Inter-provincial medium-and-long-term power transaction volume-price matching optimization method and device

Abstract

The invention discloses an inter-provincial medium-and-long-term power transaction volume-price matching optimization method and device, and the method comprises the steps: carrying out the mathematical modeling of an inter-provincial medium-and-long-term power transaction scene, and constructing a target function for minimizing the total landing cost of a purchaser and maximizing the new energy consumption; analyzing physical and policy constraints in power transmission as constraint conditions of the optimization model; based on the objective function and the constraint condition, solving to obtain an optimal inter-provincial medium-and-long-term power transaction volume-price matching parameter, and outputting an optimization execution result; the invention provides the automatic optimization matching method and device for the quantity, the electricity price and the curve of the purchaser and the seller in the inter-provincial medium and long term transaction, which help to improve the quantity and price matching efficiency and optimize the resource configuration.

Description

Technical Field

[0001] This invention belongs to the field of medium- and long-term electricity market trading technology, and particularly relates to a method and apparatus for optimizing the matching of quantity and price in inter-provincial medium- and long-term electricity trading. Background Technology

[0002] With the deepening of power market reform, the activity of power trading has significantly increased, with continuous growth in trading volume, expanding market scope, and increasingly complex trading mechanisms. Trading products have also expanded from intra-provincial medium- and long-term transactions to various types such as inter-provincial spot trading, green electricity trading, and power generation rights trading. This has significantly increased the difficulty of cross-provincial matching and curve optimization. Furthermore, the final price of power transactions involves calculations of transmission prices and network losses for the sending province, sending region, receiving region, and inter-regional channels. This requires extensive table lookups and mathematical calculations, resulting in a large computational load, low efficiency, and a high risk of errors when performed manually.

[0003] Overall, current transaction organization still relies on traditional methods, with manual calculations and decisions, which makes it difficult to adapt to the matching needs of multiple objectives and high real-time requirements, thus affecting transaction efficiency and the effectiveness of market resource allocation. Summary of the Invention

[0004] Purpose of the invention: The purpose of this invention is to provide a method and apparatus for optimizing the matching of quantity and price in inter-provincial medium- and long-term power transactions, which can improve the efficiency of quantity-price matching.

[0005] Technical solution: The present invention provides an optimization method for matching the volume and price of inter-provincial medium- and long-term electricity transactions, comprising:

[0006] Mathematical modeling is performed on inter-provincial medium- and long-term power trading scenarios to construct objective functions that minimize the total landing cost for the purchaser and maximize the consumption of new energy.

[0007] The physical and policy constraints in power transmission are analyzed and used as constraints for the optimization model.

[0008] Based on the objective function and constraints, the optimal inter-provincial medium- and long-term power transaction volume-price matching parameters are obtained, and the results of the optimized execution are output.

[0009] Furthermore, the objective function specifically includes: minimizing the buyer's total implementation cost. The expression is:

[0010]

[0011] in, This is an index for electricity purchasers, representing a single electricity purchaser. This is an index for electricity retailers, representing a single electricity retailer. It is a date index, representing a start and end date; This is a time-segment index, representing the time period from 0 to 23 of a day; This is a power source type index, indicating the selection of thermal power and new energy sources; Indicates the inter-regional power transmission route; Indicates the electricity seller Power type During the period The on-grid electricity price is expressed in yuan / MWh; This indicates that electricity is supplied by the electricity seller. via path Power is delivered to the electricity purchaser The equivalent transmission price including line loss, in yuan / MWh; For decision variables, representing the date Time period , electricity purchaser From the electricity seller via path Purchase power type The amount of electricity, expressed in MWh.

[0012] Furthermore, the constraints include:

[0013] Electricity purchaser demand balance constraint: The total electricity purchased by the electricity purchaser in each time period is equal to its electricity demand in that time period. The specific formula is as follows:

[0014]

[0015] in, For thermal power during time periods From the electricity seller Purchased electricity For new energy during the period From the electricity seller Purchased electricity For the electricity purchaser during the time period The electricity demand;

[0016] Minimum Renewable Energy Ratio Constraint: The proportion of renewable energy purchased by the power purchaser to the total purchased electricity during the entire transaction cycle shall not be less than a preset value. The specific formula is as follows:

[0017] Province-sending balance limit constraint: Each province that sends electricity must meet certain time periods. The total electricity generated from thermal power and renewable energy sold through all channels must not exceed the available transmission quota of the sending province for that period. The specific formula is as follows:

[0018]

[0019] in, For the time period Through the passage Electricity sold from thermal power plants For the time period Through the passage The electricity sold is from energy sources;

[0020] Power sellers' power transmission capacity constraints for each power type: Each power seller's power transmission capacity for each time period Thermal power sold through all channels The power transmission limit for thermal power generation during that period cannot be exceeded. or the sale of new energy electricity The power transmission limit for renewable energy sources during that period cannot be exceeded. The specific formula is as follows:

[0021] ;

[0022] Transmission channel quota constraints: in each time period Under these conditions, flow through each channel The total amount of electricity generated must not exceed the transmission capacity limit of the channel during that time period. The specific formula is as follows:

[0023] in, For the electricity purchaser From the electricity seller During the period From the passage Purchased thermal power generation For the electricity purchaser From the electricity seller During the period From the passage Purchased renewable energy electricity;

[0024] Maximum price constraint for electricity purchasers: For electricity purchasers, their weighted average landed electricity price for the entire day shall not exceed the maximum electricity price they can accept. The specific formula is as follows:

[0025]

[0026] in, For electricity sellers During the period The price of thermal power, For electricity sellers During the period The price of new energy This represents the total electricity demand of the electricity purchaser.

[0027] Furthermore, the step of solving for the optimal inter-provincial medium- and long-term electricity transaction volume-price matching parameters based on the objective function and the constraints, and outputting the results of the optimized execution, also includes:

[0028] Based on the maximum price limit constraint for electricity purchasers, by introducing auxiliary integer variables, the constraint is transformed into a set of equivalent constraints, and the original nonlinear optimization problem is transformed into a hybrid solution model.

[0029] The solution adopts a branch-and-bound approach as the main framework, traversing the feasible solution space to find the globally optimal quantity-price matching solution; at the same time, heuristic rules are added to generate high-quality solutions for key processes.

[0030] The solved quantity-price matching parameters are analyzed and merged. The solved electricity purchaser, electricity seller, energy type, channel, time and period index, as well as electricity quantity and price parameters are organized to construct a data structure that meets the specifications for medium and long-term electricity transaction data. At the same time, process calculation information is added for subsequent verification.

[0031] Based on the same inventive concept, the present invention also provides an inter-provincial medium- and long-term power trading volume-price matching optimization device, comprising:

[0032] The objective function module is used to mathematically model inter-provincial medium- and long-term power trading scenarios and construct objective functions that minimize the total landing cost for the purchaser and maximize the consumption of new energy.

[0033] The constraint module is used to analyze the physical and policy constraints in power transmission as constraints on the optimization model.

[0034] The optimization module is used to solve for the optimal inter-provincial medium- and long-term power transaction volume-price matching parameters based on the objective function and constraints, and output the results of the optimization execution.

[0035] Furthermore, the objective function specifically includes: minimizing the buyer's total implementation cost. The expression is:

[0036]

[0037] in, This is an index for electricity purchasers, representing a single electricity purchaser. This is an index for electricity retailers, representing a single electricity retailer. It is a date index, representing a start and end date; This is a time-segment index, representing the time period from 0 to 23 of a day; This is a power source type index, indicating the selection of thermal power and new energy sources; Indicates the inter-regional power transmission route; Indicates the electricity seller Power type During the period The on-grid electricity price is expressed in yuan / MWh; This indicates that electricity is supplied by the electricity seller. via path Power is delivered to the electricity purchaser The equivalent transmission price including line loss, in yuan / MWh; For decision variables, representing the date Time period , electricity purchaser From the electricity seller via path Purchase power type The amount of electricity, expressed in MWh.

