A Bipartite Graph Matching Method for Community-level P2P Energy Trading Considering Social Influence

Abstract

The present invention discloses a bipartite graph matching method for community-level P2P energy trading considering social influence, obtains data such as user load, electricity price, new energy output range, power generation and consumption preferences of users, and network parameters of the distribution network accessed by community-level microgrids, and inputs them as parameters into the optimization model; analyzes the end-to-end trading situation within the community, establishes an internal trading optimization model for the community, and analyzes the social impact of transactions within the community; uses multi-agent deep reinforcement learning to model the trading process within each community-level microgrid to obtain an energy trading model within each community-level microgrid; matches between communities to absorb the excess or deficit part of the total energy within the community; after trading the energy producers and consumers within each community, outputs the energy deficit / excess part of each community, so as to match the communities. During the matching process, the Hungarian algorithm is used to solve the optimal matching result and minimize the weight sum of the matching lines.

Description

Technical Field

[0001] The present invention belongs to the technical field of P2P trading in the power market, and particularly relates to a bipartite graph matching method for community-level P2P energy trading considering social influence. Background Art

[0002] In recent years, with the development of distributed energy technologies, end-users within the power system have become increasingly active. Many users equipped with photovoltaic panels, battery energy storage systems, and electric vehicles can sell excess electricity to the system based on the feed-in tariff. Therefore, the concept of prosumers is now commonly introduced to describe such active end-users. The local P2P energy trading market between prosumers is one of the increasingly important energy trading scenarios in the fields of distribution networks and microgrids in recent years. However, these prosumers are often distributed in different community-level microgrids, and there will be power excess and deficit in each community. The existing P2P trading does not consider the mutual influence of the electricity consumption behaviors of users within the same community, and the trading matching between communities is relatively difficult. The traditional algebraic form of trading matching model is difficult to solve for the optimal matching result in polynomial time. Summary of the Invention

[0003] To address the deficiencies mentioned in the above background art, the purpose of the present invention is to provide a bipartite graph matching method for community-level P2P energy trading considering social influence.

[0004] The purpose of the present invention can be achieved through the following technical solutions: A bipartite graph matching method for community-level P2P energy trading considering social influence, the method comprising the following steps:

[0005] Obtain usage data, where the usage data includes user load, electricity price, new energy output range, user's power generation and consumption preferences, and network parameter data of the distribution network accessed by the community-level microgrid;

[0006] Analyze the situation of end-to-end trading within the community, establish an internal trading optimization model for the community, input the obtained usage data as parameters into the internal trading optimization model for the community, and analyze the social influence of the trading within the community;

[0007] Use multi-agent deep reinforcement learning to model the trading process within each community-level microgrid to obtain an energy trading model within each community-level microgrid;

[0008] After obtaining the energy trading model within each community-level microgrid, perform matching between communities to absorb the excess or deficit part of the total energy within the community, and use the form of a bipartite graph to describe the matching problem between energy buyers and sellers, so as to maximize the revenue of the energy sellers in the matching result and minimize the electricity purchase cost of the energy buyers after matching;

[0009] After trading the prosumers within each community, the energy deficit / excess of each community is output, so as to match each community. During the matching process, the Hungarian algorithm is used to solve the optimal matching result while minimizing the weight sum of the matching lines to minimize the network passing cost during the trading process.

[0010] Preferably, the user load includes the load data of the user throughout the year, and the minimum data acquisition interval of the user load is 15 minutes.

[0011] Preferably, the electricity price includes the three-time electricity prices of peak, valley, and flat unified by the state, and the on-grid electricity price of end-users; the new energy output range includes the upper and lower limits of the power generation of renewable energy such as photovoltaic power generation and wind power generation; the power generation and consumption preferences of the user include the cost function of user power generation, the utility function of power consumption, and the social status of the user; the network parameters of the distribution network accessed by the community-level microgrid include the line parameters of the entire distribution network, the fluctuation range of the voltage of each node, and the grid structure of the distribution network.

