Method and system for calculating collaborative bidding amount of electric energy and frequency modulation market participated by virtual power plant

Abstract

The invention relates to a collaborative bidding amount calculation method and system for virtual power plants participating in electric energy and frequency modulation markets, and the method comprises the steps: generating a plurality of typical scenes with probability weights, constructing a joint opportunity constraint optimization model of bidding parameters, and setting constraints including opportunity constraints set according to preset default probability parameters, the sum of the typical scene weights exceeding the available flexibility upper limit is smaller than a default probability parameter, the bidding amount is obtained after the joint opportunity optimization model is solved, the actual default rate is counted according to the market clearing result, and the default probability parameter is dynamically adjusted. According to the method, the potential energy and frequency modulation value of the virtual power plant can be fully released, and the expected income is directly improved.

Description

Technical Field

[0001] This invention belongs to the field of power plant operation technology, specifically relating to a method and system for calculating the collaborative bidding volume of virtual power plants participating in the electricity and frequency regulation market. Background Technology

[0002] In recent years, with the rapid increase in the penetration rate of renewable energy sources such as wind power and photovoltaics in the power grid, the stable operation of the power grid faces increasingly severe challenges. The intermittent and fluctuating output of these new energy sources leads to a decrease in system inertia and a weakening of frequency stability. To ensure the safety and reliability of the power grid, the demand for ancillary services such as frequency regulation, especially the demand for flexible adjustment capabilities with minute-level rapid response, has increased significantly. Virtual Power Plants (VPPs), as an advanced energy aggregation technology, can provide reliable power regulation capabilities as a unified entity by coordinating and optimizing various distributed energy sources (DERs) such as distributed photovoltaics, energy storage systems, and adjustable loads. This is a key technical means to participate in the frequency regulation market and improve the flexibility of the power grid.

[0003] To ensure frequency stability, system operators often require that the frequency regulation reserve capacity of virtual power plants participating in bidding be strictly no less than the demand value. However, distributed virtual power plants aggregate various flexible resources such as photovoltaic arrays, energy storage systems, adjustable loads, and electric vehicles. Their available flexibility is significantly affected by factors such as weather, load, and energy storage status, resulting in considerable uncertainty. System operators typically employ a deterministic hard constraint method, which is based on predicting the worst-case scenario of future uncertainties and uses the minimum regulation capacity that the virtual power plant can provide under the most pessimistic scenario as a rigid upper limit for the bidding capacity. The advantage of this method is that it can minimize the risk of default, but its fundamental flaw lies in its excessive conservatism. In order to guard against extremely low-probability extreme events, this method severely underestimates the actual regulation potential of virtual power plants in most cases, leading to an undervaluation of bidding capacity and wasting a large number of potential revenue opportunities. This seriously affects the economic benefits of virtual power plants and their enthusiasm for participating in the market. Summary of the Invention

[0004] One objective of this invention is to at least solve one or more of the aforementioned problems existing in the prior art. In other words, one objective of this invention is to provide a method for calculating the collaborative bidding volume of a virtual power plant participating in the electricity and frequency regulation market, which satisfies one or more of the aforementioned needs. The method is characterized by comprising: The available flexibility of the virtual power plant is calculated based on real-time data from the virtual power plant. Random perturbations are applied to the price of electricity and the price of frequency regulation mileage to generate several typical scenarios with probability weights; A joint opportunity-constrained optimization model for bidding parameters is constructed. The joint opportunity-constrained optimization model aims to maximize the expected revenue of the virtual power plant in the electricity and frequency regulation market. The constraints of the joint opportunity-constrained optimization model include opportunity constraints set according to the preset default probability parameter, such that the sum of the weights of typical scenarios exceeding the available flexibility limit is less than the default probability parameter. The decision variables of the joint opportunity-constrained optimization model include the energy bidding amount and the frequency regulation capacity bidding amount in each typical scenario. Solve the joint opportunity optimization model to generate bid quantities.

