Power transaction matching method and device, equipment and medium

Abstract

The invention discloses a power transaction matching method and device, equipment and a medium. The power transaction matching method comprises the steps of obtaining first matching requirement data and power purchasing data of a plurality of power purchasing parties and second matching requirement data and power supply data of a plurality of power selling parties in a power transaction market; based on the first matching requirement data, the second matching requirement data, the electricity purchasing data and the power supply data, determining satisfaction degrees between the plurality of electricity purchasing parties and the plurality of electricity selling parties; based on the satisfaction degree, constructing a multi-objective optimization model which aims at maximizing the satisfaction degree of the electricity purchasing party and maximizing the satisfaction degree of the electricity selling party, and constructing constraint conditions of the multi-objective optimization model; and on the premise of meeting the constraint condition, solving the multi-objective optimization model to obtain a transaction matching result between the plurality of power buyers and the plurality of power sellers. The matching satisfaction degree of the two parties of the transaction subject is maximized, the benefit of the transaction subject is increased, the transaction matching time is greatly shortened, and the matching efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power, and in particular to a method, device, equipment and medium for electric power trading matching. Background Art

[0002] With the development of energy and the reform of the power market, more and more provinces and cities have started to carry out power market transactions. In the related technologies, bilateral power market transactions are usually carried out by the trading entities participating in the transactions to find trading partners independently. As the power market gradually expands, the number of participants in power trading is increasing. It takes a large amount of time cost for trading entities to find suitable trading partners, and there is information asymmetry between trading entities in this trading matching method, and it cannot be guaranteed that the matching trading partner is the optimal one among all trading parties. Summary of the Invention

[0003] The present invention provides a method, device, electronic equipment and medium for electric power trading matching to solve the technical problems that trading entities find trading partners independently for trading matching, which consumes a large amount of time cost and cannot guarantee that the found trading partner is the optimal one among all trading parties.

[0004] In a first aspect, a method for electric power trading matching is provided, including:

[0005] Obtaining first matching requirement data, power purchase data of multiple power purchasers, second matching requirement data of multiple power sellers and power supply data in the power trading market;

[0006] Determining the satisfaction degrees between multiple power purchasers and multiple power sellers based on the first matching requirement data, the second matching requirement data, the power purchase data and the power supply data;

[0007] Constructing a multi-objective optimization model with the maximization of the satisfaction degree of power purchasers and the maximization of the satisfaction degree of power sellers as the objectives, and constructing the constraint conditions of the multi-objective optimization model;

[0008] Solving the multi-objective optimization model on the premise of meeting the constraint conditions to obtain the trading matching results between multiple power purchasers and multiple power sellers.

[0009] In a second aspect, a device for electric power trading matching is provided, including:

[0010] An obtaining module, configured to obtain first matching requirement data, power purchase data of multiple power purchasers, second matching requirement data of multiple power sellers and power supply data in the power trading market;

[0011] A determining module, configured to determine the satisfaction degrees between multiple power purchasers and multiple power sellers based on the first matching requirement data, the second matching requirement data, the power purchase data and the power supply data;

[0012] A construction module is used to construct a multi-objective optimization model aiming at maximizing the satisfaction of the electricity purchaser and the satisfaction of the electricity seller based on satisfaction, and construct the constraint conditions of the multi-objective optimization model.

[0013] A generation module is used to solve the multi-objective optimization model on the premise of satisfying the constraint conditions, and obtain the transaction matching results between multiple electricity purchasers and multiple electricity sellers.

[0014] Thirdly, an electronic device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned power transaction matching method are implemented.

[0015] Fourthly, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned power transaction matching method are implemented.

[0016] In the solutions implemented by the above-mentioned power transaction matching method, device, electronic device, and storage medium, the objective function of the matching is set based on multiple transaction matching requirements, and the main bodies participating in the power transaction can be matched according to the multiple transaction matching requirements of the transaction main bodies, so as to maximize the matching satisfaction of both sides of the transaction main bodies. While increasing the interests of the transaction main bodies, the time for transaction matching is greatly shortened, and the matching efficiency is improved. It solves the problem that the transaction main bodies participating in free transactions cannot match the best transaction objects due to information asymmetry in the related art. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0018] Figure 1 It is a schematic flowchart of the power transaction matching method in an embodiment of the present invention;

[0019] Figure 2 It is a schematic structural diagram of the power transaction matching device in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0021] The power trading matching method provided by the present invention can be applied to the scenario of the energy trading market. With the development of the power market, many small power generation enterprises and many small power users have joined the power market, making the number of trading entities in the power market numerous and the transactions frequent. In the related art, under the power market trading mechanism, the trading entities in the power market need to find trading partners independently and sign bilateral trading contracts. With the gradually expanding demand of the power market entities for bilateral contracts, the number of participants in the power market is increasing. It takes a large amount of time cost for trading entities to find suitable trading partners. In addition, in the current power trading market, due to information asymmetry and market opacity, it is difficult for the two trading parties to be effectively matched, resulting in an increase in the difficulty of successful transactions, and further leading to a relatively low trading volume in the power trading market.

[0022] Based on the above problems, the embodiments of this specification provide a power trading matching method, device, equipment and medium. By setting an objective function for matching based on multiple trading matching requirements, it can match the entities participating in power trading according to the multiple trading matching requirements of the trading entities, so as to maximize the matching satisfaction of both trading entities, increase the interests of trading entities, and greatly shorten the trading matching time and improve the matching efficiency. It solves the problem in the related art that the information flow between the two parties participating in the transaction is inconvenient and the trading entities cannot match the best matching objects.

[0023] Please refer to Figure 1 , the embodiments of this specification provide a power trading matching method, which specifically includes the following steps:

[0024] S10: Obtain the first matching requirement data, power purchase data of multiple power purchasers, the second matching requirement data and power supply data of multiple power sellers in the power trading market.

[0025] It can be understood that the execution subject of the present invention can be a power trading matching device, or a terminal or a server. Specifically, it is not limited here. The embodiments of the present invention are described by taking the server as the execution subject as an example.

[0026] Specifically, the electricity purchaser can be a power user or an electricity retailer; the electricity seller can be a renewable energy power generation enterprise such as a wind power or photovoltaic power generation enterprise. In the electricity trading market, the electricity purchaser and the electricity seller have multiple transaction matching requirements for each other, and there will be corresponding weights for each transaction matching requirement. Therefore, in the matching process, both parties need to clarify their own requirements, summarize the first matching requirement data of the electricity purchaser and the second matching requirement data of the electricity seller. Among them, the first matching requirement data includes multiple matching requirements selected by each electricity purchaser and the weight values set for each matching requirement; the second matching requirement data includes multiple matching requirements selected by each electricity seller and the weight values set for each matching requirement. At the same time, based on the transaction matching requirements, collect the electricity purchase data of each electricity purchaser and the power supply data of each electricity seller.

[0027] Optionally, the first matching requirement data includes multiple first transaction matching requirements and the corresponding first weights for each first transaction matching requirement. Among them, the first transaction matching requirements include at least one of the following: the energy type of the electricity seller, the selling price, the power supply quantity, the credit value of the electricity seller, and the power supply location; the second matching requirement data includes multiple second transaction matching requirements and the corresponding second weights for each second transaction matching requirement. Among them, the second transaction matching requirements include at least one of the following: the purchase price, the demand quantity, the credit value of the electricity purchaser, and the purchase location. Specifically, the first transaction matching requirements of the electricity purchaser include at least the energy type of the electricity seller, the selling price, the power supply quantity, the information value of the electricity seller, and the power supply location. Among them, the energy type refers to the power generation type (such as thermal power, wind power, hydropower, solar power, biomass energy, etc.); the selling price refers to the selling price of the electricity seller; the power supply quantity refers to the electricity quantity that the electricity seller can sell; the credit value of the electricity seller refers to the degree of integrity obtained based on the previous transaction records of the electricity seller; the power supply location refers to the location of the electricity seller, represented by longitude and latitude. Each electricity purchaser sets the corresponding first weight value for each first transaction matching requirement. Further, the second transaction matching requirements of the electricity seller include at least the purchase price, the demand quantity, the credit value of the electricity purchaser, and the purchase location. Among them, the purchase price refers to the acceptable purchase price range of the electricity purchaser; the demand quantity refers to the electricity quantity demanded by the electricity purchaser; the credit value of the electricity purchaser refers to the degree of integrity obtained based on the previous transaction records of the electricity purchaser; the purchase location refers to the location of the electricity purchaser, represented by longitude and latitude. Each electricity seller sets the corresponding second weight value for each second transaction matching requirement.

[0028] S20: Determine the satisfaction between multiple electricity purchasers and multiple electricity sellers based on the first matching requirement data, the second matching requirement data, the electricity purchase data, and the power supply data;

[0029] In this step, in the transaction matching problem between the power seller and the power buyer, in order to enable the matching parties to obtain satisfactory matching results, some transaction matching requirements are usually considered as references. The satisfaction of each other is determined by the matching degree between the transaction matching requirements, and the size of the satisfaction will directly determine the final matching result. When the bilateral transaction parties have a high satisfaction under each transaction matching requirement, the chance of matching between the bilateral transaction parties is very high; on the contrary, when the bilateral transaction parties have a low satisfaction under each transaction matching requirement, the chance of matching between the bilateral transaction parties is very low. Therefore, based on the first matching requirement data of the power buyer, the power purchase data, the second matching requirement data of the power seller, and the power supply data, the satisfaction of each power buyer with all power sellers for any transaction matching requirement, and the satisfaction of each power seller with all power buyers for any transaction matching requirement are calculated respectively. By calculating the satisfaction, the matching degree between the power seller and the power buyer can be quantified, which helps to quickly and accurately match the best trading partner while meeting the respective needs and conditions of the power seller and the power buyer.

[0030] In an embodiment of the present application, a specific satisfaction calculation scheme is provided. In S20, that is, based on the first matching requirement data, the second matching requirement data, the power purchase data, and the power supply data, the satisfaction between multiple power buyers and multiple power sellers is determined, which specifically includes the following steps S21 - S24:

[0031] S21: Based on the power purchase data and the power supply data, determine the first feature data corresponding to each first transaction matching requirement, and the second feature data corresponding to each second transaction matching requirement;

[0032] S22: Based on the first preset matching method and the first feature data corresponding to each first transaction matching requirement, calculate the first satisfaction of each power buyer with each power seller for each first transaction matching requirement;

[0033] S23: Based on the second preset matching method and the second feature data of each second transaction matching requirement, calculate the second satisfaction of each power seller with each power buyer for each second transaction matching requirement;

[0034] For steps S21 - S23, during the matching process, corresponding matching methods are set in advance based on the actual values under different transaction matching requirements. For any first transaction matching requirement, obtain its corresponding first preset matching method, and extract the first feature data corresponding to this transaction matching requirement from the power generation data and power supply data. For example, if the first transaction matching requirement is the energy type of the power seller, then the requirements of each power buyer for the energy type of the power seller should be extracted from the power generation data, and the energy type of each power seller should be extracted from the power supply data. Then, taking the extracted first feature data as parameters, calculate the first satisfaction degree of each power buyer for each power seller under the first transaction matching requirement through the first preset matching method. Similarly, for any second transaction matching requirement, obtain its corresponding second preset matching method, and extract the second feature data corresponding to this transaction matching requirement from the power generation data and power supply data. Then, taking the extracted second feature data as parameters, calculate the second satisfaction degree of each power seller for each power buyer under the second transaction matching requirement through the second preset matching method.

[0035] Optionally, the preset matching methods for multiple transaction matching requirements can be the same or different, and this application does not make specific limitations here. For example, use numbers between 0 and 10 to represent the satisfaction degree of the power buyer for all power sellers regarding a single transaction matching requirement. When the satisfaction degree is 10, it means the highest satisfaction. If the actual value of the power seller regarding a certain transaction matching requirement fully meets the expected value of the power buyer regarding a certain transaction matching requirement, the satisfaction degree is assigned 10. If it is not fully met, the satisfaction degree is an integer less than 10, and the closer the expected value of the power buyer regarding a certain transaction matching requirement is to the actual value of the power seller regarding a certain transaction matching requirement, the greater the satisfaction degree.

[0036] In the actual application scenario, for each power buyer P i , calculate respectively the first satisfaction degree a i of the power buyer P j for each power seller S h regarding any first transaction matching requirement Q hij . And for each power seller S j , calculate respectively the second satisfaction degree b j of the power seller S i for each power buyer P q under any second transaction matching requirement I qij . Taking the first transaction matching requirement as the selling price of electricity as an example, the first extraction data obtained is: the maximum affordable purchase price of electricity (i.e., the expected value) pPrice of the power buyer, and the quoted price (i.e., the actual value) sPrice of the power seller. If sPrice ≤ pPrice, it means the selling price of electricity is lower than the expected purchase price, and the power buyer can conduct transactions at a satisfactory price, then the satisfaction degree ahij = 10; If Pricemin ≤ pPrice < sPrice, it means the selling electricity price is higher than the expected buying electricity price, and the satisfaction degree of the electricity buyer with respect to the electricity seller is represented by the proximity between the buying electricity price and the selling electricity price; at this time, the satisfaction degree of the electricity buyer with respect to the selling electricity price of the electricity seller is: ahij = pPrice / sPrice × 10. It can be understood that if the highest acceptable expectation value of the electricity buyer is closer to the actual value of the electricity seller, it means the selling price of the electricity seller is lower and more in line with the interests of the electricity buyer; if sPrice < Pricemin, it is considered that the electricity seller maliciously quotes a price to increase the selling electricity price, and at this time the satisfaction degree a hij = 0.

[0037] S24: Based on each first satisfaction degree and the first weight value corresponding to each first transaction matching requirement, calculate the third satisfaction degree of each electricity buyer with respect to each electricity seller, and based on each second satisfaction degree and the second weight value corresponding to each second transaction matching requirement, calculate the fourth satisfaction degree of each electricity seller with respect to each electricity buyer.

[0038] In this step, using the calculated satisfaction degree and the weight of a single transaction matching requirement, calculate the third satisfaction degree of each electricity buyer with respect to each electricity seller and the fourth satisfaction degree of each electricity seller with respect to each electricity buyer respectively.

[0039] In an embodiment of the present application, a specific comprehensive satisfaction degree calculation scheme is provided. In S24, that is, based on each first satisfaction degree and the first weight value corresponding to each first transaction matching requirement, calculate the third satisfaction degree of each electricity buyer with respect to each electricity seller, and based on each second satisfaction degree and the second weight value corresponding to each second transaction matching requirement, calculate the fourth satisfaction degree of each electricity seller with respect to each electricity buyer, which specifically includes the following steps S241 - S242:

[0040] S241: Substitute each first satisfaction degree and the first weight value into the first preset satisfaction degree formula to calculate the third satisfaction degree of each electricity buyer with respect to each electricity seller;

[0041] Among them, the first preset satisfaction degree formula is:

[0042]

[0043] In the formula, the above a ij is the third satisfaction degree of the i-th electricity buyer with respect to the j-th electricity seller; the above w h is the first weight value of the h-th first transaction matching requirement; the above a hij is the first satisfaction degree of the i-th electricity buyer with respect to the j-th electricity seller under the h-th first transaction matching requirement;

[0044] S242: Substitute each second satisfaction degree and second weight value into the second preset satisfaction formula to calculate the fourth satisfaction degree of each power seller for each power buyer;

[0045] Among them, the second preset satisfaction formula is:

[0046]

[0047] In the formula, the above b ij is the fourth satisfaction degree of the j-th power seller for the i-th power buyer; the above vq is the second weight value of the q-th second transaction matching requirement; the above b qij is the second satisfaction degree of the j-th power seller for the i-th power buyer under the q-th second transaction matching requirement.

[0048] For steps S241 - S242, substitute the first satisfaction degree and first weight value into the first preset satisfaction formula to calculate the third satisfaction degree of each power buyer for each power seller. Similarly, substitute the second satisfaction degree and second weight value into the second preset satisfaction formula to calculate the fourth satisfaction degree of each power seller for each power buyer.

[0049] In the above way, calculate the satisfaction degrees under different transaction matching requirements, and then aggregate them based on the weights of the transaction matching requirements to solve the overall satisfaction degree of the bilateral entities, which is convenient for the construction of the next multi-objective optimization model.

[0050] In the actual application scenario, define variables: Set of power buyers: P = {P 1 , P 2 , P 3 , …, P m}, a total of m power buyers, P i represents the i-th power buyer; Set of power sellers: S = {S 1 , S 2 , S 3 , …, S n}, a total of n power sellers, S j represents the j-th power seller; Set of transaction matching requirements of power buyers for power sellers: Q = {Q 1 , Q 2 , …, Q f}; Among them, Q h represents the h-th transaction matching requirement, h = 1, 2, …, f; Weight vector corresponding to the set of transaction matching requirements Q of power sellers: w = {w 1 , w 2 , w 3 , …, w f}; Among them, w h represents the weight of the h-th transaction matching requirement Q h ; 0 ≤ wh ≤ 1, The set of trading matching requirements of the power seller for the power buyer is \(I = \{I 1 , I 2 , I 3 , …, I k \}; where \(I q \) represents the \(q\)-th trading matching requirement, \(q = 1, 2, …, k\); the weight vector corresponding to the set of trading matching requirements \(I\): \(v=\{v 1 , v 2 , v 3 , …, v k \}; where \(v q \) represents the weight of the \(q\)-th trading matching requirement \(I q \); \(0\leq v q \leq1, The satisfaction evaluation value of the power buyer \(P i \) for the trading matching requirement \(Q j \) given by the power seller \(S h \) is \(a hij \); the satisfaction evaluation value of the power seller \(S j \) for the trading matching requirement \(I i \) given by the power buyer \(P q \) is \(b qij \); the satisfaction of the power buyer \(P i \) for the power seller \(S j \) is \(a ij \); the satisfaction of the power seller \(S j \) for the power buyer \(P i \) is \(b ij .

[0051] S30: Based on the satisfaction, construct a multi-objective optimization model with the maximization of the power buyer's satisfaction and the maximization of the power seller's satisfaction as the objectives;

[0052] In this step, the satisfaction of the trading parties is usually related to their satisfaction. The higher the satisfaction, the higher the satisfaction of the trading party with the matching object. To find the balanced solution for the maximization of the satisfaction of the power seller and the power buyer, whether the power seller and the power buyer are matched, and the satisfaction of the trading parties are used as model variables to construct a multi-objective optimization model to maximize the satisfaction of the power seller and the power buyer. In addition, fully consider the actual situation of the electricity trading market, clarify the calculation limitations of the multi-objective optimization model, and construct market matching constraint conditions to ensure that the multi-objective optimization model has accurate and meaningful feasible solutions.

[0053] In the above way, according to the bilateral satisfaction, construct a multi-objective optimization model to achieve the maximization of the satisfaction of both sides of the transaction.

[0054] In an embodiment of the present application, a construction scheme for a specific multi-objective optimization model is provided. In S30, that is, based on satisfaction, a multi-objective optimization model with the maximization of the satisfaction of the electricity purchaser and the maximization of the satisfaction of the electricity seller as the objectives is constructed, which specifically includes the following steps S31 - S34:

[0055] S31: Standardize multiple third satisfaction degrees to obtain the first objective satisfaction degree;

[0056] Among them, the expression of the first objective satisfaction degree is:

[0057]

[0058] In the formula, the above a′ ij is the first objective satisfaction degree of the i-th electricity purchaser for the j-th electricity seller; the above a ij is the third satisfaction degree of the i-th electricity purchaser for the j-th electricity seller; the above min i min j a ij is the minimum value of a ij ; the above max i max j a ij is the maximum value of aij;

[0059] S32: Take the first transaction electricity quantity between the electricity purchaser and the electricity seller as the decision variable, and based on the first objective satisfaction degree, construct a first objective function with the maximization of the electricity purchaser's satisfaction as the optimization objective;

[0060] Among them, the first objective function is:

[0061]

[0062] In the formula, the above Z 1 is the optimization objective of the multi-objective optimization model; the above MaxZ 1 means that the optimization objective is to maximize Z 1 ; the above X ij is the first transaction electricity quantity between the i-th electricity purchaser and the j-th electricity seller; the above m represents the number of electricity purchasers; the above n represents the number of electricity sellers;

[0063] S33: Standardize multiple fourth satisfaction degrees to obtain the second objective satisfaction degree;

[0064] Among them, the expression of the second objective satisfaction degree is:

[0065]

[0066] In the formula, the above b′ ijis the second target satisfaction degree of the j-th electricity seller for the i-th electricity buyer; the above b ij is the fourth satisfaction degree of the j-th electricity seller for the i-th electricity buyer; the above min i min j b ij is the minimum value in b ij ; the above max i max j b ij is the maximum value in b ij ;

[0067] S34: Taking the second transaction electricity quantity between the electricity seller and the electricity buyer as the decision variable, based on the second target satisfaction degree, construct a second objective function with the maximization of the electricity seller's satisfaction degree as the optimization objective;

[0068] Among them, the second objective function is:

[0069]

[0070] In the formula, the above Z 2 is the optimization objective of the multi-objective optimization model; the above MaxZ 2 is the optimization objective to maximize Z 2 ; the above X ij is the second transaction electricity quantity between the j-th electricity seller and the i-th electricity buyer; the above m represents the number of electricity buyers; the above n represents the number of electricity sellers.

[0071] For steps S31 - S34, the decision variable of the multi-objective optimization model can be the transaction electricity quantity between the electricity seller and the electricity buyer. Since there are multiple trading partners for both sides in the electricity trading market, taking the specific transaction electricity quantity as the decision variable is more in line with the trading rules of the free market. For the electricity buyer, it can minimize the electricity purchase cost; for the electricity seller, it can maximize the market trading volume. Furthermore, based on the satisfaction degree of multi-trading matching requirements and the trading volume of the matching parties, constructing a multi-objective optimization model can maximize the matching satisfaction degree of both trading parties and maximize the consumption of new energy on the premise of meeting the electricity purchase needs of the electricity buyer.

[0072] Specifically, standardize the satisfaction degree of each trading subject for its corresponding matching object so that it is within the range of 0 - 1, eliminate the dimension difference, and facilitate the construction of the objective function. After standardizing the third satisfaction degree and the fourth satisfaction degree respectively, combine the decision variable to construct the objective function, so that the objective optimization model can fully consider factors such as distance, energy type, credit value, etc. of multi-trading matching requirements, rather than just price factors, thus obtaining a greater matching of the satisfaction degrees of both trading parties and being more in line with the actual scenario.

[0073] In the above manner, a multi-objective optimization model is constructed. This multi-objective optimization model simultaneously considers the satisfaction of both the electricity seller and the electricity buyer. For the electricity buyer, it can minimize the electricity purchase cost; for the electricity seller, it can maximize the sold electricity volume of the electricity seller.

[0074] Optionally, the constraint conditions of the multi-objective optimization model are as follows: the sum of the electricity volumes purchased by the electricity buyer is equal to the electricity purchase demand of the electricity buyer, ensuring that the electricity purchase demand of the electricity buyer is definitely met; the sum of the sold electricity volumes of the electricity seller is less than or equal to the electricity volume that can be sold by the electricity seller, ensuring that there will be no situation where there is no electricity available for sale after the matching is formed; the sum of the electricity purchase volumes of the electricity buyer is less than or equal to the sum of the electricity volumes that can be sold by the electricity seller, ensuring that all electricity sellers can purchase the electricity they need.

[0075] S40: Obtain the power generation data of multiple electricity sellers and the electricity purchase data of multiple electricity buyers. On the premise of meeting the constraint conditions, solve the multi-objective optimization model based on the power generation data and the electricity purchase data to obtain the transaction matching results between multiple electricity sellers and multiple electricity buyers.

[0076] In this step, through the linear weighted method, the two objective functions of the multi-objective optimization model are weighted and summed to be transformed into a single-objective linear programming problem. Since the objective functions and constraint conditions are all linear, the linear programming method is used to solve the model to obtain the transaction matching results. Among them, the transaction matching results are the decision variables in the multi-objective optimization model, that is, the transaction electricity volume between the electricity buyer and the electricity seller. In practical applications, an electricity buyer can purchase electricity from one or more electricity sellers at the same time; an electricity seller can sell electricity to one or more electricity buyers at the same time. Taking the transaction electricity volume as the decision variable enables the trading entities to select one or more trading partners according to their own needs in the order from large to small of the transaction electricity volume. It is more in line with the trading rules of the free market. For the electricity buyer, it can minimize the electricity purchase cost, and for the electricity seller, it can promote the maximization of the market trading volume.

[0077] It can be seen that in the above solution, by setting the objective function of the matching based on multiple transaction matching requirements, it is possible to match the entities participating in the electricity transaction according to the multiple transaction matching requirements of the trading entities, thereby maximizing the matching satisfaction of both trading entities, increasing the interests of the trading entities, and greatly shortening the transaction matching time and improving the matching efficiency. It solves the problem in the related technology that the trading entities participating in the free transaction cannot match the best trading partners due to information asymmetry.

[0078] In an embodiment, a power trading matching device is provided, and this power trading matching device corresponds one-to-one to the power trading matching method in the above embodiment. As Figure 2As shown in the figure, the power trading matching device includes: an acquisition module 101, a determination module 102, a construction module 103, and a generation module 104. The detailed descriptions of each functional module are as follows:

[0079] The acquisition module 101 is used to acquire the first matching requirement data, power purchase data of multiple power purchasers, the second matching requirement data of multiple power sellers, and power supply data in the power trading market;

[0080] The determination module 102 is used to determine the satisfaction degree between multiple power purchasers and multiple power sellers based on the first matching requirement data, the second matching requirement data, the power purchase data, and the power supply data;

[0081] The construction module 103 is used to construct a multi-objective optimization model with the maximization of the satisfaction degree of power purchasers and the maximization of the satisfaction degree of power sellers as the objectives based on the satisfaction degree, and construct the constraint conditions of the multi-objective optimization model;

[0082] The generation module 104 is used to solve the multi-objective optimization model on the premise of meeting the constraint conditions to obtain the transaction matching results between multiple power purchasers and multiple power sellers.

[0083] In one embodiment, the first matching requirement data includes multiple first transaction matching requirements and the first weight corresponding to each first transaction matching requirement, where the first transaction matching requirement includes at least one of the following: power seller energy type, power selling price, power supply quantity, power seller credit value, and power supply location;

[0084] The second matching requirement data includes multiple second transaction matching requirements and the second weight corresponding to each second transaction matching requirement, where the second transaction matching requirement includes at least one of the following: power purchase price, demand quantity, power purchaser credit value, and power purchase location.

[0085] In one embodiment, the determination module 102 is specifically used for:

[0086] Based on the power purchase data and the power supply data, determine the first feature data corresponding to each first transaction matching requirement and the second feature data corresponding to each second transaction matching requirement;

[0087] Based on the first preset matching method corresponding to each first transaction matching requirement and the first feature data, calculate the first satisfaction degree of each power purchaser for each power seller on each first transaction matching requirement;

[0088] Based on the second preset matching method of each second transaction matching requirement and the second feature data, calculate the second satisfaction degree of each power seller for each power purchaser on each second transaction matching requirement;

[0089] Calculate the third satisfaction of each power purchaser for each power seller based on each first satisfaction and the corresponding first weight value of each first transaction matching requirement, and calculate the fourth satisfaction of each power seller for each power purchaser based on each second satisfaction and the corresponding second weight value of each second transaction matching requirement.

[0090] In one embodiment, the determination module 102 is further specifically configured to:

[0091] Substitute each first satisfaction and the first weight value into the first preset satisfaction formula to calculate the third satisfaction of each power purchaser for each power seller;

[0092] Wherein, the first preset satisfaction formula is:

[0093]

[0094] In the formula, the above a ij is the third satisfaction of the i-th power purchaser for the j-th power seller; the above w h is the first weight value of the h-th first transaction matching requirement; the above a hij is the first satisfaction of the i-th power purchaser for the j-th power seller under the h-th first transaction matching requirement;

[0095] Substitute each second satisfaction and the second weight value into the second preset satisfaction formula to calculate the fourth satisfaction of each power seller for each power purchaser;

[0096] Wherein, the second preset satisfaction formula is:

[0097]

[0098] In the formula, the above b ij is the fourth satisfaction of the j-th power seller for the i-th power purchaser; the above v q is the second weight value of the q-th second transaction matching requirement; the above b qij is the second satisfaction of the j-th power seller for the i-th power purchaser under the q-th second transaction matching requirement.

[0099] In one embodiment, the construction module 103 is specifically configured to:

[0100] Perform normalization processing on multiple third satisfactions to obtain the first target satisfaction;

[0101] Wherein, the expression of the first target satisfaction is:

[0102]

[0103] In the formula, the above a' ijis the first target satisfaction degree of the $i$-th power purchaser for the $j$-th power seller; the above $a$ ij is the third satisfaction degree of the $i$-th power purchaser for the $j$-th power seller; the above $\min$ i $\min$ j $a$ ij is the minimum value of $a$ ij ; the above $\max$ i $\max$ j $a$ ij is the maximum value of $a$ ij ;

[0104] Taking the first transaction power between the power purchaser and the power seller as the decision variable, based on the first target satisfaction degree, a first objective function with the maximization of the power purchaser's satisfaction degree as the optimization objective is constructed;

[0105] Among them, the first objective function is:

[0106]

[0107] In the formula, the above $Z$ 1 is the optimization objective of the multi-objective optimization model; the above $\max Z$ 1 means that the optimization objective is to maximize $Z$ 1 ; the above $X$ ij is the first transaction power between the $i$-th power purchaser and the $j$-th power seller; the above $m$ represents the number of power purchasers; the above $n$ represents the number of power sellers.

[0108] In an embodiment, the construction module 103 is specifically further configured to:

[0109] Standardize multiple fourth satisfaction degrees to obtain a second target satisfaction degree;

[0110] Among them, the expression of the second target satisfaction degree is:

[0111]

[0112] In the formula, the above $b'$ ij is the second target satisfaction degree of the $j$-th power seller for the $i$-th power purchaser; the above $b$ ij is the fourth satisfaction degree of the $j$-th power seller for the $i$-th power purchaser; the above $\min$ i $\min$ j $b$ ij is the minimum value of $b$ ij ; the above $\max$ i $\max$ j $b$ ij is the maximum value of $b$ ij ;

[0113] Taking the second transaction power between the power seller and the power buyer as the decision variable, based on the second objective satisfaction degree, a second objective function with the maximization of the power seller's satisfaction degree as the optimization objective is constructed;

[0114] Among them, the second objective function is:

[0115]

[0116] In the formula, the above Z 2 is the optimization objective of the multi-objective optimization model; the above MaxZ 2 means that the optimization objective is to maximize Z 2 ; the above X ij is the second transaction power between the jth power seller and the ith power buyer; the above m represents the number of power buyers; the above n represents the number of power sellers.

[0117] In an embodiment, the constraint conditions of the multi-objective optimization model are:

[0118] The sum of the electricity purchased by the power buyer is equal to the power purchase demand of the power buyer;

[0119] The sum of the electricity sold by the power seller is less than or equal to the electricity that can be sold by the power seller;

[0120] The sum of the electricity purchased by the power buyer is less than or equal to the sum of the electricity that can be sold by the power seller.

[0121] The present invention provides a power trading matching device, which sets a matching objective function based on multiple trading matching requirements, and can match the main bodies participating in the power trading according to the multi-trading matching requirements of the trading main bodies, so as to maximize the matching satisfaction degree of both trading main bodies, increase the interests of the trading main bodies, and greatly shorten the trading matching time and improve the matching efficiency. It solves the problem that the trading main bodies participating in free trading in the related art cannot match the best trading object due to information asymmetry.

[0122] For the specific limitations of the power trading matching device, reference can be made to the limitations on the power trading matching method in the above text, which will not be elaborated here. Each module in the above power trading matching device can be implemented in whole or in part by software, hardware and their combinations. The above modules can be embedded in the processor of the electronic device in the form of hardware or independent of it, or stored in the memory of the electronic device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0123] In one embodiment, an electronic device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented:

[0124] Obtain the first matching requirement data, power purchase data of multiple power purchasers, the second matching requirement data of multiple power sellers, and power supply data in the power trading market;

[0125] Based on the first matching requirement data, the second matching requirement data, the power purchase data, and the power supply data, determine the satisfaction degree between multiple power purchasers and multiple power sellers;

[0126] Based on the satisfaction degree, construct a multi-objective optimization model with the maximization of the satisfaction degree of power purchasers and the maximization of the satisfaction degree of power sellers as the objectives, and construct the constraint conditions of the multi-objective optimization model;

[0127] On the premise of satisfying the constraint conditions, solve the multi-objective optimization model to obtain the transaction matching results between multiple power purchasers and multiple power sellers.

[0128] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0129] Obtain the first matching requirement data, power purchase data of multiple power purchasers, the second matching requirement data of multiple power sellers, and power supply data in the power trading market;

[0130] Based on the first matching requirement data, the second matching requirement data, the power purchase data, and the power supply data, determine the satisfaction degree between multiple power purchasers and multiple power sellers;

[0131] Based on the satisfaction degree, construct a multi-objective optimization model with the maximization of the satisfaction degree of power purchasers and the maximization of the satisfaction degree of power sellers as the objectives, and construct the constraint conditions of the multi-objective optimization model;

[0132] On the premise of satisfying the constraint conditions, solve the multi-objective optimization model to obtain the transaction matching results between multiple power purchasers and multiple power sellers.

[0133] It should be noted that for the functions or steps that can be realized by the above computer-readable storage medium or electronic device, reference can be made to the relevant descriptions on the server side and the client side in the foregoing method embodiments. To avoid repetition, they will not be described in detail here.

[0134] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0135] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0136] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments or equivalently replace some of the technical features. These modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention and should all be included in the protection scope of the present invention.

Claims

1. A power transaction matching method, characterized in that: include: Acquire first matching requirement data and power purchase data of multiple power buyers in the power trading market, and second matching requirement data and power supply data of multiple power sellers; determining satisfaction levels between the plurality of electricity buyers and the plurality of electricity sellers based on the first matching requirement data, the second matching requirement data, the electricity purchase data, and the power supply data; Based on the satisfaction, a multi-objective optimization model with the objectives of maximizing the satisfaction of the electricity buyer and the satisfaction of the electricity seller is constructed, and constraint conditions of the multi-objective optimization model are constructed; On the premise of satisfying the constraint conditions, the multi-objective optimization model is solved to obtain transaction matching results between the multiple electricity buyers and the multiple electricity sellers.

2. The method according to claim 1, characterized in that The first matching requirement data includes a plurality of first transaction matching requirements and a first weight corresponding to each first transaction matching requirement, wherein the first transaction matching requirement includes at least one of the following: energy type of the electricity seller, electricity selling price, power supply, credibility of the electricity seller, and power supply location; The second matching requirement data includes multiple second transaction matching requirements and a second weight corresponding to each second transaction matching requirement, wherein the second transaction matching requirement includes at least one of the following: electricity purchase price, demand volume, electricity purchase party credit value and electricity purchase location.

3. The method according to claim 2, characterized in that The step of determining satisfaction levels between the plurality of electricity buyers and the plurality of electricity sellers based on the first matching requirement data, the second matching requirement data, the electricity purchase data, and the power supply data specifically includes: Based on the electricity purchase data and the power supply data, determining first feature data corresponding to each first transaction matching requirement and second feature data corresponding to each second transaction matching requirement; Calculating, based on the first preset matching mode and the first characteristic data corresponding to each first transaction matching requirement, a first satisfaction degree of each electricity buyer to each electricity seller on each first transaction matching requirement; Calculating, based on the second preset matching mode and the second characteristic data of each second transaction matching requirement, the second satisfaction of each electricity seller with respect to each electricity buyer on each second transaction matching requirement; Based on each first satisfaction degree and the first weight value corresponding to each first transaction matching requirement, the third satisfaction degree of each electricity buyer with respect to each electricity seller is calculated, and based on each second satisfaction degree and the second weight value corresponding to each second transaction matching requirement, the fourth satisfaction degree of each electricity seller with respect to each electricity buyer is calculated.

