Double-layer optimization method and system for virtual power plant to participate in market balanced transaction

Abstract

The invention is suitable for the technical field of market transaction optimization, and provides a double-layer optimization method and system for a virtual power plant to participate in market balanced transaction, and the method comprises the steps: constructing an upper-layer optimization model and a lower-layer optimization model, and enabling the upper-layer optimization model to achieve the maximization of the total profit of the virtual power plant as a target; the lower-layer optimization model takes market transaction subject benefit balance and market resource allocation optimization as targets; setting constraint conditions of the upper-layer model, wherein the constraint conditions comprise an energy market segmented quotation constraint, a standby market quotation constraint, a standby market capacity constraint and a distributed energy standby availability probability constraint; setting constraint conditions of the lower-layer model, wherein the constraint conditions comprise a node power balance constraint, a standby power consumption balance constraint, a transaction volume constraint, a capacity coupling constraint and a network topology constraint; and solving the solutions of the upper and lower optimization models by adopting a distributed iterative algorithm, and outputting the optimal transaction strategy and the market clearing price of the virtual power plant, so that the model adaptability is improved, and meanwhile, the balance of the benefits of the source-load double-side market subjects can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of market transaction optimization, and in particular to a double-layer optimization method and system for a virtual power plant to participate in market equilibrium transactions. Background Art

[0002] As a power system operation mode, virtual power plants can effectively enhance the absorption capacity of new energy by aggregating distributed energy resources. However, the access, quotation, and settlement rules for virtual power plants participating in the peak-shaving and frequency-regulation markets currently vary widely across provinces, and cross-market coordination capabilities are insufficient, which limits the benefits of virtual power plants. Furthermore, most existing research focuses on the economic benefits of virtual power plants themselves or is limited to a single market. There is a lack of effective quantitative models for promoting optimal resource allocation and improving the absorption capacity of the power grid. This makes it difficult to coordinate the interests of both the source and the load, and it is also unable to adapt to complex market environments. Furthermore, due to the volatility of wind and solar power output, static models cannot capture and adapt to these dynamic changes in real time, and lack the ability to flexibly adjust to different market environments and operating conditions.

[0003] In view of this, a two-layer optimization method and system for virtual power plants to participate in market equilibrium transactions is proposed. Summary of the Invention

[0004] The present invention provides a two-layer optimization method and system for a virtual power plant to participate in market equilibrium transactions, which is used to solve the problem that the existing model has poor adaptability and is difficult to coordinate the interests of both the source and the load.

[0005] A first aspect of the present invention provides a two-layer optimization method for a virtual power plant to participate in market equilibrium transactions, comprising:

[0006] An upper-level optimization model and a lower-level optimization model are constructed respectively. The upper-level optimization model aims to maximize the total profit of the virtual power plant, while the lower-level optimization model aims to balance the interests of market transaction entities and optimize market resource allocation.

[0007] Setting constraints for the upper-level optimization model, including energy market segmented bidding constraints, reserve market bidding constraints, reserve market capacity constraints, and distributed energy reserve availability probability constraints; setting constraints for the lower-level optimization model, including node power balance constraints, reserve power balance constraints, transaction volume constraints, capacity coupling constraints, and network topology constraints;

[0008] A distributed iterative algorithm is used to solve the double-layer equilibrium solution of the upper-layer optimization model and the lower-layer optimization model, and the optimal trading strategy of the virtual power plant and the market clearing electricity price are output.

[0009] Furthermore, the upper-level optimization model aims to maximize the total profit of the virtual power plant, and the calculation formula includes:

[0010]

[0011] Among them: F x is the total profit of virtual power plant x, and are the profit of the virtual power plant x in the electricity spot market, the profit of the reserve market, the profit of the reserve market, and the penalty for reserve response shortage at time t under the ω scenario, τ w A collection of virtual power plant transaction scenarios, π ω is the probability of scene ω appearing.