[0038] Furthermore, the constraints include:

[0039] Electricity purchaser demand balance constraint: The total electricity purchased by the electricity purchaser in each time period is equal to its electricity demand in that time period. The specific formula is as follows:

[0040]

[0041] in, For thermal power during time periods From the electricity seller Purchased electricity For new energy during the period From the electricity seller Purchased electricity For the electricity purchaser during the time period The electricity demand;

[0042] Minimum Renewable Energy Ratio Constraint: The proportion of renewable energy purchased by the power purchaser to the total purchased electricity during the entire transaction cycle shall not be less than a preset value. The specific formula is as follows:

[0043] Province-sending balance limit constraint: Each province that sends electricity must meet certain time periods. The total electricity generated from thermal power and renewable energy sold through all channels must not exceed the available transmission quota of the sending province for that period. The specific formula is as follows:

[0044]

[0045] in, For the time period Through the passage Electricity sold from thermal power plants For the time period Through the passage The electricity sold is from energy sources;

[0046] Power sellers' power transmission capacity constraints for each power type: Each power seller's power transmission capacity for each time period Thermal power sold through all channels The power transmission limit for thermal power generation during that period cannot be exceeded. or the sale of new energy electricity The power transmission limit for renewable energy sources during that period cannot be exceeded. The specific formula is as follows:

[0047] ;

[0048] Transmission channel quota constraints: in each time period Under these conditions, flow through each channel The total amount of electricity generated must not exceed the transmission capacity limit of the channel during that time period. The specific formula is as follows:

[0049] in, For the electricity purchaser From the electricity seller During the period From the passage Purchased thermal power generation For the electricity purchaser From the electricity seller During the period From the passage Purchased renewable energy electricity;

[0050] Maximum price constraint for electricity purchasers: For electricity purchasers, their weighted average landed electricity price for the entire day shall not exceed the maximum electricity price they can accept. The specific formula is as follows:

[0051]

[0052] in, For electricity sellers During the period The price of thermal power, For electricity sellers During the period The price of new energy This represents the total electricity demand of the electricity purchaser.

[0053] Furthermore, the optimization module also includes:

[0054] Based on the maximum price limit constraint for electricity purchasers, by introducing auxiliary integer variables, the constraint is transformed into a set of equivalent constraints, and the original nonlinear optimization problem is transformed into a hybrid solution model.

[0055] The solution adopts a branch-and-bound approach as the main framework, traversing the feasible solution space to find the globally optimal quantity-price matching solution; at the same time, heuristic rules are added to generate high-quality solutions for key processes.

[0056] The solved quantity-price matching parameters are analyzed and merged. The solved electricity purchaser, electricity seller, energy type, channel, time and period index, as well as electricity quantity and price parameters are organized to construct a data structure that meets the specifications for medium and long-term electricity transaction data. At the same time, process calculation information is added for subsequent verification.

[0057] Based on the same inventive concept, the present invention also provides a computing device, comprising: one or more processors, one or more memories, and one or more programs, wherein the programs are stored in the memory and configured to be executed by the processor, and when the programs are loaded onto the processor, they implement the steps of the inter-provincial medium- and long-term power transaction quantity-price matching optimization method according to any of the preceding claims.

[0058] Based on the same inventive concept, the present invention also provides a storage medium storing a computer program, the computer program including program instructions, which, when executed by a processor, cause the processor to perform the steps of the inter-provincial medium- and long-term power trading quantity-price matching optimization method according to any one of the preceding claims.

[0059] Beneficial effects: Compared with the existing technology, the inter-provincial medium and long-term power transaction matching optimization model constructed in this invention can calculate the optimal quantity and price matching scheme under multiple constraints, replacing the inefficient and non-optimal quantity and price matching method of traditional manual methods, and achieving maximum optimal allocation of resources; it can significantly reduce the mechanical workload of manual quantity and price matching, and help expand the scale of inter-regional and inter-provincial transactions and improve work efficiency. Attached Figure Description

[0060] Figure 1 This is a flowchart of a method according to an embodiment of the present invention. Detailed Implementation

[0061] To enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0062] As attached Figure 1 As shown in this embodiment, the inter-provincial medium- and long-term electricity trading volume-price matching optimization method includes:

[0063] Step 1: Conduct mathematical modeling of inter-provincial medium- and long-term power trading scenarios, and construct objective functions that minimize the total landing cost for the purchaser and maximize the consumption of new energy.

[0064] Step 2: Analyze the physical and policy constraints in power transmission as constraints for the optimization model;

[0065] Step 3: Based on the objective function and constraints, solve for the optimal inter-provincial medium- and long-term power transaction volume-price matching parameters, and output the results of the optimized execution.

