CN114529373B

CN114529373B - Priority matching-based dynamic microgrid group P2P transaction method

Info

Publication number: CN114529373B
Application number: CN202210426713.9A
Authority: CN
Inventors: 雷霞; 陈彦旭; 杨健; 钟鸿鸣; 李文星
Original assignee: Xihua University
Current assignee: Xihua University
Priority date: 2022-04-22
Filing date: 2022-04-22
Publication date: 2022-07-01
Anticipated expiration: 2042-04-22
Also published as: CN114529373A

Abstract

The invention relates to the technical field of power grids, in particular to a priority matching-based dynamic microgrid group P2P trading method, which mainly comprises the following steps: s14, each microgrid obtains the supply and demand amount of the microgrid according to the output of the optimized set, and determines transaction roles based on the supply and demand amount, wherein the transaction roles comprise buyers and sellers, and the transaction roles calculate priority indexes according to the transaction roles and sort the priority indexes according to the absolute values of the priority indexes; s15, carrying out transaction matching based on the priority index value sequence, and publishing respective quotation and supply and demand amount to matched opposite parties by the buyer and the seller after successful matching; s16, calculating a supply-demand ratio, and updating quotations of each successfully matched microgrid according to a quotation model based on the supply-demand ratio; and S17, generating a transaction according to the supply and demand quantity and the quotations of both parties and the transaction price model. In the invention, the microgrid only needs to publish quotations and supply and demand to the other party who is successfully matched, so the privacy security is better ensured.

Description

Priority matching-based dynamic microgrid group P2P transaction method

Technical Field

The invention relates to the technical field of power transaction, in particular to a priority matching-based dynamic microgrid group P2P transaction method.

Background

In recent years, technology for utilizing renewable energy Resources (RES) is continuously developed, so that distributed renewable power generation is gradually popularized, and distributed micro-grid (MG) configured with Wind Turbines (WT), Photovoltaics (PV), gas turbines (MT), Energy Storages (ES), Gas Boilers (GB), electric refrigerators (EC), and other Distributed Energy Resources (DERs) has more flexible characteristics, and resources can be adjusted to maintain supply and demand balance. However, the variability of MG adjustability is limited by the differences in the requirements of the various energy sources and the coupling between the multiple energy sources. With the continuous development of micro-grids, adjacent micro-grids can be communicated with one another to form an interconnected multi-micro-grid (IMMG) system, which can further provide an effective technical means for the comprehensive utilization of a plurality of DERs. The MG in the IMMG system can reduce the electric energy consumption cost and promote the local consumption of new energy through the coordinated operation with other MGs and a power distribution network. Meanwhile, power fluctuation and renewable energy intermittency of the interconnected IMMG can reduce safety of the interconnected IMMG, and an IMMG system is difficult to maintain. Therefore, a good electric power market trading mechanism design needs to be researched, so that multi-party behaviors are guided, the operation safety and reliability of an electric power system are ensured, and the stability of the IMMG system is maintained.

Due to the different resource configurations and the randomness of the DERs, in the IMMG system, each microgrid plays a role of a seller or buyer in real time because each microgrid has energy surplus or shortage and can consume or produce energy. To gain more benefit or reduce energy costs, and also to balance supply and demand within the microgrid, the MGs need to sell/purchase unbalanced power to other community members in the IMMG in a trading market. The P2P energy transaction allows direct transactions between consumers and producers as buyers and sellers, with benefits to both consumers and producers. The producers no longer need to accept the price offered by the utility company, so they can obtain a higher price per unit of electricity; consumers can save money because they do not have to accept various fees charged by the electric power company for electricity.

The multi-microgrid P2P energy trading market is divided into the following three types: a coordinated market, a community market, and a dispersed market. Third parties exist in the first two markets to manage the markets, so that the privacy of each MG is difficult to guarantee, the distributed markets adopt a trading mechanism based on a bilateral contract and require each MG to provide only quantitative price information, however, the method enables the key information of trading to be consulted by other participants, so that the privacy of the MG cannot be guaranteed.

Disclosure of Invention

The invention aims to provide a priority matching-based dynamic microgrid group P2P trading method, which can enhance the privacy of each microgrid member.