[0005] As a preferred implementation, the solution includes the following steps: The mixed-integer programming for each typical scenario of bidding or not is relaxed to generate a linear relaxation model that includes continuous variables; Solve the linear relaxation model based on the objective and constraints of the joint opportunity optimization model; The sum of weights for typical scenarios that exceed the available flexibility limit is calculated based on the solution results of the linear relaxation model. If the sum of the weights does not exceed the default probability parameter, the iteration stops; otherwise, the linear relaxation model is adjusted, and the continuous variables corresponding to this typical scenario are tightened or fixed to 0 and solved again.

[0006] As a preferred implementation, the flexible calculation method includes: The real-time data from the virtual power plant is input into the rolling economic dispatch model to obtain the dispatch results, which include charging and discharging power. Set the upper flexible boundary to the maximum available power minus the charging and discharging power; Set the lower flexible boundary to the charging and discharging power; The available flexibility includes the range formed by the upper flexible boundary and the lower flexible boundary.

[0007] In a preferred embodiment, step S2 includes: Apply random perturbations to the price of electricity and the price of frequency regulation mileage to generate several initial scenarios; The initial scene is clustered to generate several clusters. The center of each cluster is taken as the typical scene, and the probability weight of the corresponding typical scene is calculated based on the number of samples in the cluster.

[0008] As a further preferred implementation, clustering uses Euclidean distance as a metric between initial scenes; the number of clusters is adaptively determined by the average profile coefficient.

[0009] As a preferred implementation, the method further includes: calculating the actual default rate based on the market clearing results, and dynamically adjusting the default probability parameter based on the difference between the actual default rate and the default probability parameter.

[0010] As a further preferred implementation, the method for dynamically adjusting the default probability parameter includes: Based on the difference between the actual default rate and the default probability parameter, an adjustment amount is determined by a preset percentage to make the default probability parameter converge toward the target default rate.

[0011] As a preferred implementation, the constraints of the joint chance-constrained optimization model also include: Energy storage energy balance constraints, power ramp-up constraints, upper and lower limits of charge and discharge power constraints, and tie line capacity constraints.

[0012] As a preferred implementation, the default probability parameter is preset to a value range of 0 to 20%.

[0013] On the other hand, the present invention also provides a collaborative bidding volume calculation system for virtual power plants participating in the electricity and frequency regulation market, comprising: The flexible computing module is used to calculate the available flexibility of the virtual power plant based on real-time data from the virtual power plant. The scenario generation module is used to apply random perturbations to the price of electricity and the price of frequency regulation mileage to generate several typical scenarios with probability weights. The model building module is used to construct a joint opportunity constraint optimization model for bidding parameters. The joint opportunity constraint optimization model aims to maximize the expected revenue of the virtual power plant in the electricity and frequency regulation market. The constraints of the joint opportunity constraint optimization model include opportunity constraints set according to the preset default probability parameter, such that the sum of the weights of typical scenarios exceeding the available flexibility limit is less than the default probability parameter. The decision variables of the joint opportunity constraint optimization model include the energy bidding amount and the frequency regulation capacity bidding amount in each typical scenario. The solver module is used to solve the joint opportunity optimization model and generate bid quantities; The dynamic adjustment module is used to calculate the actual default rate based on the market clearing results, and dynamically adjust the default probability parameter based on the difference between the actual default rate and the default probability parameter.

[0014] Compared with existing technologies, the method and system for calculating the collaborative bidding volume of virtual power plants participating in the electricity and frequency regulation market provided by this invention have the following advantages: The method and system of this invention overcome the limitation of traditional hard constraint methods that must satisfy the worst case by constructing a joint opportunity constraint optimization model. The joint opportunity constraint optimization model sets an opportunity constraint of "making the sum of the weights of typical scenarios exceeding the available flexibility limit less than the default probability parameter". This allows for conscious default on some extremely low probability scenarios within a controllable risk range, thereby submitting more competitive bids under a controllable default probability, fully releasing the potential energy and frequency regulation value of the virtual power plant, and directly improving the expected returns.