[0066] Specifically, in step 1, a mathematical model is constructed for the inter-provincial medium- and long-term electricity trading scenario. The minimum landing cost condition for the buyer is established as the objective function, serving as the optimization direction for overall quantity-price matching. In the objective function, the total cost is the sum of the landing costs of all purchased electricity. The landing price is determined by factors such as the grid connection price, transmission price, and grid losses. The specific objective function is as follows:

[0067]

[0068] In the formula, the definitions of various parameters are as follows:

[0069] (1) This represents the electricity purchaser index, indicating a single electricity purchaser.

[0070] (2) This represents the electricity seller index, indicating a single electricity seller.

[0071] (3) This represents a date index, indicating a start and end date.

[0072] (4) This represents the time period index, indicating the time period from 0 to 23 of a day;

[0073] (5) This indicates the power source type index, representing the selection of thermal power and new energy sources;

[0074] (6) Indicates the power transmission path, representing an inter-regional power transmission path;

[0075] (7) Indicates the electricity seller Power type During the period Feed-in tariff (RMB / MWh);

[0076] (8) This indicates that electricity is supplied by the electricity seller. via path Power is delivered to the electricity purchaser The equivalent transmission price (RMB / MWh), including line loss.

[0077] (9) Denotes the decision variable, representing the date. Time period , electricity purchaser From the electricity seller via path Purchase power type The amount of electricity (MWh).

[0078] In step 2, considering the physical and policy constraints on power transmission, the impact of factors such as the power demand of the purchaser, the provincial balance limit for sending power, the upper limit of power transmission for each type of power source by the seller, the transmission channel limit, the minimum proportion of renewable energy, and the maximum price limit for the purchaser on power transaction matching is analyzed as a set of constraints for optimal matching. The specific constraints are as follows:

[0079] (1) The electricity purchaser's demand balance constraint: the total electricity purchased by the electricity purchaser in each time period must be equal to its demand in that time period. The specific formula is as follows:

[0080]

[0081] in, For thermal power during time periods From the electricity seller Purchased electricity For new energy during the period From the electricity seller Purchased electricity For the electricity purchaser during the time period The electricity demand;

[0082] (2) Minimum proportion constraint for new energy, throughout the entire trading cycle (24 time periods). =0,1,2,....23), the renewable energy electricity purchased by the power purchaser. The percentage must not be lower than the required minimum percentage. The specific formula is as follows:

[0083]

[0084] (3) The balancing quota constraint of the sending province, each sending province in each time period Through all channels Total electricity sold of all types (thermal power) With new energy The delivery limit cannot exceed the available delivery limit for that time period. The specific formula is as follows:

[0085]

[0086] (4) Constraints on the power transmission capacity of each power source type for each electricity seller, and the power transmission capacity of each electricity seller in each time period. Through all channels Selling electricity of a specific type of power source (thermal power) With new energy The power supply cannot exceed the power transmission limit for that type of power supply during that time period. and The specific formula is as follows:

[0087]

[0088] (5) Transmission channel quota constraints, in each time period Under these conditions, flow through each channel All electricity (from all electricity sellers) All power types (thermal power) With new energy ), transmitted to all electricity purchasers The transmission capacity of the channel cannot be exceeded. The specific formula is as follows:

[0089]

[0090] in, For the electricity purchaser From the electricity seller During the period From the passage Purchased thermal power generation For the electricity purchaser From the electricity seller During the period From the passage Purchased renewable energy electricity;

[0091] (6) Maximum price constraint for electricity purchasers: For electricity purchasers, their weighted average landed electricity price for the whole day should not exceed the maximum electricity price they can accept. It includes each time period. Below, different sellers Different energy types of supply (thermal power) With new energy ) and the corresponding energy prices (including the landed price after calculation of line loss, etc.), and The comprehensive calculation is performed under the condition that the total cost does not exceed the total cost calculated based on the maximum price limit. This is a non-linear constraint, which requires that the total cost not exceed the total cost calculated based on the maximum price limit. The specific formula is as follows:

[0092]

[0093] in, For electricity sellers During the period The price of thermal power, For electricity sellers During the period The price of new energy This represents the total electricity demand of the electricity purchaser.

[0094] In step 3, based on the objective function constructed in step 1 and the constraints analyzed in step 2, an efficient and stable solution method is designed to obtain the optimal inter-provincial medium- and long-term power transaction volume-price matching scheme and output executable results.

