CN116154760A

CN116154760A - Block chain-based distributed photovoltaic power generation point-to-point transaction method and system

Info

Publication number: CN116154760A
Application number: CN202310107174.7A
Authority: CN
Inventors: 周开乐; 孟宇繁; 虎蓉; 陆信辉
Original assignee: Hefei University of Technology
Current assignee: Hefei University of Technology
Priority date: 2023-01-30
Filing date: 2023-01-30
Publication date: 2023-05-23

Abstract

The invention provides a distributed photovoltaic power generation point-to-point transaction method and system based on a blockchain, and relates to the technical field of power transaction. The method includes acquiring initial data information; encrypting the initial data information based on an asymmetric encryption technique; acquiring predicted power generation data of photovoltaic power generation based on meteorological information and a power generation prediction model which is trained in advance; acquiring electricity load data of a user based on the electricity consumption of the user and a pre-trained user model; determining different electricity trading markets based on the predicted electricity generation data and the electricity load data; and carrying out intelligent contract transaction of the power generator and the power consumer based on intelligent contract rules. Based on the processing, the users of the distributed photovoltaic perform power transaction in a P2P mode, the supervision and the prediction of the power generation of the users are enabled through a big data technology, and intelligent contract transaction is automatically performed, so that the fair, real-time and mutually trusted power transaction among the users is realized, and the requirement of the distributed power transaction is met.

Description

Block chain-based distributed photovoltaic power generation point-to-point transaction method and system

Technical Field

The invention relates to the technical field of electric power transaction, in particular to a distributed photovoltaic power generation point-to-point transaction method and system based on a blockchain.

Background

The renewable energy source power generation has the characteristics of volatility, randomness and intermittence, and is extremely easy to generate impact on the normal operation of a power grid, so that timely electric energy transaction is needed to solve the problem of energy balance of users, and the distributed photovoltaic power generation is derived.

Because the distributed photovoltaic power generation systems are mutually independent and relatively distributed, centralized, unified and intelligent management is difficult to realize in the distributed photovoltaic power generation systems. Meanwhile, the distributed photovoltaic power generation system has more participation subjects, uncertain factors are increased, electric energy generated by the distributed photovoltaic power generation is often consumed nearby, namely 'partition wall electricity selling', so that a middle trading party in the electric power trading market is often a group or a third party in a specific range, and the trading mode faces the problem of lack of trust and centralization.

In addition, due to the lack of acquisition channels for reputation and historical transaction information of all parties participating in the power transaction, the parties participating in the power transaction have a plurality of concerns in the power transaction process, which are not beneficial to normal performance of the power transaction, and limit the range of the power transaction of the distributed photovoltaic power generation.

Based on the above factors, the safety of power transaction in the existing distributed photovoltaic power generation management system is difficult to ensure.

Disclosure of Invention

(one) solving the technical problems

Aiming at the defects of the prior art, the invention provides a distributed photovoltaic power generation point-to-point transaction method and system based on a blockchain, which solve the problem that the safety of power transaction in a distributed photovoltaic power generation management system is difficult to guarantee in the prior art.

(II) technical scheme

In order to achieve the above purpose, the invention is realized by the following technical scheme:

in a first aspect of the present invention, there is provided a blockchain-based distributed photovoltaic power generation point-to-point transaction method, the method comprising:

acquiring initial data information; the initial data information comprises meteorological information of distributed photovoltaics and electricity utilization information of users;

encrypting the initial data information based on an asymmetric encryption technology, and storing the encrypted initial data information in a data uplink based on a bottom layer block chain;

acquiring predicted power generation data of photovoltaic power generation based on the meteorological information and a power generation prediction model which is trained in advance;

acquiring electricity load data of the user based on the electricity consumption of the user and a pre-trained user model;

Determining different electricity trading markets based on the predicted electricity generation data and the electricity load data;

and carrying out intelligent contract transaction of the power generator and the power consumer based on intelligent contract rules.

Optionally, the method further comprises:

monitoring the intelligent contract transaction based on the user credit values of the power generator and the power consumer;

and monitoring whether the difference exists between the promised electric quantity and the actual electric quantity in the intelligent contract transaction, if so, collecting fine from the offender and deducting the credit value of the user of the offender.

Optionally, the trading market includes: middle and long term market, day-ahead market, real-time market;

the medium-and-long-term market includes an electricity trading market for meeting long-term planned electricity generation needs of users;

the day-ahead market includes an electricity trading market for meeting short-term electricity generation needs of users;

the real-time market includes a power trade market for solving a problem of unbalance of supply and demand amounts in a power trade.

Optionally, the user reputation value comprises a contract completion, a consensus completion rate and a node liveness;

the contract completion degree represents the completion condition of the past power transaction of the electricity consumer or the electricity generator;

The credible value calculation formula of the contract completion degree of the power generation party is as follows:

the credible value calculation formula of the contract completion degree of the electricity consumer is as follows:

wherein ,R_cc Trusted value representing contract completion, E _provide Representing the actual power supplied by the generator, E _prv Contracted electric quantity representing intelligent contract trade, E _use Representing the actual electric quantity used by the electricity consumer, R _reward A reward trusted value representing a user and a generator who complete the smart contract transaction;

the consensus completion rate indicates whether a consensus result participated by the user node is adopted by a blockchain or not;

the reliability calculation formula of the consensus completion rate is as follows:

wherein ,R_ccr Trusted value representing consensus completion rate of user node, R _receive Representing the number of blocks participating in consensus and accepted, R _total Representing the total number of blocks participating in the consensus;

node liveness represents the proportion of the number of blocks of the user node participating in consensus or transaction in the first t blocks to the first t blocks;

the trusted value calculation formula of the node liveness is as follows:

wherein ,R_na The method comprises the steps that a trusted value of node liveness is represented, t represents the first t blocks, k represents the number of blocks which the node participates in consensus, and p represents the number of blocks which the node carries out transaction;

the calculation formula of the trusted value of the node is as follows:

R＝R _last +αR _ccr +βR _na +(1-α-β)R _cc

Wherein R represents the trusted value of the node, R _last Represented as the last trusted value of the node, R _ccr Representing the consensus trusted value of a node, R _na Representing the activity confidence value of the node, R _cc Representing contract trusted values, α, β representing weight parameter values.