[0015] Furthermore, the method and system of this invention introduce a closed-loop feedback adjustment mechanism based on market clearing results, enabling the default probability to dynamically and intelligently self-adjust according to actual operating results and market environment. This adaptive learning capability avoids the bidding scheme generated by this invention, ensuring that the bidding strategy can continuously approach the optimal risk-return balance point under current market conditions, so that the virtual power plant can always maintain the highest profit potential and the strongest adaptability in a volatile market environment.

[0016] Furthermore, based on the above settings, this invention proposes an efficient iterative solution strategy for solving complex problems in joint chance-constrained optimization models. This strategy relaxes the complex mixed-integer programming problem into a linear model and adopts an iterative approach of "solving-verifying-tightening". This avoids the problems of massive combinations and excessive computation time that traditional solvers may encounter when dealing with a large number of scenarios and integer variables. This ensures that this invention can be applied to real, rolling clearing electricity markets and has extremely high engineering application value. Attached Figure Description

[0017] Figure 1 This is a flowchart of the method for calculating the collaborative bidding volume of virtual power plants participating in the electricity and frequency regulation market according to the present invention; Figure 2 This is a flowchart of step S4 of the method of the present invention; Figure 3 This is a schematic diagram of the structure of the collaborative bidding volume calculation system for virtual power plants participating in the electricity and frequency regulation market according to the present invention. Detailed Implementation

[0018] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0019] 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 function and arrangement of the described elements without departing from the scope of the invention. Various processes or components may be appropriately omitted, substituted, or added to the various examples. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Furthermore, features described with respect to some examples may be combined into other examples.

[0020] Embodiments of the present invention provide a method for calculating the collaborative bidding volume of a virtual power plant participating in the electricity and frequency regulation market, the flowchart of which is shown below. Figure 1 As shown, it includes steps S1-S5.

[0021] In step S1, the available flexibility of the virtual power plant is calculated based on the real-time data of the virtual power plant.

[0022] In one embodiment of the present invention, the virtual power plant collects real-time data such as output, state of charge, load fluctuations, and meteorological parameters of various resources, including photovoltaic arrays, energy storage systems, adjustable loads, and electric vehicles, through SCADA / EMS, WAMS, and DERMS systems.

[0023] In one embodiment of the present invention, the aforementioned real-time data is sent to the process layer, and then the available flexibility of the virtual power plant is calculated using the following method: Flexible power can be used to estimate the range of power fluctuations that a virtual power plant can provide without default during future bidding cycles. To this end, step S1 employs a rolling economic dispatch model with minute-level time steps. The process layer solves the rolling economic dispatch model based on real-time data, considering constraints such as energy storage charging and discharging efficiency limitations, state-of-charge boundaries, and the up-and-down adjustment capabilities of transferable loads during the solution process. This results in an adjustment of the energy storage charging power... and energy storage discharge power Optimize to obtain the energy storage power sequence ( , And update the state of charge (t), where the energy storage power sequence is the optimal operating plan of each device at each control time, calculated by the rolling economic dispatch model, and the state of charge is the power status of each device at each control time.

[0024] The state equation for the charged state is:

[0025] Satisfy constraints:

[0026] in, The state of charge of the stored energy at time t. This represents the maximum value of the state of charge. , These are the energy storage charging power and the maximum energy storage charging power, respectively. These are the energy storage discharge power and the maximum energy storage discharge power, respectively.

[0027] After obtaining the energy storage power sequence and state of charge, the scheduling results are obtained. The scheduling results include the charging and discharging power and state of charge of each device at each control moment. At this time, the upper flexibility and lower flexibility can be calculated according to the scheduling results.

[0028] Specifically, based on the scheduling results obtained through the above methods, the upper flexible sample Fen,t is defined as the maximum available power minus the charging and discharging power in the scheduling results, and the lower flexible sample Ffr,t is defined as the charging and discharging power in the scheduling results. Both reflect the adjustment margin of the virtual power plant in the frequency regulation market.