In order to achieve the above object, the embodiments of the present invention provide the following technical solutions:

a priority matching-based dynamic microgrid group P2P transaction method is disclosed, wherein a plurality of microgrids are included in the microgrid group, and the method comprises the following steps:

s11, initializing data, including quotation, unit parameters, loads, fan output predicted values and photovoltaic output predicted values of each micro-grid in the micro-grid group, setting transaction rounds, and enabling the initial transaction rounds to be 1;

s12, after each round of transaction is completed, adding 1 to the numerical value of the transaction round number, and updating the quotation;

s13, substituting the updated quotation into a scheduling model, and solving the model to optimize the output of each unit;

s14, each microgrid obtains the supply and demand amount of the microgrid according to the output of the optimized set, and determines transaction roles based on the supply and demand amount, wherein the transaction roles comprise buyers and sellers, and the transaction roles calculate priority indexes according to the transaction roles and sort the priority indexes according to the absolute values of the priority indexes;

s15, performing transaction matching based on the priority index value sequence, and publishing respective quotation and supply and demand amount to matched opposite parties by the buyer and the seller after the matching is successful;

s16, calculating a supply-demand ratio, and updating quotations of each successfully matched microgrid according to a quotation model based on the supply-demand ratio;

s17, generating a trading order according to the supply and demand amount and the quoted prices of both parties and a trading price model;

s18, judging whether the whole microgrid group only has supply or demand, if so, entering the step S19, and if not, returning to the step S15;

s19, judging whether the number of the current round of transaction reaches the set maximum number of the transaction rounds, if yes, ending the whole transaction flow, if not, returning to the step S12.

According to the scheme, before transaction, matching is performed based on the priority index value, transaction is performed only on the microgrid which is successfully matched, the microgrid only needs to provide quotation and supply and demand information for the opposite side which is successfully matched, and other microgrids and the distribution network in the microgrid group cannot acquire the quotation and demand information, so that the privacy security of the microgrid is effectively improved.

In step S14, the calculation formulas of the priority indexes of the buyer and the seller are:

in the formula:

and

priority indexes of a buyer and a seller respectively;

and

respectively representing the demand quantity reported by the MG of the buyer and the supply quantity reported by the MG of the seller;

is the maximum amount of transactions allowed in the P2P market;

and

the price is respectively the purchase price reported by the MG of the buyer and the sale price reported by the MG of the seller;

and

respectively the purchase price and the sale price for trading with the distribution network.

In the scheme, the priority index value is calculated based on the supply and demand quantity and the quotation of the buyer and the seller, so that the two transaction parties which are sequenced and matched according to the priority index value are closer to the actual demand, and the reliability of the whole scheme is improved.

In step S15, the process of performing transaction matching based on the ranking of priority index values includes: and sequencing the micro-grids from large to small according to the absolute values, and preferentially matching the micro-grids arranged in front successfully.

In this scheme, can not only realize the matching, through the priority matching of sequencing near the front moreover, can improve matching efficiency, improve transaction efficiency then.

In step S13, the building of the scheduling model includes:

setting an objective function

，

，

，

The total cost of the microgrid group is;

and

respectively the maintenance cost and the interaction cost,

is the probability of occurrence of scene s;

、

、

、

、

、

respectively representing the maintenance coefficients of all the units;

、

、

、

、

、

respectively the output of photovoltaic, blower, energy storage, gas turbine, gas boiler and electric refrigerator,

and

respectively buying electric quantity and selling electric quantity of each microgrid;

and

the purchase price and the sale price reported by each microgrid respectively;

and determining constraint conditions, wherein the constraint conditions comprise power balance constraint, controllable unit output constraint, interactive power constraint and energy storage constraint.

In step S13, a scene analysis method is used to describe uncertainty of fan and photovoltaic output, including: combining the output prediction of a fan before the day and the photovoltaic output prediction, and adopting Latin hypercube sampling to generate M initial scenes; and based on the principle that the Euclidean distance is shortest, reducing the initial scenes until the number of the scenes reaches a set value.

Compared with random sampling, the scene generation method in the scene analysis method can more accurately describe the distribution of the variables and improve the accuracy of output prediction.

In step S16, the supply-demand ratio is calculated using the following formula,

in the formula:

the supply quantity of the mth pair in the nth round of transaction at the moment t;

is the demanded quantity of the mth pair in the nth round of transaction at the time t.

In step S16, the process of updating the quotation according to the quotation model based on the supply-demand ratio includes:

1) the supply-demand ratio is less than 1:

in the formula:

the price is quoted for the k-th buyer in the nth round of transaction at the time t;

offering the seller the kth time in the nth round of transaction at the moment t;

2) the supply-demand ratio is more than 1:

3) supply-demand ratio equal to 1:

in the formula: k represents the number of quotes in the current round of trading.