Optionally, the method further comprises:

determining whether green energy authentication of the user is passed or not based on the electricity utilization rule, the transaction credit value and the activity degree of the user, the power generation hardware facilities and the total power generation amount of the user;

if so, the user is given additional trade discounts in the smart contract trade process.

Optionally, the method further comprises:

determining an initial incentive price;

wherein, the formula for determining the initial incentive price is:

expressed as initial incentive price; i represents a user; n (N) _u Representing the total number of users; />

Representing an initial incentive price issued to the i user t at the moment;

inputting the initial incentive price into a demand response model to obtain the response quantity of a user;

adjusting the initial incentive price based on the response quantity and a preset incentive price adjustment formula;

the preset incentive price adjustment formula is as follows:

representing the incentive price for the kth iteration; gamma represents the learning rate; />

Representing the total predicted response of the k-1 th iteration user; / >

Representing the incentive price for the k-1 th iteration; r is R _{t_T} Representing a target excitation amount;

determining a response cost of the user based on the response gradient of the user; when the response gradients of the users are equal, the response cost of the users is lowest;

the formula for determining the response gradient of the user is:

wherein ,

representing the response gradient of the kth iteration user i; />

Representing the expected response of the kth iteration user i; />

Representing the price of the incentive accepted by the user i for the kth iteration;

the incentive price update formula for each user is:

the response gradient of the user is iterated for the k-1 th time; />

Representing the incentive price for the kth iteration; />

Representing the response gradient of user i for the k-1 th iteration; />

Representing the incentive price for the k-1 th iteration; />

The k-1 th iteration user i accepts incentive price.

Optionally, the method further comprises:

dividing the user level of the user based on a clustering algorithm; wherein the user level includes a high demand response user and a low demand response user;

acquiring electricity utilization characteristics of a user, and acquiring a non-controllable equipment state vector based on the electricity utilization characteristics

Acquiring a power consumption plan of a high-demand response user, and calculating a scoring matrix of the high-demand response user;

Determining a user score for the high demand response user based on the scoring matrix;

and acquiring the electricity consumption plan with the highest score of the user as a recommendation plan.

Optionally, the method further comprises:

the formula for acquiring the electricity utilization characteristics of the user is as follows:

wherein ,

representing the probability that user u uses device k during the t-th time period of each day; />

When user u has used device k for the t-th time period on day d; />

When it means that user u does not use device k for the t-th time period on day d; d represents the d-th day of the observation period; d represents the total number of days of an observation period for acquiring the electricity utilization characteristics of the user;

wherein user u is determined ₁ And user u ₂ The similarity formula of the electricity consumption behavior is as follows:

wherein sim (u 1, u 2) represents user u ₁ And user u ₂ Similarity of electrical behavior;

representation of

Cosine value of included angle of two vectors; />

Representing the user u ₁ Electricity utilization characteristic matrix S _u1 An expanded one-dimensional vector;

representing the user u ₂ Electricity utilization characteristic matrix S _u2 An expanded one-dimensional vector; k' represents user u ₁ And user u ₂ The number of devices used together; t represents the number of divided time periods;

the calculation formula for determining the user score is:

wherein score _u，s Representing the frequency of the usage pattern s of the high-demand user u; d (D) _u，s Representing the number of times that the usage pattern s of the adjustable household electrical appliance appears in the daily life of the high-demand response user u; d represents the number of days the sample observation period contains;

Representing a low demand response user u ₀ Scoring the device usage pattern s; u represents all high-demand response users; sim (u) ₀ U) represents user u ₀ Similarity with user u's power consumption behavior;

delta represents a normalization factor, and delta = 1/Σ _u∈U |sim(u ₀ ，u)|。

In a second aspect of the present invention, there is provided a blockchain-based distributed photovoltaic power generation point-to-point transaction system, the system comprising:

the first acquisition module is used for acquiring initial data information;

the initial data information comprises power generation information, energy storage information, weather information of distributed photovoltaics and power utilization information of users;

the first encryption module is used for encrypting the initial data information based on an asymmetric encryption technology and carrying out data uplink storage on the encrypted initial data information based on a bottom layer block chain;

the second acquisition module is used for acquiring predicted power generation data of photovoltaic power generation based on the meteorological information and a power generation prediction model which is trained in advance;

the third acquisition module is used for acquiring the electricity load data of the user based on the electricity consumption of the user and a pre-trained user model;

a first determination module for determining different electricity trading markets based on the predicted electricity generation data and the electricity load data;

And the first transaction module is used for carrying out intelligent contract transaction of the power generating party and the power consuming party based on intelligent contract rules.

Optionally, the system further comprises:

the first supervision module is used for supervising the electricity utilization transaction based on the user credit values of the power generator and the electricity consumer;

and the first monitoring module is used for monitoring whether the difference exists between the promised electric quantity and the actual electric quantity, and if so, collecting fines from the offender and deducting the credit value of the user.

(III) beneficial effects

The invention provides a distributed photovoltaic power generation point-to-point transaction method and system based on a blockchain. Compared with the prior art, the method has the following beneficial effects:

the invention provides a distributed photovoltaic power generation point-to-point transaction method based on a blockchain, which comprises the following steps: acquiring initial data information; encrypting the initial data information based on an asymmetric encryption technology, and storing the encrypted initial data information in a data uplink based on a bottom layer block chain; acquiring predicted power generation data of photovoltaic power generation based on the meteorological information and a power generation prediction model which is trained in advance; acquiring electricity load data of the user based on the electricity consumption of the user and a pre-trained user model; determining different electricity trading markets based on the predicted electricity generation data and the electricity load data; and carrying out intelligent contract transaction of the power generator and the power consumer based on intelligent contract rules.

Based on the processing, the distributed photovoltaic power generation user becomes an independent power generation party and an independent power utilization party, and the user can conduct direct power transaction based on the P2P mode, so that the power transaction is more flexible. Meanwhile, the large data technology enables users to monitor and predict the power generation, supports intelligent decision of power transaction, and automatically executes the power transaction through intelligent contract transaction, so that fair, point-to-point, real-time and mutual trust transaction among users is realized, and the requirement of distributed power transaction is met.