[0012] Furthermore, the upper-level optimization model aims to maximize the total profit of the virtual power plant and further includes:

[0013]

[0014] Where: Ω X For the distributed energy collection under the virtual power plant X, is the transaction volume of the kth segment of distributed energy l on the supply side at time t under scenario ω, γ m,t,ω is the marginal electricity price of node m at time t under scenario ω, and They are the electricity selling pricing strategies of virtual power plant X for the load in its jurisdiction, and They are respectively the increase and decrease of the standby trading power of the whole network at time t under the scenario ω, and They are the upward and downward reserve transaction prices of distributed energy l on the supply side at time t, and are the cost coefficients for providing backup capacity when the distributed energy resources on the supply side are adjusted upward and downward, and are the probability of distributed energy l increasing and decreasing reserve availability at time t under scenario ω, γ P is the penalty coefficient corresponding to the shortage of virtual power plant's reserve response capacity.

[0015] Furthermore, the constraint conditions of the upper optimization model are set, including energy market segmented bidding constraints, reserve market bidding constraints, reserve market capacity constraints, and distributed energy reserve availability probability constraints, including:

[0016] Energy market segmented bidding constraints:

[0017]

[0018] in: and is the transaction price of the kth and k-1th segments of the distributed energy l on the supply side at time t, γ l,max and γL,min They are the upper and lower limits of the distributed energy market transaction quotation respectively;

[0019] Alternative market quotation constraints:

[0020]

[0021] Where: γ RU,max and γ RD,max They are the upper and lower quotation caps for distributed energy resources in the reserve market;

[0022] Reserve market capacity constraints:

[0023]

[0024] in: and are the declared capacities of the distributed energy l on the supply side in the upward and downward reserve markets at time t, and is the transaction status of distributed energy l in the upward and downward reserve markets at time t, is the maximum backup capacity of distributed energy l;

[0025] Probabilistic constraints on distributed energy backup availability:

[0026]

[0027]

[0028] in: is the basic probability of the supply-side distributed energy l being successfully called in the reserve market, is the incentive coefficient of the expected profit of distributed energy l in the reserve market.

[0029] Furthermore, the lower-level optimization model aims to achieve a balance of interests among market transaction entities and optimize market resource allocation. The calculation formula includes:

[0030]

[0031] in: is the k-th phase quotation of the distributed energy l on the supply side, is the price quoted by buyer d for the qth segment at time t in scenario ω, is the buyer's electricity transaction volume for the qth period at time t under scenario ω, and They are respectively the upward and downward adjustment of the reserve quotation for distributed energy l kth segment, and They are the upward and downward reserve trading power of distributed energy l in the kth segment at time t under scenario ω.

[0032] Furthermore, the constraint conditions of the lower-level optimization model are set, including node power balance constraint, backup power balance constraint, transaction volume constraint, capacity coupling constraint and network topology constraint, including:

[0033] Node power balance constraints:

[0034]

[0035] in: is the set of nodes associated with node m, is the supply-side distributed energy load set associated with node m, is the demand side load set associated with node m, δ m,t,ω and δ n,t,ω are the power angles of node m and node n at time t under scenario ω, B mn is the line susceptance from node m to node n, γ m,t,ω is the dual variable of the corresponding constraint;

[0036] Reserve power balance constraints:

[0037]

[0038] in: and are the upward and downward reserve requirements of the system at time t, and is the dual variable of the corresponding constraint, and The upward and downward adjustment of the reserve capacity of the kth segment of the distributed energy resource l at time t under scenario ω;

[0039] Trading volume constraints:

[0040]

[0041] in: is the upper limit of the transaction volume of the kth segment of distributed energy l on the supply side, is the upper limit of the transaction volume of buyer d in the qth segment under scenario ω, and are the upper and lower limits of the transaction volume of the kth segment of distributed energy l, respectively. The right side [·] of each constraint contains its corresponding dual variable;

[0042] Capacity coupling constraints:

[0043]

[0044] in: is the maximum load capacity of distributed energy l;

[0045] Network topology constraints:

[0046]

[0047] in: is the transmission power limit of the line from node m to node n, δ max and δ min are the upper and lower limits of the node power angle, are the corresponding dual variables.