[0012] Preferably, the process of establishing the internal trading optimization model of the community includes the following steps:

[0013] The internal trading optimization model of the community is established as follows:

[0014]

[0015] In the formula, is the net load of user i at time t, which means the utility obtained by user i consuming the net load at time t, is the electricity purchase quantity of the user in the P2P trading market within the community, is the P2P electricity purchase price of user i at time t, Ξ t is the set of decision variables of the entire community-level market, which are the net load and the electricity purchase quantity of the user in the P2P trading market within the community;

[0016] The dispatchable resources of each prosumer in the community include load curtailment, load interruption, load transfer, and distributed energy storage, and the modeling is as follows:

[0017]

[0018]

[0019]

[0020]

[0021]

[0022]

[0023]

[0024]

[0025] Wherein, represents the prosumer set of user i in community n, is the operating time of the entire market, is the total amount of flexible load resources, are the curtailable load, interruptible load, and shiftable load of user i at time t, respectively, is the load amount on the total time period required by the shiftable load, is the rigid load of user i at time t, is the net load of user i at time t, is the charging power of user i at time t, P i cha is the rated charging power of the energy storage of user i, is the charging state of the energy storage of user i, is the discharging power of user i at time t, P i dis is the rated discharging power of the energy storage of user i, is the discharging state of the energy storage of user i, is the energy storage power of user i at time t, are the charging and discharging coefficients of the energy storage of user i, respectively, and Δt is the charging and discharging time step of the energy storage, are the lower and upper limits of the energy storage power of user i.

[0026] Preferably, in addition to the operating constraints of the distributed power sources of the prosumers, the transactions within the community also need to satisfy the following power balance constraints:

[0027]

[0028]

[0029] Wherein, is the power purchase amount from the superior power grid by the nth community at time t, is the photovoltaic power generation of user i at time t, is the P2P transaction electric energy of user i at time t. When is positive, it means that user i buys electricity at time t. When is negative, it means that user i sells electricity at time t.

[0030] Preferably, the process of obtaining the energy trading model within each community-level microgrid includes:

[0031] Model the end-to-end trading process within each community as a Markov process, where the Markov observation set is as follows:

[0032]

[0033]

[0034]

[0035] In the formula, is the observation set on the power generation side, t represents the time index, represents the P2P trading electricity price of user i at time t, represents the photovoltaic power generation of user i at time t, represents the rigid load value of user i, represents the energy storage power of user i at time t, is the social network observation set within the community, respectively represent the operation difficulty, communication degree, information depth, social influence degree, and peer influence of the trading behavior. The higher the value, the greater the impact of this item on the trading result, is the observation set of the entire market;

[0036] Establish the following Markov action set:

[0037]

[0038]

[0039]

[0040]

[0041]

[0042]

[0043] In the formula, is the upper limit of the load that can be curtailed, is the upper limit of the load that can be interrupted, is the upper limit of the load that can be transferred, is the Markov action value;

[0044] After obtaining the Markov action set and the Markov observation set, the state transition process of each individual can be updated according to the actions, and the updated state transition is as follows:

[0045]

[0046]

[0047]

[0048]

[0049]

[0050] Preferably, after obtaining the state transition, the reward function corresponding to the state transition is defined as follows:

[0051]

[0052]

[0053]

[0054]

[0055]

[0056] where r i cost,t , r i uti,t , r i psy,t , r i DER,t respectively represent the revenue from selling electricity, the utility of purchasing electricity,

[0057] formula the psychological reward for maintaining consistent behavior with prosumers in the same community, and the usage and maintenance costs of distributed power sources, represents taking the average value of the net load, respectively represent the maintenance costs of photovoltaic and distributed energy storage, and taking the opposite number gives the value of the reward function.

[0058] Preferably, the process of matching between the communities includes:

[0059] The community P2P matching problem is written in the following form:

[0060]

[0061]

[0062]

[0063]

[0064]

[0065]

[0066]

[0067]

[0068] In the formula, is the price of electricity sold from community n to community m, is the electricity quantity sold from community n to community m, is the active power loss of the entire distribution network transaction, p t is the vector composed of the output powers of each community, is the Lagrangian dual variable corresponding to the active power balance, is the reactive power loss of the entire distribution network transaction, q t is the vector composed of the reactive output powers of each community, is the Lagrangian dual variable corresponding to the reactive power balance, p min,t , p max,t are the lower and upper limits of the active power output of each community respectively, is the corresponding Lagrangian dual variable, q min,t , q max,t are the lower and upper limits of the reactive power output of each community respectively, is the corresponding Lagrangian dual variable, v min,t , v max,t are the lower and upper limits of the voltage amplitude corresponding to each node respectively, is the corresponding Lagrangian dual variable, ||s start,t || 2 is the square of the magnitude of the complex power at the beginning of the branch, ||s end,t || 2 is the square of the magnitude of the complex power at the end of the branch, (S t ) 2 is the upper limit of the magnitude of the complex power of the entire branch;

[0069] In order to minimize the impact of the transaction on the distribution network when matching P2P transactions between communities, the community P2P matching problem is linearized, and then the Lagrangian function is solved as follows:

[0070]

[0071]

[0072]

[0073]

[0074]

[0075]

[0076] Wherein, π t represents the weight of the transaction of community n with respect to community m, respectively representing the impacts of energy, loss, node voltage, and line congestion reflected on the network.