Drawings

In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of a distributed photovoltaic power generation point-to-point transaction method based on blockchain provided by the invention;

FIG. 2 is a block chain based frame diagram of a distributed photovoltaic power generation point-to-point transaction management system provided by the invention;

FIG. 3 is a schematic flow chart of a transaction supervision method according to the present invention;

FIG. 4 is a schematic diagram illustrating interaction between a system and an underlying blockchain in accordance with the present invention.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention are clearly and completely described, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

The prior distributed photovoltaic point-to-point transaction system based on the blockchain has the following defects:

1. lack of supervision in the power transaction process and credit management on both transaction sides leads to frequent occurrence of default phenomena in the system of the power transaction, which affects normal power transaction.

2. The excitation mechanism of the blockchain power transaction is not fully considered, so that the enthusiasm of a transaction party is not facilitated to be improved, and the power grid regulation and the improvement of the use efficiency of the distributed photovoltaic power generation are not facilitated.

3. The problems of larger wave crest and wave trough, larger uncertainty, custom tendency of users, irrational transaction, too concentrated market force and the like in the distributed power generation are emphasized, the phenomena of power consumption peaks and sudden increase or sudden decrease of transaction demands are easy to occur, the stability of a power grid is reduced, and the normal operation of power transaction is not facilitated.

In order to solve the technical problems, the general idea of the technical scheme in the embodiment of the invention is as follows:

the invention provides a distributed photovoltaic power generation point-to-point transaction method based on a blockchain, referring to fig. 1, fig. 1 is a flow chart of the distributed photovoltaic power generation point-to-point transaction method based on the blockchain, as shown in fig. 1, the method comprises the following steps:

s1, acquiring initial data information.

The initial data information comprises power generation information, energy storage information, weather information of distributed photovoltaics and power utilization information of users.

S2, encrypting the initial data information based on an asymmetric encryption technology, and carrying out data uplink storage on the encrypted initial data information based on a bottom layer block chain.

And S3, acquiring predicted power generation data of photovoltaic power generation based on the meteorological information and a power generation prediction model which is trained in advance.

And S4, acquiring the electricity load data of the user based on the electricity information of the user and a pre-trained user model.

S5, determining different electricity trading markets based on the predicted electricity generation data and the electricity load data.

S6, carrying out intelligent contract transaction of the power generator and the power consumer based on intelligent contract rules.

Referring to fig. 2, fig. 2 is a block chain-based architecture diagram of a distributed photovoltaic power generation peer-to-peer transaction management system, which includes a base layer, a data layer, an algorithm layer, an application layer and a presentation layer, as shown in fig. 2.

For the base layer, the base layer contains the P2P network. Based on the P2P network, transaction information of a transaction party can be freely uploaded to each node in the blockchain, and new transaction information is broadcast by broadcasting. Meanwhile, the base layer contains a distributed account book, namely, each node has the same account book copy, and different nodes trust each other and encrypt each other, so that tracking prevention and tamper prevention of transaction information are realized.

In addition, when each electric power trade is completed, the management system gives a certain electric power subsidy to the trade party of the electric power trade based on the electric power trading volume of the electric power trade. Wherein, this electric power subsidy can be applied to the electric power transaction of transaction party after this system.

For a data layer, the data layer comprises a data acquisition module and a data encryption module.

The data acquisition module can be intelligent terminal equipment, such as photovoltaic power generation equipment, electric quantity transfer equipment, intelligent ammeter and other equipment. Based on the data acquisition module, the management system acquires power generation information, energy storage information, weather information and user power consumption information of the distributed photovoltaic.

Then, based on the data encryption module and the asymmetric encryption technology, the management system encrypts the acquired information, and the encrypted information is stored in a data uplink through a bottom layer block chain. Specifically, each block content includes the hash value of the previous block, the present block content (the total amount of electricity generated, transaction information, etc.), verification user information, and verification user workload certification information. Different blocks are connected end to end, and the content of each block is comprehensively determined by all the previous blocks, so that the possibility of information difference generation and artificial modification damage is eliminated.

The user can inquire the data related to the user on the whole alliance block chain through the unique private keys, so that the safety and transparency of the data are ensured. Meanwhile, the data centers of the blockchain adopt honeycomb layout, so that the safety of data storage is enhanced, and each data center performs data analysis processing in different directions according to different types and tasks (user electricity consumption prediction, user demand response recommendation and the like) of the data stored by the data centers. The system reasonably arranges calculation force distribution according to big data and manual analysis, dynamically adjusts calculation force pressure, avoids the problems of calculation errors or data loss and the like caused by overlarge data quantity of a server, and further ensures the stability and safety of power transaction.

Aiming at the step S1, based on a data acquisition module, power generation information, energy storage information, weather information and user power consumption information of the distributed photovoltaic are acquired.

Aiming at the step S2, the obtained information is subjected to data encryption based on a data encryption module and an asymmetric encryption technology, and the encrypted information is subjected to data uplink storage through a bottom layer block chain. Specifically, each block content includes the hash value of the previous block, the present block content (the total amount of electricity generated, transaction information, etc.), verification user information, and verification user workload certification information.

Aiming at an algorithm layer, the algorithm layer comprises a photovoltaic power generation prediction module, an electric load prediction module and a demand response strategy recommendation module. A plurality of modules in the algorithm layer perform operations such as classification processing, analysis, management, prediction and the like by calling encrypted information stored on the blockchain.

The photovoltaic power generation prediction module comprises a power generation prediction model which is trained in advance. The training process for the power generation prediction model includes: firstly, training a power generation prediction model based on weather information acquired in a data layer based on a k-means clustering algorithm and a long-short-term memory model algorithm to realize prediction of future weather information by the power generation prediction model. And then, continuously performing model training on the power generation prediction model based on the collected historical photovoltaic power generation data and the air image information. The power generation prediction model also comprises multiple linear regression, stepwise regression, an artificial neural network and other models.

In actual operation, the system inputs the acquired meteorological information to a pre-trained power generation prediction model. Then, the power generation prediction model acquires future weather information, and outputs predicted power generation data of photovoltaic power generation based on the future weather information.

The electricity load prediction module comprises a user model which is trained in advance, and the training process aiming at the user model comprises the following steps: and carrying out user portraits and training a user model on the power consumption conditions of different users based on the historical power consumption information of the users acquired by the data layer. In actual work, based on the electricity consumption information of the user, the user portrait of the user and the pre-trained user model, the management system acquires the electricity consumption load data of the user. The user model comprises a gray prediction model, a multivariable linear regression model and an ARIMA model, and long-term, medium-term and short-term prediction of the electricity load data of the user is respectively realized.

