AI Large Model-Based Green Electricity Real-Time Trading Method, System, Medium and Device

Abstract

The present invention discloses a method, system, medium and device for real-time trading of green electricity based on an AI large model, including: using each trading node to respectively obtain the demand characteristics of each load node, the quality characteristics of each energy storage node, and the quality characteristics of each green electricity node; constructing a training sample set and a training model based on the AI large model, training the training model using the training sample set, and using the trained training model as the green electricity real-time trading model; using each trading node to respectively input the demand characteristics of each load node, the quality characteristics of each energy storage node, and the quality characteristics of each green electricity node into the green electricity real-time trading model to obtain the optimal trading combination and quotation plan for each load node. The present invention can give the economically optimal trading combination and quotation plan in real time according to the demands of each load node, and the user cost is the lowest under the condition of meeting the load demand.

Description

Technical Field

[0001] The present invention relates to the technical field of green power trading, and in particular to a green power real-time trading method, system, medium and device based on an AI large model. Background Art

[0002] With the continuous development of the power market, green power (hereinafter referred to as green electricity) is becoming increasingly important as an energy entity participating in power market transactions. However, green power, including photovoltaic power generation, wind power generation, etc., has randomness, volatility and intermittency, and the characteristic of being unable to be stored, resulting in the inability to fully participate in the power trading market for real-time power trading. At the same time, the user load demand is also random, resulting in a serious mismatch between the load demand and the green power supply, making the current utilization rate of green power low, the degree of participation in the market weak, and the construction economy poor and other problems. Summary of the Invention

[0003] To solve the technical problems existing in the background art, the present invention proposes a green power real-time trading method, system, medium and device based on an AI large model.

[0004] In a first aspect, a green power real-time trading method based on an AI large model proposed by the present invention, the main bodies participating in the green power real-time trading include N green power nodes, K energy storage nodes, M load nodes and X trading nodes, and each trading node is communicatively connected to N green power nodes, K energy storage nodes and M load nodes;

[0005] The method includes:

[0006] Using each trading node to respectively obtain the demand characteristics of each load node, the quality characteristics of each energy storage node, and the quality characteristics of each green power node;

[0007] Construct a training sample set and Based on the training model of the AI large model, and using the training sample set to train the training model, and taking the trained training model as the green power real-time trading model.

[0008] Using each trading node to respectively input the demand characteristics of each load node, the quality characteristics of each energy storage node, and the quality characteristics of each green power node into the green power real-time trading model to obtain the optimal trading combination and quotation plan for each load node.

[0009] Preferably, the demand characteristics of the load node include: the location of the load node, the maximum demand power, the duration of the maximum demand power, the continuous operating power, the duration of the continuous operating power, and the load conversion efficiency;

[0010] The quality characteristics of green power nodes include the location of green power nodes, the initial power generation cost coefficient, the installed age of green power nodes, the real-time maximum output power, the sustainable time of the maximum output power, the rated output power, the rated output duration, and the green power conversion efficiency;

[0011] The quality characteristics of energy storage nodes include the location of energy storage nodes, the initial investment cost coefficient, the installed age of energy storage nodes, the rated capacity of energy storage nodes, the initial rated power, SOC, SOH, self-power consumption, and the energy storage conversion efficiency.

[0012] Preferably, a training sample set is constructed, specifically including:

[0013] Use each trading node to obtain the historical demand characteristics of each load node, the historical quality characteristics of each energy storage node, and the historical quality characteristics of each green power node respectively;

[0014] According to the historical demand characteristics of each load node, the historical quality characteristics of each energy storage node, the historical quality characteristics of each green power node, and each trading node and the preset negotiation and trading strategy, obtain the historical optimal trading combination and quotation plan for each load node;

[0015] Construct a training sample set according to the historical demand characteristics of each load node, the historical quality characteristics of each energy storage node, the historical quality characteristics of each green power node, and each trading node and the corresponding historical optimal trading combination and quotation plan.

[0016] Preferably, the preset negotiation and trading strategy specifically includes:

[0017] According to the historical demand characteristics of each load node, the historical quality characteristics of each energy storage node, and the historical quality characteristics of each green power node, obtain the demand characteristic set of each load node, the quality characteristic set of each energy storage node, and the historical quality characteristic set of each green power node respectively;

[0018] According to the historical demand characteristic set of each load node, obtain the load node demand data set; among them, the load node demand data set includes the demand data for each load node to participate in the transaction;

[0019] According to the historical quality characteristic set of each green power node, obtain the green power trading form data set; among them, the green power trading form data set includes the green power trading form data for each green power node to respond to the demands of each load node;

[0020] According to the historical quality feature set of each energy storage node, an energy storage transaction form data set is obtained; the energy storage transaction form data set includes energy storage transaction form data in which each energy storage node can respond to the discharge demands of each load node or the charging demands of each green power node; according to the load node demand data set, the energy storage transaction form data set, and the green power transaction form data set, a transaction cost data set is obtained;

[0021] A load transaction operating unit cost set, an energy storage transaction scheduling unit cost set, and a green power transaction scheduling unit cost set are established;

[0022] According to the transaction cost data set, the load transaction operating unit cost set, the energy storage transaction scheduling unit cost set, and the green power transaction scheduling unit cost set, a total transaction cost set is obtained; wherein, the total transaction cost set includes the total transaction cost of each load node when participating in transactions at each transaction node;

[0023] The total transaction costs of each load node at each transaction node in the total transaction cost set are respectively sorted by priority, and the lowest total cost is used as the final cost of each load node;

[0024] Each load node, the transaction node, the green power node, and the energy storage node corresponding to its final cost form the historical optimal transaction combination of each load node, and the transaction node corresponding to the final cost of each load node makes an offer according to a preset offer strategy.