[0048] Furthermore, the distributed iterative algorithm is used to solve the double-layer equilibrium solution of the upper optimization model and the lower optimization model, and output the optimal trading strategy of the virtual power plant and the market clearing electricity price, including:

[0049] Setting the initial quotation strategy and initial standby transaction capacity of the upper-layer optimization model and transmitting them to the lower-layer optimization model;

[0050] Solving the objective function of the lower optimization model based on the initial bidding strategy to obtain the market clearing electricity price, transaction volume and spare capacity, and passing the dual variables to the upper optimization model;

[0051] Based on the dual variables and clearing results of the lower-level optimization model, the bidding strategy and spare capacity allocation of the upper-level optimization model are updated to generate new decision variables, which are then passed to the lower-level optimization model;

[0052] Repeat the steps of solving the lower-level optimization model and updating the upper-level optimization model until the objective function difference meets the convergence condition and the market clearing electricity price fluctuation is less than the preset threshold, and output the final virtual power plant optimal trading strategy and market clearing electricity price.

[0053] Furthermore, the method of using a distributed iterative algorithm to solve the double-layer equilibrium solution of the upper-layer optimization model and the lower-layer optimization model, and outputting the optimal trading strategy of the virtual power plant and the market-clearing electricity price, further includes:

[0054] The update formula of the upper optimization model is:

[0055]

[0056] Among them: η and λ are the iteration step coefficients, and are the quotation strategies for the n+1th and nth iterations respectively, is the dual variable corresponding to the lower limit of energy market transaction volume, and are the spare transaction capacities for the n+1th and nth iterations respectively.

[0057] Furthermore, after using the distributed iterative algorithm to solve the double-layer equilibrium solution of the upper-layer optimization model and the lower-layer optimization model and outputting the optimal trading strategy of the virtual power plant and the market clearing electricity price, the method further includes:

[0058] Adjust the weight probability of each scenario based on real-time fluctuation data of wind and photovoltaic output;

[0059] Based on the actual output fluctuations of distributed energy and the market clearing electricity price, the availability probability of upward and downward reserve is dynamically adjusted;

[0060] The adjusted weight probability and backup availability probability of each scenario are input into the upper optimization model and the lower optimization model to determine the optimal trading strategy and market clearing electricity price of the optimized virtual power plant.

[0061] A second aspect of the present invention provides a two-tier optimization system for a virtual power plant to participate in market equilibrium transactions, comprising:

[0062] An upper-layer optimization model and a lower-layer optimization model construction unit, for respectively constructing an upper-layer optimization model and a lower-layer optimization model, wherein the upper-layer optimization model aims to maximize the total profit of the virtual power plant, and the lower-layer optimization model aims to balance the interests of market transaction entities and optimize the allocation of market resources;

[0063] A constraint setting unit is used to set the constraints of the upper-layer optimization model, including energy market segmented bidding constraints, reserve market bidding constraints, reserve market capacity constraints, and distributed energy reserve availability probability constraints; and set the constraints of the lower-layer optimization model, including node power balance constraints, reserve power balance constraints, transaction volume constraints, capacity coupling constraints, and network topology constraints;

[0064] The virtual power plant optimal trading strategy and market clearing electricity price output unit is used to use a distributed iterative algorithm to solve the double-layer equilibrium solution of the upper optimization model and the lower optimization model, and output the virtual power plant optimal trading strategy and market clearing electricity price.

[0065] It can be seen from the above technical solutions that the present invention has the following advantages:

[0066] This invention constructs a two-layer collaborative optimization model consisting of an upper-layer optimization model and a lower-layer optimization model, combining the constraints of the energy market and the reserve market to effectively coordinate the interests of virtual power plants and market entities, improving the capacity to absorb new energy and the efficiency of resource allocation. By optimizing trading strategies and clearing electricity prices in real time through a distributed iterative algorithm, the model's flexibility and adaptability are enhanced, addressing issues such as insufficient cross-market coordination, poor adaptability of static models, and imbalanced interests between sources and loads. This maximizes the economic benefits of virtual power plants while improving the stability of power system operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 This is a flow chart of an embodiment of a two-layer optimization method for a virtual power plant to participate in market equilibrium transactions in the present invention. DETAILED DESCRIPTION