Aiming at step S3, the predicted power generation data of the photovoltaic power generation is obtained based on the meteorological information obtained by the data layer and the photovoltaic power generation prediction module of the algorithm layer.

Aiming at step S4, based on the electricity consumption information of the user acquired by the data layer and the electricity consumption load prediction module of the algorithm layer, the electricity consumption load data of the user is acquired.

The power demand response refers to a short-term behavior that a user temporarily changes its power consumption behavior according to a power price or an incentive measure to reduce or increase the power consumption when the power price is significantly raised (lowered) in the power trade market or the safety reliability of the trade system is at risk, so as to promote the power supply and demand balance, ensure the stable operation of the power grid, and inhibit the rise of the power price. Briefly, the power demand response is that a user responds to the call of a power grid, and the power consumption condition (including reducing and increasing the power consumption) of the user is temporarily adjusted in a planned manner, so that the stable behavior of the power system is promoted.

In order to realize the power demand response, a demand response strategy recommendation module is constructed in the technical scheme of the invention. Specifically, the demand response strategy recommendation module comprises an incentive type demand response based on optimal incentive iterative learning and a personalized recommendation model based on neural network collaborative filtering.

The demand response can be classified into an incentive type demand response and a price type demand response in terms of driving methods. Wherein the incentive type demand response can understand the load reduction items required for attracting users to participate in the transaction system based on incentive policies and compensation modes. For example, when the electricity consumption peak requires reduction of the electricity load, the user obtains a discount on the electricity fee or directly obtains a "prize" by adjusting or reducing the electricity consumption. Price type demand response can be understood as based on a change in price of electricity to allow a user to actively change electricity consumption behavior. In actual life, price type demand response is a non-voluntary regulation mode of users, has poor user-friendliness degree on weak rigid load and decision capability, and is unfavorable for attracting massive users to participate.

Compared with price type demand response, the incentive type demand response adopted in the technical scheme of the invention does not relate to the price of the electric power, so that the user does not bear the risk of benefit loss, and the acceptance degree is higher.

When the business of the incentive type demand response is developed, the management system needs to deliver the optimal incentive price according to the demand response model of the user. Wherein the demand response cost of the incentive type demand response corresponds to the combination of the economic benefits of the users in values, and when the target load reduction amount is determined, the demand response cost is expressed as the following formula.

wherein ,C_t A demand response cost representing an incentive type demand response; n (N) _u Representing the total number of users; i represents a user; r is R _i，t Indicating i the total load reduction amount at the time t of the user; i _i，t Representing a challenge issued at a time t of an i userPrice is excited.

In formulating an incentive strategy for incentive type demand response, it is necessary to minimize the cost of demand response for the entire user while satisfying the target reduction amount, and thus the corresponding optimization problem can be expressed by the following formula.

Wherein, minC _t Representing a minimized demand response cost; s.t. represents the corresponding formula as constraint condition; r is R _{t_T} Representing the target excitation amount.

The optimal function constructed according to the Lagrangian multiplier method is as follows:

/>

wherein L represents a lagrangian function; λ represents the lagrange multiplier;

represents an optimal function constructed according to Lagrangian multiplier method, and the argument of the optimal function is +. >

It is worth noting that in the technical solution of the present invention, the constraint problem is converted into an unconstrained problem based on the lagrangian function, so as to solve the unconstrained problem. The specific equality constraint is converted to a lagrangian function.

Representing constraint problems to be solved, +.>

Constraints representing the constraint problem.

The Lagrangian function format is: l (independent variables, lagrangian multiplier) =solution problem expression+lagrangian multiplier constraint, extremum is obtained when the lagrangian function derives 0 for all independent variables, respectively (i.e., the minimum demand response cost in the present invention).

The extremum can be found by the following formula when the function satisfies the following conditions:

when the unit cost of the load reduction increment of the user is the same, the optimal solution of the formula is obtained. Because the network of the demand response has no clear analytical expression, the optimal incentive price corresponding to each demand response cannot be directly solved.

In order to solve the problem that the optimal incentive price corresponding to each demand response cannot be directly solved, the optimal solution (namely, the optimal incentive price in the invention) is approximated by using an optimal incentive iteration method in the technical scheme of the invention.

Specifically, the process of the optimal excitation iteration method comprises the following steps:

first, an initial incentive price is determined

And equally distributes the initial incentive price to each user, at this time +.>

wherein ,/>

Expressed as initial incentive price; />

Representing the initial incentive price to be placed for the i user t time.

And inputting the initial incentive price into a demand response model to obtain the response quantity of each user.

Then, based on the obtained user response, the formula is updated based on the incentive price, and the adjusted incentive price. Wherein the incentive price updating formula is as follows.

/>

wherein ,

Representing the total predicted response of the k-1 th iteration user; />

Representing the incentive price for the k-1 th iteration; r is R _{t_T} Representing the target excitation amount.

After determining the updated formula for incentive prices described above, it is necessary to determine how to assign the total incentive price to each user based upon the user's response. It is known that when the user unit increment response costs are equal, the corresponding incentive price is optimally distributed.

Because the prediction of the user's response behavior has some error, there may be a large volatility in the marginal incremental cost of the user. In order to reduce the influence of the prediction error on the allocation strategy, the response gradient of the user is defined as follows:

wherein ,

representing the response gradient of the kth iteration user i; />

Representing the expected response of the kth iteration user i; />

Representing the price of the incentive accepted by user i for the kth iteration.

When the response gradients of the users are equal, the response cost of the users is the lowest, and the incentive price updating formula of each user is as follows.

wherein ,

representing the price of the incentive accepted by the user i for the kth iteration; />

Representing the response gradient of the k-1 th iteration user; />

Representing the incentive price for the kth iteration; />

Representing the response gradient of user i for the k-1 th iteration;

representation ofIncentive price of the k-1 th iteration; />

The k-1 th iteration user i accepts incentive price.

After a number of iterations, the total predicted response of the user reaches the target response value. In the demand response implementation stage, the system pushes the optimized optimal incentive price to the user, and the user performs actual response.