[0077] Preferably, a device includes:

[0078] one or more processors;

[0079] a memory for storing one or more programs;

[0080] When the one or more programs are executed by the one or more processors, the one or more processors implement a bipartite graph matching method for community-level P2P energy trading considering social impacts as described above.

[0081] Preferably, a storage medium containing computer-executable instructions, the computer-executable instructions being used to execute a bipartite graph matching method for community-level P2P energy trading considering social impacts as described above when executed by a computer processor.

[0082] Advantages of the present invention:

[0083] The present invention analyzes the social relationships within each community that determine the energy consumption decisions of individual prosumers, creates a multi-agent deep reinforcement learning model based on these social relationships, accurately depicts the trading process while ensuring normal transactions among users within the community. By considering the line parameters in the network during the matching optimization process and using the Lagrangian duality and Hungarian algorithms for optimization and matching, the impact of P2P energy matching between communities on the distribution network is fully considered. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0085] Figure 1 is a schematic diagram of a three-layer structure including communities, prosumers, and distribution network operators in the P2P trading market of the present invention;

[0086] Figure 2 is a modeling structure diagram of the information-physical-social system corresponding to the present invention;

[0087] Figure 3 It is a schematic diagram of a bipartite graph of buyers and sellers formed by each community-level microgrid according to the deficit and excess of internal energy in the embodiments of the present invention;

[0088] Figure 4 It is a flow chart of a coupling algorithm of multi-agent deep reinforcement learning and the Hungarian algorithm in the embodiments of the present invention. Specific embodiments

[0089] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0090] As Figure 1 shown, a bipartite graph matching method for community-level P2P energy trading considering social influence includes the following steps:

[0091] (1) Obtain user load, electricity price, new energy output range, user's power generation and consumption preferences, and network parameter data of the distribution network accessed by the community-level microgrid, and input the collected data as parameters into the optimization model;

[0092] Furthermore, the user load data includes the load data of the user throughout the year, and the minimum data collection interval is 15 minutes;

[0093] Furthermore, the electricity price information includes the national unified peak-valley-flat three-time electricity price and the on-grid electricity price of end users;

[0094] Furthermore, the new energy output range includes the upper and lower limits of power generation of renewable energy such as photovoltaic power generation and wind power generation;

[0095] Furthermore, the user's power generation and consumption preferences include the cost function of user power generation, the utility function of power consumption, and the social status of the user;

[0096] Furthermore, the network parameters of the distribution network include the line parameters of the entire distribution network, the fluctuation range of the voltage of each node, and the grid structure of the distribution network.

[0097] (2) Analyze the end-to-end transactions within the community, establish an internal transaction optimization model for the community, and analyze the social impact of transactions within the community;

[0098] (21) The optimization goal of the internal optimization model of the community is to maximize the welfare of prosumers. Therefore, the model of each community can be established as follows:

[0099]

[0100] where is the net load of user i at time t, which represents the utility obtained by user i for consuming such a load at time t, is the electricity purchase volume of the user in the P2P trading market within the community, is the P2P electricity purchase price of user i at time t, Ξ t is the set of decision variables in the entire community-level market.

[0101] (22) The dispatchable resources of each prosumer in the community mainly include curtailable load, interruptible load, shiftable load, and distributed energy storage, which can be modeled as follows:

[0102]

[0103]

[0104]

[0105]

[0106]

[0107]

[0108]

[0109]

[0110] where represents the set of prosumers of user i in community n, is the operating time of the entire market, is the total amount of flexible load resources, are the curtailable load, interruptible load, and shiftable load of user i at time t, respectively, is the load volume on the total time period required for the shiftable load, is the inflexible load of user i at time t, is the net load of user i at time t, is the charging power of user i at time t, P i cha is the rated charging power of the energy storage of user i, is the charging state of the energy storage of user i, is the discharging power of user i at time t, P i dis is the rated discharging power of the energy storage of user i, is the discharging state of the energy storage of user i, The stored energy of user \(i\) at time \(t\). The charging and discharging coefficients of the energy storage of user \(i\) respectively, \(\Delta t\) is the charging and discharging time step of the energy storage. The lower and upper limits of the stored energy of user \(i\)'s energy storage.