[0068] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "corresponding to" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0069] Example 1

[0070] The implementation method in this embodiment can be implemented in the system, can be implemented in the server, and can also be implemented in the terminal, and the specific implementation is not clearly limited. The following will introduce the two-layer optimization method of the virtual power plant participating in the market equilibrium transaction in this application from the perspective of system implementation. Figure 1 , the method provided in the embodiment of the present application includes the following steps:

[0071] S11. Construct an upper-level optimization model and a lower-level optimization model respectively. The upper-level optimization model aims to maximize the total profit of the virtual power plant, while the lower-level optimization model aims to balance the interests of market transaction entities and optimize market resource allocation.

[0072] S12. Set constraints for the upper-level optimization model, including energy market segmented bidding constraints, reserve market bidding constraints, reserve market capacity constraints, and distributed energy reserve availability probability constraints; set constraints for the lower-level optimization model, including node power balance constraints, reserve power balance constraints, transaction volume constraints, capacity coupling constraints, and network topology constraints;

[0073] The objective function of the upper optimization model is:

[0074]

[0075] Among them: F x is the total profit of virtual power plant x, and are the profit of the virtual power plant x in the electricity spot market, the profit of the reserve market, the profit of the reserve market, and the penalty for reserve response shortage at time t under the ω scenario, τw A collection of virtual power plant transaction scenarios, π ω is the probability of scene ω appearing.

[0076]

[0077] Where: Ω X For the distributed energy collection under the virtual power plant X, is the transaction volume of the kth segment of distributed energy l on the supply side at time t under scenario ω, γ m,t,ω is the marginal electricity price of node m at time t under scenario ω, and They are the electricity selling pricing strategies of virtual power plant X for the load in its jurisdiction, and They are respectively the increase and decrease of the standby trading power of the whole network at time t under the scenario ω, and They are the upward and downward reserve transaction prices of distributed energy l on the supply side at time t, and are the cost coefficients for providing backup capacity when the distributed energy resources on the supply side are adjusted upward and downward, and are the probability of distributed energy l increasing and decreasing reserve availability at time t under scenario ω, γ P is the penalty coefficient corresponding to the shortage of virtual power plant's reserve response capacity.

[0078] The constraints of the upper optimization model are as follows:

[0079] Energy market segmented bidding constraints:

[0080]

[0081] in: and is the transaction price of the kth and k-1th segments of the distributed energy l on the supply side at time t, γ l,max and γ L,min They are the upper and lower limits of the distributed energy market transaction quotation respectively;

[0082] Alternative market quotation constraints:

[0083]

[0084] Where: γ RU,max and γ RD,max They are the upper and lower quotation caps for distributed energy resources in the reserve market;

[0085] Reserve market capacity constraints:

[0086]

[0087] in: and are the declared capacities of the distributed energy l on the supply side in the upward and downward reserve markets at time t, and is the transaction status of distributed energy l in the upward and downward reserve markets at time t, is the maximum backup capacity of distributed energy l;

[0088] The different regulation characteristics of loads under the control of virtual power plants will lead to significant changes in the reserve market quota constraints. For loads with strong controllability, in order to facilitate the formulation of load transfer strategies, it is assumed that each load can only participate in the reserve market in a single time period, and the total transaction volume of reserve increase and reserve decrease within a week is guaranteed to be consistent:

[0089]

[0090] For loads that can be curtailed, the virtual power plant can sign a number of curtailment contracts with them, thereby providing system upward reserve by reducing online load on the load side:

[0091]

[0092] For directly controlled loads, virtual power plants can directly control them and therefore participate in both the upward and downward reserve markets:

[0093]

[0094] In actual transactions, distributed energy is affected by factors such as instability and volatility, and there is also uncertainty in responding to transactions in the reserve market. Considering the expected market returns can, to a certain extent, increase the enthusiasm of distributed energy to participate in energy market transactions and auxiliary markets, and enhance the success probability of distributed energy being called. Therefore, the probability constraint of distributed energy reserve availability is:

[0095]

[0096] in: is the basic probability of the supply-side distributed energy l being successfully called in the reserve market, is the incentive coefficient of the expected profit of distributed energy l in the reserve market.