In addition, when the demand response recommendation is carried out, firstly, dividing the user level of the user based on a clustering algorithm; wherein the user level includes a high demand response user and a low demand response user. For example, households having similar household appliance types are selected, and the monthly average power consumption and the monthly average power rate of each household are calculated. If the electricity consumption of two families is close and the electricity charge difference is larger, the family with lower electricity charge better utilizes the demand response plan, and belongs to the corresponding user with high demand. In contrast, a household with a higher electricity fee is considered as a low demand response user.

The recommendation system then recommends the appliance usage experience of the high demand response user to the low demand response user.

The invention adopts the cosine similarity calculation. The similarity of life modes of two residents is quantified by calculating the similarity of the service time of the non-adjustable equipment, and the working state matrix S of the non-adjustable equipment of the user is obtained _u，d The working state matrix S _u，d The elements in (a) are expressed as

Representing whether device k is used for user u, day d, time t, when the element value is 0, indicating that it is unused; the use is indicated when the element value is 1.

Since the time of day the user uses the device cannot be completely consistent, the daily device operating state is averaged to represent. After the mean value is calculated

Indicating that user u is using during the t-th time period of each dayProbability of device k. Matrix S _u The daily electricity utilization characteristics of the user are represented, and the actual living habits of the user are reflected.

wherein ,

When user u has used device k for the t-th time period on day d; />

When it means that user u does not use device k for the t-th time period on day d; d represents the d-th day of the observation period; d represents the total number of days of the observation period for which the electricity usage characteristics of the user are acquired.

To calculate user u ₁ and u₂ The similarity of the power consumption behavior requires two users u to be selected from k non-dispatchable devices ₁ and u₂ A common use device. Suppose user u ₁ and u₂ The commonly used devices are k' and then a matrix S is calculated _u1 and S_u2 Is a distance of (3). Expanding matrices into one-dimensional vectors expressed as

And the one-dimensional vector has a length of KxT, thereby calculating user u ₁ and u₂ The similarity problem of the electricity behavior is converted into a vector>

and />

Distance between them.

representation of

Cosine value of included angle of two vectors; />

representing the user u ₂ Electricity utilization characteristic matrix S _u2 An expanded one-dimensional vector; k' represents user u ₁ And user u ₂ The number of devices used together; t represents the number of divided time periods; (e.g., dividing a day by a period of 2 hours, then t=12).

In the technical scheme of the invention, the score of the user on the equipment using mode is calculated according to the frequency of occurrence of the equipment using mode of the user in a period of time. The specific formula for obtaining the frequency is as follows:

wherein score _i.s Representing the frequency value; d (D) _i.s The number of times that the usage pattern s of the adjustable household electrical appliance appears in the daily life of the ith high-demand response user is represented, and D represents the number of days contained in the sample observation period. The frequency value may be expressed as a preference of user i for the device usage pattern s, representing a higher user score when the value of the frequency value is larger.

Responding to a user for a low demandu ₀ Scoring device usage patterns s

Can be expressed by the following formula.

representing a low demand response user u ₀ Scoring the device usage pattern s; u represents all high-demand response users; sim (u) ₀ U) represents user u ₀ And similarity of the electricity consumption behavior of the user u.

The coefficient delta is a normalization factor, and the normalization process is performed on the user scores in the collaborative filtering algorithm based on the normalization factor, wherein the calculation method of the coefficient delta is as follows.

δ＝1/∑ _u∈U |sim(u ₀ ，u)|

According to the scoring method, the scores of the users to be recommended on all the adjustable household electrical appliances can be obtained, then n using modes with the largest scores are selected to be recommended to the target users, the quality of user demand response decisions can be improved through the recommendation results, and the users are helped to finish the demand response plans better.

The actual recommendation process may include:

dividing user levels based on a clustering algorithm; wherein the user level includes a high demand response user and a low demand response user.

Acquiring electricity utilization characteristics of a user and generating an unregulated equipment state vector based on the electricity utilization characteristics

And obtaining the electricity consumption plan of the high-demand response user, and calculating a scoring matrix of the high-demand response user.

A user score for the high demand response user is determined based on the scoring matrix.

Based on the processing, the invention uses the excitation type demand response based on the optimal excitation iterative learning and the personalized recommendation model based on the neural network collaborative filtering to accurately recommend the excitation type demand response strategy to the user, so as to comprehensively and dynamically integrate the resources of the demand side and the supply side, regulate and control the electric power market through electric load prediction and electricity utilization optimization, realize peak clipping and valley removing of the current consumption amount, improve the stability of the power grid and maintain the normal operation of the transaction system.

Aiming at an application layer, the application layer comprises an information release module, a power transaction module and a demand response module.

The information release module is used for releasing related data information, and the related data information comprises: the power generation data and the energy storage data of the users on the same day, the output prediction data of photovoltaic power generation, the long-term, medium-term and short-term power load prediction data, the power transaction data of the users and the participation demand response condition of the users. Meanwhile, the user can inquire the related data information through the information release module.

The power transaction module comprises an intelligent contract sub-module, a transaction supervision sub-module, a power patch sub-module and a green energy authentication sub-module.

The power transaction module automatically performs power transaction through the intelligent contract module, and the power transaction market can be divided into: middle and long term market, day-ahead market, real-time market.

The medium-and-long-term market is used for meeting the long-term planned power generation requirement of users, namely, the users continuously supply power according to the contracted electric quantity and the price in a specified time so as to avoid the risk of market price fluctuation.

The market in the future is used for meeting the short-term electricity demand of the production and life of the user, namely, the two-way quotation and price alignment report of the electricity consumer and the electricity generator for electric power transaction are matched by the management system according to the preset rule, and a corresponding electric power transaction order is formed.

The real-time market is used for solving the problem that the electricity supply and demand are unbalanced in the electricity transaction, namely, the management system adopts unified real-time electricity price to distribute electricity for the electricity consumption of the electricity consumer for the transaction.

For step S5, different electricity trading markets are determined based on the predicted electricity generation data of the electricity generating party and the electricity load data of the electricity consuming party.

Specifically, the power generating party and the power consuming party (i.e. the trading party in the invention) participating in the power trade can select different trading markets to purchase and sell power by themselves. The medium-long-term market and the real-time market need to be manually initiated by a user in a management system, and the market in the future carries out point-to-point automatic transaction through an intelligent contract based on a blockchain algorithm type trust mechanism.

