A method for quantitatively analyzing risks of a power seller including a VPP aggregation risk

Abstract

The application discloses a kind of risk quantification analysis methods of electricity seller including VPP aggregation risk, comprising, establish VPP operation framework;Establish the operation framework of electricity selling company;Based on the utility model of user side resource in the distributed resource bidding process in VPP operation framework, establish the bidding model of electricity seller considering VPP aggregation risk based on the external market bidding process in the operation framework of electricity selling company, obtain the risk cost curve of electricity seller;Based on the risk cost curve of electricity seller, the risk that may exist to electricity seller is quantitatively analyzed;The application optimizes the report of distributed resource in VPP and the bidding strategy of electricity seller in external market, improves user utility and reduces the aggregation risk of electricity selling company;Quantify the degree of uncertainty in aggregation risk, calculate the change of aggregation risk of electricity selling company under the same bidding result, help electricity selling company more comprehensive understanding of economic risk when bidding, avoid risk, improve efficiency.

Description

Technical Field

[0001] This invention relates to the technical field of risk quantification analysis for e-commerce businesses, and more particularly to a method for risk quantification analysis of e-commerce businesses that includes VPP aggregation risk. Background Technology

[0002] To address the severe challenges of energy shortages and environmental pollution, the vigorous development of clean energy and the promotion of coordinated development of "source-grid-load-storage" have received widespread attention from the industry. Electricity sales companies, as key players in breaking the objective monopoly of the electricity market and improving the level of electricity sales services, utilize their affiliated Virtual Power Plants (VPPs) to aggregate and optimize the development of clean energy across "source-grid-load-storage" and participate in commercial interactions.

[0003] The uncertainty of electricity prices in the external electricity market in which electricity sales companies participate, the response characteristics of multiple entities within their VPPs, the uncertainty of electricity load, and the uncertainty of load response make it difficult for electricity sales companies to assess the magnitude of their risks and quantify the degree to which various factors affect their risks after formulating bidding strategies. Summary of the Invention

[0004] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0005] In view of the aforementioned existing problems, the present invention is proposed.

[0006] Therefore, this invention provides a method for quantitative analysis of electricity sales company risk that includes VPP aggregation risk. It can calculate the aggregation risk caused by the uncertainty of user load and response in the lower-level bidding and the uncertainty of external market electricity price in the upper-level bidding, so that electricity sales companies can have a more comprehensive understanding of the economic risks they bear when bidding.

[0007] To address the aforementioned technical problems, this invention provides the following technical solution: It includes establishing a VPP (Virtual Power Provider) operation framework; establishing a power sales company operation framework; establishing a utility model for user-side resources based on the decentralized resource bidding process within the VPP operation framework; establishing a power sales company bidding model considering VPP aggregation risks based on the bidding process within the power sales company operation framework and the external market, and obtaining the risk cost curve of the power sales company; and quantitatively analyzing the potential risks of the power sales company based on the risk cost curve of the power sales company.

[0008] As a preferred embodiment of the risk quantification analysis method for electricity retailers including VPP aggregation risk described in this invention, the following steps are taken: Establishing the operational framework of the VPP includes: the user-side resources aggregated by the VPP include distributed power sources and controllable loads, wherein the controllable loads include interruptible loads and shiftable loads; the VPP quotes prices to the distributed power sources and controllable loads within its jurisdiction, and internal users quote prices based on the VPP and declare the electricity consumption of distributed power sources and controllable loads to the VPP; the VPP, combining its own wind and solar power output, interacts with the internal users regarding electricity prices and consumption, and, provided that the photovoltaic and wind power output of the distributed power sources exceeds the actual electricity consumption of the users, declares the remaining unbalanced electricity consumption to the electricity retailer based on the quoted price; the economic risk of the electricity retailer participating in external market transactions based on the declared electricity consumption is obtained based on the fluctuation of the actual electricity consumption of the user-side load aggregated by the VPP and the subjectivity of user responses.

