A market transaction method and system of a load aggregator based on P2P

Abstract

The application discloses a market transaction method and system of a load aggregator based on P2P, wherein an initial value of a transaction price is issued by a load aggregator end to a producer-consumer end, price information of other producer-consumer ends is obtained through information exchange between the producer-consumer ends, a power plan is independently formulated by each producer-consumer end, and point-to-point price and plan exchange is carried out between the producer-consumer ends, when the adjustment result meets the convergence condition, the final power plan and transaction price of each producer-consumer end are reported to the load aggregator end, in this way, the calculation of the load aggregator end in the day-ahead stage is dispersed to the producer-consumer ends, and limited information exchange protects the user privacy and ensures the safety and timeliness. In the real-time stage, the load aggregator end counts the deviation power and informs other load aggregator ends; the load aggregator end of the power purchase type submits a sealed bid to a transaction platform; the platform determines a transaction result based on the VCG auction rule, and the electric energy deviation can be effectively eliminated.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of point-to-point transaction of electricity market, and particularly relates to a market transaction method and system of a load aggregator based on P2P. BACKGROUND

[0002] At present, microgrids and active distribution networks, as important access points for photovoltaic and wind power, play a key role in improving the consumption level of new energy. With the further development of new energy, the proportion of producers and consumers with power generation and power consumption capabilities in the distribution network is gradually increasing. Current research shows that by reasonably coordinating and scheduling these producers and consumers, local power sharing can be achieved, thereby bringing multiple advantages such as reducing system operation cost, enhancing distribution network capacity, and promoting local consumption of distributed energy.

[0003] Although producers and consumers play an increasingly important role in the power system, their enthusiasm for responding to market electricity price information is relatively low due to their distribution at the bottom of the system structure. At the same time, due to the small size of the producers and consumers, the flexibility level of the flexible energy resources they manage is often insufficient to meet the standard for participating in the electricity market. In addition, numerous small-scale transactions also bring problems such as computational redundancy and information security risks to the electricity market. Therefore, as a bridge between the electricity market and producers and consumers, the load aggregator can effectively integrate producer and consumer resources and participate in market operation.

[0004] In some current research, the load aggregator adopts a centralized scheduling model, aiming to optimize management with the smallest scheduling cost. However, when facing a high proportion of producer and consumer resources, this optimization model may face the challenge of "curse of dimensionality", i.e. with the increase in the number of variables, the computational complexity and difficulty increase sharply. In addition, there is a trust problem between the end users and the operator. The load aggregator also faces information security risks in the management process, which may lead to the leakage of private data of end users, causing unnecessary losses and worries to the producers and consumers.

[0005] With the introduction of the interactive energy mechanism into the marketization operation of the power system, the load aggregator can use market incentive rules to coordinate and optimize scheduling through value signals, thereby controlling the operation of the cluster of producers and consumers participating in the electricity market. At present, the peer-to-peer (P2P) market transaction mode is considered as an effective way to promote local power sharing and effective use of distributed energy. However, most P2P transactions currently use Lagrange relaxation and multiplier alternating direction methods and other strategies based on dual price variable updates. Although these strategies can be considered as a competitive auction mechanism in theory, the large number of iterations of the algorithm leads to a long operation time, so they are not suitable for real-time operation in the actual electricity market. SUMMARY

[0006] The technical problem solved by the present application is to provide a market transaction method and system of a load aggregator based on P2P, which can ensure the security and timeliness of the electricity market transaction and further reduce the risk of information leakage in the transaction process.

[0007] To solve the above technical problems, the technical scheme adopted by the present application is:

[0008] A market transaction method of a load aggregator based on P2P, comprising the steps of:

[0009] S1. The load aggregator end issues an initial value of a transaction price to the producer-consumer end belonging to it;

[0010] S2. The transaction price of each producer-consumer end is transmitted between the producer-consumer ends belonging to the load aggregator end to calculate the power plan of each producer-consumer end; the power plan and the transaction price of each producer-consumer end are transmitted between the producer-consumer ends through point-to-point to adjust the transaction price of each producer-consumer end, and when the adjustment result meets the convergence condition, the final power plan and transaction price of each producer-consumer end are reported to the load aggregator end;

[0011] S3. The load aggregator end calculates the real-time power deviation and classifies the load aggregator end according to the real-time power deviation; if the load aggregator end is a power supply type, the load aggregator end provides a sealed bid to the transaction platform; if the load aggregator end is a power purchase type, the load aggregator end reports the demand power to the transaction platform;

[0012] S4. The total demand of the load aggregator end is calculated in the transaction platform, and the VCG is used for auction to obtain an auction result.