In step S17, the transaction price model is:

1) when the seller available quantity is more than the buyer's required quantity:

2) when the seller salable amount is less than the buyer's demanded amount:

3) when the seller salable amount equals the buyer demand amount:

wherein the content of the first and second substances,

，

for the kth buyer quote in the last round of transaction at time t,

for the kth seller offer in the last round of transaction at time t,

the supply amount reported by the buyer at the kth time in the last round of transaction at the time t,

the demand reported by the seller at the kth time in the last round of transaction at the time t.

In step S19, if the number of transaction rounds reaches the set maximum number of transaction rounds after the current round of transaction, the remaining supply or demand is transacted with the distribution network.

According to the scheme, when residual supply or the demand is not met after the transaction in the micro-grid group is completed, the transaction with the distribution network is carried out, so that the application demand and benefit maximization in the micro-grid group are met.

Compared with the prior art, the method has the following advantages:

1. through rapid and repeated matching of priority indexes of buyers and sellers, an improved distributed P2P energy transaction bidding mechanism is provided, and because the micro-grid only needs to provide information of quotation and supply and demand for the other party who is successfully matched, other micro-grids and distribution networks in the micro-grid group cannot be obtained, so that the privacy security of the micro-grid is effectively improved. And because information interaction is not needed to be carried out on other micro-grids, the frequency of information interaction is reduced, and the transaction efficiency is improved.

2. In the autonomous scheduling model of each microgrid, a scene analysis method is adopted to describe the uncertainty of wind-solar output and analyze the influence of the uncertainty on the total cost of the multi-microgrid system. In addition, in consideration of privacy of each microgrid, supply and demand and quotation after autonomous scheduling are quantified in the form of priority indexes, dynamic composition of microgrid groups can be guided, and flexibility and willingness of each microgrid in transaction are improved.

3. The whole transaction process is divided into two stages of quotation and transaction. In the bidding stage of each round of transaction, each bidding is updated according to the supply-demand ratio of the two parties of the transaction; in the trading stage of each round of trading, an improved intermediate market interest rate model is designed, trading prices are calculated according to the quotations and supply-demand ratios of two trading parties, and finally local consumption of renewable energy sources is promoted, and an optimal operation strategy is realized.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.

Fig. 1 is an exemplary internal architecture diagram of a multi-piconet group.

Fig. 2 is a flowchart of a dynamic piconet group P2P transaction method based on priority matching.

Fig. 3 is a graph of predicted values of electrical load over time in an application example.

Fig. 4 is a graph showing the predicted values of the thermal load and the cooling load over time in an application example.

Fig. 5a, 5b, 5c, and 5d are schematic diagrams of costs of MG1, MG2, MG3, and MG4 in application examples, respectively.

Fig. 6 is a diagram of the total MMG cost in an application example.

Fig. 7a and 7b are diagrams illustrating matching between the first transaction and the second transaction when t =1 in the application example.

Fig. 8a and 8b are diagrams illustrating matching between the first transaction and the second transaction when t =4 in the application example.

Fig. 9 is a diagram of transaction matching at t =15 in an application example.

Fig. 10 is a graph comparing transaction prices at t =1 in an application example.

Fig. 11 is a graph comparing transaction prices at t =4 in the application example.

Fig. 12 is a comparison graph of transaction prices at t =15 in an application example.

Fig. 13 is a schematic diagram of an initial scene generated in an application example.

Fig. 14 is a schematic diagram of a scene after clipping in an application example.

Fig. 15 is a diagram illustrating the result of autonomous scheduling by MG1 in an application example.

Fig. 16 is a diagram illustrating the result of MG2 autonomous scheduling in an application example.

Fig. 17 is a diagram illustrating a result of MG3 autonomous scheduling in an application example.

Fig. 18 is a diagram illustrating the result of autonomous scheduling by MG4 in an application example.

FIG. 19 is a graph of interaction power versus power in an application example.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.

As shown in fig. 1, for an exemplary architecture diagram of a piconet group, there are a plurality of piconets, such as MG 1-MG 7 in the diagram, and types of piconets may be different, for example, the number of loads is different, the types of devices are different, and the like.