[0025] Preferably, the transaction cost data set includes a first transaction cost data set and a second transaction cost data set;

[0026] Among them, obtaining the transaction cost data set according to the load node demand data set, the energy storage transaction form data set, and the green power transaction form data set specifically includes:

[0027] The demand data of each load node in the load node demand data set is divided into first demand or second demand;

[0028] For each transaction node, A1 first demands of A1 load nodes are randomly extracted from the load node demand data set, and the first demands of the A1 load nodes are combined to form a first demand set; where A1 < M;

[0029] According to the first preset condition, the quality feature set of the green power nodes that meet the first demand set is screened out from the green power transaction form data set; the number of the screened quality feature sets of the green power nodes is B1, B1 < N; and the quality feature sets of the B1 screened green power nodes are combined to form a first green power data set;

[0030] Among them, the first preset condition is and ; Wherein, represents the maximum power demand of the j-th load node, ; , represents the maximum demand power of the j-th load node, represents the load conversion efficiency of the j-th load node; represents the duration of the maximum power demand of the j-th load node, represents the real-time maximum output power of the i-th green power node, ; represents the green power conversion efficiency of the i-th green power node; represents the sustainable time of the maximum output power of the i-th green power node; represents the load conversion efficiency of the j-th load node;

[0031] According to the first green power data set and the demand characteristic set of the load nodes in the first demand set it satisfies, obtain the first two-dimensional power generation tradable data set between each load node and each green power node trading at this trading node;

[0032] Judge whether B1 is greater than or equal to A1; if so, combine the first two-dimensional power generation tradable data sets of all trading nodes to form the first trading cost data set;

[0033] For each trading node, randomly extract the second demands of A2 load nodes from the load node demand data set, and combine the second demands of the A2 load nodes to form the second demand set; where A2 < M;

[0034] According to the second preset condition, screen out the quality characteristic set of the green power nodes that meet the second demand set from the green power trading form data set; where the number of the quality characteristic sets of the green power nodes that meet the second demand set screened out is B2, B2 < N; and combine the quality characteristic sets of the B2 green power nodes screened out to form the second green power data set; where the second preset condition is and ; Wherein, represents the continuous operating power demand of the j-th load node, ; Wherein, , represents the continuous operating demand power of the j-th load node; represents the rated output power of the i-th green power node, represents the rated output duration of the i-th green power node; where, , represents the continuous operating demand power of the j-th load node;

[0035] According to the demand characteristic sets of the load nodes in the second green power data set and the second demand set it satisfies, obtain the second two-dimensional power generation tradable data set between each load node and each green power node trading at this trading node;

[0036] Judge whether B2 is greater than or equal to A2; if so, combine the second two-dimensional power generation tradable data sets of all trading nodes to form the second trading cost data set.

[0037] Preferably, when judging whether B1 is greater than or equal to A1, if not, according to the third preset condition, screen out the quality characteristic sets of the energy storage nodes that meet the demand characteristic sets of the remaining load nodes in the first demand set from the energy storage trading form data set; among them, the number of quality characteristic sets of the energy storage nodes that meet the demand characteristic sets of the remaining load nodes in the first demand set is C1; judge whether the sum of B1 and C1 is less than A1; if so, this trading node does not respond to the trading of the remaining A1 - B1 - C1 load nodes in the first demand set; if not, according to the quality characteristic sets of the C1 screened energy storage nodes, obtain the first two-dimensional energy storage compensation data set between each load node and each energy storage node trading energy storage compensation at this trading node; the third preset condition is and ; in the formula, represents the rated energy storage power of the kth energy storage node, represents the dischargeable capacity of the kth energy storage node;

[0038] Combine all the first two-dimensional power generation tradable data sets and the first two-dimensional energy storage compensation data sets to obtain the first trading cost set.

[0039] Preferably, when judging whether B2 is greater than or equal to A2, if it is determined that B2 is less than A2, then according to the fourth preset condition, screen out the quality characteristic sets of the energy storage nodes that meet the demand characteristic sets of the remaining load nodes in the second demand set from the energy storage trading form data set; among them, the number of quality characteristic sets of the energy storage nodes that meet the demand characteristic sets of the remaining load nodes in the second demand set is C2; judge whether the sum of B2 and C3 is less than A2; if so, this trading node does not respond to the remaining A2 - B2 - C2 load nodes in the second demand set; if not, according to the quality characteristic sets of the C2 screened energy storage nodes, obtain the second two-dimensional energy storage compensation data set between each load node and each energy storage node trading energy storage compensation at this trading node; the fourth preset condition is and ;

[0040] And combine the second two-dimensional power generation tradable data sets and the second two-dimensional energy storage compensation data sets on each trading node to obtain the second trading cost set.

[0041] Preferably, according to the transaction cost data set, the load trading operation unit cost set, the energy storage trading scheduling unit cost set, and the green power trading scheduling unit cost set, a total transaction cost set is obtained, which specifically includes: according to the first transaction cost data set, the load trading operation unit cost set, the energy storage trading scheduling unit cost set, and the green power trading scheduling unit cost set, a first total transaction cost set is obtained; according to the second transaction cost data set, the load trading operation unit cost set, the energy storage trading scheduling unit cost set, and the green power trading scheduling unit cost set, a second total transaction cost set is obtained.

[0042] Among them, the total cost of each load node in each trading node in the total transaction cost set is sorted by priority respectively, and the lowest total cost is used as the final cost of each load node, which specifically includes:

[0043] The total cost of each load node in each trading node in the first total transaction cost set and the second total transaction cost set is sorted by priority respectively; the lowest total cost of each load node in the first total transaction cost set is used as the first total cost of each load node; the lowest total cost of each load node in the second total transaction cost set is used as the second total cost of each load node; according to the first total cost and the second total cost of each load node, the lowest total cost among the first total cost and the second total cost is used as the final cost of each load node.

[0044] Among them, the first total transaction cost set is ;

[0045] In the formula, represents the load trading operation unit cost of each load node and the xth trading node, represents the energy storage trading scheduling unit cost of each energy storage node and the xth trading node, represents the green power trading scheduling unit cost of each green power node and the xth trading node, represents the green power generation unit cost; among them, , represents the initial power generation cost coefficient, represents the green power installation life; represents the energy storage unit cost; among them, ; in the formula, represents the initial investment cost coefficient, represents the energy storage installation life.

[0046] Among them, the second total transaction cost data set is .

[0047] Preferably, after forming the first set of transaction cost data and the second set of transaction cost data, it further includes:

[0048] If B1 + B2 < N and K > C1 + C2 and the rechargeable capacity of the remaining K - C1 - C2 energy storage nodes > 0, then store the electricity generated by the remaining N - B1 - B2 green power nodes into the remaining K - C1 - C2 energy storage nodes, and obtain the green power charging demand scheduling cost data set for the remaining N - B1 - B2 green power nodes to schedule energy storage to store electrical energy at each trading node;

[0049] According to the energy storage transaction scheduling unit cost set, the green power transaction scheduling unit cost set, and the green power charging demand scheduling cost data set, obtain the total green power charging demand cost set; wherein, the total green power charging demand cost set includes the total green power charging demand cost for each green power node to schedule energy storage to store electrical energy at each trading node;

[0050] Sort the total green power charging demand costs for each green power node to schedule energy storage to store electrical energy at each trading node in the total green power charging demand cost data set by priority, and select the lowest total green power charging demand cost as the final total green power charging demand cost for each green power node to schedule energy storage to store electrical energy, and use the energy storage node corresponding to the final total green power charging demand cost as the charging response for each green power node;

[0051] Use the trading node corresponding to the final green power charging demand cost to quote the unit price of the charging demand for each green power node according to the preset quotation strategy, and quote the unit price of the charging and energy storage scheduling for the energy storage node of the charging response of each green power node.