[0009] As a preferred embodiment of the method for quantitatively analyzing the risk of electricity retailers including VPP aggregation risk as described in this invention, the following steps are taken: Establishing the electricity retailer's operational framework includes: the electricity retailer utilizing the user-side resources aggregated by the VPPs to form a resource aggregate that meets the electricity market access rules; after the electricity retailer transmits the electricity price from the external day-ahead electricity market to each of its subordinate VPPs, the VPPs, based on the VPP operational framework, interact with the aggregated user-side electricity price and report the remaining unbalanced electricity volume to the electricity retailer; the electricity retailer bids for electricity volume with the external day-ahead and real-time electricity markets and trades the unbalanced electricity volume in the real-time electricity market; and the economic risk during the trading process is obtained based on the uncertainty of the day-ahead market electricity price and the uncertainty of the electricity volume reported by the VPPs.

[0010] As a preferred embodiment of the method for quantitative analysis of e-commerce risk including VPP aggregation risk described in this invention, the establishment of the utility model for the user-side resources includes designing an objective function based on the maximum utility of distributed power sources and controllable loads:

[0011] max U = U DG +U DL

[0012] Where U represents the total utility of the VPP on the user side owned by the electricity sales company, U DG For the benefit of all distributed power users, U DL The utility for all controllable load users.

[0013] As a preferred embodiment of the risk quantification analysis method for e-commerce platforms including VPP aggregation risk described in this invention, it further includes: the utility U of all distributed power users DG for:

[0014]

[0015] The output constraints of distributed power sources are:

[0016]

[0017]

[0018] The utility U of all controllable load users DL for:

[0019]

[0020] The controllable load constraint is:

[0021]

[0022]

[0023]

[0024]

[0025]

[0026] Where T represents the bidding period, and N represents the total number of distributed power users within the VPP jurisdiction owned by the electricity sales company. Let t be the bidding electricity amount reported by the i-th distributed power user to the VPP during time period t. Let A be the electricity price quoted by VPP to the i-th distributed power user during time period t. i B i C i These are the output cost coefficients for the i-th distributed power user; M is the total number of controllable load users under the VPP owned by the electricity sales company. and These represent the actual electricity consumption and rigid electricity consumption of the j-th controllable load user during time period t, respectively; q represents the user's electricity consumption; a is a constant; ε is the demand-price elasticity coefficient; P RT,t This refers to the retail electricity price that the VPP charges users during time period t. and These are the interruptible load price and the shiftable load price that VPP offers to user j during time period t, respectively. and These represent the interruptible load bidding volume and the shiftable load bidding volume for user j during time period t, respectively. and These represent the maximum and minimum output values ​​of the i-th distributed power user during the entire T-period; and These are the downslope rate and upslope rate of the power output of the i-th distributed power user, respectively; These are the maximum and minimum interruptible load values ​​for the j-th controllable load user, respectively; Let be the maximum and minimum values ​​of the transferable load for the j-th controllable load user, respectively. Let t be the time interval for the transferable load of the j-th controllable load user during time period t; These are the lower and upper limits of the load shifting time interval for the j-th controllable load user, respectively. Let t be the initial load of the j-th controllable load user during time period t.

[0027] As a preferred embodiment of the risk quantification analysis method for electricity retailers including VPP aggregation risk described in this invention, obtaining the risk cost curve of the electricity retailer includes designing an objective function based on the maximum operating revenue of the electricity retailer:

[0028] max f1=C RT -(C DG +C DL +C MT )

[0029] Calculate the electricity sales company's various revenues and expenses using the following formula:

[0030]

[0031]

[0032]

[0033]

[0034] The aggregate risk of VPP is calculated using conditional value at risk (VaR), and the objective function is designed to minimize the conditional VaR.

[0035]

[0036] Define the negative of the VPP's operating revenue f1, -f1, as the VPP's operating cost. Design the objective function to minimize the VPP's operating cost and risk:

[0037] min R = (-f1, f2);

[0038] Where f1 is the VPP operating revenue, C RT C represents the revenue of a VPP from retail electricity sales to load users at retail prices. DG C is the fee charged by the VPP for purchasing electricity from distributed power sources. DL C is the fee paid by the VPP to users of the controllable load during demand response. MTThe payment made by the VPP for purchasing and selling electricity from the day-ahead external market; P M,t The current electricity price in the external market, σ t Standard deviation Let be the actual electricity consumption of the j-th user during time period t, and let its value follow a normal distribution. Let σ be the average battery consumption of this user. RE,t Q is the standard deviation. M,t f2 represents the amount of electricity purchased and sold by the electricity sales company to the day-ahead market; a positive value indicates purchasing electricity from the market, while a negative value indicates selling electricity to the market. f2 represents the risk borne by the VPP during operation. This is an approximate estimate of the conditional value at risk, where α is the maximum possible loss per unit of VPP; f 1,k For the total VPP running profit under the k-th sample from K types of historical or simulated future sample data, [-f 1,k -α] + max(0, -f) 1,k -α).