As shown in fig. 2, in the dynamic piconet group P2P trading method based on priority matching, the execution subjects of each step are all the piconets in the piconet group, and thus descriptions may be omitted in some steps. The method comprises the following steps:

and S11, initializing data, wherein the data comprise quoted prices of each micro-grid in the micro-grid group, unit parameters (such as maintenance coefficients, upper output limits and lower output limits of each unit), loads, predicted output values of fans (wind power) and predicted photovoltaic output values, setting the number of trading rounds, and setting the number of initial trading rounds to be 1. The purpose of setting the number of transaction rounds is to set the number of iterations to avoid endless loops. The purpose of the initial quotation is to give each microgrid initial value to carry out scheduling, and then the quotation is continuously updated, and finally the optimal operation state is achieved.

And S12, adding 1 to the numerical value of the transaction rounds after each round of transaction is completed, and updating the quotation.

It is to be understood that for a first transaction, the quote refers to the quote at the time of data initialization without an update quote process, and for a non-first transaction, the quote is updated once per transaction, so the update quote herein refers to the update quote after completing one transaction.

And S13, optimizing the scheduling model through quotation updating for each microgrid, and realizing optimization of output of each unit. And after the quotation of each round is updated, substituting the updated quotation into the scheduling model, and solving the model to optimize the output of each unit.

And S14, each microgrid obtains the supply and demand amount of the microgrid according to the optimized unit output, transaction roles are determined based on the supply and demand amount, the transaction roles comprise buyers and sellers, priority indexes are calculated according to the transaction roles, one microgrid correspondingly obtains a priority index value, and the priority index value is sorted according to the absolute value of the priority index.

The priority index calculation formula is as follows:

（24）

（25）

in the formula:

and

priority indexes of the buyer and the seller respectively;

and

is the maximum amount of transactions allowed in the P2P market;

and

the price is respectively the buying price reported by the MG of the buyer and the selling price reported by the MG of the seller;

and

respectively the purchase price and the sale price of the transaction with the distribution network.

According to the model, the buyer and the seller calculate the priority index values respectively, and the higher the absolute value of the priority index value is, the higher the priority is for carrying out the transaction. That is, if the two piconets are sequentially ranked from large to small according to the absolute value, the prior matching between the two piconets is successful.

And S15, performing transaction matching based on the priority index value sequence, and publishing respective specific information to matched parties by the matched buyer and seller, wherein the specific information comprises quotation and supply and demand.

According to the scheme, matching is carried out based on the priority indexes, then only the buyer and the seller publish own specific information to the other party who is successfully matched, and other micro-grids cannot learn related information, so that the privacy of the two parties of the transaction is protected to the great extent, and the privacy of the two parties of the transaction is improved.

And S16, updating the quotation according to the quotation model by each microgrid (the microgrid which is successfully matched is referred to herein). After each pairing, each microgrid updates its own price according to the supply-demand ratio, and the calculation method of the supply-demand ratio is described later.

And S17, generating a trading order according to the trading price model according to the supply and demand amount and the quoted prices of both parties. A trade price is calculated based on the supply and demand amount and the quote, and a trade order is generated.

And S18, judging whether the whole microgrid group only has supply or demand, if so, entering the step S19, and if not, returning to the step S15.

And S19, judging whether the number of the current round of transaction reaches the set maximum number of the transaction rounds, if so, ending the whole transaction process, and if not, returning to the step S12.

In step S19, if the microgrid group is still to transact with the distribution network, when the transaction round number reaches the set maximum transaction round number, the entire transaction flow is not ended, but the remaining supply or demand is transacted to the distribution network, as shown in fig. 2.

It should be noted that, as shown in fig. 2, one or more transactions performed within the piconet may be counted as one transaction.

In the above steps, unless otherwise specified, the next step is performed in sequence after the previous step is completed, for example, step S16 is performed after step S15 is completed.

The predicted values of the output of the fan (wind power) and the photovoltaic output describe the uncertainty of the fan processing and the photovoltaic output respectively. In the embodiment, when the dispatching model is optimized, uncertainty of fan and photovoltaic output is described by adopting a scene analysis method, the scene represents output results of different fans and different photovoltaic outputs, and the running economy of each micro-grid and the running stability of the distribution network can be effectively improved. Specifically, the step of describing the uncertainty of the wind-solar output based on the scene analysis method comprises the following steps:

(1) scene generation: and combining the day-ahead (next day) wind and light prediction (a fan output predicted value and a photovoltaic output predicted value), and generating a wind and light output curve of the next day by adopting Latin hypercube sampling. Assuming that the wind power output prediction error meets

Normal distribution of wherein

Is a prediction wind power output value in the day ahead,

the standard deviation of the possible output of the wind power is used for representing the randomness of the wind power. Similarly, assume photovoltaic output prediction error compliance

The normal distribution of the number of the channels is,

the standard deviation of the photovoltaic possible contribution is then used to predict the error through the generated photovoltaic contribution

Calculating the actual photovoltaic output

. Unlike random sampling, the method of scene generation in the scene analysis method can describe the distribution of variables more accurately.