[0111] (23) In addition to the operating constraints of the distributed power sources of each prosumer, the transactions within the community also need to satisfy the following power balance constraints:

[0112]

[0113]

[0114] In the formula, The power purchase from the superior power grid by the \(n\)th community at time \(t\). The photovoltaic power generation of user \(i\) at time \(t\). The P2P transaction electric energy of user \(i\) at time \(t\). When it is positive, it means user \(i\) buys electricity at time \(t\), and when it is negative, it means user \(i\) sells electricity at time \(t\).

[0115] (3) As Figure 2 shown, in order to consider the social influence among energy users within each community-level microgrid, the present invention uses multi-agent deep reinforcement learning to model the transaction process within each microgrid, and models the entire community-level microgrid as an information-physical-social system.

[0116] (31) In order to model deep reinforcement learning, the present invention models the end-to-end transaction process within each community as a Markov process, and the Markov observation set is as follows:

[0117]

[0118]

[0119]

[0120] In the formula, The observation set on the power generation side, where the meanings of the variables are the same as those of the corresponding variables in (2). The social network observation set within the community. respectively represent the operation difficulty, communication degree, information depth, social influence degree and peer influence of the transaction behavior. The higher the value, the greater the influence of this item on the transaction result. The observation set of the entire market.

[0121] (32) In addition to the observation set, the present invention also establishes the following Markov action set:

[0122]

[0123]

[0124]

[0125]

[0126]

[0127]

[0128] wherein, is the upper limit of the load that can be curtailed, is the upper limit of the interruptible load, is the upper limit of the load that can be transferred, is the Markov action value.

[0129] (33) After obtaining the action set and state set of the Markov process, the present invention can update the state transition process of each individual according to the action, and the updated state transition is as follows:

[0130]

[0131]

[0132]

[0133]

[0134]

[0135] The meanings of the variables in the formula are the same as those of the corresponding variables in (31) and (32).

[0136] (34) After obtaining the state transition action, the present invention defines the reward function corresponding to the state transition as follows:

[0137]

[0138]

[0139]

[0140]

[0141]

[0142] wherein, r i cost,t , r i uti,t,r i psy,t ,r i DER,t respectively represent the revenue from selling electricity, the utility of purchasing electricity, the psychological reward for maintaining behavior consistency with prosumers in the same community, and the usage and maintenance cost of distributed power sources. denotes taking the average value of the net load. respectively represent the maintenance costs of photovoltaic and distributed energy storage. Taking the opposite values can obtain the return function values.

[0143] (4) After obtaining the energy trading model within each community-level microgrid, communities can be matched to further absorb the excess or deficit part of the total energy within the community. The present invention uses Figure 3 the form of a bipartite graph in

[0144] (41) The community P2P matching problem at the entire distribution network level can be written in the following form:

[0145]

[0146]

[0147]

[0148]

[0149]

[0150]

[0151]

[0152]

[0153] In the formula, is the price at which community n sells electricity to community m, is the amount of electricity sold by community n to community m, is the active power loss of the entire distribution network transaction, p t is the vector composed of the output powers of each community, is the Lagrangian dual variable corresponding to the active power balance, is the reactive power loss of the entire distribution network transaction, q t is the vector composed of the reactive output powers of each community, is the Lagrangian dual variable corresponding to the reactive power balance, p min,t ,p max,t are respectively the lower and upper limits of the active power output of each community, is the corresponding Lagrangian dual variable, qmin,t , q max,t are the lower and upper limits of the reactive power output for each community respectively is the corresponding Lagrangian dual variable, v min,t , v max,t are the lower and upper limits of the voltage magnitude corresponding to each node respectively is the corresponding Lagrangian dual variable, ||s start,t || 2 is the square of the complex power magnitude at the beginning of the branch, ||s end,t || 2 is the square of the complex power magnitude at the end of the branch, (S t ) 2 is the upper limit of the complex power magnitude of the entire branch