[0029] With "net external injection power" as positive: This indicates the amount of electricity transmitted to external sources during the current period (mainly power generation / discharge). This indicates that power is drawn from external sources during the current period (primarily for load / charging). This is based on the baseline given by the rolling economic dispatch model. Above, the upper and lower flexibility of sample n and time period t are defined as follows:

[0030] in, and These represent the maximum / minimum net injection power that can be achieved in time period t, under sample n, after considering the upper / lower bounds of power for various adjustable resources, ramping limitations, and energy sustainability; and The upper / lower limits of the power injected into the tie line with the upper-level power grid are given by network / security constraints. Based on this, the flexibility interval for sample n and time period t is... Together, they depict the instantaneous up / down adjustment margin of virtual power plants in the frequency regulation market.

[0031] In step S2, random perturbations are applied to the price of electricity and the price of frequency regulation mileage to generate several typical scenarios with probability weights.

[0032] In one embodiment of the present invention, step S2 includes the following process: Based on historical market data and existing price forecasts, the price of electricity... and frequency adjustment mileage price By superimposing zero-mean normal perturbations, N0 initial scenarios are generated.

[0033] Using K Means or hierarchical clustering algorithms cluster the initial scene to generate K Each cluster is used as a typical scenario, and the center of each cluster is extracted. K For each typical scenario, the probability weight w is calculated based on the number of samples within the cluster. k And make the sum of the weights equal to 1, that is .

[0034] In a further embodiment of the present invention, the above clustering algorithm uses Euclidean distance as the initial metric between scenes, and the number of clusters... K The average profile coefficient is adaptively determined.

[0035] The formula for calculating Euclidean distance is:

[0036] in, and Let represent the energy price and frequency regulation mileage price for the s-th scenario, respectively.

[0037] By using clustering in step S2, the number of scenarios can be significantly reduced while maintaining the price distribution characteristics, thereby reducing the size of the subsequent optimization model.

[0038] In step S3, a joint opportunity constraint optimization model for bidding parameters is constructed. This model aims to maximize the expected revenue of the virtual power plant in the electricity and frequency regulation markets. Joint opportunity constraints are set according to preset default probability parameters, ensuring that the sum of probability weights for the set of typical scenarios that violate the available flexibility boundary does not exceed the default probability parameters. "Violation of the available flexibility boundary" refers to either the energy bid amount or / or the frequency regulation capacity bid amount exceeding the corresponding upper or lower flexibility limit in that scenario. The decision variables of the joint opportunity constraint optimization model include the energy bid amount and the frequency regulation capacity bid amount in each typical scenario.

[0039] In one embodiment of the present invention, step S3 constructs an objective function for a joint chance-constrained optimization model with the goal of maximizing the expected revenue of the virtual power plant in the electricity and frequency regulation market. Specifically, the revenue includes revenue based on energy prices. Payment of revenue from electricity sales and prices based on frequency regulation mileage The FM mileage payment is calculated by weighting the two parts of the payment together according to the scenario probability k. The objective function formula is as follows:

[0040] The opportunity constraints of the joint opportunity constraint optimization model are set according to a preset default probability parameter:

[0041] in It is an adjustable default probability parameter, and its value range is preferably 0%–20%.

[0042] The decision variables of the joint opportunity-constrained optimization model are set as the energy bid amount p in each typical scenario. en,k And frequency modulation capacity bid amount p fr,k .

[0043] In addition, the model also includes hard constraints such as energy storage balance constraints, power ramp-up constraints, upper and lower limits of charging and discharging power constraints, and tie-line capacity, as well as non-scenario coupling constraints to ensure that the same bid quantity is shared among different scenarios.

[0044] Opportunity constraints ensure that the sum of the energy bids and frequency regulation capacity bids of the virtual power plant does not exceed the probability of available flexibility information, and is not lower than the confidence level determined by the adjustable default probability parameter. This limits the solution process of the joint opportunity constraint optimization model to allow bidding scenarios to appropriately default within the opportunity constraints, thereby making fuller use of the virtual power plant's regulation margin.

[0045] By adjusting the default probability parameter α between 0 and 20%, a bidding amount scheme that better suits risk expectations can be selected between zero default and maximizing returns.

[0046] In step S4, the joint opportunity optimization model is solved to generate the bid quantity.

[0047] In one embodiment of the present invention, the solution flowchart for step S4 is as follows: Figure 2 As shown, it includes the following steps: S41. Relax the mixed integer programming for each typical scenario of bidding or not, and relax the 0 / 1 variables in the mixed integer programming to continuous variables in [0,1].

[0048] Since the chance constraint model contains a large number of 0 / 1 variables and probability constraints, the computational complexity of direct solution is high. Therefore, step S4 first relaxes the 0 / 1 bidding decision variables, extending their domain from (0,1) to [0,1], to obtain a set of linear relaxation models.

[0049] Based on the relaxed continuous variables for each typical bidding scenario, a linear relaxation model is generated. By solving this model, the bidding decision can be initially obtained and which scenarios violate the opportunity constraint can be identified.

[0050] S42. Solve the linear relaxation model.

[0051] Based on the solution results, the sum of the weights of typical scenarios that exceed the available flexibility limit is calculated. Specifically, the solution results can be used to initially obtain bidding decisions and identify which scenarios violate the opportunity constraint, i.e., in which scenarios p en,k >F en or p fr,k >F fr These scenarios constitute a set of default sample cases.

[0052] S43. If the sum of the weights of the defaulted samples does not exceed the threshold specified by the adjustable default probability parameter, the current solution satisfies the opportunity constraint and the iteration can be stopped; otherwise, the bidding variable corresponding to the defaulted sample is tightened or fixed to 0, that is, bidding is not allowed to exceed the flexibility limit in these scenarios, and then the model is solved again.

[0053] Through the above iterative process, default samples are continuously eliminated, gradually reducing the set of scenarios that need to be processed in the model. As a result, the size of integer variables decreases, and finally, a bid quantity scheme that satisfies the opportunity constraint can be obtained within minutes.

[0054] The bidding plan includes the energy bids and frequency regulation capacity bids submitted in the electricity market and the frequency regulation market, respectively.

[0055] In a further embodiment, the method also includes step S5, which involves calculating the actual default rate based on the market clearing results, and dynamically adjusting the default probability parameter based on the difference between the actual default rate and the default probability parameter.

[0056] Specifically, in each rolling period t, the default probability parameter α from step S4 is first obtained, and then the market clearing result of the previous rolling period is obtained. Based on the market clearing result, the revenue Rt and the actual default rate αt are calculated.

[0057] Then adjust the default probability parameter α according to the following formula.

[0058]

[0059] Where, α tar The target default rate preset for the operator. This is the adjustment coefficient. The above formula determines the adjustment amount by a preset proportion based on the difference between the actual default rate and the default probability parameter, so that the default probability parameter approaches the actual default rate. Through the above closed-loop adjustment mechanism, the method of this invention can use actual operating results to learn and update the risk parameters online. If the actual default rate is lower than the target default rate α, tar This indicates that the system is operating conservatively and could be improved appropriately. To increase bidding capacity; conversely, if the actual default rate is too high, it is necessary to reduce it. This is to mitigate risk. By implementing this method and updating it over multiple rolling cycles, the probability of default gradually converges to a value suitable for the current market conditions, thereby achieving a dynamic balance between risk and return.

[0060] Another embodiment of the present invention provides a collaborative bidding quantity calculation system for virtual power plants participating in the electricity and frequency regulation market, the structural schematic diagram of which is shown below. Figure 3 As shown, it includes: The flexible computing module is used to calculate the available flexibility of the virtual power plant based on real-time data from the virtual power plant. The scenario generation module is used to apply random perturbations to the price of electricity and the price of frequency regulation mileage to generate several typical scenarios with probability weights. The model building module is used to construct a joint opportunity constraint optimization model for bidding parameters. The joint opportunity constraint optimization model aims to maximize the expected revenue of the virtual power plant in the electricity and frequency regulation market. The constraints of the joint opportunity constraint optimization model include opportunity constraints set according to the preset default probability parameter, such that the sum of the weights of typical scenarios exceeding the available flexibility limit is less than the default probability parameter. The decision variables of the joint opportunity constraint optimization model include the energy bidding amount and the frequency regulation capacity bidding amount in each typical scenario. The solver module is used to solve the joint chance optimization model.

[0061] In one embodiment of the present invention, the solving module solves the joint chance optimization model using the following method: The mixed-integer programming for each typical scenario of bidding or not is relaxed to generate a linear relaxation model that includes continuous variables; Solve the linear relaxation model based on the objective and constraints of the joint opportunity optimization model; The sum of weights for typical scenarios that exceed the available flexibility limit is calculated based on the solution results of the linear relaxation model. If the sum of the weights does not exceed the default probability parameter, the iteration stops; otherwise, the linear relaxation model is adjusted, and the continuous variables corresponding to this typical scenario are tightened or fixed to 0 and solved again.

[0062] In one embodiment of the present invention, the flexible computing module calculates the available flexibility using the following methods: The real-time data of the virtual power plant is input into the rolling economic dispatch model to obtain the dispatch results, which include charging and discharging power. Set the upper flexible boundary to the maximum available power minus the charging and discharging power; Set the lower flexible boundary to the charging and discharging power; The available flexibility includes the range formed by the upper flexible boundary and the lower flexible boundary.

[0063] In one embodiment of the present invention, the scene generation module generates a typical scene using the following method: Apply random perturbations to the price of electricity and the price of frequency regulation mileage to generate several initial scenarios; The initial scene is clustered to generate several clusters. The center of each cluster is taken as the typical scene, and the probability weight of the corresponding typical scene is calculated based on the number of samples in the cluster.

[0064] In one embodiment of the present invention, the system further includes a dynamic adjustment module, which is used to calculate the actual default rate based on the market clearing results and dynamically adjust the default probability parameter based on the difference between the actual default rate and the default probability parameter.

[0065] Furthermore, the dynamic adjustment module's method for dynamically adjusting the default probability parameter includes: determining an adjustment amount based on a preset certain proportion according to the difference between the actual default rate and the target default rate, so that the default probability parameter converges towards the target default rate.

[0066] In one embodiment of the present invention, the constraints of the joint chance-constrained optimization model in the model building module further include: Energy storage energy balance constraints, power ramp-up constraints, upper and lower limits of charge and discharge power constraints, and tie line capacity constraints.

[0067] In one embodiment of the present invention, in the model building module, the default probability parameter is preset to a value range of 0 to 20%.

[0068] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Those skilled in the art will readily conceive of other embodiments of this disclosure upon considering the specification and practicing the disclosure herein. This invention is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described herein. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A method for calculating the collaborative bidding volume of a virtual power plant participating in the electricity and frequency regulation market, characterized in that, include: The available flexibility of the virtual power plant is calculated based on real-time data from the virtual power plant. Random perturbations are applied to the price of electricity and the price of frequency regulation mileage to generate several typical scenarios with probability weights; A joint opportunity constraint optimization model for bidding parameters is constructed, the joint opportunity constraint optimization model aims to maximize the expected revenue of the virtual power plant in the electricity and frequency regulation market; the constraints of the joint opportunity constraint optimization model include at least opportunity constraints set according to a preset default probability parameter, the opportunity constraints ensuring that the sum of the weights of typical scenarios exceeding the available flexibility limit is less than the default probability parameter; the decision variables of the joint opportunity constraint optimization model include the energy bidding amount and the frequency regulation capacity bidding amount in each typical scenario; Under the constraints of the joint opportunity-constrained optimization model, the joint opportunity-constrained optimization model is solved to generate the bid quantity.