(2) Scene reduction: and the M initial scenes are reduced by adopting a synchronous back substitution elimination method so as to reduce the calculation amount of optimized scheduling, and the scenes reserved after reduction can better reflect various conditions. The scene reduction steps are as follows:

1) initializing, importing the generated M initial scenes, setting iteration times, and equalizing the probability of any scene, namely:

（1）

2) optionally a scene

And calculating other scenes with the shortest Euclidean distance to the scenes:

（2）

in the formula:

is the probability of occurrence of scene j;

the Euclidean distance between the scene i and the scene j is shown, and M is a positive integer.

3) Determining scenes to delete

：

（3）

4) The number of updated scenes M = M-1, and the probability of the deleted scene appearing is added to the scene closest to the Euclidean distance of the deleted scene, so that the sum of the probabilities of all the scenes appearing is 1. And repeating the steps until the number of scenes reaches the set value of the cut scenes.

The method for building the autonomous scheduling model comprises the following steps:

1) setting an objective function: the microgrid group aims at minimizing the operation cost of each microgrid, and comprises maintenance cost and interaction cost.

（4）

（5）

（6）

In the formula:

the total cost of the microgrid group is obtained;

and

maintenance costs and interaction costs, respectively;

is the probability of occurrence of scene s;

、

、

、

、

、

respectively representing the maintenance coefficients of all the units;

、

、

、

、

、

respectively the output of photovoltaic, fan, energy storage, gas turbine, gas boiler and electric refrigerator, wherein

；

And

and

the purchase price and the sale price reported by each microgrid respectively.

2) Determining constraints

a. And power balance constraint: including cold, hot, electrical power balance constraints.

（7）

（8）

（9）

In the formula:

、

、

respectively predicting values of cold load, heat load and electric load of each micro-grid;

、

respectively the refrigerating capacity and the heating capacity of the gas turbine.

b. Controlled unit output constraint

（10）

（11）

（12）

In the formula:

and

respectively the upper and lower limits of the MT force,

and

respectively the upper limit and the lower limit of GB output,

and

the upper and lower limits of the EC output, respectively.

c. Interactive power constraints

（13）

（14）

（15）

（16）

In the formula:

is the maximum value of the interaction power;

and

is a binary variable.

d. Restraint of stored energy

The capacity of the ES at the time t is related to the capacity at the time t-1, and the ES operation has periodicity, so that the capacities at the beginning and the end of the ES are consistent:

（17）

（18）

in the formula:

and

the energy storage capacities at the time t and the time t-1 are respectively;

and

charging power and discharging power of ES respectively;

and

respectively, a charge coefficient and a discharge coefficient;

is the self-discharge coefficient of ES.

The ES operation must satisfy the charge-discharge power constraint, and at the same time, charge and discharge cannot be performed at the same time, so the following constraints are satisfied:

（19）

（20）

（21）

（22）

（23）

in the formula:

and

upper and lower capacity limits of ES, respectively;

and

respectively is the maximum value of the charging power and the maximum value of the discharging power;

and

is a binary variable.

In the step S15, a dynamic microgrid group is formed under the guidance of the priority index, and each participant can independently select the most appropriate party to trade according to the own supply demand and quotation at each moment, so that the reasonable interaction of the redundant energy of each microgrid in the microgrid group is realized, and the safety and stability of the operation of the distribution network are further improved.

The step S16 is a quotation stage, that is, the two parties after the priority matching first disclose the specific information of volume and price with the other party, and since the supply and demand of the two parties for trading are not necessarily the same, and the fairness of the trading price to the two parties needs to be considered, the trading price after each trade is achieved is calculated according to the volume and price reported by the two parties, and finally the optimal operation of the microgrid group is realized by performing multiple rounds of quotations.

And in the quotation stage, from the perspective of supply and demand of the two parties after matching, the supply and demand ratio of the two parties is calculated firstly:

（26）

（27）

in the formula:

the demanded quantity of the mth pair in the nth round of transaction at the moment t;

the supply-demand ratio of the m-th pair;

is composed of

Is inversely proportional to the ratio of (a) to (b).