[0097] In the lower-level model, the virtual power plant, as an independent system operator, integrates the transaction information of market entities on both sides of the load supply and demand, takes into account the interests of the transaction entities on both sides and the overall social resource allocation, and establishes a function model with the goal of achieving a balance of interests among transaction entities and optimal allocation of social resources.

[0098] The objective function of the lower optimization model is:

[0099]

[0100] in: is the k-th phase quotation of the distributed energy l on the supply side, is the price quoted by buyer d for the qth segment at time t in scenario ω, is the buyer's electricity transaction volume for the qth period at time t under scenario ω, and They are respectively the upward and downward adjustment of the reserve quotation for distributed energy l kth segment, and They are the upward and downward reserve trading power of distributed energy l in the kth segment at time t under scenario ω.

[0101] The constraints of the lower-level optimization model are as follows:

[0102] Node power balance constraints:

[0103]

[0104] in: is the set of nodes associated with node m, is the supply-side distributed energy load set associated with node m, is the demand side load set associated with node m, δ m,t,ω and δ n,t,ω are the power angles of node m and node n at time t under scenario ω, B mn is the line susceptance from node m to node n, γ m,t,ω is the dual variable of the corresponding constraint;

[0105] Reserve power balance constraints:

[0106]

[0107] in: and are the upward and downward reserve requirements of the system at time t, and is the dual variable of the corresponding constraint, and The upward and downward adjustment of the reserve capacity of the kth segment of the distributed energy resource l at time t under scenario ω;

[0108] Trading volume constraints:

[0109]

[0110] in: is the upper limit of the transaction volume of the kth segment of distributed energy l on the supply side, is the upper limit of the transaction volume of buyer d in the qth segment under scenario ω, and are the upper and lower limits of the transaction volume of the kth segment of distributed energy l, respectively. The right side [·] of each constraint contains its corresponding dual variable;

[0111] Capacity coupling constraints:

[0112]

[0113] in: is the maximum load capacity of distributed energy l;

[0114] Network topology constraints:

[0115]

[0116]

[0117] in: is the transmission power limit of the line from node m to node n, δ max and δ min are the upper and lower limits of the node power angle, are the corresponding dual variables.

[0118] S13. A distributed iterative algorithm is used to solve the double-layer equilibrium solution of the upper-layer optimization model and the lower-layer optimization model, and output the optimal trading strategy of the virtual power plant and the market clearing electricity price.

[0119] In this embodiment, solving the dual-layer equilibrium solution includes the following steps:

[0120] 1. Set the initial quotation strategy of the upper optimization model and initial spare transaction capacity And pass it to the lower optimization model;

[0121] 2. Solve the objective function of the lower optimization model based on the initial bidding strategy to obtain the market clearing electricity price Trading volume and spare capacity And the dual variable Passed to the upper optimization model;

[0122] 3. Based on the dual variables and clearing results of the lower-level optimization model, update the bidding strategy and spare capacity allocation of the upper-level optimization model to generate new decision variables And pass it to the lower optimization model;

[0123] Update formula of the upper optimization model:

[0124]

[0125] Among them: η and λ are the iteration step coefficients, and are the quotation strategies for the n+1th and nth iterations respectively, is the dual variable corresponding to the lower limit of energy market transaction volume, and are the spare transaction capacities for the n+1th and nth iterations respectively.

[0126] 4. Repeat the steps of solving the lower optimization model and updating the upper optimization model until the difference of the objective function meets the convergence condition And the market clearing price fluctuation is less than the preset threshold Among them, ∈ and δ are the preset profit and electricity price convergence thresholds, is the total profit of the upper layer at the nth iteration, The marginal electricity price of node m in the nth iteration is used to output the final optimal trading strategy of the virtual power plant and the market clearing electricity price.

[0127] The above steps realize the dynamic interaction between the upper and lower models through a distributed iterative algorithm, and use dual variables to associate the market clearing electricity price with the virtual power plant strategy to ensure the convergence and computational efficiency of the two-layer equilibrium solution.