2. The method for calculating the collaborative bidding volume of a virtual power plant participating in the electricity and frequency regulation market as described in claim 1, characterized in that, Solving the joint chance optimization model includes the following steps: The mixed-integer programming for each of the typical scenarios, whether to bid or not, is relaxed to generate a linear relaxation model that includes continuous variables; Solve the linear relaxation model based on the objective and constraints of the joint opportunity optimization model; The sum of weights for typical scenarios that exceed the available flexibility limit is calculated based on the solution results of the linear relaxation model. If the sum of the weights does not exceed the default probability parameter, the iteration stops; otherwise, the linear relaxation model is adjusted, and the continuous variables corresponding to this typical scenario are tightened or fixed to 0 and solved again.

3. The method for calculating the collaborative bidding volume of a virtual power plant participating in the electricity and frequency regulation market as described in claim 1, characterized in that, The methods for calculating available flexibility include: The real-time data of the virtual power plant is input into the rolling economic dispatch model to obtain the dispatch results, which include charging and discharging power. Set the upper flexible boundary to the maximum available power minus the charging and discharging power; Set the lower flexible boundary to the charging and discharging power; The available flexibility includes the range formed by the upper flexible boundary and the lower flexible boundary.

4. The method for calculating the collaborative bidding volume of a virtual power plant participating in the electricity and frequency regulation market as described in claim 1, characterized in that, Step S2 includes: Apply random perturbations to the price of electricity and the price of frequency regulation mileage to generate several initial scenarios; The initial scene is clustered to generate several clusters. The center of each cluster is taken as the typical scene, and the probability weight of the corresponding typical scene is calculated based on the number of samples in the cluster.

5. The method for calculating the collaborative bidding volume of a virtual power plant participating in the electricity and frequency regulation market as described in claim 4, characterized in that, The clustering uses Euclidean distance as a metric between the initial scenes; the number of clusters is adaptively determined by the average profile coefficient.

6. The method for calculating the collaborative bidding volume of a virtual power plant participating in the electricity and frequency regulation market as described in claim 1, characterized in that, The method also includes: calculating the actual default rate based on the market clearing results, and dynamically adjusting the default probability parameter based on the difference between the actual default rate and the default probability parameter.

7. The method for calculating the collaborative bidding volume of a virtual power plant participating in the electricity and frequency regulation market as described in claim 6, characterized in that, The dynamic adjustment method for the default probability parameter includes: Based on the difference between the actual default rate and the target default rate, an adjustment amount is determined by a preset certain percentage to make the default probability parameter converge toward the target default rate.

8. The method for calculating the collaborative bidding volume of a virtual power plant participating in the electricity and frequency regulation market as described in claim 1, characterized in that, The constraints of the joint chance-constrained optimization model also include: Energy storage energy balance constraints, power ramp-up constraints, upper and lower limits of charge and discharge power constraints, and tie line capacity constraints.

9. The method for calculating the collaborative bidding volume of a virtual power plant participating in the electricity and frequency regulation market as described in claim 1, characterized in that, The preset default probability parameter ranges from 0 to 20%.

10. A collaborative bidding volume calculation system for virtual power plants participating in the electricity and frequency regulation market, characterized in that, include: The flexible computing module is used to calculate the available flexibility of the virtual power plant based on real-time data from the virtual power plant. The scenario generation module is used to apply random perturbations to the price of electricity and the price of frequency regulation mileage to generate several typical scenarios with probability weights. The model building module is used to construct a joint opportunity constraint optimization model for bidding parameters. The joint opportunity constraint optimization model aims to maximize the expected revenue of the virtual power plant in the electricity and frequency regulation market. The constraints of the joint opportunity constraint optimization model include at least opportunity constraints set according to a preset default probability parameter, such that the sum of the weights of typical scenarios exceeding the available flexibility limit is less than the default probability parameter. The decision variables of the joint opportunity constraint optimization model include the energy bidding amount and the frequency regulation capacity bidding amount in each typical scenario. The solution module is used to solve the joint opportunity optimization model and generate bid quantities; The dynamic adjustment module is used to calculate the actual default rate based on the market clearing results, and to dynamically adjust the default probability parameter based on the difference between the actual default rate and the default probability parameter.

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