When the whole micro-grid group has no demand or supply, each micro-grid does not participate in the P2P trading market, and trading is directly carried out with the distribution network at the internet price. When there is demand or supply in the entire microgrid group, the following 3 cases are classified:

1) the supply-demand ratio is less than 1: at this time, since the insufficient part still needs to be traded again after the buyer purchases electricity from the seller, the seller price should be updated first.

（28）

（29）

In the formula:

quoted price for the kth buyer in the nth round of transaction at the moment t;

and offering the k-th seller in the nth round of transaction at the moment t.

2) The supply-demand ratio is more than 1: at this time, since the surplus portion still needs to be traded again after the seller sells electricity to the buyer, the buyer's price should be updated first.

（30）

（31）

3) Supply-demand ratio equal to 1: in order to embody fairness, the updated quotations of the buyer and the seller are the average value of the quotations.

（32）

In the formula: k represents the number of quotes in the current round of transaction and may also be understood as the number of matches, since the quote is updated every time a match is successful.

After N (maximum value of set transaction rounds) rounds of transactions, the design of each transaction price also needs to consider the supply and demand of both parties. The traditional method for setting the trading price is to take the average value of the quoted prices of the buyer and the seller, but when the supply and demand of the buyer and the seller are unequal, the incentive effect of the price is not strong and unfair for the party with less quantity, so from the perspective of supply and demand, the reasonable trading price is set by improving the model of the middle market rate, each trading price is ensured not to exceed the quoted price range of the two parties, the enthusiasm of trading can be promoted, the internal consumption rate of the microgrid group is improved, and the safe operation of the distribution network is ensured.

First, the average of the quotes of both parties is calculated:

（33）

in the formula:

is the average trade price for the mth pair at time t.

Secondly, according to the supply and demand of both parties, the following 3 cases are divided:

1) when the seller is available for sale more than the buyer is required, and the buyer wants the transaction price to be lower than the average value, the transaction price should be biased toward the buyer:

（34）

2) when the seller is less available than the buyer is demanding, and the seller wants the transaction price to be higher than the average, the transaction price should be biased toward the seller:

（35）

3) when the seller sales amount equals the buyer demand amount, the transaction price is the average of the two quotes:

（36）

wherein the content of the first and second substances,

is last at time tThe k-th buyer's bid in the round-robin transaction,

for the kth seller offer in the last round of transaction at time t,

According to the model, willingness of each microgrid to participate in trading can be effectively improved, and the trading pricing model is more reasonable than a traditional trading pricing model.

Examples of specific applications

Assume a piconet group of 4 piconets, the specific load types and device configurations of which are shown in table 1 below. Assuming that both PV and WT operate in the maximum power tracking mode, the prediction values of the electrical load, the thermal load and the cold load of each microgrid are shown in fig. 3 and 4. The maximum output of MT, GB and the refrigerator is set to be 500 kW. For energy storage, the minimum capacity is 100kW, the initial capacity is set to 20% of the total capacity, the maximum charge and discharge power is set to 100kW, the charge and discharge efficiency is 0.9, and u = 0.001.

TABLE 1

1. Cost comparison

Comparing results of different methods:

the initial scheme is as follows: interaction does not exist in the microgrid group, and the supply and demand quantity and the distribution network trade according to the power price of the internet;

scheme A: the micro-grid group adopts a uniform price and then carries out transaction through a pricing model;

scheme B: the reported amount of the microgrid group is distributed in proportion, and then trade is carried out through a pricing model;

scheme C: and matching according to the priority model of the invention, and then carrying out transaction through a pricing model.

The cost results are shown in fig. 5a, 5b, 5c, 5d and 6. As can be seen, the entire P2P market is in a relatively steady state after approximately 5 rounds of trading. Firstly, compared with the scheme A, the scheme B and the scheme C adopt a mutual bargaining mode, so that the cost of each microgrid is reduced, the flexibility of each microgrid participating in internal transaction is ensured, and the participation willingness of each microgrid is improved; secondly, according to the comparison between the scheme B and the scheme C, the total cost after the microgrid group is guided to trade according to the priority model is reduced in a small scale, in the aspect of privacy protection, the scheme B distributes supply and demand according to the proportion and requires that each microgrid disclose the supply and demand and the price to all other microgrids in the whole system, the scheme C only needs to provide one index of the scheme C, then the information of the scheme C is disclosed to the opposite side in the trade, and the privacy is greatly improved.

The model considering the wind-solar output uncertainty is regarded as a scheme D, and the comparison result of the cost of each microgrid and the scheme C is shown in table 2. As can be seen from the table, when the uncertainty of the wind-solar output is considered, the cost thereof is slightly increased, and the increased part of the cost can be understood as: when the wind and light output fluctuates, the safe operation cost is increased to ensure that the system can still normally operate.

TABLE 2

Scheme(s)	MG1	MG2	MG3	MG4	MMG
						C (yuan)	4028.83	7280.66	3595.22	4743.15	19647.86
D (yuan)	4033.98	7339.35	3654.31	4802.03	19829.67

2. Transaction process

The matching process when trading when n =10 is illustrated by taking the time instants t =1, t =4, and t =15 as an example.

As shown in fig. 7a and 7b, when t =1, the MG1 and the MG4 respectively need 4.905kW and 93kW to satisfy the supply and demand balance, the MG2 has 114.235kW for sale, and the MG3 reaches the supply and demand balance at this time, so that the MG1, the MG2 and the MG4 participating in the P2P market trading firstly issue respective priority indexes-0.5501, 2 and-1.4499, and then under the guidance of the signal, the MG2 and the MG4 carry out the first trading, the deal order is <93,0.0787>, the MG4 exits the P2P trade market after reaching the supply and demand balance, the MG2 and the MG1 carry out the second trading, the deal order is <4.905, 0.0787>, and finally the MG2 sells 16.33kW to the distribution network according to the price of the network.

As shown in fig. 8a and 8b, at t =4, 200.69kW and 102.67kW are required for MG1 and MG4 respectively to meet the balance between supply and demand, 152.835kW and 184.355kW are available for MG2 and MG3 respectively, and then 4 piconets participate in the P2P market for trading. The priority indexes of the 4 microgrids are-1.1616, 2.4533, 2.5467 and 0.8384, the MG3 and the MG1, and the MG2 and the MG4 are traded at the same time, the achieved orders are <184.355, 0.0692>, <102.67 and 0.0692>, and then the MG3 and the MG4 meet the demand balance and exit the trading market, at the moment, the MG1 needs 16.335kW, the MG2 can sell 50.165kW, so the users can release the priority indexes of the 4 microgrids in the P2P trading market again, the users can directly achieve the trading due to the fact that the whole distribution network group is a buyer and a seller, the trading orders of the MG1 and the MG2 are <16.335,0.0692>, and finally the MG2 sells 33.83kW to the microgrids according to the price of the internet electricity.

As shown in fig. 9, at t =15, the MG1 and the MG2 respectively need 106.7857kW and 7.935kW to meet the balance of supply and demand, the MG3 and the MG4 are respectively available at 33.005kW and 374.565kW, and at this time, 4 piconets all participate in the P2P market for trading. The priority indexes of the 4 microgrids are-1.4277, -0.5723, 2.0685 and 2.9317 respectively, the MG1, the MG4, the MG2 and the MG3 trade at the same time, the bargain orders are <106.7857, 0.6381>, and <7.935 and 0.6462 respectively, after trading, the MG1 and the MG2 reach supply and demand balance and exit the P2P trading market, and the MG3 and the MG4 sell 25.07kW and 267.7793kW to the distribution network according to the price of power on the Internet.

3. Transaction price comparison

The above-mentioned trade price results at three time points are compared with the traditional average price results as shown in fig. 10, fig. 11 and fig. 12. According to the graph, the transaction price of each transaction order takes the offered demand and price of both transaction parties into account, and the transaction price calculated according to the model is more beneficial to promoting each microgrid to generate/use more electricity to realize local balance.

At t =1, MG2 and MG4 quote 0.07 yuan and 0.09 yuan respectively, and at this time, since the supply amount is larger than the demand amount, the traditional trade price of 0.08 yuan is not as good as the trade price of 0.0781 yuan in the present invention, and the trade price is biased to promote the buyer to use more electricity to satisfy the supply and demand balance. Similarly, in the MG2 and MG1 transaction after the quotation is updated, MG2 quotation is 0.0765 yuan, MG1 quotation is 0.08 yuan, at this time, because the supply is less than the demand, the traditional trade price 0.0782 yuan is not as good as the trade price 0.0792 yuan in the invention, the trade price is biased to the seller to promote the seller to generate more electricity to realize supply and demand balance, and the same applies when t =4 and t = 15.

4. Autonomous scheduling results

Setting 1000 scene generations by using a scene generation method in a scene analysis method, as shown in fig. 13; the scene is cut down to 10 by adopting a synchronous back substitution elimination method, as shown in FIG. 14; the probabilities corresponding to the 10 scenarios are shown in table 3, and the scenario 6 with the maximum probability value is selected as an example to explain the scheduling result. As shown in fig. 15, 16, 17, and 18, the ES power is positive indicating discharge and negative indicating charge. MG1 has large PV output value, but because there are 3 types of load, the load is large, so it is basically in buyer state, the energy storage device is charged selectively when the electricity price is low (eg.4: 00~ 6: 00), and discharged selectively when the electricity price is high (eg.11: 00~ 15: 00) to reduce the electricity purchasing cost; since the MG2 cooling load demand is only chiller supply, there is both a buyer status and a seller status; when the requirement of MG3 is large, the energy storage is discharged selectively, when the PV output is large and the requirement can be satisfied, the energy storage is charged selectively; the MG4 is in the seller state when the PV output is large (eg.10: 00-16: 00) because the internal equipment is single, and is in the buyer state when no supply is supplied to meet the load demand at the rest time.

TABLE 3

Scene	S1	S2	S3	S4	S5
						Probability of occurrence	0.052	0.121	0.074	0.1	0.047
Scene	S6	S7	S8	S9	S10
						Probability of	0.178	0.091	0.077	0.15	0.11

The interaction power of the MMG with the distribution network before and after coordination is shown in fig. 19. Compared with the prior art, the interaction power after coordination is greatly reduced, and the permeability of the power distribution network after redundant distributed resources are fully consumed on site is reduced, so that the safe and stable operation of the power distribution network is facilitated.

The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily think of the changes or substitutions within the technical scope of the present invention, and shall cover the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A priority matching-based dynamic microgrid group P2P transaction method is characterized in that the microgrid group comprises a plurality of microgrids and comprises the following steps:

s19, judging whether the number of the current round of transaction reaches the set maximum number of the transaction rounds, if yes, ending the whole transaction process, and if not, returning to the step S12;

in step S14, the calculation formulas of the priority indexes of the buyer and the seller are respectively:

in the formula:

and

priority indexes of the buyer and the seller respectively;

and

is the maximum amount of transactions allowed in the P2P market;

and

and

the purchase price and the selling price of the transaction with the distribution network are respectively;

in step S16, the supply-demand ratio is calculated using the following formula,

in the formula:

for the requirement of the m pair in the n round of transaction at the moment tCalculating the quantity;

the process of updating the quotation according to a quotation model based on the supply-demand ratio comprises the following steps:

1) the supply-demand ratio is less than 1:

in the formula:

quoted price for the kth buyer in the nth round of transaction at the moment t;

2) the supply-demand ratio is more than 1:

3) supply-demand ratio equal to 1:

in the formula: k represents the number of quotes in the current round of trading;

in step S17, the transaction price model is:

2) when the seller salable amount is less than the buyer's demanded amount:

3) when the seller salable amount equals the buyer demand amount:

wherein the content of the first and second substances,

，

for the k-th bid of the buyer in the last round of transaction at time t,

for the kth seller offer in the last round of transaction at time t,

2. The dynamic microgrid group P2P transaction method based on priority matching according to claim 1, wherein in the step S15, the process of performing transaction matching based on the ranking of priority index values includes: and sequencing the micro-grids from large to small according to the absolute values, and preferentially matching the micro-grids arranged in front successfully.

3. The priority matching based dynamic microgrid group P2P transaction method according to claim 1, wherein in the step S13, the building of the scheduling model comprises the steps of:

setting an objective function

，

，

，

The total cost of the microgrid group is obtained;

and

respectively the maintenance cost and the interaction cost,

is the probability of occurrence of scene s;

、

、

、

、

、

respectively representing the maintenance coefficients of all the units;

、

、

、

、

、

and

respectively buying electric quantity and selling electric quantity for each microgrid;

and

the purchase price and the sale price reported by each microgrid respectively;

4. The priority matching-based dynamic microgrid group P2P trading method of claim 3, wherein in the step S13, a scene analysis method is adopted to describe uncertainty of wind turbine and photovoltaic output, and the method comprises the following steps: combining the output prediction of a fan before the day and the photovoltaic output prediction, and adopting Latin hypercube sampling to generate M initial scenes; and based on the principle that the Euclidean distance is shortest, reducing the initial scenes until the number of the scenes reaches a set value.

5. The priority matching based transaction method of the dynamic microgrid group P2P of claim 1, wherein in the step S19, if a set maximum number of transaction rounds is reached after the current round of transaction, the remaining supply or demand is transacted with the distribution network.