4. The method according to claim 3, characterized in that The step of calculating the third satisfaction degree of each electricity buyer for each electricity seller based on each first satisfaction degree and the first weight value corresponding to each first transaction matching requirement, and calculating the fourth satisfaction degree of each electricity seller for each electricity buyer based on each second satisfaction degree and the second weight value corresponding to each second transaction matching requirement, specifically includes: Substituting each first satisfaction level and first weight value into a first preset satisfaction level formula, and calculating a third satisfaction level of each electricity buyer for each electricity seller; Wherein, the first preset satisfaction formula is: In the formula, the above a ij is the third satisfaction of the i-th electricity buyer for the j-th electricity seller; the above w h is the first weight value of the hth first transaction matching requirement; the above a hij is the first satisfaction of the i-th power buyer to the j-th power seller under the h-th first transaction matching requirement; Substituting each second satisfaction level and the second weight value into a second preset satisfaction level formula, and calculating a fourth satisfaction level of each electricity seller for each electricity buyer; Wherein, the second preset satisfaction formula is: In the formula, the above b ij is the fourth satisfaction of the j-th electricity seller for the i-th electricity buyer; the above v q is the second weight value required for the qth second transaction match; the above b qij is the second satisfaction of the j-th electricity seller with the i-th electricity buyer under the q-th second transaction matching requirement.

5. The method according to claim 4, characterized in that The step of constructing a multi-objective optimization model based on the satisfaction level with the goal of maximizing the satisfaction level of the electricity buyer and the satisfaction level of the electricity seller specifically includes: Standardize multiple third satisfaction levels to obtain the first target satisfaction level; Among them, the first target satisfaction expression is: In the formula, the above a′ ij is the first target satisfaction of the i-th power buyer for the j-th power seller; ij is the third satisfaction of the i-th electricity buyer for the j-th electricity seller; the above min i min j a ij for a ij The minimum value among the above max i max j a ij for a ij The maximum value in ; Taking the first transaction volume between the electricity buyer and the electricity seller as the decision variable, and based on the first target satisfaction, constructing the first objective function with maximizing the electricity buyer's satisfaction as the optimization target; Wherein, the first objective function is: Wherein, Z1 is the optimization target of the multi-objective optimization model; MaxZ1 is the optimization target to maximize Z1; X ij is the first transaction electricity amount between the ith electricity buyer and the jth electricity seller; the above m represents the number of electricity buyers; and the above n represents the number of electricity sellers.

6. The method according to claim 5, characterized in that The step of constructing a multi-objective optimization model based on the satisfaction level with the goal of maximizing the satisfaction level of the electricity buyer and the satisfaction level of the electricity seller specifically includes: Standardize multiple fourth satisfaction levels to obtain the second target satisfaction level; Among them, the second target satisfaction expression is: In the formula, the above b′ ij is the second target satisfaction of the j-th electricity seller for the i-th electricity buyer; ij is the fourth satisfaction of the j-th electricity seller for the i-th electricity buyer; the above min i min j b ij for b ij The minimum value among the above max i max j b ij for b ij The maximum value in ; Taking the second transaction volume between the electricity seller and the electricity buyer as the decision variable, and based on the second target satisfaction, constructing a second objective function with maximizing the electricity seller's satisfaction as the optimization goal; Wherein, the second objective function is: Wherein, Z2 is the optimization target of the multi-objective optimization model; MaxZ2 is the optimization target to maximize Z2; X ij is the second transaction electricity amount between the j-th electricity seller and the i-th electricity buyer; the above m represents the number of electricity buyers; and the above n represents the number of electricity sellers.

7. The method according to any one of claims 1 to 6, characterized in that The constraints of the multi-objective optimization model are: The sum of the electricity purchased by the electricity purchaser is equal to the electricity demand of the electricity purchaser; The total amount of electricity sold by the electricity seller is less than or equal to the electricity seller's saleable electricity; The sum of the electricity purchased by the electricity buyers is less than or equal to the sum of the electricity available for sale by the electricity sellers.

8. An electric power trading matching device, characterized in that: include: An acquisition module, used to acquire first matching requirement data and power purchase data of multiple power buyers in the power trading market, and second matching requirement data and power supply data of multiple power sellers; a determination module, configured to determine satisfaction levels between the plurality of electricity buyers and the plurality of electricity sellers based on the first matching requirement data, the second matching requirement data, the electricity purchase data, and the power supply data; A construction module, used to construct a multi-objective optimization model with the objectives of maximizing the satisfaction of the electricity buyer and the satisfaction of the electricity seller based on the satisfaction, and to construct constraint conditions of the multi-objective optimization model; A generation module is used to solve the multi-objective optimization model on the premise of satisfying the constraint conditions to obtain transaction matching results between the multiple electricity buyers and the multiple electricity sellers.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the power transaction matching method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the power transaction matching method according to any one of claims 1 to 7 are implemented.

Images