The transaction supervision submodule supervises the transaction process based on a user credit value, wherein the credit value consists of three credibility factor contract completions, consensus completion rates and node liveness.

The contract completion degree indicates the completion of the past power transaction of the electricity consumer or the electricity generator, specifically, based on an agreement reached by both parties of the transaction for carrying out the power transaction, the agreement prescribes the amount of electric energy, and the contract is automatically executed by means of intelligent contract rules in the management system, and the completion of the agreement in the term of the power transaction of the user is obtained.

The transaction of the electric quantity is allowed to be carried out only when the credibility of the user node reaches a threshold value, wherein the threshold value can be set by a power generating party and a power using party in the intelligent contract transaction. Wherein, the smart contract transaction in the invention represents the power transaction performed in the system. And after the intelligent contract transaction is finished, updating the node credit value according to the electricity consumption conditions of the power generator and the power consumer. And introducing a credit value integrating mechanism in the power transaction, namely displaying the finally calculated user credit value at the last of each power transaction, and matching the user credit value with the next intelligent contract transaction by different users.

wherein ,R_cc Trusted value representing contract completion, E _provide Representing the actual power supplied by the generator, E _prv Contracted electric quantity representing intelligent contract trade, E _use Representing the actual electric quantity used by the electricity consumer, R _reward And the rewards trusted values of the electricity consumers and the electricity generators for completing the intelligent contract transaction are represented.

The consensus completion rate indicates whether the consensus result of the user node participation is employed by the blockchain. Specifically, if the consensus reached by the user node is a final consensus result, it indicates that the user node is an honest node. When the agreed block has malicious transaction and other actions, the user node agreed with the block is identified as a malicious node to a certain extent, and the user node identified as the malicious node is not adopted even if the agreed block is agreed.

In order to avoid accidents caused by faults of the user nodes, the management system can prohibit the user nodes from participating in consensus for a certain time. If the number of consensus times that a user node is prohibited from participating is excessive, the user node may be considered a malicious node and may be deleted from the blockchain network.

If a user node is considered as honest, but the consensus completion rate of the user node is less than 0, the reason is due to the existence of bifurcation. When the consensus completion rate of the user node is larger, the number of branches is smaller, so that the consensus is facilitated to be rapidly achieved, and the longest block chain is generated.

The confidence calculation formula of the consensus completion rate is as follows:

wherein ,R_ccr Trusted value representing consensus completion rate of user node, R _receive Representing the number of blocks participating in consensus and accepted, R _total Indicating the total number of blocks participating in the consensus.

Node liveness represents the proportion of the number of blocks that the user node participates in consensus or transacts in the first t blocks to the first t blocks. Based on the node liveness, the possibility that a certain user node participates in a time period all the time to obtain higher credibility is avoided, so that the user node with higher node liveness is ensured to be a user participating in the blockchain for a long time, and the network security is maintained to a certain extent.

The calculation formula of the credible value of the node liveness is as follows:

wherein ,R_na The method comprises the steps of representing the credible value of the liveness of a node, t representing the first t blocks, k representing the number of blocks the node participates in consensus, and p representing the number of blocks the node performs transaction.

The calculation formula of the trusted value of the user node is as follows:

R＝R _last +αR _ccr +βR _na +(1-α-β)R _cc

wherein R represents the trusted value of the user node, R _last Represented as the last trusted value of the user node, R _ccr Representing a consensus trusted value of a user node, R _na An activity confidence value representing a user node, R _cc And the contract credible value is expressed, and alpha and beta are weight parameter values.

The credit value evaluation electric energy transaction flow specifically comprises the following steps:

orders for power transactions are made to be submitted according to a matching machine, and the submitted information will be published for future reference. The amount of power that is not met in the bidding of the power transaction is centrally traded by the energy suppliers and forms an order according to the agreed power price. The management system performs matching and authentication of the power transaction according to the transaction application function and the user declaration content based on the intelligent contract rule.

The user forms a link power generation status report or a power utilization status report locally and issues to the blockchain. And sending the signature to a checking staff by the status report meeting the requirements, and issuing the report to the blockchain network when the report meets the standard. For the difference between the promised electric quantity and the actual electric quantity, the offender will trade with the upper level distribution network to make up the deficiency and collect certain fine. The transaction supervision submodule deducts or increases credit points of the users according to the reward and punishment rules. When the power transaction is successfully matched, a certain user node stores and joins the block under a consensus mechanism, each user node generates an effective power transaction contract after the block verification is successful, and the block chain enters the next stage to execute the power transaction by the user.

After all the processes of the power transaction are completed, or after the sales user receives the corresponding amount of electric energy, the checking personnel sends a power generation state report or a power utilization state report of the checked link to the transaction supervision submodule and obtains an authentication signature. The transaction supervision sub-module comprises a standard link region data model to update credit points and match corresponding users, and calculates the contribution degree of the inspection personnel. In addition, the transaction supervision sub-module can check the data model of the user to form an auxiliary verification data model, update the credit points of the check personnel, and calculate the contribution degree to form a trusted transaction supervision sub-model.

Referring to fig. 3, fig. 3 is a flow chart of a transaction supervision method according to the present invention. As shown in FIG. 3, users within the trading system submit demands containing node information, reference offers, receive minimum points (i.e., user reputation values), etc. to the trading monitoring submodule. The requirements are verified by a logging module in the management system and by a supervisor in the transaction system. If the demand authentication is not passed, the demand is directly rejected. If the demand verification is passed, the management system gives the unique code to the user and the contract and uploads the unique code to the blockchain. The management system matches the demands according to the transaction mechanism and gathers and clears the electric power transaction orders which fail to be matched. For a successfully matched power transaction, the power transaction is performed according to a contract in the power transaction, and then a power utilization status report or a power generation status report is issued to the blockchain.

For a transacting party who violates a contract, the management system will transact by the offending party and the upper level distribution network to make up for the absence and collect a certain fine based on the difference between the promised power and the actual power. In addition, for the offender, the management system will deduct the offender's reputation value and update the power transaction both sides and supervisor reputation value model. For the transaction party which does not violate the contract, the management system gives the transaction party of the electric power transaction a power subsidy and promotes the credit value based on a preset rule, and updates the credit value model of the power transaction party and the supervisor.