[0052] Among them, the total green power charging demand cost set is ;

[0053] In the formula, represents the continuous operating demand power of the jth load node, ; represents the dischargeable capacity of the kth energy storage node, represents the rated power of the energy storage of the kth energy storage node, ; Among them, ; In the formula, represents the initial rated power of the energy storage of the kth energy storage node, represents the energy storage conversion efficiency of the kth energy storage node, represents the energy storage life correction coefficient of the kth energy storage node.

[0054] Second aspect, the present invention also proposes a green electricity real-time trading system based on an AI large model. The entities participating in the green electricity real-time trading include N green electricity nodes, K energy storage nodes, M load nodes, and X trading nodes. Each trading node is communicatively connected to the N green electricity nodes, the K energy storage nodes, and the M load nodes. The system includes:

[0055] An acquisition module, configured to respectively use each trading node to acquire the demand characteristics of each load node, the quality characteristics of each energy storage node, and the quality characteristics of each green electricity node;

[0056] A model construction module, configured to construct a training model based on an AI large model;

[0057] A training set construction module, configured to construct a training sample set;

[0058] A training module, configured to use the training sample set to train the training model, and use the training model that passes the training as the green electricity real-time trading model.

[0059] A processing module, storing the green electricity real-time trading model, configured to respectively use each trading node to input the demand characteristics of each load node, the quality characteristics of each energy storage node, and the quality characteristics of each green electricity node into the green electricity real-time trading model to obtain the optimal trading combination and quotation plan for each load node.

[0060] Third aspect, the present invention also proposes a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the green electricity real-time trading method based on an AI large model as described in any item of the first aspect.

[0061] Fourth aspect, the present invention also proposes an electronic device, including: a processor and a memory. The memory is used to store one or more programs; when the one or more programs are executed by the processor, it implements the green electricity real-time trading method based on an AI large model as described in any item of the first aspect.

[0062] In the present invention, the proposed green electricity real-time trading method, system, medium, and device use the green electricity real-time trading model based on an AI large model, which can give the economically optimal trading combination and quotation plan in real time according to the demands of each load node. Under the condition of meeting the load demand, the user cost is the lowest, and it ensures the best win-win situation of green electricity benefits, promotes the full participation of green electricity in real-time market-based power trading, and advances the process of green electricity marketization. Description of the Drawings

[0063] Figure 1 It is a schematic flowchart of a green electricity real-time trading method based on an AI large model proposed by the present invention. Detailed Embodiments

[0064] It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the drawings and in combination with the embodiments.

[0065] Refer to Figure 1 , a green electricity real-time trading method based on an AI large model proposed by the present invention includes: The main bodies participating in the green electricity real-time trading include N green electricity nodes, K energy storage nodes, M load nodes, and X trading nodes, and each trading node is communicatively connected to the N green electricity nodes, the K energy storage nodes, and the M load nodes;

[0066] The method includes:

[0067] Using each trading node to respectively obtain the demand characteristics of each load node, the quality characteristics of each energy storage node, and the quality characteristics of each green electricity node;

[0068] Construct a training sample set and a training model based on the AI large model, and use the training sample set to train the training model, and use the training model that passes the training as the green electricity real-time trading model;

[0069] Using each trading node to respectively input the demand characteristics of each load node, the quality characteristics of each energy storage node, and the quality characteristics of each green electricity node into the green electricity real-time trading model to obtain the optimal trading combination and quotation plan for each load node.

[0070] Aiming at the problems that the randomness, intermittency, volatility, and non-storable characteristics of green electricity lead to its inability to participate in real-time power trading, the present invention proposes to use a green electricity real-time trading model based on an AI large model, which can give an economically optimal optimal trading combination and quotation plan in real time according to the demands of each load node. Under the condition of meeting the load demand, the user cost is the lowest, and the best win-win situation of ensuring the best green electricity revenue is achieved, promoting the full participation of green electricity in real-time market-based power trading and advancing the process of green electricity marketization development.

[0071] It should be understood that the trading node is each trading node of a centralized single-set trading system or a distributed collaborative decision-making trading system, and each trading node is not restricted by regions such as the country's geography. Each trading node includes the geographical location of the system , where x is the xth of all the X trading nodes.

[0072] Among them, the quality characteristics of each green electricity node include the location where the green electricity node is located , the initial power generation cost coefficient , the installation age of the green electricity node , the real-time maximum output power , the sustainable time of the maximum output power 、Rated output power 、Rated output duration and green power conversion efficiency 。

[0073] Among them, the quality characteristics of each energy storage node include the location where the energy storage node is located 、Initial investment cost coefficient 、Installation life of the energy storage node 、Rated capacity of the energy storage node 、Initial rated power 、SOC, SOH, self-power consumption and energy storage conversion efficiency 。

[0074] Among them, the demand characteristics of each load node include the location where the load node is located 、Maximum demand power 、Duration of maximum demand power 、Continuous operation power 、Duration of continuous operation power and load conversion efficiency 。

[0075] In this embodiment, training the training model using the training sample set specifically includes:

[0076] Training the training model using the training sample set to obtain a training model that passes the first training, so as to use the training model that passes the first training as the green power real-time trading model.

[0077] In a further embodiment, after obtaining the training model that passes the first training, it further includes:

[0078] Using the fine-tuning network to perform reinforcement correction on the training model that passes the first training to obtain a training model that passes the second training, so as to use the training model that passes the second training as the green power real-time trading model to further improve the accuracy of the green power real-time trading model.

[0079] In order to further improve the accuracy of the green power real-time trading model, it is necessary to improve the accuracy of the data in the training sample set. In order to improve the accuracy of the data in the training sample set, in this embodiment, constructing the training sample set specifically includes:

[0080] Respectively using each trading node to obtain the historical demand characteristics of each load node, the historical quality characteristics of each energy storage node, and the historical quality characteristics of each green power node;

[0081] Based on the historical demand characteristics of each load node, the historical quality characteristics of each energy storage node, the historical quality characteristics of each green power node, and each trading node and the preset negotiation and trading strategy, obtain the historical optimal trading combination and quotation plan for each load node;

[0082] Construct a training sample set according to the historical demand characteristics of each load node, the historical quality characteristics of each energy storage node, the historical quality characteristics of each green power node, and each trading node and the corresponding historical optimal trading combination and quotation plan.