[0013] To solve the above technical problems, another technical scheme adopted by the present application is:

[0014] A market transaction system of a load aggregator based on P2P, comprising a transaction platform, a load aggregator end and a producer-consumer end thereof;

[0015] The transaction platform executes each step in the above market transaction method of a load aggregator based on P2P with the transaction platform as the execution subject;

[0016] The load aggregator end executes each step in the above market transaction method of a load aggregator based on P2P with the load aggregator end as the execution subject;

[0017] The producer-consumer end executes each step in the above market transaction method of a load aggregator based on P2P with the producer-consumer end as the execution subject.

[0018] The beneficial effects of the present application are that the load aggregator end issues the initial value of the transaction price for the producer-consumer end belonging to the load aggregator end, the price information of other producer-consumer ends is obtained through information exchange between the producer-consumer ends, the power plan of each producer-consumer end is independently formulated, and the price and plan are exchanged point-to-point between the producer-consumer ends, when the adjustment result meets the convergence condition, the final power plan and transaction price of each producer-consumer end are reported to the load aggregator end, in this way, the calculation of the load aggregator end in the day-ahead stage is dispersed to the producer-consumer end, the load aggregator end does not need a complex calculation process, and the operation burden is reduced, at the same time, limited information exchange protects the user privacy, and ensures the security and timeliness. In the real-time stage, the load aggregator end statistics the deviation power and informs other load aggregator ends; then, the load aggregator end of the power purchase type submits a sealed bid to the transaction platform; finally, the platform determines the transaction result based on the VCG auction rule. In this way, the real-time stage promotes fair competition, encourages real bidding, effectively eliminates the power deviation, and realizes the incentive compatibility of the user and the power market. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 A flow chart of a market transaction method of a P2P-based load aggregator according to an embodiment of the present application;

[0020] Figure 2 A schematic diagram of a market transaction system of a P2P-based load aggregator according to an embodiment of the present application;

[0021] Figure 3 A specific step flow chart of a market transaction method of a P2P-based load aggregator according to an embodiment of the present application. DETAILED DESCRIPTION

[0022] To make the technical content, the achieved purposes and effects of the present application clear, the following will be described in detail in combination with the embodiments and the accompanying drawings.

[0023] Please refer to Figure 1 The embodiment of the present application provides a market transaction method of a P2P-based load aggregator, which comprises the following steps:

[0024] S1, issuing, by the load aggregator end, the initial value of the transaction price for the producer-consumer end belonging to the load aggregator end;

[0025] S2, transmitting the respective transaction prices between the producer-consumer ends belonging to the load aggregator end to calculate the power plan of each producer-consumer end; transmitting the respective power plans and transaction prices between the producer-consumer ends through point-to-point to adjust the transaction price of each producer-consumer end, and when the adjustment result meets the convergence condition, reporting the final power plan and transaction price of each producer-consumer end to the load aggregator end;

[0026] S3, the load aggregator end counts real-time power deviation, and classifies the load aggregator end according to the real-time power deviation, if the load aggregator end is a power supply type, the load aggregator end provides sealed bid to the trading platform, if the load aggregator end is a power purchase type, the load aggregator end reports demand power to the trading platform;

[0027] S4, the total demand of the load aggregator end is counted in the trading platform, and VCG is used for auction, and an auction result is obtained.

[0028] From the above description, the beneficial effects of the present application are that the load aggregator end issues the initial value of the transaction price for the producer-consumer end, the producer-consumer end obtains the price information of other producer-consumer ends through information exchange, independently formulates the power plan, and exchanges the price and plan with other producer-consumer ends, when the adjustment result meets the convergence condition, each producer-consumer end reports the final power plan and transaction price to the load aggregator end, in this way, the calculation of the load aggregator end in the day-ahead stage is dispersed to the producer-consumer end, the load aggregator end does not need complex calculation process, and the operation burden is reduced, at the same time, limited information exchange protects the user privacy, and ensures the safety and timeliness. In the real-time stage, the load aggregator end counts the deviation power and informs other load aggregator ends; then, the load aggregator end of the power purchase type submits sealed bid to the trading platform; finally, the platform determines the transaction result based on the VCG auction rule. In this way, the real-time stage promotes fair competition, encourages real bid, effectively eliminates the power deviation, and realizes the incentive compatibility of the user and the power market.

[0029] Further, the step S2 further comprises:

[0030] When the adjustment result does not meet the convergence condition, the step S2 is re-executed according to the adjustment result.

[0031] Further, the step S2 further comprises:

[0032] The transaction price of each producer-consumer end is transmitted between the producer-consumer ends managed by the load aggregator end to calculate the power plan of each producer-consumer end.

[0033] The transaction price of each producer-consumer end is transmitted between the producer-consumer ends managed by the load aggregator end to calculate the power plan of each producer-consumer end.

[0034]

[0035] The Lagrange objective function is decomposed into a sub-problem of maximizing the benefit of each producer-consumer end:

[0036]

[0037] wherein, denotes the total power sold by prosumer h in time period t, λ i,h,t denotes the transaction price of prosumer h in time period t, g i,hp denotes the network loss parameter between prosumer h and prosumer p, denotes the power purchased by prosumer h from prosumer p in time period t, N i,pro denotes the number of prosumers;

[0038] The power plan of each prosumer of the load aggregator is obtained by solving the sub-problems, and the power plan includes a power generation and consumption plan and a power purchase and sale plan.

[0039] As can be seen from the above description, in order to maximize the benefits of the prosumers managed by the load aggregator, a Lagrange multiplier is introduced to establish a Lagrange objective function, and the objective function is decomposed into multiple independent sub-problems with the goal of maximizing the benefits of the prosumers by using the dual decomposition principle, so that the power plan of each prosumer of the load aggregator can be more reasonably solved.

[0040] Further, the power plan and the transaction price of each prosumer are transmitted between the prosumers in a point-to-point manner in step S2, including:

[0041] The power plan and the transaction price of each prosumer are transmitted between the prosumers in a point-to-point manner, and each prosumer adjusts the transaction price of the prosumer itself iteratively according to the received power plan and transaction price of other prosumers in combination with the sub-gradient method:

[0042]

[0043] wherein, α h is a step factor, and k denotes the number of iterations.

[0044] Further, the convergence condition is:

[0045] |λ i,h,t [k+1]-λ i,h,t [k]|≤μ h

[0046] wherein, μ h denotes an iteration convergence criterion parameter.

[0047] As can be seen from the above description, the sub-problems are iteratively solved in combination with the sub-gradient method, and the Lagrange multiplier is updated at each step of the iteration, so that the optimization goal of the entire system can be ensured to be achieved.

[0048] Further, the step S4 includes counting the total demand of the load aggregators in the transaction platform, and conducting the auction based on VCG, including:

[0049] Setting a target function in the transaction platform to eliminate the total cost of the power deviation of the multiple load aggregators participating in the market operation as the target:

[0050]

[0051] D i represents the power selling cost function of the i-th LA, represents the actual power provided by the i-th load aggregator in the time period t, e i represents the cost coefficient, M o,LA represents the set of load aggregators of the power supply type.

[0052] As can be seen from the above description, the transaction platform sets the target function to eliminate the total cost of the power deviation of the multiple load aggregators participating in the market operation as the target, and in this way, the power deviation can be efficiently eliminated.

[0053] Further, the step S4 further includes:

[0054] Setting a power balance constraint for the VCG auction:

[0055]

[0056] M a,LA represents the set of load aggregators of the power purchase type, represents the power required by the j-th load aggregator in the time period t, and represents the upper and lower limits of the power that the load aggregator can provide.

[0057] Further, the load aggregator provides a sealed bid to the transaction platform, including:

[0058] The load aggregator encrypts the bid by using the MD2 hash function:

[0059] H = S (y, s)

[0060] Where y represents the bid of the load aggregator, and s represents a random string defined by the load aggregator of the power purchase type.

[0061] As can be seen from the above description, the step S4 further includes:

[0062] Calculating the revenue of each winning load aggregator:

[0063] Z i = V' - V"

[0064] In the formula, Z i V represents the total income of the rest of the winning load aggregators in the clearing queue, and V' represents the total income of the new clearing queue formed according to the clearing rule when the i-th winning load aggregator does not participate in bidding;

[0065]

[0066] In the formula, p t V represents the final transaction price of the publisher; V represents the total income of each winning load aggregator in the clearing queue; V represents the total clearing power.