[0039] As a preferred embodiment of the e-commerce risk quantification analysis method including VPP aggregation risk described in this invention, it further includes: market access constraints:

[0040]

[0041]

[0042]

[0043] Q M,min ≤Q M,t ≤Q M,max

[0044] P RT,min ≤P RT,t ≤P RT,max

[0045]

[0046]

[0047] in, Let $t$ be the lower limit and upper limit of the price quoted by the VPP to the i-th distributed power user during time period $t$. These represent the lower and upper limits of the interruptible load quote that the VPP provides to the j-th controllable load user during time period t. Let Q be the lower and upper limits of the load transferable price quoted by the VPP to the j-th controllable load user during time period t. M,min , These represent the minimum and maximum electricity volumes for VPP to participate in day-ahead external market transactions, respectively, P RT,min ,P RT,max ,P RT,avg Let Q be the minimum, maximum, and average retail electricity price that the VPP charges its internal users. W,t +Q PV,t The output of wind power (W) and photovoltaic power (PV) during time period t are respectively.

[0048] As a preferred embodiment of the quantitative analysis method for electricity retailers including VPP aggregation risk described in this invention, the quantitative analysis includes: obtaining the bidding results adopted by the electricity retailer and its subordinate VPPs based on the bidding model considering VPP aggregation risk; when the VPP aggregation risk increases... Satisfies a normal distribution Furthermore, the bidding results adopted by the aforementioned electricity sales company and its subordinate VPPs remain unchanged, thus yielding a new aggregation risk for the electricity sales company. This new aggregation risk is expressed as conditional risk value:

[0049]

[0050] Where δ is the uncertainty factor, representing the degree of risk increase; f 2，new When the bidding result remains unchanged while δ changes, the new aggregation risk value for e-commerce platforms is determined.

[0051] The beneficial effects of this invention are as follows: By considering the risks caused by user load, response uncertainty, and external market electricity price uncertainty, this invention optimizes the reporting of distributed resources within a VPP and the bidding strategies of electricity retailers in the external market, which helps to improve user utility and reduce the aggregation risk of electricity retailers. Quantifying the degree of uncertainty in aggregation risk and calculating the change in aggregation risk of electricity retailers under the same bidding results helps electricity retailers to have a more comprehensive understanding of the economic risks they bear when bidding, avoid risks, and improve efficiency. Attached Figure Description

[0052] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0053] Figure 1 This is a flowchart illustrating a method for quantitative analysis of e-commerce risk including VPP aggregation risk, as described in the first embodiment of the present invention.

[0054] Figure 2This is a schematic diagram of the VPP operation framework of a method for quantitative analysis of e-commerce risk including VPP aggregation risk, as described in the first embodiment of the present invention.

[0055] Figure 3 This is a schematic diagram of the operational framework of a power sales company containing multiple VPPs, as described in the first embodiment of the present invention, which is a method for quantitative analysis of the risk of power sales companies including VPP aggregation risk.

[0056] Figure 4 This is a Pareto front diagram illustrating the bidding strategy of a power sales company, which includes a method for quantitative analysis of the risk of VPP aggregation in power sales companies, as described in the second embodiment of the present invention.

[0057] Figure 5 This is a schematic diagram of the internal resource reporting of an electricity sales company, as described in the second embodiment of the present invention, which includes a method for quantitatively analyzing the risk of VPP aggregation in electricity sales companies.

[0058] Figure 6 This is a schematic diagram of a power sales company's VPP quotation, as described in the second embodiment of the present invention, which includes a method for quantitatively analyzing the risk of VPP aggregation in power sales companies.

[0059] Figure 7 This is a schematic diagram of the external market electricity purchase and sale volume of an electricity sales company, as described in the second embodiment of the present invention, which includes a method for quantitative analysis of the risk of VPP aggregation in electricity sales companies.

[0060] Figure 8 This is a schematic diagram of the VPP retail electricity price of an electricity sales company, as described in the second embodiment of the present invention, which includes a method for quantitative analysis of VPP aggregation risk in electricity sales companies. Detailed Implementation

[0061] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0062] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0063] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0064] This invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of this invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not adhering to the usual scale. Furthermore, the schematic diagrams are merely examples and should not be construed as limiting the scope of protection of this invention. In actual fabrication, the three-dimensional spatial dimensions of length, width, and depth should be included.

[0065] Furthermore, in the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used solely for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0066] Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "joining" in this invention should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; similarly, they can refer to mechanical connections, electrical connections, or direct connections, or indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0067] Example 1

[0068] Reference Figures 1 to 3 This is the first embodiment of the present invention, which provides a method for quantitative analysis of e-commerce risk including VPP aggregation risk, comprising:

[0069] S1: Establish the VPP operation framework.

[0070] It should be noted that the user-side resources aggregated by VPP (Virtual Power Plant) mainly include distributed power (DG) and dispatchable load (DL). DL mainly includes interruptible load (IL) and translatable load (TL).

[0071] Reference Figure 2 The specific steps for establishing a VPP operation framework are as follows:

[0072] (1) The VPP quotes prices to the distributed power sources and controllable loads of the users within its jurisdiction, and the internal users quote prices according to the VPP and report the power consumption of distributed power sources and controllable loads to the VPP.

[0073] (2) The VPP combines its own wind and solar power output, interacts with internal users on electricity price and electricity volume, and, under the condition that the photovoltaic and wind power output of the distributed power source is greater than the actual electricity consumption of the users, declares the remaining unbalanced electricity volume to the electricity sales company based on the electricity price quoted by the electricity sales company.

[0074] (3) Based on the fluctuation of the actual electricity volume of the user-side load aggregated by VPP and the subjectivity of user response, the economic risk of the electricity sales company participating in external market transactions based on the declared electricity volume is obtained.

[0075] On the one hand, although the electricity consumption of user-side loads aggregated by VPP can be estimated from historical averages, the actual electricity consumption fluctuates. On the other hand, due to the subjectivity of user response, the IL and TL electricity consumption reported by DL loads to VPP is also uncertain. The combination of these two factors causes VPP to deviate in coordinating internal and external electricity consumption, resulting in economic risks for electricity sales companies participating in external market transactions based on the reported electricity consumption.

[0076] S2: Establish the operational framework for the electricity sales company.

[0077] Reference Figure 3 The specific steps for establishing the operational framework of the electricity sales company are as follows:

[0078] (1) The electricity sales company includes multiple virtual power plants and uses the user-side resources aggregated by the VPP to form a resource aggregate that meets the electricity market access rules;

[0079] (2) After the electricity sales company transmits the electricity price from the external day-ahead electricity market to each of its VPPs, the VPPs interact with the aggregated user-side electricity price based on the VPP operation framework and report the remaining unbalanced electricity to the electricity sales company.

[0080] (3) The electricity sales company bids with the external day-ahead and real-time electricity markets to purchase electricity, and trades the unbalanced electricity in the real-time electricity market.

[0081] (4) Based on the uncertainty of the current market electricity price and the uncertainty of the VPP's declared electricity volume, obtain the economic risks in the transaction process.

[0082] When electricity sales companies trade surplus unbalanced electricity with external markets, they may face certain economic risks during the trading process due to the uncertainty of day-ahead market electricity prices and the uncertainty of electricity declared by VPPs.

[0083] S3: Based on the decentralized resource bidding process within the VPP operation framework, establish a utility model for user-side resources.

[0084] Specifically, the steps to establish a utility model for user-side resources are as follows:

[0085] (1) The VPP operation framework clarifies the method of aggregating user resources within the VPP under the jurisdiction of the electricity sales company and the process of bidding for electricity volume and price between the VPP and internal user-side resources at the lower level. The objective function is designed to maximize the utility of distributed power sources and controllable loads.

[0086] max U = U DG +U DL

[0087] Where U represents the total utility of the VPP on the user side owned by the electricity sales company, U DG For the benefit of all distributed power users, U DL The utility for all controllable load users.

[0088] (2) Utility U of all distributed power users DG for:

[0089]

[0090] Where T represents the bidding period, and N represents the total number of distributed power users within the VPP jurisdiction owned by the electricity sales company. Let t be the bidding electricity amount reported by the i-th distributed power user to the VPP during time period t. Let A be the electricity price quoted by VPP to the i-th distributed power user during time period t. i B i C i These are the output cost coefficients for the i-th distributed power user.

[0091] (3) Utility U of all controllable load users DL for:

[0092]

[0093] Where M represents the total number of controllable load users under the VPP owned by the electricity sales company. and Let q be the actual electricity consumption and rigid electricity consumption of the j-th controllable load user in time period t, a be a constant, ε be the demand-price elasticity coefficient, and P be the load factor. RT,t This refers to the retail electricity price that the VPP pays to users during time period t. and These represent the interruptible load price and the shiftable load price that VPP offers to user j during time period t, respectively. and These represent the interruptible load bidding amount and the shiftable load bidding amount for user j during time period t, respectively.

[0094] (4) The output constraint of distributed power sources is:

[0095]

[0096]

[0097] in, and Let be the maximum and minimum power output of the i-th distributed power user during the entire time period T. and These are the downslope rate and upslope rate of the power output of the i-th distributed power user, respectively;

[0098] (5) The controllable load constraint is:

[0099]

[0100]

[0101]

[0102]

[0103]

[0104] in, These are the maximum and minimum interruptible load values ​​for the j-th controllable load user, respectively; Let be the maximum and minimum values ​​of the transferable load for the j-th controllable load user, respectively. Let t be the time interval for the transferable load of the j-th controllable load user during time period t; These are the lower and upper limits of the load shifting time interval for the j-th controllable load user, respectively. Let t be the initial load of the j-th controllable load user during time period t.

[0105] S4: Based on the bidding process within and outside the electricity sales company's operational framework, establish a bidding model for electricity sales companies that considers the aggregation risk of VPPs, and obtain the risk cost curve of the electricity sales companies.

[0106] (1) The process of bidding for upper-level electricity volume and price in the day-ahead market outside the electricity sales company and the occurrence of electricity transactions, and the VPP under its jurisdiction aggregating resources and bidding with users at the lower level, design the objective function to maximize the operating revenue of the electricity sales company:

[0107] max f1=C RT -(C DG +C DL+C MT )

[0108] Where f1 is the VPP operating revenue, C RT C represents the revenue of a VPP from retail electricity sales to load users at retail prices. DG C is the fee charged by the VPP for purchasing electricity from distributed power sources. DL C is the fee paid by the VPP to users of the controllable load during demand response. MT Payments made by the VPP for purchasing and selling electricity from the day-ahead external market;

[0109] (2) Calculate the various revenues and expenditures of the electricity sales company according to the following formula:

[0110]

[0111]

[0112]

[0113]

[0114] Among them, P M,t The current electricity price in the external market, σ t Standard deviation Let be the actual electricity consumption of the j-th user during time period t, and let its value follow a normal distribution. Let σ be the average battery consumption of this user. RE,t The standard deviation is given by Q. The reported IL and TL values ​​for DL ​​users vary with their actual electricity consumption. M,t This represents the amount of electricity a power company purchases or sells to the day-ahead market. A positive value indicates that the company is purchasing electricity from the market, while a negative value indicates that the company is selling electricity to the market.

[0115] (3) The aggregate risk of VPP is calculated using conditional value at risk (VaR), and the objective function is designed to minimize the conditional VaR.

[0116]

[0117] Where f2 represents the risk undertaken by VPP during runtime. This is an approximate estimate of the conditional value at risk, where α is the VaR value under certain confidence level β and risk level constraints, i.e., the maximum possible loss per unit of VPP; f 1,k For the total VPP running profit under the k-th sample from K types of historical or simulated future sample data, [-f 1,k -α] + max(0, -f) 1,k -α).

[0118] (4) Define the negative of the VPP operating revenue f1, -f1, as the VPP operating cost, and design the objective function to minimize the VPP operating cost and risk:

[0119] minR = (-f1, f2);

[0120] (5) Market access constraints:

[0121]

[0122]

[0123]

[0124] Q M,min ≤Q M,t ≤Q M,max

[0125] P RT,min ≤P RT,t ≤P RT,max

[0126]

[0127]

[0128] in, Let $t$ be the lower limit and upper limit of the price quoted by the VPP to the i-th distributed power user during time period $t$. These represent the lower and upper limits of the interruptible load quote that the VPP provides to the j-th controllable load user during time period t. Let Q be the lower and upper limits of the load transferable price quoted by the VPP to the j-th controllable load user during time period t. M,min , These represent the minimum and maximum electricity volumes for VPP to participate in day-ahead external market transactions, respectively, P RT,min ,P RT,max ,P RT,avg Let Q be the minimum, maximum, and average retail electricity price that the VPP charges its internal users. W,t +Q PV,t The output of wind power (W) and photovoltaic power (PV) during time period t are respectively.

[0129] S5: Based on the risk cost curve of e-commerce platforms, conduct quantitative analysis of the potential risks of e-commerce platforms.

[0130] (1) Based on the bidding model of the electricity retailer considering the aggregation risk of VPP, the bidding results adopted by the electricity retailer and its subordinate VPP are obtained; the specific bidding results adopted by the electricity retailer and its subordinate VPP include retail electricity price, DG, PV, W power, DGIL and TL electricity price and power.

[0131] (2) When the risk of VPP aggregation increases Satisfies a normal distribution Furthermore, the bidding results adopted by the electricity sales company and its subordinate VPPs remain unchanged, thus obtaining a new aggregation risk for the electricity sales company. The new aggregation risk of the electricity sales company is expressed by the CVaR risk value as follows:

[0132]

[0133] Where δ is the uncertainty factor, representing the degree of risk increase; f 2，new When the bidding result remains unchanged while δ changes, the new aggregation risk value for e-commerce platforms is determined.

[0134] Because the e-commerce strategy remains unchanged and the corresponding costs remain unchanged, the uncertainty factor δ of the risk from user reports affects the e-commerce risk value and increases the conditional risk value.

[0135] Example 2

[0136] To verify the effectiveness of the technology used in this method, this embodiment selects a technical solution that does not consider the risk of VPP polymerization and compares it with this method for testing. The test results are compared using scientific demonstration methods to verify the real effect of this method.

[0137] Reference Figure 4 The diagram illustrates the Pareto frontier of a power retailer's bidding strategy considering the risk of VPP aggregation.

[0138] The power sales company's VPP includes one 200kW controllable generator unit DG1 and one 150kW controllable generator unit DG2, one 150kW photovoltaic (PV) unit, and one 200kW wind turbine unit (W). The generation cost function A for DG1 and DG2 is... 1 B 1 C 1 and A 2 B 2 C 2 The values ​​are 0.022, 4.1, 200 and 0.032, 4.1, 180, respectively. The average values ​​of PV and W outputs and VPP participation in the day-ahead market electricity price are known. The variance of the day-ahead market electricity price is taken as 0.1, and the confidence level is taken as 0.95.

[0139] Reference Figures 5 to 8 The diagram illustrates the bidding results for electricity retailers considering the risks of VPP aggregation.

[0140] Based on the above bidding results for e-commerce platforms, a comparison is made between the technical solution that does not consider the risk of VPP aggregation (adopting the same strategy) and the e-commerce platform risk quantification results obtained by this method, as shown in Table 1.

[0141] Table 1: Comparison of risk quantification results for e-commerce retailers.

[0142] Scenario Cost (yuan) δ = 1, CVaR (yuan) δ = 5, CVaR (yuan) Increment Considering the VPP aggregation risk -2747.32 45191.14 52939.24 17.15％ Not considering the VPP aggregation risk -2747.32 45191.14 45191.14 0％

[0143] When the uncertainty factor δ changes from 1 to 5, the aggregate risk CVaR of the electricity retailer in this method increases from 45191.14 to 52939.24, an increase of 17.15%. However, the CVaR without considering the aggregate risk remains constant at 45191.14. The change in uncertainty is not passed on to the final aggregate risk. It can be seen that by considering the VPP aggregate risk, the risk caused by the uncertainty of electricity volume and price can be more comprehensively quantified, and the change in aggregate risk caused by the change in the uncertainty of the electricity retailer under the same strategy can be calculated.

[0144] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for quantitatively analyzing risks of a power seller including a vpp aggregation risk, characterized in that: The utility model relates to a kind of VPP operation framework and the operation framework of retail electricity supplier based on VPP operation framework, and the utility model also provides a kind of risk quantitative analysis method based on the operation framework of retail electricity supplier. The utility model includes, Establish VPP operation framework; Establish the operation framework of retail electricity supplier; Based on the bidding process of distributed resources in VPP operation framework, establish the utility model of user-side resources; Based on the bidding process of external market in the operation framework of retail electricity supplier, establish the bidding model of retail electricity supplier considering VPP aggregation risk, obtain the risk cost curve of retail electricity supplier; Based on the risk cost curve of the retail electricity supplier, the risk of the retail electricity supplier is quantitatively analyzed to obtain the risk quantification result of the retail electricity supplier; The establishment of the operation framework of the VPP includes, The user-side resources aggregated by the VPP include distributed power sources and controllable loads, wherein the controllable loads include interruptible loads and translatable loads; The VPP quotes to the distributed power sources and controllable loads of the user side within its jurisdiction respectively, and the internal users report the power of distributed power sources and controllable loads to the VPP according to the quotation of the VPP; The VPP interacts with the internal users in terms of electricity price and quantity, and reports the remaining unbalanced power to the retail electricity supplier if the output of wind power and photovoltaic power of the VPP is greater than the actual power consumption of the users; The economic risk of the retail electricity supplier participating in the external market transaction according to the reported power is obtained according to the fluctuation of the actual power of the user-side loads aggregated by the VPP and the subjectivity of the user response; The establishment of the operation framework of the retail electricity supplier includes, The retail electricity supplier aggregates the user-side resources aggregated by the VPP to form a resource aggregation body that meets the access rules of the electricity market; After the retail electricity supplier transmits the electricity price from the external day-ahead electricity market to each VPP under its jurisdiction, the VPP interacts with the aggregated user side in terms of electricity price and quantity based on the operation framework of the VPP, and reports the remaining unbalanced power to the retail electricity supplier; The retail electricity supplier purchases power in the external day-ahead and real-time electricity market through bidding, and trades the unbalanced power in the real-time electricity market; The economic risk in the transaction process is obtained according to the uncertainty of the day-ahead market electricity price and the uncertainty of the reported power of the VPP; The establishment of the utility model of the user-side resources includes, max U = U DG + U DL where U is the total utility of VPP users owned by the electricity retailer, U DG is the utility of all distributed generation users, U DL is the utility of all controllable load users; The objective function is designed based on the maximum utility of the distributed power sources and controllable loads: The utility U of all distributed power source users DG is: It also includes, The utility U of the total controllable load users DL is: The output constraint of the distributed power sources is: Where T is the bidding period, and N is the total number of distributed power users within the VPP jurisdiction owned by the electricity sales company. Let t be the bidding electricity amount reported by the i-th distributed power user to the VPP during time period t. Let A be the electricity price quoted by VPP to the i-th distributed power user during time period t. i B i C i These are the output cost coefficients for the i-th distributed power user; M is the total number of controllable load users under the VPP owned by the electricity sales company. and Let q be the actual electricity consumption and rigid electricity consumption of the j-th controllable load user in time period t, a be a constant, ε be the demand-price elasticity coefficient, and P be the load factor. RT,t This refers to the retail electricity price that the VPP pays to users during time period t. and These represent the interruptible load price and the shiftable load price that VPP offers to user j during time period t, respectively. and These represent the interruptible load bidding volume and the shiftable load bidding volume for user j during time period t, respectively. and These represent the maximum and minimum output values ​​of the i-th distributed power user during the entire T-period; and These are the downslope rate and upslope rate of the power output of the i-th distributed power user, respectively; These are the maximum and minimum interruptible load values ​​for the j-th controllable load user, respectively; Let be the maximum and minimum values ​​of the transferable load for the j-th controllable load user, respectively. Let t be the time interval for the transferable load of the j-th controllable load user during time period t; These are the lower and upper limits of the load shifting time interval for the j-th controllable load user, respectively. The initial load of the j-th controllable load user in time period t; The constraint of the controllable loads is: The obtaining of the risk cost curve of the retail electricity supplier includes, max f1 = C RT - (C DG + C DL + C MT ) The objective function is designed based on the maximum operation income of the retail electricity supplier: The income and expenditure of the retail electricity supplier are calculated according to the following formula: The aggregation risk of the VPP is quantitatively calculated by using conditional value at risk, and the objective function is designed based on the minimum conditional value at risk: The opposite number-f1 of the operation income f1 of the VPP is defined as the operation cost of the VPP, and the objective function is designed based on the minimum cost and minimum risk of the operation of the VPP: where f1is the VPP operation revenue, C RT is the income of VPP retailing electricity to load users at the retail price, C DG is the cost of VPP purchasing electricity from distributed power sources, C DL is the cost of VPP paying to controllable load users in demand response, C MT is the payment cost of VPP purchasing and selling electricity from day-ahead external market; P M,t is the electricity price of day-ahead external market, σ t is the standard deviation, is the actual electricity consumption of the jth user in the t period, which is subject to normal distribution is the mean of the user's electricity consumption, σ RE,t is the standard deviation, Q M,t is the purchasing and selling electricity of the VPP to the day-ahead market, which is positive indicating purchasing electricity from the market and negative indicating selling electricity to the market; f2is the risk borne by the VPP in operation, is the estimated approximation of conditional risk value, α is the VaR value under certain confidence level β and risk level constraint, i.e. the maximum possible loss per unit of the VPP; f 1,k is the VPP operation revenue of the kth sample in the total K samples from historical or simulated future sample data, [-f 1,k - α] + is max(0, -f 1,k - α). min R=(-f1,f2); It also includes, Q M,min ≤Q M,t ≤Q M,max P RT,min ≤P RT,t ≤P RT,max wherein, are the lower and upper limits of the offer of the VPP to the ith distributed generation consumer at time period t, are the lower and upper limits of the offer of the VPP to the interruptible load of the jth controllable load consumer at time period t, are the lower and upper limits of the offer of the VPP to the shiftable load of the jth controllable load consumer at time period t, Q M,min , are the minimum and maximum values of the electricity quantity of the VPP participating in the day-ahead external market transaction, P RT,min ,P RT,max ,P RT,avg are the minimum, maximum and average values of the retail electricity price of the VPP to the internal consumer, Q W,t +Q PV,t are the output of wind power (W) and photovoltaic (PV) at time period t; The market access constraint is: The quantitative analysis includes, Based on the bidding model of the retail electricity supplier considering VPP aggregation risk, the bidding results taken by the retail electricity supplier and the VPPs under it are obtained;The specific bidding results taken by the retail electricity supplier and the VPPs under it include retail electricity price, DG, PV, W power, DGIL and TL electricity price and power; When the VPP aggregation risk increases, satisfies normal distribution and the bidding results taken by the electricity retailer and the affiliated VPP remain unchanged, the new aggregation risk of the electricity retailer is obtained, and the new aggregation risk of the electricity retailer is expressed by the conditional risk value as follows: Wherein, δ is the uncertainty degree factor, representing the degree of risk increase; f 2，new The new aggregated risk value of the electricity seller when the bidding result is unchanged for δ change; The risk of the electricity seller may exist quantitative analysis, including the following steps: Initialize all VPPs owned by the electricity seller to report the price of the internal aggregated user-side resources, denoted as k, and the user reports DG and DL power according to the price, denoted as l; then calculate the user-side utility U, Determine whether the user utility is maximized, if not, return to update the DG and DL declaration amount l until the user-side utility is maximized; If the maximization is reached, determine the DG and DL power corresponding to the maximum user-side utility U according to the VPP price k, and enter the next operation; The electricity seller makes decisions on the day-ahead market purchase and sale of electricity and retail electricity price, denoted as m, and optimizes the total operating cost and conditional risk value CVaR of the electricity seller and all VPPs under it based on the genetic algorithm, denoted as-f1 and f2, respectively; Determine whether it converges, if not, return to update the day-ahead market purchase and sale of electricity and retail electricity price decision m of the electricity seller in the upper layer; If it converges, calculate the non-dominated solution set virtual congestion degree of the objective function-f1 and f2, combine the parent and child populations to obtain the elite strategy, and enter the next operation; Determine whether it reaches the non-inferior solution set of the multi-objective model, if it is the inferior solution set, return to update the price k of all VPPs owned by the electricity seller to report the internal aggregated user-side resources, until it reaches the non-inferior solution set of the multi-objective model, and enter the next operation; Determine whether the iteration number i reaches the maximum value I, if the iteration number does not reach the maximum value I, return to re-initialize the price of all VPPs owned by the electricity seller to report the internal aggregated user-side resources; If the iteration number reaches the maximum value I, output the VPP internal resource price and quantity, the electricity seller and external market purchase and sale strategy, and the risk cost curve of the electricity seller; Calculate the aggregation risk of the electricity seller due to the uncertainty of user load, user response and external market price, and realize the quantification of the change of the aggregation risk according to the uncertainty degree of the aggregation risk.

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