[0083] Among them, the preset negotiation and trading strategy specifically includes:

[0084] Based on the historical demand characteristics of each load node, the historical quality characteristics of each energy storage node, and the historical quality characteristics of each green power node, obtain the demand characteristic set of each load node, the quality characteristic set of each energy storage node, and the historical quality characteristic set of each green power node respectively;

[0085] Based on the historical demand characteristic set of each load node, obtain the load node demand data set; based on the historical quality characteristic set of each energy storage node, obtain the energy storage trading form data set; based on the historical quality characteristic set of each green power node, obtain the green power trading form data set; and obtain the trading cost data set according to the load node demand data set, the energy storage trading form data set, and the green power trading form data set;

[0086] Establish a set of unit costs for load trading operation, a set of unit costs for energy storage trading scheduling, and a set of unit costs for green power trading scheduling; obtain the total trading cost set according to the trading cost data set, the set of unit costs for load trading operation, the set of unit costs for energy storage trading scheduling, and the set of unit costs for green power trading scheduling;

[0087] Perform priority sorting on the total costs of each load node in each trading node in the total trading cost set, and take the lowest total cost as the final cost of each load node;

[0088] Form the historical optimal trading combination of each load node with each load node and the trading node, green power node, and energy storage node corresponding to its final cost, and make a quotation according to the preset quotation strategy using the trading node corresponding to the final cost of each load node.

[0089] Among them, the quality characteristic set of each green power node is ; where i represents the i-th in each green power node; among them, ; where represents the green power generation unit cost of the i-th green power node, represents the initial power generation cost coefficient, represents the green power installation life.

[0090] Among them, the mass characteristic set of each energy storage node is , where k represents the k-th of all K energy storage nodes, represents the unit cost of energy storage, represents the dischargeable capacity of energy storage; represents the rated power of energy storage. Among them, the unit cost of energy storage is ; in the formula, represents the initial investment cost coefficient, represents the installation life of energy storage.

[0091] Among them, the dischargeable capacity of energy storage is ; in the formula, represents the self-power consumption of energy storage. Among them, , represents the conversion efficiency of the k-th energy storage node, is the energy storage life correction coefficient.

[0092] Among them, the demand characteristic set of each load node is , where j is the j-th of all N load nodes, represents the maximum power demand, represents the continuous operating power demand, represents the duration of the maximum power demand, represents the duration of the continuous operating power demand. Among them, , in the formula, represents the maximum power demand of the j-th load node, represents the maximum demand power of the j-th load node, represents the load conversion efficiency of the j-th load node. Among them, , in the formula, represents the continuous operating power demand of the j-th load node, represents the continuous operating demand power of the j-th load node.

[0093] Among them, taking any load node as a reference, according to the physical distance between any trading node around the load node j and the load node j, the trading operation unit cost between the trading node and the j-th load node is obtained; among them, the trading operation unit cost includes the operation cost of the load participating in the transaction and the line transmission cost between the two; according to the trading operation unit cost between each load node and any trading node, the load trading operation unit cost set is obtained. Among them, ; in the formula, represents the load trading operation unit cost set, represents the load trading operation unit cost between the j-th load node and the x-th trading node.

[0094] Based on the set of unit costs for load trading operation, taking any x-th trading node as a benchmark, the unit cost of trading scheduling between the k-th energy storage node and the x-th trading node is obtained; among them, the unit cost of trading scheduling between the k-th energy storage node and the x-th trading node includes the line transmission cost during the period and the operating cost of the trading pair for energy storage; according to the unit cost of trading scheduling between the k-th energy storage node and the x-th trading node, the set of unit costs for energy storage trading scheduling is obtained. Among them, ; in the formula, represents the set of unit costs for energy storage trading scheduling, represents the unit cost of energy storage trading scheduling between the k-th energy storage node and the x-th trading node.

[0095] Based on the set of unit costs for load trading operation, taking any x-th trading node as a benchmark, the unit cost of green power trading scheduling between the i-th green power node and the x-th trading node is obtained; among them, the unit cost of green power trading scheduling includes the line transmission cost during the period and the operating cost of the trading pair for energy storage; according to the unit cost of green power trading scheduling between the i-th green power node and the x-th trading node, the set of unit costs for green power trading scheduling is obtained. Among them, ; in the formula, represents the set of unit costs for green power trading scheduling, represents the unit cost of green power trading scheduling between the i-th green power node and the x-th trading node.

[0096] Specifically, the set of load node demand data includes the demand data for each load node participating in the transaction. Specifically, the set of load node demand data is ; in the formula, represents the set of load node demand data, j represents the j-th load node, represents the maximum power demand, represents the continuous operating power demand, represents the duration of the maximum power demand, represents the duration of the continuous operating power demand.

[0097] Since there are two types of power for the i-th green power node participating in the transaction, including running at power with a sustainable running time of or running at less than the rated power for at least a sustainable running time of . Therefore, the set of green power trading form data includes the green power trading form data for each green power node to respond to the demand of the load node. Specifically, the set of green power trading form data is ; in the formula, represents the set of green power trading form data, represents the unit cost of green power generation, Indicates the real-time maximum output power, Indicates the sustainable time of the maximum output power, Indicates the rated output power, Indicates the rated output duration, Indicates the green power conversion efficiency.

[0098] Among them, the energy storage transaction form data set includes the energy storage transaction form data in which each energy storage node can respond to the discharge demand of each load node or the charging demand of each green power node. Specifically, the energy storage transaction form data set is ; In the formula, k represents the kth of all K energy storage nodes, Indicates the unit cost of energy storage, Indicates the dischargeable capacity participating in the transaction, Indicates the rechargeable capacity, Indicates the rated power of energy storage. Among them, the rated power of energy storage is ; In the formula, Indicates the initial rated power of energy storage, Indicates the conversion efficiency of energy storage, Indicates the energy storage life correction coefficient. Among them, the dischargeable capacity of the kth energy storage node The corresponding dischargeable power is any power less than ; Among them, the rechargeable capacity is .