The electric power subsidy management sub-module comprises a certain amount of electric power subsidy amount. And according to the user transaction activity degree and the transaction quantity of the transaction platform, the system subsidizes the user to a certain electric subsidy.

For the green energy authentication sub-module, based on the green energy authentication sub-module, a user participating in the power transaction can perform "green energy authentication". Wherein green energy authentication is only for users who use new energy to generate electricity. When the user finishes registering in the management system, green authentication can be performed. Wherein, the user can select whether to perform green authentication or not by himself.

In the green energy authentication process, the green energy authentication submodule can carry out green energy authentication by detecting parameters such as electricity utilization rule, electric energy transaction credit value and activity of a user, power generation hardware facilities, power generation total amount and the like. When the green authentication is successful, the address of the user can obtain a green NFT (NFT) marked as the "verify" passing authentication, which indicates the approval of the emission reduction contribution of the system to the user. In addition, green-capable authenticated users may receive additional trade discounts and trade patches during the power trade to encourage users to transition to a power generation mode, such as to a new energy power generation mode.

In some embodiments, the distributed photovoltaic power generation point-to-point transaction method of the present invention further comprises the steps of:

step one: determining whether green energy authentication of the user is passed or not based on the electricity utilization rule, the transaction credit value and the activity degree of the user, the power generation hardware facilities and the total power generation amount of the user;

step two: if so, the user is given additional trade discounts in the smart contract trade process.

Based on the above processing, the user can perform green authentication in the transaction system and ensure closed-loop operation of the transaction system. In addition, the technical scheme of the invention is additionally provided with the electric power patch, so that the social benefit and the environmental protection benefit in the electric power transaction process are promoted, and users are encouraged to use green energy.

The demand response module recommends a demand response strategy to the user based on the result of the demand response strategy recommendation module in the algorithm layer. Specifically, the neural network in the demand response recommendation module predicts the user scores according to the user power consumption habit, the power consumption level of the surrounding area of the user and other parameters, and then the demand response module in the application layer selects the power consumption plan with the highest predicted score for recommendation. The demand response module can also combine the electricity utilization rule of other users to recommend the energy saving experience and reasonable electricity utilization time to the users.

Based on the processing, the long, medium and short-term prediction is carried out on the distributed photovoltaic power generation amount and the power consumption of the user, so that game strategies and intelligent suggestions are provided for the power transaction of the user in the medium and long-term market, the day-ahead market and the real-time market. And automatically executing the electric power transaction in an intelligent contract mode and the like, supervising the completion condition of the transaction through a transaction supervision module to regulate and control the transaction progress, and updating the credit value of the user.

Referring to fig. 4, fig. 4 is a schematic diagram illustrating interaction between a system and an underlying blockchain according to an embodiment of the present invention. As shown in fig. 4, information such as power generation data, power consumption data and the like is acquired based on a data acquisition module in a data layer in the management system. And then, based on an encryption module, carrying out asymmetric encryption on the information, and carrying out data uplink storage on the encrypted information through a bottom layer block chain. Specifically, each block includes information such as a hash value of the last block, a user identification, power generation data, transaction data, a workload certification and the like.

And then, the encrypted information is subjected to data export and sent to an algorithm layer and an application layer. The algorithm layer comprises a photovoltaic power generation prediction module, a power load prediction module and a demand response strategy recommendation module, and is used for generating predicted power generation data of photovoltaic power generation, power load data of a user and an optimal demand response strategy, and assisting the application layer in completing power transaction.

It is noted that the application layer includes a power transaction module, and the power transaction market is divided into a medium-long term market, a day-ahead market and a real-time market. The medium-and-long-term market and the real-time market need to be manually initiated (i.e. manually transacted) by users in a management system, and the market in the past carries out point-to-point automatic transacting through intelligent contracts based on a blockchain algorithm type trust mechanism.

Aiming at the presentation layer, the presentation layer provides system management, transaction and query services for a user interface, namely, a layer of direct interaction of the system with a user. The presentation layer is an operation platform and a feedback interface for users to conduct transactions, inquire transaction information, power generation forecast information and the like. The terminal has a web end and a mobile end. And the interface is interacted with a user, so that the user operation is convenient.

Specifically, the presentation layer includes four functional modules, respectively

And the personal information interface module is used for containing registration information, personal power generation data, forecast data, credit value, electric subsidy balance and other data.

The transaction interface module is used for recording transaction data and information display, providing a transaction operation interface and connecting a transaction contract end.

And the intelligent contract interface module is used for providing a code-free writing intelligent contract and providing setting of related parameters in the transaction contract.

And the demand response interface module is used for completing the signing of the demand response contract and displaying the participation condition of the demand response.

In one implementation, the distributed photovoltaic power generation point-to-point transaction method of the invention further comprises the following steps:

and S7, supervising the electricity utilization transaction based on the user credit values of the power generator and the electricity consumer.

And S8, monitoring whether the difference exists between the promised electric quantity and the actual electric quantity, if so, collecting fines from the offender and deducting the credit value of the user.

Specifically, after the user determines the electricity trading market, the system automatically performs intelligent contract trading for the electricity generator and the electricity consumer according to the trading market selected by the user and the intelligent contract rule. The process of the intelligent contract transaction is performed under the underlying blockchain algorithm type trust mechanism to ensure the transparency and the security of the data chain.

Notably, the trade of medium and long term markets and real-time markets must be initiated manually by the user. Specifically, the user submits the demand to the record intelligent contract module, applies for basic information such as issuing contracts, node attributes, reference electricity prices, minimum credit score of the received contracts and the like, and reports quotations according to the prediction results. In this process, the user can edit the order content at will and resubmit, but cannot learn other user orders. And after the intelligent contract module passes the verification information, the basic information is issued to the blockchain, otherwise, the information is refused.

After the credit points of various users meet the requirements, the codes and the unique hash values of the users are given to the applications. When the business of the power transaction starts, the related data information is uploaded to the blockchain, and each participating user can inquire the specific information of other users.

For step S8, the transaction supervision module supervises whether the committed power and the actual power are different, and the offender will transact with the upper distribution network to make up for the deficiency and collect a certain fine and deduct or increase the credit value of the user according to the reward and punishment rule.