[0154] (42) In order to minimize the impact of transactions on the distribution network when matching P2P transactions between communities, the present invention linearizes the original problem and then solves the Lagrangian function as follows:

[0155]

[0156]

[0157]

[0158]

[0159]

[0160]

[0161] wherein, π t represents the weight of the transaction from community n to community m respectively represent the impacts of energy, losses, node voltages, and line congestion on the network. The present invention reduces the impact of energy transactions between communities on the distribution network by minimizing the sum of these matching weights during matching

[0162] (5) After the energy producers and consumers within each community conduct transactions according to the multi-agent deep learning algorithm in Figure 4 , the energy deficit / excess of each community can be output, and then each community can be matched. During the matching process, the Hungarian algorithm is used to solve the optimal matching result while minimizing the sum of the weights of the matching lines

[0163] Preferably, a device includes:

[0164] one or more processors

[0165] a memory for storing one or more programs

[0166] When one or more of the said programs are executed by one or more of the said processors, the one or more processors implement a bipartite graph matching method for community-level P2P energy trading considering social impacts as described above.

[0167] Preferably, a storage medium containing computer-executable instructions which, when executed by a computer processor, are used to execute a bipartite graph matching method for community-level P2P energy trading considering social impacts as described above.

[0168] In the description of this specification, the descriptions referring to terms such as "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in a suitable manner in any one or more embodiments or examples.

[0169] The above has shown and described the basic principles, main features and advantages of the present invention. Those skilled in the art of this industry should understand that the present invention is not limited by the above embodiments, and what is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will also have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed.

Claims

1. A bipartite graph matching method for community-level P2P energy trading considering social impacts, characterized in that, The method includes the following steps: Obtain usage data, where the usage data includes user load, electricity price, new energy output range, user's power generation and consumption preferences, and network parameter data of the distribution network accessed by the community-level microgrid; Analyze the end-to-end transactions within the community, establish an internal community transaction optimization model, input the obtained usage data as parameters into the internal community transaction optimization model, and analyze the social impact of transactions within the community; Use multi-agent deep reinforcement learning to model the transaction process within each community-level microgrid to obtain an energy transaction model within each community-level microgrid; The process of obtaining the energy transaction model within each community-level microgrid includes: Model the end-to-end transaction process within each community as a Markov process, where the Markov observation set is as follows: In the formula, is the observation set on the power generation side, t represents the time index, represents the P2P trading electricity price of user i at time t, represents the photovoltaic power generation of user i at time t, represents the rigid load value of user i, represents the energy storage power of user i at time t, is the social network observation set within the community, respectively represent the operation difficulty, communication degree, information depth, social influence degree and peer influence of the trading behavior. The higher the value, the greater the impact on the trading result, is the observation set of the entire market; Establish the following Markov action set: In the formula, is the upper limit of the load that can be curtailed, is the upper limit of the interruptible load, is the upper limit of the load that can be shifted, is the Markov action value; After obtaining the Markov action set and Markov observation set, the state transition process of each individual can be updated according to the action, and the updated state transition is as follows: After obtaining the state transition, define the following reward function corresponding to the state transition: where r i cost,t , r i uti,t , r i psy,t , r i DER,t represent the revenue from selling electricity and the utility of purchasing electricity, respectively Formula The psychological rewards for maintaining consistent behavior with prosumers in the same community and the usage and maintenance costs of distributed power sources Denotes taking the average value of the net load Respectively represent the maintenance costs of photovoltaics and distributed energy storage, and taking the opposite number gives the value of the reward function After obtaining the energy transaction model within each community-level microgrid, match between communities to accommodate the excess or deficit of the total energy within the community, and use the form of a bipartite graph to describe the matching problem between energy buyers and sellers to maximize the revenue of energy sellers in the matching result and minimize the electricity purchase cost of energy buyers after matching; After trading the energy prosumers within each community, output the energy deficit / excess part of each community, so as to match each community. During the matching process, use the Hungarian algorithm to solve the best matching result while minimizing the sum of the weights of the matching lines to minimize the network passing cost during the transaction process.

2. The bipartite graph matching method for community-level P2P energy trading considering social impacts according to claim 1, characterized in that The user load includes the load data of the user throughout the year, and the minimum data acquisition interval of the user load is 15 minutes.

3. The bipartite graph matching method for community-level P2P energy trading considering social impacts according to claim 1, characterized in that, The electricity price includes the national unified peak-valley-flat three-time electricity price and the on-grid electricity price of end-users; the new energy output range includes the upper and lower limits of power generation of renewable energy such as photovoltaic power generation and wind power generation; the user's power generation and consumption preferences include the cost function of user power generation, the utility function of power consumption, and the social status of the user; the network parameters of the distribution network accessed by the community-level microgrid include the line parameters of the entire distribution network, the fluctuation range of node voltages, and the grid structure of the distribution network.

4. The bipartite graph matching method for community-level P2P energy trading considering social impacts according to claim 1, characterized in that, The process of establishing the internal community transaction optimization model includes the following steps: The internal community transaction optimization model is established as follows: wherein, is the net load of user i at time t, i.e., represents the utility obtained by user i from consuming the net load at time t, is the electricity purchase quantity of the user in the P2P trading market within the community, is the P2P electricity purchase price of user i at time t, Ξ t is the set of decision variables in the entire community-level market, which are the net load and the electricity purchase quantity of the user in the P2P trading market within the community; The dispatchable resources of each energy prosumer in the community include load shedding, load interruption, load transfer, and distributed energy storage, and the modeling is as follows: In the formula, represents the set of prosumers of user i in community n, is the operating time of the entire market, is the total amount of flexible load resources, are the curtailable load, interruptible load, and shiftable load of user i at time t respectively, is the load amount on the total time period required by the shiftable load, is the rigid load of user i at time t, is the net load of user i at time t, is the charging power of user i at time t, P i cha is the rated charging power of the energy storage of user i, is the charging state of the energy storage of user i, is the discharging power of user i at time t, P i dis is the rated discharging power of the energy storage of user i, is the discharging state of the energy storage of user i, is the energy storage power of user i at time t, are the charging and discharging coefficients of the energy storage of user i respectively, and Δt is the charging and discharging time step of the energy storage, are the lower and upper limits of the energy storage power of user i.

5. A bipartite graph matching method for community-level P2P energy trading considering social impacts according to claim 4, characterized in that In addition to the operation constraints of the distributed power sources of energy prosumers, the internal community transactions also need to satisfy the following power balance constraints: Wherein, is the power purchase from the superior power grid by the nth community at time t, is the photovoltaic power generation of user i at time t, is the P2P trading electric energy of user i at time t. When is positive, it means that user i buys electricity at time t. When is negative, it means that user i sells electricity at time t.

6. The bipartite graph matching method for community-level P2P energy trading considering social impacts according to claim 1, characterized in that The process of matching between communities includes: The community P2P matching problem is written in the following form: Wherein, is the price at which community n sells electricity to community m, is the electricity quantity sold by community n to community m, is the active power loss of the entire distribution network transaction, p t is the vector composed of the output power of each community, is the Lagrangian dual variable corresponding to the active power balance, is the reactive power loss of the entire distribution network transaction, q t is the vector composed of the reactive output power of each community, is the Lagrangian dual variable corresponding to the reactive power balance, p min,t , p max,t are respectively the lower limit and upper limit of the active power output of each community, is the corresponding Lagrangian dual variable, q min,t , q max,t are respectively the lower limit and upper limit of the reactive power output of each community, is the Lagrangian dual variable corresponding to it, v min,t , v max,t are respectively the lower limit and upper limit of the voltage amplitude corresponding to each node, is the corresponding Lagrangian dual variable, ||s start,t || 2 is the square of the complex power amplitude at the beginning of the branch, ||s end,t || 2 is the square of the complex power amplitude at the end of the branch, (S t ) 2 is the upper limit of the complex power amplitude of the entire branch; In order to minimize the impact of transactions on the distribution network when matching P2P transactions between communities, linearize the community P2P matching problem, and then solve the Lagrangian function as follows: where, π t represents the weight of the transaction of community n with respect to community m, respectively represent the impacts of energy, loss, node voltage, and line congestion reflected on the network.

7. A computer device, characterized in that, Includes: One or more processors; A memory for storing one or more programs; When one or more of the said programs are executed by one or more of the said processors, such that one or more of the said processors implement a bipartite graph matching method for community-level P2P energy trading considering social impacts as described in any one of claims 1-6.

8. A storage medium containing computer-executable instructions, characterized in that, The computer-executable instructions, when executed by a computer processor, are used to execute a bipartite graph matching method for community-level P2P energy trading considering social impacts as described in any one of claims 1-6.

Images