[0099] In order to promote the full participation of green power in real-time market-based power transactions, in this embodiment, among them, the transaction cost data set includes the first transaction cost data set and the second transaction cost data set;

[0100] The transaction cost data set is obtained according to the load node demand data set, the energy storage transaction form data set, and the green power transaction form data set, specifically including:

[0101] Divide the demand data of each load node in the load node demand data set into the first demand or the second demand;

[0102] For each trading node, randomly extract the first demands of A1 load nodes from the load node demand data set, and combine the first demands of the A1 load nodes to form the first demand set; where A1 < M;

[0103] According to the first preset condition, from the green power transaction form data set Filter out the quality feature set of green power nodes that meet the first demand set; where the number of the quality feature sets of the filtered green power nodes is B1, B1 < N; and combine the quality feature sets of the B1 green power nodes selected to form the first green power data set ; Among them, the first preset condition is and ;

[0104] According to the first green power dataset and the demand feature set of the load nodes in the first demand set it satisfies, obtain the first two-dimensional power generation tradable dataset between each load node and each green power node trading at this trading node;

[0105] Judge whether B1 is greater than or equal to A1; if so, combine the first two-dimensional power generation tradable datasets of all trading nodes to form the first trading cost dataset;

[0106] For each trading node, randomly extract the second demands of A2 load nodes from the load node demand dataset, and combine the second demands of the A2 load nodes to form the second demand set; where A2 < M;

[0107] According to the second preset condition, screen out the quality feature set of the green power nodes that meet the second demand set from the green power trading form dataset ; where the number of the quality feature sets of the green power nodes that meet the second demand set screened out is B2, B2 < N; and combine the quality feature sets of the B2 green power nodes screened out to form the second green power dataset ; where the second preset condition is ;

[0108] According to the second green power dataset and the demand feature set of the load nodes in the second demand set it satisfies, obtain the second two-dimensional power generation tradable dataset between each load node and each green power node trading at this trading node; judge whether B2 is greater than or equal to A2; if so, combine the second two-dimensional power generation tradable datasets of all trading nodes to form the second trading cost dataset.

[0109] Wherein, the first two-dimensional power generation tradable dataset is ; represents the first two-dimensional power generation tradable dataset between the jth load node and the ith green power node; the second two-dimensional power generation tradable dataset is ; represents the second two-dimensional power generation tradable dataset between the jth load node and the ith green power node.

[0110] In a further embodiment, when judging whether B1 is greater than or equal to A1, if not, according to the third preset condition, from the energy storage trading form dataset Screen out the quality feature set of energy storage nodes that meet the demand feature set of the remaining load nodes in the first demand set; among them, the number of quality feature sets of energy storage nodes that meet the demand feature set of the remaining load nodes in the first demand set is C1;

[0111] Judge whether the sum of B1 and C1 is less than A1; if so, the trading node does not respond to the trading of the remaining A1 - B1 - C1 load nodes in the first demand set; if not, according to the quality feature sets of the C1 screened energy storage nodes, obtain the first two-dimensional energy storage compensation data set between each load node and each energy storage node for energy storage compensation trading on this trading node; where the third preset condition is ; Combine the first two-dimensional power generation tradable data sets and the first two-dimensional energy storage compensation data sets on each trading node to obtain the first trading cost set;

[0112] Similarly, when judging whether B2 is greater than or equal to A2, if it is determined that B2 is less than A2, then according to the fourth preset condition, from the energy storage trading form data set Screen out the quality feature set of energy storage nodes that meet the demand feature set of the remaining load nodes in the second demand set; among them, the number of quality feature sets of energy storage nodes that meet the demand feature set of the remaining load nodes in the second demand set is C2;

[0113] Judge whether the sum of B2 and C3 is less than A2; if so, the trading node does not respond to the remaining A2 - B2 - C2 load nodes in the second demand set; if not, according to the quality feature sets of the C2 screened energy storage nodes, obtain the second two-dimensional energy storage compensation data set between each load node and each energy storage node for energy storage compensation trading on this trading node; The fourth preset condition is and ; And combine the second two-dimensional power generation tradable data sets and the second two-dimensional energy storage compensation data sets on each trading node to obtain the second trading cost set.

[0114] Among them, the first two-dimensional energy storage compensation data set is ; In the formula, represents the first two-dimensional energy storage compensation data set; where r is the node after removing i from the load nodes, and k is any energy storage node. Among them, the second two-dimensional energy storage compensation data set is ; In the formula, represents the second two-dimensional energy storage compensation data set.

[0115] Therefore, in this embodiment, according to the trading cost data set, the load trading operation unit cost set, the energy storage trading scheduling unit cost set, and the green power trading scheduling unit cost set, obtain the total trading cost set, which specifically includes:

[0116] Based on the first transaction cost data set, the load transaction operation unit cost set, the energy storage transaction scheduling unit cost set, and the green power transaction scheduling unit cost set, obtain the first total transaction cost set;

[0117] Based on the second transaction cost data set, the load transaction operation unit cost set, the energy storage transaction scheduling unit cost set, and the green power transaction scheduling unit cost set, obtain the second total transaction cost set.

[0118] In a further embodiment, perform a priority ranking on the total costs of each load node in each transaction node in the total transaction cost set, and take the lowest total cost as the final cost of each load node, specifically including:

[0119] Perform a priority ranking on the total costs of each load node in each transaction node in the first total transaction cost set and the second total transaction cost set respectively; take the lowest total cost of each load node in the first total transaction cost set as the first total cost of each load node; take the lowest total cost of each load node in the second total transaction cost set as the second total cost of each load node; according to the first total cost and the second total cost of each load node, take the lowest total cost among the first total cost and the second total cost as the final cost of each load node.

[0120] In this embodiment, in the preset quotation strategy, use the demand of the transaction node for its load node, the power generation selling price of the green power node, and the discharge of the energy storage node in each historical optimal transaction combination to make quotations;

[0121] The demand quotation unit price of each load node is the sum of the final cost and the preset first profit coefficient; the power generation selling price quotation of each green power node is the sum of the power generation unit cost and the preset second profit coefficient; the discharge quotation of each energy storage node is the sum of the energy storage unit cost and the preset third profit coefficient; the first profit coefficient is greater than the sum of the second profit coefficient and the third profit coefficient.

[0122] Among them, the first total transaction cost set is ; in the formula, represents the load transaction operation unit cost of each load node and the x-th transaction node, represents the energy storage transaction scheduling unit cost of each energy storage node and the x-th transaction node, represents the green power transaction scheduling unit cost of each green power node and the x-th transaction node.

[0123] Among them, the second total transaction cost data set is .