Under the consensus mechanism, the system stores and adds the successfully matched transaction to the blockchain by a certain user node. After the block verification is successful, each user node generates an effective power transaction contract by the block chain. The power trade contract defines the contents such as the power price and the total amount of the trade. After the contract of the power transaction is achieved, digital signatures of both parties of the transaction are generated and uploaded to the blockchain. When the power transaction starts, the intelligent contract is automatically executed to carry out corresponding transfer transaction of electric energy and value.

The settlement results after the electric power transaction are stored in a distributed mode through the bottom layer blockchain and are synchronous with other data chains, and the electric power transaction and the settlement results are published through the information publishing module.

The demand response module of the application layer calls the data obtained by the demand response recommendation module of the algorithm layer, pushes the energy saving experience to the user, and pushes demand response strategy recommendation according to the user electricity consumption portrait. Wherein the user can choose whether to participate in the demand response strategy or not by himself.

In addition, the user can perform operations such as information inquiry, power transaction, signing of demand response contracts and the like through the web terminal or the mobile terminal.

Based on the same inventive concept, the invention provides a distributed photovoltaic power generation point-to-point transaction system based on a blockchain, which comprises:

the first acquisition module is used for acquiring initial data information;

The first encryption module is used for encrypting the initial data information based on an asymmetric encryption technology and carrying out data uplink storage on the encrypted initial data information based on a bottom layer block chain.

And the second acquisition module is used for acquiring predicted power generation data of photovoltaic power generation based on the meteorological information and a power generation prediction model which is trained in advance.

And the third acquisition module is used for acquiring the electricity load data of the user based on the electricity consumption information of the user and a pre-trained user model.

The first determining module is used for determining different electricity trading markets based on the predicted electricity generation data and the electricity load data.

The system further comprises:

the first supervision module is used for supervising the intelligent contract transaction based on the user credit values of the power generator and the power consumer;

and the first monitoring module is used for monitoring whether the promised electric quantity and the actual electric quantity of the intelligent contract transaction are different, if so, collecting fines from the offender and deducting the credit value of the user.

It can be understood that the blockchain-based distributed photovoltaic power generation point-to-point transaction system provided by the embodiment of the invention corresponds to the blockchain-based distributed photovoltaic power generation point-to-point transaction method, and the explanation, the examples, the beneficial effects and the like of the relevant content can refer to the corresponding content in the blockchain-based distributed photovoltaic power generation point-to-point transaction method, which is not repeated herein.

In summary, compared with the prior art, the method has the following beneficial effects:

1. the users of the distributed photovoltaic become independent generators and consumers, direct energy transaction is carried out in a P2P mode, and electricity transaction can be flexibly carried out by electricity consumers and electricity generators. The intelligent decision of the transaction is supported by enabling the power generation supervision and prediction of the users through the big data technology, and the power transaction is automatically executed through the intelligent contract, so that fair, point-to-point, real-time and mutual trust transaction among the users is realized, and the requirement of distributed power transaction is met.

2. Users can carry out green energy authentication in the system, the attribute of the green energy authentication is circulated, and the business mode of green consumption can be continuously innovated, so that identity information identification and policy guarantee are provided for promoting the green consumption, and the green value of clean energy can be smoothly reflected as market value. In addition, based on the electric power subsidy, the enthusiasm of a user for using green energy sources can be improved.

3. According to the invention, the excitation type demand response strategy is accurately recommended to the user, the resources of the electricity consumer and the electricity generator are comprehensively and dynamically integrated, and the electricity market is regulated and controlled through the electricity load prediction module and electricity optimization, so that the peak clipping and valley removing of electricity consumption amount are realized, and the stability of the power grid is improved. In addition, the technical scheme of the invention supports the distributed photovoltaic large-scale access energy system so as to ensure that the transaction system is clean, low-carbon, safe and efficient to operate.

4. The user can conduct services such as power transaction, information inquiry, signing of demand response contracts and the like through the web terminal or the mobile terminal, so that the flow of power transaction is simplified, and the user can conduct power transaction conveniently.

It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A blockchain-based distributed photovoltaic power generation point-to-point transaction method, the method comprising:

2. The distributed photovoltaic power generation point-to-point transaction method according to claim 1, further comprising:

3. The distributed photovoltaic power generation point-to-point transaction method according to claim 1, wherein the transaction marketplace comprises: middle and long term market, day-ahead market, real-time market;

4. The distributed photovoltaic power generation point-to-point transaction method of claim 1, wherein the user reputation value comprises contract completion, consensus completion rate, and node liveness;

/>

the trusted value calculation formula of the node liveness is as follows:

the calculation formula of the trusted value of the node is as follows:

R＝R _last +αR _ccr +βR _na +(1-α-β)R _cc

5. The distributed photovoltaic power generation point-to-point transaction method according to claim 1, further comprising:

6. The distributed photovoltaic power generation point-to-point transaction method according to claim 1, further comprising:

determining an initial incentive price;

wherein, the formula for determining the initial incentive price is:

Representing an initial incentive price issued to the i user t at the moment;

the preset incentive price adjustment formula is as follows:

Representing the total predicted response of the k-1 th iteration user; />

the formula for determining the response gradient of the user is:

/>

wherein ,

representing the response gradient of the kth iteration user i; / >

Representing the expected response of the kth iteration user i;

the incentive price update formula for each user is:

wherein ,

Representing the response gradient of the k-1 th iteration user; />

Representing the incentive price for the kth iteration; />

Representing the response gradient of user i for the k-1 th iteration;

representing the incentive price for the k-1 th iteration; />

Represents the k-1 th iterationPrice of incentive accepted by user i.

7. The distributed photovoltaic power generation point-to-point transaction method according to claim 1, further comprising:

8. The distributed photovoltaic power generation point-to-point transaction method according to claim 7, further comprising:

wherein ,

When user u has used device k for the t-th time period on day d; />

representation of

Cosine value of included angle of two vectors; />

representing the user u ₂ Electricity utilization characteristic matrix S _u2 An expanded one-dimensional vector; k' represents user u ₁ And user u ₂ The number of devices used together; t represents the number of divided time periods; />

The calculation formula for determining the user score is:

9. A blockchain-based distributed photovoltaic power generation point-to-point transaction system, the system comprising:

the first acquisition module is used for acquiring initial data information;

10. The distributed photovoltaic power generation point-to-point transaction system according to claim 9, wherein the system further comprises: