A virtual power plant inter-provincial and intra-provincial two-level market joint bidding decision model construction method and virtual power plant joint bidding method

Abstract

This invention discloses a method for constructing a joint bidding decision-making model for virtual power plants in inter-provincial and intra-provincial markets. Relating to the field of virtual power plants, the method first systematically models the bidding decision-making process of virtual power plants participating in inter-provincial and intra-provincial markets, clearly defining their dual role as price setters in the inter-provincial market and price takers in the intra-provincial market. Simultaneously, it introduces the Conditional Value at Risk (CVaR) method to accurately characterize the multiple uncertainties existing in inter-provincial electricity prices, intra-provincial electricity prices, and internal resource output. By quantifying and effectively controlling potential economic losses under extreme scenarios, the overall robustness of the bidding model is improved, ensuring the reliability of strategy implementation.

Description

Technical Field

[0001] This invention relates to virtual power plants, specifically a method for constructing a joint bidding decision-making model for inter-provincial and intra-provincial markets for virtual power plants, and a method for joint bidding in virtual power plants. Background Technology

[0002] Significant spatial differences exist in energy resource endowments and electricity supply and demand patterns among provinces in my country. Inter-provincial electricity trading can break down regional barriers, optimize the allocation of electricity resources on a larger scale, and play a vital supporting role in promoting the consumption of renewable energy and ensuring the security of electricity supply. Against this backdrop, my country is accelerating the construction of a unified national electricity market system, and has basically formed an overall framework for the coordinated operation of inter-provincial and intra-provincial electricity markets under a unified market and two-tier operation model.

[0003] To guide new market players such as distributed resources and adjustable loads to participate in the optimal allocation of resources across provinces and regions, various regions have innovatively launched diversified inter-provincial trading products based on their own characteristics. Taking the East China Power Grid as an example, the Yangtze River Delta region has established a mutual assistance trading market for surplus renewable energy consumption and a mutual assistance trading market for surplus demand-side adjustable resources. The former aims to improve the region's renewable energy consumption level, while the latter focuses on alleviating power supply pressure during peak hours. Both types of transactions are initiated by the power-receiving provinces within the region, and new players participate by independently submitting quantity and price quotations, with clearing prices significantly higher than intra-provincial market prices.

[0004] As a typical new type of market entity, virtual power plants can efficiently aggregate diverse resources such as distributed generation, adjustable load, and energy storage, and are characterized by abundant resource types, outstanding regulation potential, and flexible response. However, in actual operation, virtual power plants generally have low enthusiasm for participating in the inter-provincial market, and the actual transaction volume and frequency of participation are far lower than expected. The core constraint is the profit risk brought about by multiple uncertainties.

[0005] Taking the demand-side adjustable resource surplus mutual assistance transaction in the Yangtze River Delta region as an example: On the one hand, the market mechanism itself has strong uncertainties. First, the various participants are all renewable energy-related entities, with significant differences in adjustment costs and bidding strategies; due to market size limitations, participants have a certain price influence, and their bidding behavior significantly affects the clearing results, making it difficult to accurately predict the clearing price and trading volume. Second, virtual power plants participating in inter-provincial transactions must first meet the clearing constraints within their respective provinces. If the actual response volume does not reach 30% of the winning bid volume in the inter-provincial market, settlement will not be made for that period, resulting in significant revenue losses. On the other hand, the internal resources aggregated by virtual power plants, such as wind and solar power output and load response, are inherently random and volatile, further exacerbating operational and revenue risks.

[0006] Due to the combined effects of the aforementioned multiple uncertainties, virtual power plants are prone to problems such as large output fluctuations, excessive regulation response deviations, and low efficiency in cross-regional resource coordination and allocation during cross-market dispatch and power allocation. They tend to participate only in the intra-provincial market for local consumption and find it difficult to participate stably in the inter-provincial market for optimal allocation of power resources and cross-provincial ancillary services. Summary of the Invention

[0007] The purpose of this invention is to provide a method for constructing a joint bidding decision model for virtual power plants in inter-provincial and intra-provincial markets, and a method for joint bidding of virtual power plants, so as to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides the following technical solution: A method for constructing a joint bidding decision-making model for virtual power plants in inter-provincial and intra-provincial markets, characterized by the following steps: Step 1: Establish a joint bidding decision model for virtual power plants in inter-provincial and intra-provincial markets: With the goal of maximizing the comprehensive expected return of virtual power plants, construct the objective function by weighted summation of expected return and conditional value at risk (CVaR) under each scenario. Without considering energy storage arbitrage behavior, virtual power plants are uniformly set as electricity sellers in inter-provincial and intra-provincial markets. Step 2: Construct the physical constraints for the operation of the virtual power plant: covering the upper and lower limits of output of wind power, photovoltaic, adjustable load, and energy storage, mutual exclusion constraints for energy storage charging and discharging, continuous dynamic constraints of state of charge (SOC) and energy balance constraints, establishing a two-level market bidding coupling constraint between the province and the province, clarifying that the sum of the day-ahead bid volume within the province and the day-ahead bid volume between the province equals the total adjustable output of the virtual power plant, while also meeting the upper limit constraints of the declared capacity in the province and between the province. Step 3: Construct real-time deviation and inter-provincial three-stage settlement rules: Calculate the excess electricity sales and shortfall in the real-time market within the province based on the deviation between the actual output of the virtual power plant and the winning bid amount; determine the valid settlement amount according to the inter-provincial settlement threshold, and do not settle if the amount is below the threshold, settle according to the actual amount if the amount is between the threshold and the winning bid amount, and settle according to the winning bid amount if the amount exceeds the winning bid amount. Step 4: Quantify uncertainty and control risk based on Conditional Value at Risk (CVaR): Characterize the random fluctuations of inter-provincial electricity prices, intra-provincial electricity prices, and internal resource output in a scenario-based manner, calculate the tail loss under extreme scenarios, balance returns and risks through the risk aversion coefficient, and output the optimal joint bidding strategy.

[0009] As a further preferred embodiment of the present invention, the objective of the joint bidding decision model for virtual power plants in inter-provincial and intra-provincial markets is to maximize the overall expected profit of virtual power plants (VPPs) participating in the two-tier markets.

[0010] As a further preferred embodiment of the present invention: the objective is expressed as the weighted sum of expected profit and value at risk (CVaR) under all scenarios; the virtual power plant is considered as a seller in inter-provincial and intra-provincial markets, and arbitrage activities involving energy storage are not considered. The objective function F is expressed as follows: (1) Where S and s represent the total number and sequence number of the random scenarios, respectively. This represents the risk aversion coefficient. Let represent the probability of scenario s occurring. This represents the total profit under a single scenario s, and CVaR represents the worst-case scenario. The expected profit, CVaR is calculated using the following formula: (2) in Value at Risk (VaR) It is an auxiliary variable when the tail loss exceeds VaR in scenario s. , and Meet the conditions ; The expression is: (3) Where T and t represent the total number and sequence number of the random scenarios, respectively. and Revenue from the provincial market and the inter-provincial market, respectively. This represents the operating cost of internal resources of the virtual power plant (VPP) under scenario s; The specific expressions for revenue and cost items are as follows: (4) This indicates the current day's price within the province. and They represent positive and negative deviations, respectively. and negative deviation The real-time market clearing price is defined in equations (16) and (17); (5) in It is a unified settlement price in the inter-provincial market. This refers to the effective electricity volume settled between provinces. The inter-provincial market adopts a unified marginal pricing mechanism based on the intersection of the aggregated supply and demand price curves. (6) in and These represent the unit charging and discharging costs of the energy storage system (ESS), respectively. Indicates the compensation cost of demand response (DR) , and These represent the previous day's scheduled power for ESS and DR, respectively.

[0011] As a further preferred embodiment of the present invention, the objective function F is subject to the following constraints: Runtime constraints The current scheduling and bidding processes satisfy the energy balance and physical boundary constraints of internal resources: (7) in , , These represent the response values ​​of wind power, solar power, and adjustable load within the VPP, respectively. , , These represent the upper limits of the previous day's forecast for wind power, solar power, and adjustable load, respectively. and These represent the charging power and discharging power of the ESS in the VPP, respectively. and These represent the upper limits of charging power and discharging power, respectively. It is an exclusive binary variable for ESS charging / discharging. State of Charge (SOC) and These represent the charging / discharging efficiency, respectively. and They represent The lower and upper limits; Quotation constraints (8) (9) (10) in , These represent the day-to-day bidding volume in the provincial market and the inter-provincial market, respectively. This represents the total available capacity of a virtual power plant (VPP), which is equal to the sum of the installed capacity of wind power, solar power, energy storage, and the maximum load response capacity within the VPP. This indicates demand in inter-provincial markets; Real-time deviation and inter-provincial settlement constraints (11) (12) (13) (14) (15) (16) (17) , , , These represent the actual output responses of the virtual power plant (VPP), wind power, solar power, and demand response, respectively. and These represent VPP's actual output in the provincial market and the inter-provincial market, respectively. This indicates the amount of electricity that can be used in inter-provincial markets after meeting the needs of the provincial market. This represents the winning bid volume in the inter-provincial market settlement, multiplied by the safety inspection reduction factor. The final settlement amount can then be obtained. , , These represent the surplus electricity generated after meeting inter-provincial market demand and the shortfall when meeting intra-provincial market demand, respectively, which are traded in the intra-provincial real-time market. Inter-provincial three-stage settlement (18) in This indicates the settlement volume in the inter-provincial market. Less than of If, then no settlement will be made. Between of Between 100% and 100%, according to Settlement, if Exceed Then, according to 100% Settlement.

[0012] As a further preferred embodiment of the present invention: the solution of the virtual power plant inter-provincial and intra-provincial two-level market joint bidding decision model is as follows: The physical parameters and operational constraints of various resources within a virtual power plant, including wind and solar power output, adjustable load, and energy storage systems, are initialized. Key data such as day-ahead and real-time electricity prices in inter-provincial and intra-provincial electricity markets, market clearing rules, and inter-provincial three-stage settlement thresholds are input. The Gurobi solver is used to solve the joint bidding optimization model of the inter-provincial and intra-provincial two-tier markets based on Conditional Value at Risk (CVaR). The outputs the optimal day-ahead bid volume of the virtual power plant in the inter-provincial and intra-provincial markets, internal resource scheduling plans, and revenue and risk levels under various scenarios. Based on the solution results, the differences in bidding strategies of the virtual power plant under different risk preferences, the characteristics of power output allocation in the inter-provincial and intra-provincial two-tier markets, and economic losses under extreme scenarios are quantitatively analyzed, thus verifying the effectiveness of the joint bidding strategy under uncertainty.

[0013] Furthermore, this invention also provides a virtual power plant joint bidding method, which adopts the virtual power plant inter-provincial and intra-provincial two-level market joint bidding decision model, clearly defining its dual role as a price setter in the inter-provincial market and a price taker in the intra-provincial market; at the same time, it introduces the Conditional Value at Risk (CVaR) method to accurately characterize the multiple uncertainties existing in inter-provincial electricity prices, intra-provincial electricity prices and internal resource output, quantifying and controlling economic losses in extreme scenarios.

[0014] Compared with the prior art, the beneficial effects of the present invention are: (1) By constructing a two-tiered pricing decision model for inter-provincial and intra-provincial markets, the role of virtual power plants as price setters in inter-provincial markets and price takers in intra-provincial markets is clarified. Conditional value at risk method is introduced to quantify and control multiple uncertainties, effectively suppressing operational deviations caused by fluctuations in wind and solar power output and load response, reducing the risk of cross-market dispatch mismatch, and improving the stability of power output response and the robustness of strategies.

[0015] (2) This invention constructs internal operational physical constraints, inter-provincial and intra-provincial two-tier market bidding coupling constraints, and inter-provincial three-stage settlement rules, which accurately align with the actual power market operation mechanism and assessment requirements, and realize coordinated allocation and reasonable settlement of power output in inter-provincial and intra-provincial markets. The proposed strategy can provide a scientific basis for virtual power plants in cross-level power markets, improve the efficiency of cross-regional resource optimization and allocation, smooth power fluctuations, and promote the efficient consumption of renewable energy. Attached Figure Description

[0016] Figure 1 This is a diagram showing the expected return composition and tail risk indicators for different trading models in this embodiment of the invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Please see Figure 1 In this embodiment of the invention, the present invention addresses the problems faced by virtual power plants in inter-provincial and intra-provincial coupled electricity markets, including multiple uncertainties in electricity prices, wind and solar power output, and load response, leading to conservative bidding, high profit and risk, and insufficient enthusiasm for inter-provincial transactions. The invention proposes a joint bidding method for virtual power plants in inter-provincial and intra-provincial markets that considers these uncertainties. This method constructs a two-tiered bidding decision model for inter-provincial and intra-provincial markets, clarifying the role of virtual power plants as price setters in the inter-provincial market and price takers in the intra-provincial market. It also introduces the Conditional Value at Risk (CVaR) method to quantify and manage economic losses in extreme scenarios, significantly enhancing the robustness of the bidding strategy while improving returns. The above-mentioned technical problems of the present invention are mainly solved through the following technical solutions: Step 1: Establish a joint bidding decision model for virtual power plants in inter-provincial and intra-provincial markets. With the goal of maximizing the overall expected return of virtual power plants, the objective function is constructed by weighted summation of expected return and conditional value at risk (CVaR) under each scenario. Without considering energy storage arbitrage, virtual power plants are uniformly set as electricity sellers in both inter-provincial and intra-provincial markets.

[0019] Step 2: Construct the physical constraints for the virtual power plant's internal operation. This includes upper and lower limits for wind power, photovoltaic power, adjustable loads, and energy storage output; mutual exclusion constraints for energy storage charging and discharging; continuous dynamic constraints for state of charge (SOC); and energy balance constraints to ensure the feasibility of internal resource scheduling. Establish coupling constraints for inter-provincial and intra-provincial market bidding. It is stipulated that the sum of the intra-provincial day-ahead bids and the inter-provincial day-ahead bids equals the total adjustable output within the virtual power plant, while simultaneously satisfying the upper limit constraints for both intra-provincial and inter-provincial market declared capacity, thus achieving coordinated output allocation between the inter-provincial and intra-provincial markets.

[0020] Step 3: Construct real-time deviation and inter-provincial three-stage settlement rules. Based on the deviation between the actual output of the virtual power plant and the winning bid amount, calculate the excess electricity sales and insufficient electricity purchases in the provincial real-time market; determine the valid settlement electricity volume according to the inter-provincial settlement threshold, if it is below the threshold, no settlement will be made, if it is between the threshold and the winning bid amount, settlement will be made according to the actual amount, and if it exceeds the winning bid amount, settlement will be made according to the winning bid amount.

[0021] Step 4: Quantify uncertainty and control risk based on Conditional Value at Risk (CVaR). The random fluctuations in inter-provincial electricity prices, intra-provincial electricity prices, and internal resource output are characterized in a scenario-based manner. Tail losses under extreme scenarios are calculated, and returns and risks are balanced through the risk aversion coefficient to output the optimal joint bidding strategy.

[0022] 3. Method Implementation Instructions Consider a virtual power plant joint bidding model for an inter-provincial / intra-provincial coupled market: In the context of coordinated operation of inter-provincial and intra-provincial power markets, virtual power plants, as rational market players, make bidding decisions based on price signals from the inter-provincial and intra-provincial markets, internal resource output characteristics, and market rules. Their decision-making objective is to maximize the overall expected return throughout the day through coordinated bidding in the inter-provincial and intra-provincial markets, while effectively managing the extreme return risks brought about by multiple uncertainties, under the premise of satisfying internal resource physical constraints, inter-provincial and intra-provincial market bidding coupling constraints, and inter-provincial settlement assessment constraints.

[0023] The objective of the proposed model is to maximize the overall expected profit of virtual power plants (VPPs) participating in both the inter- and intra-provincial markets. This objective is expressed as a weighted sum of expected profit and value at risk (CVaR) across all scenarios. To simplify the analysis and reflect the actual development of virtual power plants, this model treats them as sellers in both inter- and intra-provincial markets and does not consider arbitrage activities involving energy storage. The objective function F is expressed as follows: (1) Where S and s represent the total number and sequence number of the random scenarios, respectively. This represents the risk aversion coefficient. This represents the probability of scenario s occurring. This represents the total profit under a single scenario s. CVaR represents the worst-case scenario. The expected profit. The formula for calculating CVaR is: (2) in Value at Risk (VaR) It is an auxiliary variable in scenario s when the tail loss exceeds VaR. , and Meet the conditions . The expression is: (3) Where T and t represent the total number and sequence number of the random scenarios, respectively. and Revenue from the provincial market and the inter-provincial market, respectively. This represents the operating cost of resources within the Virtual Power Plant (VPP) under scenario s.

[0024] The specific expressions for revenue and cost items are as follows: (4) This indicates the current day's price within the province. and They represent positive and negative deviations, respectively. and negative deviation The real-time market clearing price. Its definition is given in equations (16) and (17).

[0025] (5) in It is a unified settlement price in the inter-provincial market. This refers to the effective electricity volume settled between provinces. The inter-provincial market adopts a unified marginal pricing mechanism based on the intersection of the aggregated supply and demand price curves.

[0026] (6) in and These represent the unit charging and discharging costs of an energy storage system (ESS), respectively. Represents the compensation cost of demand response (DR) , and These represent the previous day's scheduled power for ESS and DR, respectively.

[0027] constraint Runtime constraints Current scheduling and bidding must satisfy the energy balance and physical boundary constraints of internal resources: (7) in , , These represent the response values ​​of wind power, photovoltaic power, and adjustable load within the VPP, respectively. , , These represent the upper limit of the previous day's forecast for wind power, solar power, and adjustable load, respectively. and These represent the charging power and discharging power of the ESS in the VPP, respectively. and These represent the upper limits of charging power and discharging power, respectively. It is a binary variable that enables exclusive charging / discharging of ESS. It indicates the State of Charge (SOC). and These represent the charging / discharging efficiency, respectively. and They represent The lower and upper limits.

[0028] Quotation constraints (8) (9) (10) in , These represent the day-to-day bidding volume in the provincial market and the inter-provincial market, respectively. The total available capacity of a virtual power plant (VPP) is equal to the sum of the installed capacity of wind power, solar power, energy storage, and the maximum load response capacity within the VPP. This indicates the demand in the inter-provincial market.

[0029] Real-time deviation and inter-provincial settlement constraints (11) (12) (13) (14) (15) (16) (17) , , , These represent the actual output (response) of the virtual power plant (VPP), wind power, photovoltaic power, and demand response, respectively. and These represent the actual output of VPP in the provincial market and the inter-provincial market, respectively. This indicates the amount of electricity that can be used in inter-provincial markets after meeting the needs of the provincial market. This represents the winning bid volume in the inter-provincial market clearing. Multiply this by the safety inspection reduction factor. The final settlement amount can then be obtained. . , These represent the surplus electricity generated after meeting inter-provincial market demand and the shortfall when meeting intra-provincial market demand, respectively. They are traded on the intra-provincial real-time market.

[0030] Inter-provincial three-stage settlement (18) in This indicates the settlement volume in inter-provincial markets. If... Less than of If no settlement is made, then no settlement will be made. Between of Between 100% and 100%, according to Settlement. If Exceed Then, according to 100% Settlement.

[0031] Model solution: The process initializes the physical parameters and operational constraints of various resources within the virtual power plant, including wind and solar power output, adjustable loads, and energy storage systems. Key data input includes day-ahead and real-time electricity prices in inter-provincial and intra-provincial electricity markets, market clearing rules, and inter-provincial three-tier settlement thresholds. The Gurobi solver is used to solve the joint bidding optimization model for the inter-provincial and intra-provincial markets based on Conditional Value at Risk (CVaR). The outputs the virtual power plant's optimal day-ahead bid volume in both inter-provincial and intra-provincial markets, internal resource scheduling plans, and profit and risk levels under various scenarios. Based on the solution results, the differences in bidding strategies of the virtual power plant under different risk preferences, the characteristics of power output allocation in the inter-provincial and intra-provincial markets, and economic losses under extreme scenarios are quantitatively analyzed, thus verifying the effectiveness of the joint bidding strategy under uncertainty.

[0032] Experimental Results: To verify the effectiveness of the proposed framework and strategy, three comparative models were set up for verification: Model 1 (Deterministic Intra-Provincial Market Model, Det. Intra) participates in the intra-provincial electricity market based solely on deterministic point predictions, without considering uncertainties and without risk control measures; Model 2 (Deterministic Two-Tier Market Model, Det. Dual) participates in both intra-provincial and inter-provincial electricity markets, but makes decisions based solely on day-ahead deterministic predictions, without explicit control over uncertainties such as wind and solar output, electricity prices, and load, resulting in weak resistance to random fluctuations; Model 3 (Stochastic Two-Tier Market Model, Sto. Dual) is the proposed stochastic optimization framework, which can effectively coordinate the cross-market bidding strategies of virtual power plants. By integrating uncertainties from multiple scenarios into the decision-making process, it fully considers the fluctuation characteristics and correlations of various random variables, achieving robust mitigation of market risks.

[0033] Comparative verification revealed significant differences in the economic benefits and risk resilience of different models (see Figure f1 for results): Model 1, which only participates in the provincial market, has the lowest theoretical expected return; Model 2, which adopts an aggressive pricing strategy, can achieve the highest theoretical expected return, but has the longest risk threshold and extremely high financial risk exposure; the expected return of the proposed Model 3 is slightly lower than that of Model 2, but the risk indicators are significantly optimized and the risk level is greatly reduced, effectively achieving a balance between profitability and robustness, fully demonstrating the effectiveness and superiority of the proposed framework and strategy.

[0034] Experimental Conclusions: This model and method effectively address the problems of conservative bidding, high return-risk, and insufficient enthusiasm for inter-provincial transactions caused by multiple uncertainties in electricity prices, wind and solar output, and load response in the inter-provincial-intra-provincial coupled electricity market. The proposed method constructs a two-tiered decision-making model for the inter-provincial and intra-provincial markets, clarifying the roles of virtual power plants as price setters in the inter-provincial market and price takers in the intra-provincial market. Combined with the CVaR method, it quantifies and manages risk, significantly enhancing the robustness of bidding strategies while improving overall returns. This provides scientific decision-making support for virtual power plants to participate in cross-regional electricity markets and efficiently participate in resource optimization.

[0035] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0036] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for constructing a virtual power plant inter-provincial and intra-provincial two-level market joint bidding decision model, characterized in that, Includes the following steps: Step 1: Establish a joint bidding decision model for virtual power plants in inter-provincial and intra-provincial markets: With the goal of maximizing the comprehensive expected return of virtual power plants, construct the objective function by weighted summation of expected return and conditional value at risk (CVaR) under each scenario. Without considering energy storage arbitrage behavior, virtual power plants are uniformly set as electricity sellers in inter-provincial and intra-provincial markets. Step 2: Construct the physical constraints for the operation of the virtual power plant: covering the upper and lower limits of output of wind power, photovoltaic, adjustable load, and energy storage, mutual exclusion constraints for energy storage charging and discharging, continuous dynamic constraints of state of charge (SOC) and energy balance constraints, establishing a two-level market bidding coupling constraint between the province and the province, clarifying that the sum of the day-ahead bid volume within the province and the day-ahead bid volume between the province equals the total adjustable output of the virtual power plant, while also meeting the upper limit constraints of the declared capacity in the province and between the province. Step 3: Construct real-time deviation and inter-provincial three-stage settlement rules: Calculate the excess electricity sales and shortfall in the real-time market within the province based on the deviation between the actual output of the virtual power plant and the winning bid amount; determine the valid settlement amount according to the inter-provincial settlement threshold, and do not settle if the amount is below the threshold, settle according to the actual amount if the amount is between the threshold and the winning bid amount, and settle according to the winning bid amount if the amount exceeds the winning bid amount. Step 4: Quantify uncertainty and control risk based on Conditional Value at Risk (CVaR): Characterize the random fluctuations of inter-provincial electricity prices, intra-provincial electricity prices, and internal resource output in a scenario-based manner, calculate the tail loss under extreme scenarios, balance returns and risks through the risk aversion coefficient, and output the optimal joint bidding strategy.

2. The method according to claim 1, wherein The objective of the proposed virtual power plant inter-provincial and intra-provincial two-tier market joint bidding decision model is to maximize the overall expected profit of virtual power plant (VPP) participating in both markets.

3. The method for constructing a joint bidding decision-making model for virtual power plant inter-provincial and intra-provincial two-tier markets according to claim 2, characterized in that, The objective is expressed as a weighted sum of expected profit and value at risk (CVaR) under all scenarios; the virtual power plant is considered as a seller in both inter-provincial and intra-provincial markets, and arbitrage activities involving energy storage are not considered. The objective function is... F The statement is as follows: (1) Where S and s represent the total number and sequence number of the random scenarios, respectively. This represents the risk aversion coefficient. Let represent the probability of scenario s occurring. This represents the total profit under a single scenario s, and CVaR represents the worst-case scenario. The expected profit, CVaR is calculated using the following formula: (2) in Value at Risk (VaR) It is an auxiliary variable when the tail loss exceeds VaR in scenario s. , and Meet the conditions ; The expression is: (3) Where T and t represent the total number and sequence number of the random scenarios, respectively. and Revenue from the provincial market and the inter-provincial market, respectively. This represents the operating cost of internal resources of the virtual power plant (VPP) under scenario s; The specific expressions for revenue and cost items are as follows: (4) This indicates the current day's price within the province. and They represent positive and negative deviations, respectively. and negative deviation The real-time market clearing price is defined in equations (16) and (17); (5) in It is a unified settlement price in the inter-provincial market. This refers to the effective electricity volume settled between provinces. The inter-provincial market adopts a unified marginal pricing mechanism based on the intersection of the aggregated supply and demand price curves. (6) in and These represent the unit charging and discharging costs of the energy storage system (ESS), respectively. Indicates the compensation cost of demand response (DR) , and These represent the previous day's scheduled power for ESS and DR, respectively.

4. The method for constructing a joint bidding decision-making model for virtual power plant inter-provincial and intra-provincial two-tier markets according to claim 3, characterized in that, The objective function F The constraints are as follows: Runtime constraints The current scheduling and bidding processes satisfy the energy balance and physical boundary constraints of internal resources: (7) in , , These represent the response values ​​of wind power, solar power, and adjustable load within the VPP, respectively. , , These represent the upper limits of the previous day's forecast for wind power, solar power, and adjustable load, respectively. and These represent the charging power and discharging power of the ESS in the VPP, respectively. and These represent the upper limits of charging power and discharging power, respectively. It is an exclusive binary variable for ESS charging / discharging. State of Charge (SOC) and These represent the charging / discharging efficiency, respectively. and They represent The lower and upper limits; Quotation constraints (8) (9) (10) in , These represent the day-to-day bidding volume in the provincial market and the inter-provincial market, respectively. This represents the total available capacity of a virtual power plant (VPP), which is equal to the sum of the installed capacity of wind power, solar power, energy storage, and the maximum load response capacity within the VPP. This indicates demand in inter-provincial markets; Real-time deviation and inter-provincial settlement constraints (11) (12) (13) (14) (15) (16) (17) , , , These represent the actual output responses of the virtual power plant (VPP), wind power, solar power, and demand response, respectively. and These represent VPP's actual output in the provincial market and the inter-provincial market, respectively. This indicates the amount of electricity that can be used in inter-provincial markets after meeting the needs of the provincial market. This represents the winning bid volume in the inter-provincial market settlement, multiplied by the safety inspection reduction factor. The final settlement amount can then be obtained. , , These represent the surplus electricity generated after meeting inter-provincial market demand and the shortfall when meeting intra-provincial market demand, respectively, which are traded in the intra-provincial real-time market. Inter-provincial three-stage settlement (18) in This indicates the settlement volume in the inter-provincial market. Less than of If, then no settlement will be made. Between of Between 100% and 100%, according to Settlement, if Exceed Then, according to 100% Settlement.

5. The method for constructing a joint bidding decision-making model for virtual power plant inter-provincial and intra-provincial two-tier markets according to claim 4, characterized in that, The solution to the virtual power plant's inter-provincial and intra-provincial two-tier market joint bidding decision model is as follows: The physical parameters and operational constraints of various resources within a virtual power plant, including wind and solar power output, adjustable load, and energy storage systems, are initialized. Key data such as day-ahead and real-time electricity prices in inter-provincial and intra-provincial electricity markets, market clearing rules, and inter-provincial three-stage settlement thresholds are input. The Gurobi solver is used to solve the joint bidding optimization model of the inter-provincial and intra-provincial two-tier markets based on Conditional Value at Risk (CVaR). The outputs the optimal day-ahead bid volume of the virtual power plant in the inter-provincial and intra-provincial markets, internal resource scheduling plans, and revenue and risk levels under various scenarios. Based on the solution results, the differences in bidding strategies of the virtual power plant under different risk preferences, the characteristics of power output allocation in the inter-provincial and intra-provincial two-tier markets, and economic losses under extreme scenarios are quantitatively analyzed, thus verifying the effectiveness of the joint bidding strategy under uncertainty.

6. A method for joint bidding of virtual power plants, characterized in that, The virtual power plant adopts the inter-provincial and intra-provincial two-level market joint bidding decision model as described in any one of claims 1-5, which clearly defines its dual role as a price setter in the inter-provincial market and a price taker in the intra-provincial market. At the same time, the Conditional Value at Risk (CVaR) method is introduced to accurately characterize the multiple uncertainties in inter-provincial electricity prices, intra-provincial electricity prices and internal resource output, and to quantify and control economic losses in extreme scenarios.

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