[0128] In this embodiment, after using a distributed iterative algorithm to solve the double-layer equilibrium solution of the upper-layer optimization model and the lower-layer optimization model and outputting the optimal trading strategy of the virtual power plant and the market-clearing electricity price, the following steps are also included:

[0129] 1. Adjust the weight probability of each scenario based on the real-time fluctuation data of wind and photovoltaic output; the calculation formula is as follows:

[0130]

[0131] in: and are the probabilities of each scene ω before and after updating, ΔP ω is the deviation between the predicted value and the actual value of wind and solar power output under scenario ω, and θ is the adjustment coefficient.

[0132] 2. Dynamically adjust the availability probability of upward and downward reserve based on the actual output fluctuations of distributed energy resources and the market clearing electricity price;

[0133] According to the actual output fluctuation of distributed energy and the market clearing electricity price Update upward and downward adjustments to the probability of standby availability The calculation formula is:

[0134]

[0135] in: is the base probability of standby call, is the market return incentive coefficient.

[0136] 3. Input the adjusted weight probability and backup availability probability of each scenario into the upper-level optimization model and the lower-level optimization model to determine the optimal trading strategy and market clearing electricity price of the optimized virtual power plant.

[0137] The updated and Input the two-level optimization model and re-solve the equilibrium solution to adapt to the dynamic market environment and resource fluctuations.

[0138] The above steps enhance the model's adaptability to wind and solar power output fluctuations and market changes through real-time data-driven scenario probability updates and dynamic adjustment of backup availability probability, ensuring the flexibility of virtual power plants in cross-market transactions.

[0139] Example 2

[0140] An embodiment of a two-tier optimization system for a virtual power plant to participate in market equilibrium transactions in the present invention includes the following steps:

[0141] The upper optimization model and lower optimization model construction units are used to construct the upper optimization model and the lower optimization model respectively. The upper optimization model aims to maximize the total profit of the virtual power plant, while the lower optimization model aims to balance the interests of market transaction entities and optimize market resource allocation.

[0142] The constraint setting unit is used to set the constraints of the upper-level optimization model, including energy market segmented bidding constraints, reserve market bidding constraints, reserve market capacity constraints, and distributed energy reserve availability probability constraints; and set the constraints of the lower-level optimization model, including node power balance constraints, reserve power balance constraints, transaction volume constraints, capacity coupling constraints, and network topology constraints;

[0143] The virtual power plant optimal trading strategy and market clearing electricity price output unit is used to use a distributed iterative algorithm to solve the double-layer equilibrium solution of the upper optimization model and the lower optimization model, and output the virtual power plant optimal trading strategy and market clearing electricity price.

[0144] For the specific definition of the optimization system, please refer to the definition of the optimization method above and will not be repeated here. Each module in the above-mentioned optimization system can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software so that the processor can call and execute the operations corresponding to each of the above modules.

[0145] It is understandable that those skilled in the art can, under the guidance of the above embodiments, combine various implementation methods in the above embodiments to obtain technical solutions of multiple implementation methods.

[0146] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A two-layer optimization method for virtual power plants to participate in market equilibrium transactions, characterized in that: include: An upper-level optimization model and a lower-level optimization model are constructed respectively. The upper-level optimization model aims to maximize the total profit of the virtual power plant, while the lower-level optimization model aims to balance the interests of market transaction entities and optimize market resource allocation. Setting constraints for the upper-level optimization model, including energy market segmented bidding constraints, reserve market bidding constraints, reserve market capacity constraints, and distributed energy reserve availability probability constraints; setting constraints for the lower-level optimization model, including node power balance constraints, reserve power balance constraints, transaction volume constraints, capacity coupling constraints, and network topology constraints; A distributed iterative algorithm is used to solve the double-layer equilibrium solution of the upper-layer optimization model and the lower-layer optimization model, and the optimal trading strategy of the virtual power plant and the market clearing electricity price are output.