[0124] In order to further ensure the best green power revenue and promote the full participation of green power in real-time market-based power trading, in a further embodiment, if B1 + B2 < N and K > C1 + C2 and the rechargeable capacity of the remaining K - C1 - C2 energy storage nodes > 0, then the electricity generated by the remaining N - B1 - B2 green power nodes is stored in the remaining K - C1 - C2 energy storage nodes, and a set of green power charging demand scheduling cost data for the remaining N - B1 - B2 green power nodes to schedule energy storage to store electrical energy at each trading node is obtained;

[0125] According to the energy storage trading scheduling unit cost set, the green power trading scheduling unit cost set, and the green power charging demand scheduling cost data set, a set of total green power charging demand costs is obtained; among them, the set of total green power charging demand costs includes the total green power charging demand costs for each green power node to schedule energy storage to store electrical energy at each trading node;

[0126] Respectively perform priority sorting on the total green power charging demand costs for each green power node to schedule energy storage to store electrical energy at each trading node in the green power charging demand scheduling cost data set, and select the lowest total green power charging demand cost as the final total green power charging demand cost for each green power node to schedule energy storage to store electrical energy, and use the energy storage node corresponding to the final total green power charging demand cost as the charging response for each green power node;

[0127] Use the trading node corresponding to the final green power charging demand cost to quote the unit price of the charging demand for each green power node according to a preset quotation strategy, and quote the unit price of the charging energy storage scheduling for the charging response of each green power node.

[0128] Among them, the cost data set for the remaining N - B1 - B2 green power nodes to schedule energy storage to store electrical energy at each trading node is 。

[0129] Specifically, in the preset quotation strategy, the unit price of the charging demand for each green power node is the sum of the final cost for each green power node to schedule energy storage to store electrical energy and a preset first profit coefficient; the unit price quotation of the charging energy storage scheduling for each energy storage node is the sum of the unit price of the charging demand for each energy storage node and a preset second profit coefficient.

[0130] In a second aspect, the present invention also proposes a green power real-time trading system based on an AI large model. The main bodies participating in the green power real-time trading include N green power nodes, K energy storage nodes, M load nodes, and X trading nodes. Each trading node is communicatively connected to N green power nodes, K energy storage nodes, and M load nodes; the system includes:

[0131] An acquisition module, configured to respectively acquire the demand characteristics of each load node, the quality characteristics of each energy storage node, and the quality characteristics of each green power node by using each trading node;

[0132] A model construction module, configured to construct a training model based on an AI large model;

[0133] A training set construction module, configured to construct a training sample set;

[0134] A training module, configured to train the training model by using the training sample set, and use the trained training model as a green power real-time trading model.

[0135] A processing module, storing the green power real-time trading model, configured to respectively input the demand characteristics of each load node, the quality characteristics of each energy storage node, and the quality characteristics of each green power node into the green power real-time trading model by using each trading node, so as to obtain the optimal trading combination and quotation plan of each load node.

[0136] It should be understood that the method for the training set construction module to construct the training sample set in this embodiment is the same as the construction process of the training sample set described in the first aspect, and will not be elaborated here.

[0137] In a third aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the green power real-time trading method based on an AI large model as described in any item of the first aspect.

[0138] In a fourth aspect, the present invention further provides an electronic device, including: a processor and a memory, where the memory is used to store one or more programs; when the one or more programs are executed by the processor, it implements the green power real-time trading method based on an AI large model as described in any item of the first aspect.

[0139] As mentioned above, the above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and all should be covered by the protection scope of the present invention.

Claims

1. A green electricity real-time trading method based on AI big model, characterized in that: The entities participating in the real-time green electricity transaction include N green electricity nodes, K energy storage nodes, M load nodes and X transaction nodes. Each transaction node is connected to N green electricity nodes, K energy storage nodes and M load nodes. The method comprises: Using each trading node to obtain the demand characteristics of each load node, the quality characteristics of each energy storage node, and the quality characteristics of each green power node; wherein each trading node includes the geographical location of the system; The demand characteristics of the load node include: the location of the load node, the maximum demand power, the duration of the maximum demand power, the continuous operation power, the duration of the continuous operation power, and the load conversion efficiency; The quality characteristics of green power nodes include the location of the green power node, the initial power generation cost coefficient, the installed age of the green power node, the real-time maximum output power, the duration of maximum output power, the rated output power, the rated output duration, and the green power conversion efficiency; The quality characteristics of energy storage nodes include the location of the energy storage node, the initial investment cost coefficient, the installed age of the energy storage node, the rated capacity of the energy storage node, the initial rated power, SOC, SOH, self-consumption and energy storage conversion efficiency; Construct a training sample set and a training model based on the AI ​​big model, use the training sample set to train the training model, and use the trained training model as the green electricity real-time trading model; Using each trading node, the demand characteristics of each load node, the quality characteristics of each energy storage node, and the quality characteristics of each green electricity node are input into the green electricity real-time trading model to obtain the optimal trading combination and quotation plan for each load node; Among them, the training model is trained using the training sample set, specifically including: The training model is trained using the training sample set to obtain a training model that passes the training once; The training model that has passed the first training is enhanced and corrected by using the fine-tuning network to obtain a training model that has passed the second training, and the training model that has passed the second training is used as the green electricity real-time trading model; Among them, constructing a training sample set specifically includes: Use each transaction node to obtain the historical demand characteristics of each load node, the historical quality characteristics of each energy storage node, and the historical quality characteristics of each green power node; According to the historical demand characteristics of each load node, the historical quality characteristics of each energy storage node, the historical quality characteristics of each green power node, and each transaction node and the preset negotiation transaction strategy, the historical optimal transaction combination and quotation plan for each load node are obtained; Construct a training sample set based on the historical demand characteristics of each load node, the historical quality characteristics of each energy storage node, the historical quality characteristics of each green power node, and each transaction node and the corresponding historical optimal transaction combination and quotation scheme; The preset negotiation and transaction strategies specifically include: According to the historical demand characteristics of each load node, the historical quality characteristics of each energy storage node, and the historical quality characteristics of each green electricity node, the demand characteristic set of each load node, the quality characteristic set of each energy storage node, and the historical quality characteristic set of each green electricity node are obtained respectively; According to the historical demand feature set of each load node, a load node demand data set is obtained; wherein the load node demand data set includes demand data of each load node participating in the transaction; According to the historical quality feature set of each green power node, the green power transaction form data set is obtained; among them, The green electricity transaction form data set includes green electricity transaction form data that each green electricity node can respond to the needs of each load node; According to the historical quality feature set of each energy storage node, a set of energy storage transaction form data is obtained; wherein the set of energy storage transaction form data includes energy storage transaction form data that each energy storage node can respond to the discharge demand of each load node or the charging demand of each green power node; Obtain a transaction cost data set based on a load node demand data set, an energy storage transaction form data set, and a green electricity transaction form data set; Establish a load trading operation unit cost set, a storage trading dispatch unit cost set, and a green power trading dispatch unit cost set; According to the transaction cost data set, the load transaction operation unit cost set, the energy storage transaction dispatch unit cost set, and the green electricity transaction dispatch unit cost set, a total transaction cost set is obtained; wherein the total transaction cost set includes the total transaction cost of each load node when participating in the transaction at each transaction node; The total transaction costs of each load node in the total transaction cost set at each transaction node are prioritized respectively, and the lowest total cost is taken as the final cost of each load node; Each load node and the transaction nodes, green power nodes and energy storage nodes corresponding to its final cost are combined to form the historical optimal transaction combination of each load node, and the transaction nodes corresponding to the final cost of each load node are used to make quotations according to the preset quotation strategy; The transaction cost data set includes a first transaction cost data set and a second transaction cost data set; the total transaction cost set is obtained according to the transaction cost data set, the load transaction operation unit cost set, the energy storage transaction dispatch unit cost set, and the green electricity transaction dispatch unit cost set, which specifically includes: Obtaining a first transaction total cost set according to the first transaction cost data set, the load transaction operation unit cost set, the energy storage transaction dispatch unit cost set, and the green electricity transaction dispatch unit cost set; Obtain a second transaction total cost set according to the second transaction cost data set, the load transaction operation unit cost set, the energy storage transaction dispatch unit cost set, and the green electricity transaction dispatch unit cost set; The total costs of each load node in the transaction total cost set at each transaction node are prioritized and the lowest total cost is taken as the final cost of each load node, including: Prioritize the total cost of each load node in each transaction node in the first transaction total cost set and the second transaction total cost set respectively; Taking the lowest total cost of each load node in the first transaction total cost set as the first total cost of each load node; Taking the lowest total cost of each load node in the second transaction total cost set as the second total cost of each load node; According to the first total cost and the second total cost of each load node, the lowest total cost between the first total cost and the second total cost is taken as the final cost of each load node.

2. The green electricity real-time trading method based on AI big model according to claim 1 is characterized in that: The transaction cost data set is obtained based on the load node demand data set, the energy storage transaction form data set, and the green electricity transaction form data set, including: Classifying the demand data of each load node in the load node demand data set into a first demand or a second demand; For each transaction node, the first demands of A1 load nodes are randomly extracted from the load node demand data set, and the first demands of A1 load nodes are combined to form a first demand set; wherein A1 <M; According to the first preset condition, screen out the quality feature set of green power nodes that meet the first demand set from the green power trading form data set; among them, the number of the quality feature sets of the screened green power nodes is B1, and B1 < N; and combine the quality feature sets of the B1 screened green power nodes to form the first green power data set; where the first preset condition is and ; in the formula, represents the maximum power demand of the jth load node, ; represents the duration of the maximum power demand of the jth load node, represents the real-time maximum output power of the ith green power node, ; represents the green power conversion efficiency of the ith green power node; represents the sustainable time of the maximum output power of the ith green power node; And according to the first green electricity data set and the demand feature set of the load nodes in the first demand set satisfied by the first green electricity data set, a first two-dimensional power generation tradable data set between each load node and each green electricity node traded on the transaction node is obtained; Determine whether B1 is greater than or equal to A1; if so, combine the first two-dimensional power generation tradable data sets of all transaction nodes to form a first transaction cost data set; For each transaction node, the second demands of A2 load nodes are randomly extracted from the load node demand data set, and the second demands of A2 load nodes are combined to form a second demand set; wherein A2 <M; According to the second preset condition, screen out the quality feature set of green power nodes that meet the second requirement set from the green power trading form data set; wherein, the number of quality feature sets of green power nodes that meet the second requirement set is B2, and B2 < N; and combine the quality feature sets of the B2 green power nodes screened out to form a second green power data set; wherein, the second preset condition is and ; where represents the continuous operating power demand of the jth load node, represents the rated output power of the ith green power node, represents the continuous operating power duration of the jth load node, represents the rated output duration of the ith green power node; According to the second green electricity data set and the demand feature set of the load node of the second demand set satisfied by the second green electricity data set, a second two-dimensional power generation tradable data set between each load node and each green electricity node that performs transactions on the transaction node is obtained; Determine whether B2 is greater than or equal to A2; if so, combine the second two-dimensional power generation tradable data sets of all transaction nodes to form a second transaction cost data set.

3. The green electricity real-time trading method based on AI big model according to claim 2 is characterized in that: When judging whether B1 is greater than or equal to A1, if not, the quality feature sets of energy storage nodes that meet the demand feature sets of the remaining load nodes in the first demand set are screened out from the energy storage transaction form data set according to the third preset condition; wherein the number of the quality feature sets of energy storage nodes that meet the demand feature sets of the remaining load nodes in the first demand set is C1; Determine whether the sum of B1 and C1 is less than A1; if not, obtain the first two-dimensional energy storage compensation data set between each load node and each energy storage node that conducts energy storage compensation transactions on the transaction node based on the quality feature set of the screened C1 energy storage nodes; wherein the third preset condition is and ; In the formula, represents the energy storage rated power of the kth energy storage node, represents the dischargeable capacity of the kth energy storage node; Combining the first two-dimensional power generation tradable data set and the first two-dimensional energy storage compensation data set on each transaction node to obtain a first transaction cost set; When judging whether B2 is greater than or equal to A2, if it is judged that B2 is less than A2, then according to the fourth preset condition, the quality feature sets of energy storage nodes that meet the demand feature sets of the remaining load nodes in the second demand set are screened out from the energy storage transaction form data set; wherein the number of the quality feature sets of energy storage nodes that meet the demand feature sets of the remaining load nodes in the second demand set is C2; Determine whether the sum of B2 and C3 is less than A2; if not, obtain the second two-dimensional energy storage compensation data set between each load node and each energy storage node that conducts energy storage compensation transactions on the transaction node based on the quality feature set of the screened C2 energy storage nodes; the fourth preset condition is and ; The second two-dimensional power generation tradable data set and the second two-dimensional energy storage compensation data set on each transaction node are combined to obtain a second transaction cost set.

4. The green electricity real-time trading method based on AI big model according to claim 3 is characterized in that: After forming the first transaction cost data set and the second transaction cost data set, the method further includes: If B1 + B2 < N, K > C1 + C2, and the rechargeable capacity of the remaining K - C1 - C2 energy storage nodes > 0, then store the electricity generated by the remaining N - B1 - B2 green power nodes into the remaining K - C1 - C2 energy storage nodes, and obtain the green power charging demand scheduling cost data set for the remaining N - B1 - B2 green power nodes to schedule energy storage to store electrical energy at each trading node; Based on the energy storage trading scheduling unit cost set, the green power trading scheduling unit cost set, and the green power charging demand scheduling cost data set, obtain the total green power charging demand cost set; among them, the total green power charging demand cost set includes the total green power charging demand cost for each green power node to schedule energy storage to store electrical energy at each trading node; Sort the total green power charging demand costs for each green power node to schedule energy storage to store electrical energy at each trading node in the total green power charging demand cost data set by priority, and select the lowest total green power charging demand cost as the final total green power charging demand cost for each green power node to schedule energy storage to store electrical energy, and use the energy storage node corresponding to the final total green power charging demand cost as the charging response for each green power node; Use the trading node corresponding to the final green power charging demand cost to quote the unit price of the charging demand for each green power node according to the preset quoting strategy, and quote the charging and energy storage scheduling unit price of the energy storage node for the charging response of each green power node.

5. A green electricity real-time trading system based on AI big model, characterized in that: The entities participating in the green power real-time trading include N green power nodes, K energy storage nodes, M load nodes, and X trading nodes. Each trading node is communicatively connected to N green power nodes, K energy storage nodes, and M load nodes, including: An acquisition module, configured to use each trading node to respectively acquire the demand characteristics of each load node, the quality characteristics of each energy storage node, and the quality characteristics of each green power node; A model construction module, configured to construct a training model based on the AI large model; A training set construction module, configured to construct a training sample set; A training module, configured to use the training sample set to train the training model, and use the trained training model as the green power real-time trading model; A processing module, storing the green power real-time trading model, configured to use each trading node to respectively input the demand characteristics of each load node, the quality characteristics of each energy storage node, and the quality characteristics of each green power node into the green power real-time trading model to obtain the optimal trading combination and quoting scheme for each load node; Among them, the training process of the training model specifically includes: Use the training sample set to train the training model to obtain a once-trained training model; Use the fine-tuning network to perform reinforcement correction on the once-trained training model to obtain a twice-trained training model, and use the twice-trained training model as the green power real-time trading model; Among them, the construction process of the training sample set specifically includes: Use each trading node to respectively acquire the historical demand characteristics of each load node, the historical quality characteristics of each energy storage node, and the historical quality characteristics of each green power node; According to the historical demand characteristics of each load node, the historical quality characteristics of each energy storage node, the historical quality characteristics of each green power node, and each transaction node and the preset negotiation transaction strategy, the historical optimal transaction combination and quotation plan for each load node are obtained; Construct a training sample set based on the historical demand characteristics of each load node, the historical quality characteristics of each energy storage node, the historical quality characteristics of each green power node, and each transaction node and the corresponding historical optimal transaction combination and quotation scheme; The preset negotiation and transaction strategies specifically include: According to the historical demand characteristics of each load node, the historical quality characteristics of each energy storage node, and the historical quality characteristics of each green electricity node, the demand characteristic set of each load node, the quality characteristic set of each energy storage node, and the historical quality characteristic set of each green electricity node are obtained respectively; According to the historical demand feature set of each load node, a load node demand data set is obtained; wherein the load node demand data set includes demand data of each load node participating in the transaction; According to the historical quality feature set of each green power node, the green power transaction form data set is obtained; among them, The green electricity transaction form data set includes green electricity transaction form data that each green electricity node can respond to the needs of each load node; According to the historical quality feature set of each energy storage node, a set of energy storage transaction form data is obtained; wherein the set of energy storage transaction form data includes energy storage transaction form data that each energy storage node can respond to the discharge demand of each load node or the charging demand of each green power node; Obtain a transaction cost data set based on a load node demand data set, an energy storage transaction form data set, and a green electricity transaction form data set; Establish a load trading operation unit cost set, a storage trading dispatch unit cost set, and a green power trading dispatch unit cost set; According to the transaction cost data set, the load transaction operation unit cost set, the energy storage transaction dispatch unit cost set, and the green electricity transaction dispatch unit cost set, a total transaction cost set is obtained; wherein the total transaction cost set includes the total transaction cost of each load node when participating in the transaction at each transaction node; The total transaction costs of each load node in the total transaction cost set at each transaction node are prioritized respectively, and the lowest total cost is taken as the final cost of each load node; Each load node and the transaction nodes, green power nodes and energy storage nodes corresponding to its final cost are combined to form the historical optimal transaction combination of each load node, and the transaction nodes corresponding to the final cost of each load node are used to make quotations according to the preset quotation strategy; The transaction cost data set includes a first transaction cost data set and a second transaction cost data set; the total transaction cost set is obtained according to the transaction cost data set, the load transaction operation unit cost set, the energy storage transaction dispatch unit cost set, and the green electricity transaction dispatch unit cost set, which specifically includes: Obtaining a first transaction total cost set according to the first transaction cost data set, the load transaction operation unit cost set, the energy storage transaction dispatch unit cost set, and the green electricity transaction dispatch unit cost set; Obtain a second transaction total cost set according to the second transaction cost data set, the load transaction operation unit cost set, the energy storage transaction dispatch unit cost set, and the green electricity transaction dispatch unit cost set; The total costs of each load node in the transaction total cost set at each transaction node are prioritized and the lowest total cost is taken as the final cost of each load node, including: Prioritize the total cost of each load node in each transaction node in the first transaction total cost set and the second transaction total cost set respectively; Taking the lowest total cost of each load node in the first transaction total cost set as the first total cost of each load node; Taking the lowest total cost of each load node in the second transaction total cost set as the second total cost of each load node; According to the first total cost and the second total cost of each load node, the lowest total cost between the first total cost and the second total cost is taken as the final cost of each load node.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the green electricity real-time trading method based on the AI ​​big model as described in any one of claims 1 to 4 is implemented.

7. An electronic device, characterized in that: include: A processor and a memory, the memory is used to store one or more programs; when the one or more programs are executed by the processor, the green electricity real-time trading method based on the AI ​​big model as described in any one of claims 1-4 is implemented.

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