A method for point-to-point transaction of regional virtual power plant considering tie-line constraint

Abstract

The application discloses a kind of considering intertie constraint's regional virtual power plant point-to-point transaction method, comprising the following steps: step one, resource declaration;Step two, summary refinement;Step three, centralized clearing;Step four, point-to-point transaction;Step five, call resource;Wherein in the above step one, each virtual power plant internal resource is declared to virtual power plant;Wherein in the above step two, all declaration information is summarized and refined by virtual power plant through virtual power plant management platform, and the quantity-price curve of virtual power plant power is formed;Wherein in the above step three, virtual power plant participates in power market participation centralized market clearing using the formed electricity price curve;The considering intertie constraint's regional virtual power plant point-to-point transaction method provides method support for virtual power plant in a region to carry out point-to-point transaction under the transmission capacity constraint of intertie, realizes the coordination interaction optimization between different virtual power plants.

Description

Technical Field

[0001] This invention relates to the field of electricity market trading technology, specifically to a method for point-to-point trading of virtual power plants within a region, taking into account tie-line constraints. Background Technology

[0002] With the development of renewable energy sources such as wind power and solar power, the proportion of output from traditional generating units is gradually decreasing. To compensate for the lack of flexibility in the power system, it is necessary to utilize massive, random distributed resources to increase the flexibility of the power system. Virtual power plants are an energy utilization model that integrates and manages resources. Virtual power plants can break down the regional limitations of distributed energy sources and aggregate distributed resources such as distributed generation, energy storage, and flexible loads using advanced metering, communication, and control technologies to achieve coordinated optimization and rational utilization of distributed resources.

[0003] Existing research on virtual power plants focuses on the mechanisms and strategies for virtual power plants to participate in centralized markets. In the context of massive distributed resources, virtual power plants are inevitably constructed by different stakeholders. In this case, a peer-to-peer trading method for virtual power plants within a region that considers tie-line constraints is designed to provide methodological support for peer-to-peer trading of virtual power plants within a region under tie-line transmission capacity constraints, thereby achieving coordinated interaction and optimization between different virtual power plants. Summary of the Invention

[0004] The purpose of this invention is to provide a method for point-to-point trading of virtual power plants within a region, taking into account tie line constraints, in order to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for point-to-point trading of virtual power plants within a region considering tie-line constraints, comprising the following steps: Step 1, resource declaration; Step 2, aggregation and refinement; Step 3, centralized clearing; Step 4, point-to-point trading; Step 5, resource allocation;

[0006] In step one above, each virtual power plant submits its internal resources to the virtual power plant.

[0007] In step two above, the virtual power plant summarizes and refines all the application information through the virtual power plant management platform to form the quantity-price curve of the virtual power plant's electricity.

[0008] In step three above, the virtual power plant uses the generated electricity price curve to participate in the electricity market and participate in the centralized market clearing.

[0009] In step four above, virtual power plants within the region determine their roles in the peer-to-peer trading market based on the market clearing results and engage in peer-to-peer transactions with each other.

[0010] In step five above, each virtual power plant obtains the additional electricity volume that needs to be traded based on the pre-clearing results under the constraints of the tie line, and completes the transaction.

[0011] Preferably, in step one, the submission of internal resources of each virtual power plant to the virtual power plant specifically includes:

[0012] 1) Virtual power plant resources include resources with adjustable capabilities such as gas turbines, distributed photovoltaic power generation, distributed wind power generation, energy storage devices, and adjustable load users;

[0013] 2) The information required to be declared for gas turbines includes: the upper and lower limits of the gas turbine's output, the gas turbine's cost curve, and the gas turbine's ramp-up capability data;

[0014] 3) The information required for reporting distributed photovoltaic power generation includes: photovoltaic power generation output forecast curve and cost;

[0015] 4) The information required for distributed wind power generation includes: wind power output forecast curve and cost;

[0016] 5) The information required to be declared for energy storage devices includes: maximum charging and discharging power, rated capacity, maximum state of charge, minimum state of charge, charging and discharging efficiency, and energy storage charging and discharging call-up cost;

[0017] 6) The information that adjustable load users need to report includes: maximum interruptible load reduction, maximum transferable load, allowed interruptible load reduction period, allowed transferable load transfer period, interruptible load reduction call cost, and transferable load transfer call cost.

[0018] Preferably, in step two, the virtual power plant summarizes and refines all declared information through the virtual power plant management platform to form the quantity-price curve of virtual power plant electricity. The specific process includes:

[0019] 1) The quantity-price curve of virtual power plant electricity is mainly formed by the cost curve based on the information declared by gas turbines, distributed photovoltaic power generation, distributed wind power generation, energy storage devices and adjustable load users with adjustable resources in S1.

[0020] 2) The cost curve of a gas turbine is represented by a quadratic function with power generation as the variable, as follows:

[0021]

[0022] Among them, f MT,i P represents the power generation cost of the i-th gas turbine within one dispatch cycle. MT,t,i Let a be the output power of the i-th gas turbine during time period t; MT,ib is the coefficient of the quadratic term in the power generation cost of the i-th gas turbine; MT,i c is the coefficient of the first-order term in the power generation cost of the i-th gas turbine; MT,i This is a constant term in the power generation cost of the i-th gas turbine;

[0023] 3) Distributed generation is divided into two categories: user-side power sources that primarily operate on a self-consumption basis and independently operated distributed power sources. Their costs are as follows:

[0024] For power supplies that primarily operate on a self-consumption basis, the cost is equivalent to the cost of directly reducing the user's flexible load.

[0025] For independently operated distributed power sources, considering that increasing the output of distributed power sources will not increase their cost per kilowatt-hour, their costs can be determined by each operator.

[0026] 4) Energy storage devices are divided into two categories: user-side energy storage devices and independent grid-side energy storage devices. Their costs are expressed as follows:

[0027] The load that the energy storage system installed on the user side uses to participate in demand-side response actually comes from the transfer of user load. Therefore, the cost of the user-side energy storage system participating in demand-side response is completely equivalent to the cost of directly reducing the user's flexible load.

[0028] For independent energy storage power stations, based on the current ancillary services market, the main profit model is providing frequency regulation ancillary services. Currently, the revenue that energy storage can obtain from participating in AGC frequency regulation mainly includes frequency regulation mileage compensation and frequency regulation capacity compensation. Therefore, the cost for this type of energy storage to participate in demand-side response peak shaving during peak electricity consumption periods by accepting virtual power plant dispatch is the reduced frequency regulation revenue during this period and the increased battery loss cost due to participating in demand-side response peak shaving.

[0029]

[0030] Where, N F This indicates the average number of times energy storage participates in frequency regulation per day; P F1 Indicates the FM mileage compensation price; T F P represents the average duration of each frequency modulation; F2 This represents the frequency regulation capacity compensation price. Considering that energy storage needs an additional 1 hour for charging to participate in 1 hour of demand-side response peak shaving, during which time the energy storage cannot participate in the peak shaving market, compensation is needed for this period, hence multiplied by a coefficient of 2; Furthermore, P B Indicates the cost of energy storage batteries; N B Indicates the number of cycles of the energy storage battery; This indicates the loss that occurs when an energy storage battery completes one more cycle;

[0031] 5) The cost of flexible loads from adjustable load users participating in demand-side response is mainly measured by calculating the economic losses caused to users by load losses. Estimated from the perspective of power generation ratio, the cost can be expressed in the following form:

[0032]

[0033] Where C1 represents the average value of user flexible load loss; ΔQ represents the amount of load reduction by the user; Q i0 N represents the user's initial electricity load; G represents the department's total consumer surplus, i.e., the department's added value; G represents the department's annual electricity consumption.

[0034] 6) Combine all the above information to form the total cost curve.

[0035] Preferably, in step three, the specific process of the virtual power plant participating in the electricity market and clearing the centralized market using the generated electricity price curve includes:

[0036] 1) The Safety Constrained Unit Combination (SCUC) procedure is used to calculate the 96-point unit start-up combination for the operating day;

[0037] 2) Based on the daily unit start-up combination, calculate the pre-clearing results of the ancillary services market and modify the upper and lower limits of the output of the corresponding units;

[0038] 3) After modifying the upper and lower limits of the output of the corresponding units, the Safety Constrained Economic Dispatch (SCED) program is used to calculate the output curves of the 96 units and the time-of-use electricity price for the operating day;

[0039] 4) Perform AC power flow safety verification on the unit start-up combination and unit output curve of the operating day. If the AC power flow safety constraints are not met, add the corresponding constraints to the calculation model and repeat the calculation process from step one to step four above until the AC power flow safety constraints are met and the market clearing result is obtained.

[0040] Preferably, in step four, the specific process by which virtual power plants within the region determine their roles in the peer-to-peer trading market based on market clearing results and conduct peer-to-peer transactions with each other includes:

[0041] At a certain transaction time t, when virtual power plant n acts as a buyer:

[0042] n∈N b (t)

[0043] Where, N b (t) is the set of IDs of all virtual power plants that act as buyers within time period t;

[0044] When virtual power plant n acts as the seller:

[0045] n∈N s (t)

[0046] Where, N s (t) is the set of IDs of all virtual power plants that act as sellers within time period t;

[0047] Since the same virtual power plant cannot act as both a buyer and a seller simultaneously, there are constraints between the seller set and the buyer set:

[0048] N s (t)∩N b (t)=φ

[0049] If a virtual power plant neither sells nor buys electricity, meaning it is neither a buyer nor a seller, then:

[0050] n s (t)+n b (t)≤N

[0051] Where, n s (t) represents the number of virtual power plants acting as sellers in the region during time period t, i.e., the set N. s The number of elements in (t); n b (t) represents the number of virtual power plants acting as buyers in the region during time period t, i.e., set N. b The number of elements in (t); N is the total number of virtual power plants in the region.

[0052] Preferably, in step five, each virtual power plant obtains the additional electricity volume that needs to be traded based on the pre-clearing results under the tie-line constraints. The specific process for completing the transaction includes:

[0053] During the transaction, the tie-line transmission power for all virtual power plants' external interactions must meet the following tie-line constraints:

[0054] Q line ≤Q limit

[0055] Among them, Q line Q is the transmission power of the tie line; limit It is the maximum transmission power of the tie line.

[0056] Compared with the prior art, the beneficial effects of the present invention are: the present invention provides a point-to-point trading method for virtual power plants within a region that considers tie line constraints. By aggregating the resource declaration information within the virtual power plants, a cost curve for the virtual power plants to participate in the centralized market is formed. Based on this, a method is designed for virtual power plants within a region to conduct point-to-point trading under tie line transmission capacity constraints based on the centralized clearing results. This provides methodological support for point-to-point trading of virtual power plants within a region under tie line transmission capacity constraints. Attached Figure Description

[0057] Figure 1 This is a flowchart of the method of the present invention;

[0058] Figure 2 This is a flowchart of a transaction method according to an embodiment of the present invention;

[0059] Figure 3 This is a virtual power plant cost curve diagram according to an embodiment of the present invention;

[0060] Figure 4 This is the result of the transmission capacity of the virtual power plant transaction link according to an example of the present invention. Detailed Implementation

[0061] 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.

[0062] Please see Figure 1-4 The present invention provides an embodiment of a method for point-to-point trading of virtual power plants within a region, considering tie-line constraints, comprising the following steps: Step 1, resource declaration; Step 2, aggregation and refinement; Step 3, centralized clearing; Step 4, point-to-point trading; Step 5, resource allocation.

[0063] In step one above, each virtual power plant submits its internal resources to the virtual power plant. This submission specifically includes:

[0064] 1) Virtual power plant resources include resources with adjustable capabilities such as gas turbines, distributed photovoltaic power generation, distributed wind power generation, energy storage devices, and adjustable load users;

[0065] 2) The information required to be declared for gas turbines includes: the upper and lower limits of the gas turbine's output, the gas turbine's cost curve, and the gas turbine's ramp-up capability data;

[0066] 3) The information required for reporting distributed photovoltaic power generation includes: photovoltaic power generation output forecast curve and cost;

[0067] 4) The information required for distributed wind power generation includes: wind power output forecast curve and cost;

[0068] 5) The information required to be declared for energy storage devices includes: maximum charging and discharging power, rated capacity, maximum state of charge, minimum state of charge, charging and discharging efficiency, and energy storage charging and discharging call-up cost;

[0069] 6) The information that adjustable load users need to report includes: maximum interruptible load reduction, maximum transferable load, allowed interruptible load reduction period, allowed transferable load transfer period, interruptible load reduction call cost, and transferable load transfer call cost;

[0070] In step two above, the virtual power plant summarizes and refines all the declared information through the virtual power plant management platform to form the quantity-price curve of the virtual power plant's electricity. The specific process of summarizing and refining all the declared information through the virtual power plant management platform to form the quantity-price curve of the virtual power plant's electricity includes:

[0071] 1) The quantity-price curve of virtual power plant electricity is mainly formed by the cost curve based on the information declared by gas turbines, distributed photovoltaic power generation, distributed wind power generation, energy storage devices and adjustable load users with adjustable resources in S1.

[0072] 2) The cost curve of a gas turbine is represented by a quadratic function with power generation as the variable, as follows:

[0073]

[0074] Among them, f MT,i P represents the power generation cost of the i-th gas turbine within one dispatch cycle. MT,t,i Let a be the output power of the i-th gas turbine during time period t; MT,i b is the coefficient of the quadratic term in the power generation cost of the i-th gas turbine; MT,i c is the coefficient of the first-order term in the power generation cost of the i-th gas turbine; MT,i This is a constant term in the power generation cost of the i-th gas turbine;

[0075] 3) Distributed generation is divided into two categories: user-side power sources that primarily operate on a self-consumption basis and independently operated distributed power sources. Their costs are as follows:

[0076] For power supplies that primarily operate on a self-consumption basis, the cost is equivalent to the cost of directly reducing the user's flexible load.

[0077] For independently operated distributed power sources, considering that increasing the output of distributed power sources will not increase their cost per kilowatt-hour, their costs can be determined by each operator.

[0078] 4) Energy storage devices are divided into two categories: user-side energy storage devices and independent grid-side energy storage devices. Their costs are expressed as follows:

[0079] The load that the energy storage system installed on the user side uses to participate in demand-side response actually comes from the transfer of user load. Therefore, the cost of the user-side energy storage system participating in demand-side response is completely equivalent to the cost of directly reducing the user's flexible load.

[0080] For independent energy storage power stations, based on the current ancillary services market, the main profit model is providing frequency regulation ancillary services. Currently, the revenue that energy storage can obtain from participating in AGC frequency regulation mainly includes frequency regulation mileage compensation and frequency regulation capacity compensation. Therefore, the cost for this type of energy storage to participate in demand-side response peak shaving during peak electricity consumption periods by accepting virtual power plant dispatch is the reduced frequency regulation revenue during this period and the increased battery loss cost due to participating in demand-side response peak shaving.

[0081]

[0082] Where, N F This indicates the average number of times energy storage participates in frequency regulation per day; P F1 Indicates the FM mileage compensation price; T F P represents the average duration of each frequency modulation; F2 This represents the frequency regulation capacity compensation price. Considering that energy storage needs an additional 1 hour for charging to participate in 1 hour of demand-side response peak shaving, during which time the energy storage cannot participate in the peak shaving market, compensation is needed for this period, hence multiplied by a coefficient of 2; Furthermore, P B Indicates the cost of energy storage batteries; N B Indicates the number of cycles of the energy storage battery; This indicates the loss that occurs when an energy storage battery completes one more cycle;

[0083] 5) The cost of flexible loads from adjustable load users participating in demand-side response is mainly measured by calculating the economic losses caused to users by load losses. Estimated from the perspective of power generation ratio, the cost can be expressed in the following form:

[0084]

[0085] Where C1 represents the average value of user flexible load loss; ΔQ represents the amount of load reduction by the user; Q i0 N represents the user's initial electricity load; G represents the department's total consumer surplus, i.e., the department's added value; G represents the department's annual electricity consumption.

[0086] 6) Combine all the above information to form a total cost curve;

[0087] In step three above, the virtual power plant uses the generated electricity price curve to participate in the electricity market and participate in the centralized market clearing process. The specific process of the virtual power plant using the generated electricity price curve to participate in the electricity market and participate in the centralized market clearing process includes:

[0088] 1) The Safety Constrained Unit Combination (SCUC) procedure is used to calculate the 96-point unit start-up combination for the operating day;

[0089] 2) Based on the daily unit start-up combination, calculate the pre-clearing results of the ancillary services market and modify the upper and lower limits of the output of the corresponding units;

[0090] 3) After modifying the upper and lower limits of the output of the corresponding units, the Safety Constrained Economic Dispatch (SCED) program is used to calculate the output curves of the 96 units and the time-of-use electricity price for the operating day;

[0091] 4) Perform AC power flow safety verification on the unit start-up combination and unit output curve of the operating day. If the AC power flow safety constraints are not met, add the corresponding constraints to the calculation model and repeat the calculation process from step one to step four above until the AC power flow safety constraints are met and the market clearing result is obtained.

[0092] In step four above, virtual power plants within the region determine their roles in the peer-to-peer trading market based on the market clearing results and engage in peer-to-peer transactions with each other. The specific process of virtual power plants within the region determining their roles in the peer-to-peer trading market based on the market clearing results and engaging in peer-to-peer transactions includes:

[0093] At a certain transaction time t, when virtual power plant n acts as a buyer:

[0094] n∈N b (t)

[0095] Where, N b (t) is the set of IDs of all virtual power plants that act as buyers within time period t;

[0096] When virtual power plant n acts as the seller:

[0097] n∈N s (t)

[0098] Where, N s (t) is the set of IDs of all virtual power plants that act as sellers within time period t;

[0099] Since the same virtual power plant cannot act as both a buyer and a seller simultaneously, there are constraints between the seller set and the buyer set:

[0100] N s (t)∩N b (t)=φ

[0101] If a virtual power plant neither sells nor buys electricity, meaning it is neither a buyer nor a seller, then:

[0102] n s (t)+n b (t)≤N

[0103] Where, n s(t) represents the number of virtual power plants acting as sellers in the region during time period t, i.e., the set N. s The number of elements in (t); n b (t) represents the number of virtual power plants acting as buyers in the region during time period t, i.e., set N. b The number of elements in (t); N is the total number of virtual power plants in the region;

[0104] In step five above, each virtual power plant, under tie-line constraints, obtains the additional electricity volume that needs to be traded based on the pre-clearing results and completes the transaction. The specific process of each virtual power plant obtaining the additional electricity volume that needs to be traded based on the pre-clearing results and completing the transaction includes:

[0105] During the transaction, the tie-line transmission power for all virtual power plants' external interactions must meet the following tie-line constraints:

[0106] Q line ≤Q limit

[0107] Among them, Q line Q is the transmission power of the tie line; limit It is the maximum transmission power of the tie line.

[0108] Based on the above, the advantages of this invention are that, when used, it aggregates the resource application information within virtual power plants to form a cost curve for virtual power plants to participate in the centralized market. Furthermore, it designs a method for point-to-point transactions between virtual power plants within a region, considering tie-line transmission capacity constraints based on centralized clearing results. This method provides support for point-to-point transactions between virtual power plants within a region under tie-line transmission capacity constraints, enabling coordinated interaction and optimization between different virtual power plants.

[0109] 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.

Claims

1. A method for point-to-point trading of virtual power plants within a region, considering tie-line constraints, characterized in that: Includes the following steps: Step 1: Each virtual power plant submits its internal resources to the virtual power plant. Step two: The virtual power plant summarizes and refines all the application information through the virtual power plant management platform to form the quantity-price curve of the virtual power plant's electricity. Step 3: Virtual power plants utilize the resulting electricity price curves to participate in the electricity market and participate in centralized market clearing. Step four: Based on the market clearing results, virtual power plants within the region determine their roles in the peer-to-peer trading market and engage in peer-to-peer transactions with each other. Step 5: Under the constraints of the tie line, each virtual power plant obtains the additional electricity volume that it needs to trade based on the pre-clearing results, and completes the transaction; In step two, the virtual power plant summarizes and refines all the declared information through the virtual power plant management platform to form the quantity-price curve of the virtual power plant's electricity. The specific process includes: 1) The quantity-price curve for virtual power plant electricity is formed by the information declared by gas turbines, distributed photovoltaic power generation, distributed wind power generation, energy storage devices, and adjustable load users with adjustable resources to form the cost curve; 2) The cost curve of a gas turbine is represented by a quadratic function with power generation as the variable, as follows: ； in, For the first The power generation cost of a gas turbine within one commissioning cycle; For the first Taiwan gas turbine Output power during the time period; For the first The coefficient of the quadratic term in the cost of gas turbine power generation; For the first The coefficient of the primary term in the cost of gas turbine power generation; For the first A constant term in the cost of generating electricity using a gas turbine. 3) Distributed generation is divided into two categories: user-side power sources that primarily operate on a self-consumption basis and independently operated distributed power sources. Their costs are as follows: For power supplies that primarily operate on a self-consumption basis, the cost is equivalent to the cost of directly reducing the user's flexible load. For independently operated distributed power sources, considering that increasing the output of distributed power sources will not increase their cost per kilowatt-hour, their costs can be determined by each operator. 4) Energy storage devices are divided into two categories: user-side energy storage devices and independent grid-side energy storage devices. Their costs are expressed as follows: The load that the energy storage system installed on the user side uses to participate in demand-side response actually comes from the transfer of user load. Therefore, the cost of the user-side energy storage system participating in demand-side response is completely equivalent to the cost of directly reducing the user's flexible load. For independent energy storage power stations, based on the current ancillary services market, the main profit model is providing frequency regulation ancillary services. Currently, the revenue that energy storage can obtain from participating in AGC frequency regulation mainly includes frequency regulation mileage compensation and frequency regulation capacity compensation. Therefore, the cost for this type of energy storage to participate in demand-side response peak shaving during peak electricity consumption periods through virtual power plant dispatch is the reduced frequency regulation revenue during this period plus the increased battery wear cost due to participation in demand-side response peak shaving. ； in, This indicates the average number of times energy storage participates in frequency regulation per day; Indicates the FM mileage compensation price; This indicates the average duration of each frequency modulation. This represents the frequency regulation capacity compensation price. Considering that energy storage needs an additional 1 hour for charging to participate in demand-side peak shaving (1 hour of peak response), during which time energy storage cannot participate in the peak shaving market, compensation is needed for this loss, hence the multiplication by a coefficient of 2. Furthermore, Indicates the cost of energy storage batteries; Indicates the number of cycles of the energy storage battery; This indicates the loss that occurs when an energy storage battery completes one more cycle; 5) The cost of flexible loads from adjustable load users participating in demand-side response is mainly measured by calculating the economic losses caused to users by load losses. Estimated from the perspective of power generation ratio, the cost can be expressed in the following form: ； in, This represents the average value of a user's flexible load loss. This indicates that the user is reducing their load. Indicates the user's initial electrical load; This represents the total consumer surplus of the department, i.e., the added value of the department. This represents the department's annual electricity consumption. 6) Combine all the above information to form the total cost curve.

2. The method for point-to-point trading of virtual power plants within a region considering tie-line constraints according to claim 1, characterized in that: In step one, the submission of internal resources from each virtual power plant to the virtual power plant specifically includes: 1) Virtual power plant resources include gas turbines, distributed photovoltaic power generation, distributed wind power generation, energy storage devices, and resources with adjustable load users that have adjustable capabilities; 2) The information required to be declared for gas turbines includes: the upper and lower limits of the gas turbine's output, the gas turbine's cost curve, and the gas turbine's ramp-up capability data; 3) The information required for reporting distributed photovoltaic power generation includes: photovoltaic power generation output forecast curve and cost; 4) The information required for distributed wind power generation includes: wind power output forecast curve and cost; 5) The information required to be declared for energy storage devices includes: maximum charging and discharging power, rated capacity, maximum state of charge, minimum state of charge, charging and discharging efficiency, and energy storage charging and discharging call-up cost; 6) The information that adjustable load users need to report includes: maximum interruptible load reduction, maximum transferable load, allowed interruptible load reduction period, allowed transferable load transfer period, interruptible load reduction call cost, and transferable load transfer call cost.

3. The method for point-to-point trading of virtual power plants within a region considering tie-line constraints according to claim 1, characterized in that: In step three, the specific process by which the virtual power plant participates in the electricity market and participates in the centralized market clearing using the generated electricity price curve includes: 1) The safety-constrained unit combination program is used to calculate the 96-point unit start-up combination for the operating day; 2) Based on the daily unit start-up combination, calculate the pre-clearing results of the ancillary services market and modify the upper and lower limits of the output of the corresponding units; 3) After modifying the upper and lower limits of the output of the corresponding units, the safety-constrained economic dispatch program is used to calculate the output curves of the 96 units and the time-of-use electricity price for the operating day; 4) Perform AC power flow safety verification on the unit start-up combination and unit output curve of the operating day. If the AC power flow safety constraints are not met, add the corresponding constraints to the calculation model and repeat the calculation process of the first to fourth steps of step three above until the AC power flow safety constraints are met and the market clearing result is obtained.

4. The method for point-to-point trading of virtual power plants within a region considering tie-line constraints according to claim 1, characterized in that: In step four, the specific process by which virtual power plants within the region determine their roles in the peer-to-peer trading market based on the market clearing results and engage in peer-to-peer transactions includes: At a certain trading time When virtual power plants When acting as a buyer: ； in, It is a time period A collection of the IDs of all virtual power plants that are buyers within the same domain; When virtual power plants When acting as a seller: ； in, It is a time period A collection of IDs for all virtual power plants that act as sellers within the same domain; Since the same virtual power plant cannot act as both a buyer and a seller simultaneously, there are constraints between the seller set and the buyer set: ； If a virtual power plant neither sells nor buys electricity, meaning it is neither a buyer nor a seller, then: ； in, yes The number of virtual power plants acting as sellers in a region within a given time period, i.e., the collection. The number of elements; yes The number of virtual power plants acting as buyers within a given time period, i.e., the collection. The number of elements; It represents the total number of virtual power plants in the region.

5. The method for point-to-point trading of virtual power plants within a region considering tie-line constraints according to claim 1, characterized in that: In step five, each virtual power plant obtains the additional electricity volume that needs to be traded based on the pre-clearing results under the tie-line constraints. The specific process of completing the transaction includes: During the transaction, the tie-line transmission power for all virtual power plants' external interactions must meet the following tie-line constraints: ； in, It is the transmission power of the tie line; It is the maximum transmission power of the tie line.

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