[0067] As can be seen from the above description, the transaction platform can perform a fast and large-scale electric energy clearing based on the deviation electric quantity auction result according to the VCG rule, so as to realize the minimization of the total cost of eliminating electric energy deviation and the maximization of the individual income of the load aggregator end.

[0068] Please refer to Figure 2 Another embodiment of the present application provides a P2P-based load aggregator market transaction system, comprising a transaction platform, a load aggregator end and a producer-consumer end thereof;

[0069] The transaction platform performs each step of the above-mentioned P2P-based load aggregator market transaction method with the transaction platform as the execution subject;

[0070] The load aggregator end performs each step of the above-mentioned P2P-based load aggregator market transaction method with the load aggregator end as the execution subject;

[0071] The producer-consumer end performs each step of the above-mentioned P2P-based load aggregator market transaction method with the producer-consumer end as the execution subject.

[0072] The above-mentioned P2P-based load aggregator market transaction method and system of the present application are suitable for real-time operation of actual power market transactions, can guarantee the safety and timeliness of the power market transaction, and further reduce the risk of information leakage in the transaction process. The following will be described through specific implementation manners:

[0073] Embodiment one

[0074] Please refer to Figure 1 and Figure 3 A P2P-based load aggregator market transaction method, characterized in that it comprises the following steps:

[0075] S1, the load aggregator end issues an initial value of the transaction price to the producer-consumer end belonging to it.

[0076] In this embodiment, take the producer-consumer side 1 as an example. The producer-consumer side 1 first receives the initial value of the transaction price λ (i.e. the Lagrange multiplier) issued by the load aggregator side. At the same time, the producer-consumer side 1 collects information of various flexible resources and regular electricity demand data.

[0077] In this embodiment, the corresponding producer-consumer side model is set for the two distributed resources, i.e. photovoltaic power generation and energy storage system.

[0078] (1) Producer-consumer side model of photovoltaic power generation

[0079] Let the number of producer-consumer sides in the i-th load aggregator side be N i,pro For one of the producer-consumer sides h, the load aggregator side model is:

[0080]

[0081] In the formula, represents the actual output of the photovoltaic power generation of the producer-consumer side h in the i-th load aggregator side at time period t, represents the upper limit of the predicted output of the photovoltaic power generation of the producer-consumer side h in the i-th load aggregator side at time period t.

[0082] (2) Producer-consumer side model of energy storage system

[0083] In the load aggregator side, the energy storage models of the producer-consumer sides share the same energy storage device, and the energy storage model of the producer-consumer side is:

[0084]

[0085] In the formula, represents the energy storage power of the producer-consumer side h in the i-th load aggregator side at time period t, and represents the upper and lower limits of the energy storage power of the producer-consumer side h in the i-th load aggregator side at time period t. Here, in order to simplify the processing, it is assumed that the charging and discharging efficiency of the energy storage system is 1.

[0086]

[0087] In the formula, represents the energy storage of the energy storage device in the i-th load aggregator side at time period t; represents the upper limit of the energy storage of the energy storage device in the i-th load aggregator side at time period t.

[0088] (3) Set the energy balance constraint of the producer-consumer side for the above model

[0089]

[0090] wherein, for ensuring line safety, represents the total power sold by producer-consumer end h to producer-consumer end p in time period t; represents the upper limit of the expected power sold by producer-consumer end h to producer-consumer end p in time period t; represents the power purchased by producer-consumer end h from producer-consumer end p in time period t; represents the upper limit of the power expected to be purchased by producer-consumer end h from producer-consumer end p in time period t; represents the total power sold by producer-consumer end h in time period t. Equation (7) indicates that, for producer-consumer end h, the total power sold to all producer-consumer ends should be equal to the sum of the power purchased from producer-consumer end h by all producer-consumer ends.

[0091] For each producer-consumer end, the following power balance constraint needs to be satisfied:

[0092]

[0093] The daily power of a user is generally a fixed load, wherein the daily load of the i-th load aggregator end is represented by represents the daily load of producer-consumer end h in time period t in the i-th load aggregator end.

[0094] S2, transmitting the respective transaction prices between each producer-consumer end to which the load aggregator end belongs, to calculate the power plan of each producer-consumer end; transmitting the respective power plans and transaction prices between each producer-consumer end through point-to-point, to adjust the transaction price of each producer-consumer end, and when the adjustment result satisfies the convergence condition, reporting the final power plan and transaction price of each producer-consumer end to the load aggregator end.

[0095] When the adjustment result does not satisfy the convergence condition, re-executing step S2 according to the adjustment result.

[0096] In the day-ahead stage, the producer-consumer end not only depends on the distributed power (photovoltaic) to meet its own power demand, but also can trade electric energy with other producer-consumer ends in P2P mode.

[0097] In this embodiment, the energy sharing of the producer-consumer end in the i-th load aggregator end is taken as an example for illustration. For the sake of simplicity, the subscript i of all variables involved in the subsequent description will not be repeatedly explained. In addition, since the time of one day is divided into 24 time periods in this embodiment, the time interval Δt of each time period is 1 hour, so the identification of the time interval will be omitted in the subsequent formula description.

[0098] In order to maximize the social benefit of the producer-consumer end group managed by the i-th load aggregator end, the objective function is:

[0099]

[0100] In the formula, g i,hp Let h represent the network loss parameter between the producer-consumer end and p. The constraints of the objective function are given by equations (1)(2)(5)(6)(8).

[0101] By introducing Lagrange multipliers into equation (9) using constraint (7), we obtain the Lagrange function form of the optimization problem:

[0102]

[0103] In the formula, λ i,h,t This represents the transaction price of producer-consumer (h) during time period t.

[0104] When solving the optimization problem with the objective of equation (10), the Lagrange duality principle can be used to decompose the optimization problem into multiple independent subproblems with the objective of maximizing the revenue of the producer and consumer. Then, these subproblems are solved iteratively using the subgradient method. In each step of the iteration, the Lagrange multipliers are updated according to equation (12) to ensure that the optimization objective of the entire system is achieved.

[0105]

[0106] The convergence criterion is:

[0107] |λ i,h,t [k+1]-λ i,h,t [k]|≤μ h (13)

[0108] In the formula, α h μ is the step size factor. h The parameter represents the convergence criterion for the iteration, and k represents the number of iterations. Once the Lagrange multipliers at each producer-consumer end meet the convergence requirements, a definite power plan is obtained, and the load aggregator end can formulate the ESS power plan based on the power plan at the producer-consumer end.

[0109] In this embodiment, after the producer-consumer end 1 transmits its initial transaction price information to other producer-consumer ends, it optimizes the power generation and consumption plan and the power purchase and sale plan based on the optimization objective formula (11) formed by decoupling.

[0110] Producer-consumer 1 will transmit the formed electricity purchase and sale plan and its own transaction price to other producer-consumers in a point-to-point manner. For example, it will transmit the electricity purchase (sale) and the transaction price of producer-consumer 1 to producer-consumer 3. Subsequently, producer-consumer 1 will make necessary adjustments or updates to its own transaction price based on the information obtained from other producer-consumers and formula (12).

[0111] The producer-consumer end 1 performs convergence test using formula (13). When the Lagrange multiplier of the producer-consumer end 1 does not satisfy the convergence condition, the producer-consumer end 1 returns to step 2 to re-optimize the power generation plan and the power purchase and sale plan. If the Lagrange multiplier of the producer-consumer end 1 satisfies the convergence condition, it indicates that the producer-consumer end 1 has obtained the final power plan, and the plan is reported to the load aggregator end thereof. Subsequently, the load aggregator end further formulates the power plan of the ESS according to the reported power plan.

[0112] S3, the load aggregator end counts real-time power deviation and classifies the load aggregator end according to the real-time power deviation. If the load aggregator end is of the power supply type, the load aggregator end provides sealed bid to the trading platform. If the load aggregator end is of the power purchase type, the load aggregator end reports demand power to the trading platform.

[0113] In the real-time stage, each producer-consumer end reports its current energy state to the load aggregator end. According to the power supply and demand situation, the load aggregator end is divided into two parts: M a,LA represents a set of load aggregator ends that need to purchase power; and M o,LA represents a set of load aggregator ends that can supply power.

[0114] S4, the total demand of the load aggregator end is counted in the trading platform, and VCG is used for auction to obtain the auction result.

[0115] In order to cope with the uncertainty of day-ahead prediction data, the trading platform sets a target of minimizing the total cost of eliminating the power deviation of multiple load aggregator ends participating in market operation, and the objective function is:

[0116]

[0117] In formula (14), D i represents the power supply cost function of the i-th LA, which describes the power cost in the form of a quadratic function, as shown in formula (15); represents the actual power provided by the i-th load aggregator end; e i represents the cost coefficient.

[0118]

[0119] Formula (16) is the power balance that needs to be satisfied in VCG auction, wherein represents the power required by the j-th load aggregator end at time period t. Formula (17) is the power supply constraint of the load aggregator end, wherein and represent the upper and lower limits of the power that the load aggregator end can supply.

[0120] Because the VCG mechanism has the advantage of encouraging the load aggregator to make a true bid, the bid of the load aggregator will tend to be the marginal cost of the load aggregator. The marginal cost is defined as the total cost increase per product variation. For ease of processing, y i,t As LA i The unit bid at time period t, i.e. y i,t (kWh) is required to be in the form of a sealed bid, which is encrypted by the MD2 hash function:

[0121] H = S(y, s) (18)

[0122] In the formula, y represents the bid of the load aggregator y i,t ; s represents a random string defined by the bidder.

[0123] Under the VCG auction rule, all valid bids are considered in order from low to high to form a clearing queue until the deviation power in the system reaches a balanced state. The revenue of each winning bidder is calculated based on the revenue loss they bring to other non-winning bidders, which ensures the fairness and efficiency of the auction.

[0124] Z i = V' - V" (19)

[0125] In the formula, Z i represents the revenue of the i-th winning bidder; V" represents the total revenue of the remaining winning bidders in the clearing queue; V' is the total revenue of the new clearing queue formed according to the clearing rule when the i-th winning bidder does not participate in bidding.

[0126]

[0127] In the formula, p t represents the final transaction price of the publisher; represents the total revenue of each winning load aggregator in the clearing queue; represents the total clearing power.

[0128] At this point, the transaction platform can quickly and large-scale clear the electricity according to the deviation power auction result of the VCG rule, which not only realizes the minimization of the total cost of eliminating the deviation of electricity, but also maximizes the individual revenue of the load aggregator.

[0129] Embodiment Two

[0130] Please refer to Figure 2 , a market transaction system for load aggregators based on P2P, including a transaction platform, load aggregators and their producer-consumer ends;

[0131] The transaction platform executes each step in the market transaction method of the P2P-based load aggregator in embodiment one, with the transaction platform as the execution subject;

[0132] The load aggregator end executes each step in the market transaction method of the P2P-based load aggregator in embodiment one, with the load aggregator end as the execution subject;

[0133] The producer and consumer end executes each step in the market transaction method of the P2P-based load aggregator in embodiment one, with the producer and consumer end as the execution subject.

[0134] In summary, the market transaction method and system of the P2P-based load aggregator provided by the application are based on the self-sufficiency of each producer and consumer in the load aggregator end, and the transaction mechanism is divided into two stages, day-ahead and real-time: in the day-ahead stage, the producer and consumer end in the load aggregator end autonomously plans its power plan through the P2P mode, and the load aggregator end formulates a power dispatch scheme of the energy storage system based on the plan. In the real-time stage, due to factors such as the uncertainty of photovoltaic output, it is difficult for the load aggregator end to balance the power deviation by itself, so the load aggregator ends will carry out multi-party transactions based on the incentive compatibility theory to eliminate the power deviation. In this way, the day-ahead stage disperses the calculation burden to each producer and consumer end through P2P transactions, and limited information exchange protects user privacy; the real-time stage promotes fair competition, encourages real bidding, effectively eliminates power deviation, and realizes the incentive compatibility of users and the electricity market through VCG rules.

[0135] The above description is only an embodiment of the application, and does not limit the patent scope of the application, and any equivalent transformation or direct or indirect application in the related technical field based on the content of the specification and drawings of the application is also included in the patent protection scope of the application.

Claims

1. A market trading method based on a P2P load aggregator, characterized in that, Including the following steps: S1, The initial value of the transaction price is issued by the load aggregator to the corresponding producer-consumer end; S2. Transmit the respective transaction prices between the various prosumers belonging to the load aggregator to calculate the power plan of each prosumer. Each producer-consumer transmits its power plan and transaction price to the other in a point-to-point manner to adjust the transaction price of each producer-consumer. When the adjustment results meet the convergence conditions, each producer-consumer reports the final power plan and transaction price to the load aggregator. S3. The load aggregator calculates the real-time power deviation and classifies the load aggregator according to the real-time power deviation. If the load aggregator is a power supply type, the load aggregator provides a sealed bid to the trading platform. If the load aggregator is a power purchase type, the load aggregator reports the required electricity to the trading platform. S4. Calculate the total demand of the load aggregator in the trading platform, and conduct an auction based on VCG to obtain the auction results; Step S2 involves transmitting the respective transaction prices between the various prosumers belonging to the load aggregator to calculate the power plan for each prosumer, including: The transaction prices are transmitted between the various prosumers belonging to the load aggregator. With the first i To maximize the benefits for producers and consumers managed by a single load aggregator, a Lagrange objective function is established: The Lagrange objective function is decomposed into subproblems aimed at maximizing the revenue of each producer and consumer: In the formula, Indicates the producer-consumer side h In time period t Total power sold, λ i,h,t Indicates the producer-consumer side h In time period t The transaction price g i,hp Indicates the producer-consumer side h and consumer end p Network loss parameters between Indicates the producer-consumer side h In time period t From the producer-consumer side p Purchased power, N i,pro Indicates the number of producers and consumers; The power plans for each producer and consumer at the load aggregator end are obtained by solving the sub-problems. The power plans include power generation and consumption plans and power purchase and sale plans. Step S4 involves calculating the total demand from the load aggregator on the trading platform and conducting an auction based on the VCG, including: In the trading platform, set an objective function that minimizes the total cost of eliminating power deviations among multiple load aggregators participating in the market operation: In the formula, D i Indicates the first i The electricity sales cost function for LA, Indicates the first i Individual load aggregator terminals during time period t Actual power supplied e i Indicates the cost coefficient. This represents the set of load aggregators for different power supply types. Set power balance constraints for VCG auctions: In the formula, This represents the set of load aggregators for different electricity purchase types. Indicates the first j Individual load aggregator terminals during time period t Required power and This indicates the upper and lower limits of the power that the load aggregator can provide.

2. The market trading method based on P2P load aggregator according to claim 1, characterized in that, Step S2 also includes: If the adjustment result does not meet the convergence condition, step S2 is re-executed based on the adjustment result.

3. The market trading method based on a P2P load aggregator according to claim 1, characterized in that, Step S2 involves transmitting power plans and transaction prices between each producer and consumer through a peer-to-peer mechanism, including: Each prosumer transmits its power plan and transaction price to each other point-to-point. Based on the power plans and transaction prices received from other prosumers, each prosumer iteratively adjusts its own transaction price using a subgradient method. In the formula, α h Step size factor k Indicates the number of iterations.

4. A market trading method based on a P2P load aggregator according to claim 3, characterized in that, The convergence condition is: In the formula, μ h This represents the parameters of the iterative convergence criterion.

5. A market trading method based on a P2P load aggregator according to claim 1, characterized in that, The load aggregator provides a sealed bid to the trading platform, including: The load aggregator encrypts the quote using the MD2 hash function: H = S ( y , s ) In the formula, y This indicates the quote from the load aggregator. s This represents a random string defined by the load aggregator on the electricity purchase side.

6. A market trading method based on a P2P load aggregator according to claim 1, characterized in that, Step S4 is followed by: Calculate the revenue for each winning load aggregator: In the formula, Z i Indicates the first i The revenue of each winning bidder's load aggregator. This represents the total revenue of the remaining winning load aggregators in the clearing queue. Indicates the first i When a winning load aggregator does not participate in the bidding, the total revenue of the new clearing queue formed according to the clearing rules; In the formula, p t Indicates the final transaction price from the publisher; This represents the total revenue of each winning bidder in the clearing queue; This indicates the total clearing power.

7. A market trading system based on a P2P load aggregator, characterized in that, This includes trading platforms, load aggregators, and their producers and consumers; The trading platform performs each step of the market trading method based on a P2P load aggregator as described in any one of claims 1 to 6, with the trading platform as the executing entity. The load aggregator performs each step of the market trading method based on P2P load aggregator as described in any one of claims 1 to 6, with the load aggregator as the execution subject; The prosumer-consumer side performs each step of the market transaction method based on P2P load aggregator as described in any one of claims 1 to 6, with the prosumer-consumer side as the executing entity.

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