2. The two-layer optimization method for virtual power plants to participate in market equilibrium transactions according to claim 1 is characterized in that: The upper-level optimization model aims to maximize the total profit of the virtual power plant, and the calculation formula includes: Among them: F x is the total profit of virtual power plant x, and are the profit of the virtual power plant x in the electricity spot market, the profit of the reserve market, the profit of the reserve market, and the penalty for reserve response shortage at time t under the ω scenario, τ w A collection of virtual power plant transaction scenarios, π ω is the probability of scene ω appearing.

3. The two-layer optimization method for virtual power plants to participate in market equilibrium transactions according to claim 2 is characterized in that: The upper-level optimization model aims to maximize the total profit of the virtual power plant and also includes: Where: Ω X For the distributed energy collection under the virtual power plant X, is the transaction volume of the kth segment of distributed energy l on the supply side at time t under scenario ω, γ m,t,ω is the marginal electricity price of node m at time t under scenario ω, and They are the electricity selling pricing strategies of virtual power plant X for the load in its jurisdiction, and They are respectively the increase and decrease of the standby trading power of the whole network at time t under the scenario ω, and They are the upward and downward reserve transaction prices of distributed energy l on the supply side at time t, and are the cost coefficients for providing backup capacity when the distributed energy resources on the supply side are adjusted upward and downward, and are the probability of distributed energy l increasing and decreasing reserve availability at time t under scenario ω, γ P is the penalty coefficient corresponding to the shortage of virtual power plant's reserve response capacity.

4. The two-layer optimization method for virtual power plants to participate in market equilibrium transactions according to claim 3 is characterized in that: The constraint conditions for setting the upper optimization model include energy market segmented bidding constraints, reserve market bidding constraints, reserve market capacity constraints, and distributed energy reserve availability probability constraints, including: Energy market segmented bidding constraints: in: and is the transaction price of the kth and k-1th segments of the distributed energy l on the supply side at time t, γ l,max and γ L,min They are the upper and lower limits of the distributed energy market transaction quotation respectively; Alternative market quotation constraints: Where: γ RU,max and γ RD,max They are the upper and lower quotation caps for distributed energy resources in the reserve market; Reserve market capacity constraints: in: and are the declared capacities of the distributed energy l on the supply side in the upward and downward reserve markets at time t, and is the transaction status of distributed energy l in the upward and downward reserve markets at time t, is the maximum backup capacity of distributed energy l; Probabilistic constraints on distributed energy backup availability: in: is the basic probability of the supply-side distributed energy l being successfully called in the reserve market, is the incentive coefficient of the expected profit of distributed energy l in the reserve market.

5. The two-layer optimization method for virtual power plants to participate in market equilibrium transactions according to claim 1 is characterized in that: The lower-level optimization model aims to balance the interests of market transaction entities and optimize market resource allocation. The calculation formula includes: in: is the k-th phase quotation of the distributed energy l on the supply side, is the price quoted by buyer d for the qth segment at time t in scenario ω, is the buyer's electricity transaction volume for the qth period at time t under scenario ω, and They are respectively the upward and downward adjustment of the reserve quotation for distributed energy l kth segment, and They are the upward and downward reserve trading power of distributed energy l in the kth segment at time t under scenario ω.

6. The two-layer optimization method for virtual power plants to participate in market equilibrium transactions according to claim 5 is characterized in that: The constraint conditions for setting the lower-level optimization model include node power balance constraints, standby power balance constraints, transaction volume constraints, capacity coupling constraints, and network topology constraints, including: Node power balance constraints: in: is the set of nodes associated with node m, is the supply-side distributed energy load set associated with node m, is the demand side load set associated with node m, δ m,t,ω and δ n,t,ω are the power angles of node m and node n at time t under scenario ω, B mn is the line susceptance from node m to node n, γ m,t,ω is the dual variable of the corresponding constraint; Reserve power balance constraints: in: and are the upward and downward reserve requirements of the system at time t, and is the dual variable of the corresponding constraint, and The upward and downward adjustment of the reserve capacity of the kth segment of the distributed energy resource l at time t under scenario ω; Trading volume constraints: in: is the upper limit of the transaction volume of the kth segment of distributed energy l on the supply side, is the upper limit of the transaction volume of buyer d in the qth segment under scenario ω, and are the upper and lower limits of the transaction volume of the kth segment of distributed energy l, respectively. The right side [·] of each constraint contains its corresponding dual variable; Capacity coupling constraints: in: is the maximum load capacity of distributed energy l; Network topology constraints: in: is the transmission power limit of the line from node m to node n, δ max and δ min are the upper and lower limits of the node power angle, are the corresponding dual variables.

7. The two-layer optimization method for virtual power plants to participate in market equilibrium transactions according to claim 1 is characterized in that: The distributed iterative algorithm is used to solve the double-layer equilibrium solution of the upper optimization model and the lower optimization model, and output the optimal trading strategy of the virtual power plant and the market clearing electricity price, including: Setting the initial quotation strategy and initial standby transaction capacity of the upper-layer optimization model and transmitting them to the lower-layer optimization model; Solving the objective function of the lower optimization model based on the initial bidding strategy to obtain the market clearing electricity price, transaction volume and spare capacity, and passing the dual variables to the upper optimization model; Based on the dual variables and clearing results of the lower-level optimization model, the bidding strategy and spare capacity allocation of the upper-level optimization model are updated to generate new decision variables, which are then passed to the lower-level optimization model; Repeat the steps of solving the lower-level optimization model and updating the upper-level optimization model until the objective function difference meets the convergence condition and the market clearing electricity price fluctuation is less than the preset threshold, and output the final virtual power plant optimal trading strategy and market clearing electricity price.

8. The two-layer optimization method for virtual power plants to participate in market equilibrium transactions according to claim 7 is characterized in that: The method of using a distributed iterative algorithm to solve the double-layer equilibrium solution of the upper-layer optimization model and the lower-layer optimization model, and outputting the optimal trading strategy of the virtual power plant and the market clearing electricity price, further includes: The update formula of the upper optimization model is: Among them: η and λ are the iteration step coefficients, and are the quotation strategies for the n+1th and nth iterations respectively, is the dual variable corresponding to the lower limit of energy market transaction volume, and are the spare transaction capacities for the n+1th and nth iterations respectively.

9. The two-layer optimization method for virtual power plants to participate in market equilibrium transactions according to claim 1 is characterized in that: After using the distributed iterative algorithm to solve the double-layer equilibrium solution of the upper-layer optimization model and the lower-layer optimization model and outputting the optimal trading strategy of the virtual power plant and the market-clearing electricity price, the method further includes: Adjust the weight probability of each scenario based on real-time fluctuation data of wind and photovoltaic output; Based on the actual output fluctuations of distributed energy and the market clearing electricity price, the availability probability of upward and downward reserve is dynamically adjusted; The adjusted weight probability and backup availability probability of each scenario are input into the upper optimization model and the lower optimization model to determine the optimal trading strategy and market clearing electricity price of the optimized virtual power plant.

10. A two-layer optimization system for a virtual power plant to participate in market equilibrium transactions, using the two-layer optimization method for a virtual power plant to participate in market equilibrium transactions according to any one of claims 1 to 9, characterized in that: include: An upper-layer optimization model and a lower-layer optimization model construction unit, for respectively constructing an upper-layer optimization model and a lower-layer optimization model, wherein the upper-layer optimization model aims to maximize the total profit of the virtual power plant, and the lower-layer optimization model aims to balance the interests of market transaction entities and optimize the allocation of market resources; A constraint setting unit is used to set the constraints of the upper-layer optimization model, including energy market segmented bidding constraints, reserve market bidding constraints, reserve market capacity constraints, and distributed energy reserve availability probability constraints; and set the constraints of the lower-layer optimization model, including node power balance constraints, reserve power balance constraints, transaction volume constraints, capacity coupling constraints, and network topology constraints; The virtual power plant optimal trading strategy and market clearing electricity price output unit is used to use a distributed iterative algorithm to solve the double-layer equilibrium solution of the upper optimization model and the lower optimization model, and output the virtual power plant optimal trading strategy and market clearing electricity price.

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