[0095] (1) Transformation processing: In response to the characteristics of the maximum price constraint of the electricity purchaser in step 2, the constraint is transformed into a set of equivalent constraints by introducing auxiliary integer variables, thereby transforming the original nonlinear optimization problem into a hybrid solution model, laying the foundation for subsequent solutions.

[0096] (2) A hybrid solution strategy is adopted, using a branch and bound approach as the main framework to accurately solve the model, systematically traversing the feasible solution space to find the globally optimal quantity-price matching solution. At the same time, heuristic rules are added to generate high-quality solutions for key processes. These rules prioritize matching the electricity purchaser with renewable energy with lower marginal costs to quickly approach the optimal solution region, reduce the number of iterations, and improve solution efficiency.

[0097] (3) Organize the result set, analyze and merge the solved quantity and price matching parameters, organize the buyer and seller, energy type, channel, time, time period index, and electricity and price parameters after the model is solved, and construct a data structure that meets the specifications of medium and long-term power trading data, so that it can be used in the power trading system in the future. At the same time, add process calculation information to facilitate verification.

[0098] Based on the same inventive concept, this embodiment also provides an inter-provincial medium- and long-term power trading volume-price matching optimization device, comprising:

[0099] The objective function module is used to mathematically model inter-provincial medium- and long-term power trading scenarios and construct objective functions that minimize the total landing cost for the purchaser and maximize the consumption of new energy.

[0100] The constraint module is used to analyze the physical and policy constraints in power transmission as constraints on the optimization model.

[0101] The optimization module is used to solve for the optimal inter-provincial medium- and long-term power transaction volume-price matching parameters based on the objective function and constraints, and output the results of the optimization execution.

[0102] Furthermore, the objective function specifically includes: minimizing the buyer's total implementation cost. The expression is:

[0103]

[0104] in, This is an index for electricity purchasers, representing a single electricity purchaser. This is an index for electricity retailers, representing a single electricity retailer. It is a date index, representing a start and end date; This is a time-segment index, representing the time period from 0 to 23 of a day; This is a power source type index, indicating the selection of thermal power and new energy sources; Indicates the inter-regional power transmission route; Indicates the electricity seller Power type During the period The on-grid electricity price is expressed in yuan / MWh; This indicates that electricity is supplied by the electricity seller. via path Power is delivered to the electricity purchaser The equivalent transmission price including line loss, in yuan / MWh; For decision variables, representing the date Time period , electricity purchaser From the electricity seller via path Purchase power type The amount of electricity, expressed in MWh.

[0105] Furthermore, the constraints include:

[0106] Electricity purchaser demand balance constraint: The total electricity purchased by the electricity purchaser in each time period is equal to its electricity demand in that time period. The specific formula is as follows:

[0107]

[0108] in, For thermal power during time periods From the electricity seller Purchased electricity For new energy during the period From the electricity seller Purchased electricity For the electricity purchaser during the time period The electricity demand;

[0109] Minimum Renewable Energy Ratio Constraint: The proportion of renewable energy purchased by the power purchaser to the total purchased electricity during the entire transaction cycle shall not be less than a preset value. The specific formula is as follows:

[0110] Province-sending balance limit constraint: Each province that sends electricity must meet certain time periods. The total electricity generated from thermal power and renewable energy sold through all channels must not exceed the available transmission quota of the sending province for that period. The specific formula is as follows:

[0111]

[0112] in, For the time period Through the passage Electricity sold from thermal power plants For the time period Through the passage The electricity sold is from energy sources;

[0113] Power sellers' power transmission capacity constraints for each power type: Each power seller's power transmission capacity for each time period Thermal power sold through all channels The power transmission limit for thermal power generation during that period cannot be exceeded. or the sale of new energy electricity The power transmission limit for renewable energy sources during that period cannot be exceeded. The specific formula is as follows:

[0114] ;

[0115] Transmission channel quota constraints: in each time period Under these conditions, flow through each channel The total amount of electricity generated must not exceed the transmission capacity limit of the channel during that time period. The specific formula is as follows:

[0116] in, For the electricity purchaser From the electricity seller During the period From the passage Purchased thermal power generation For the electricity purchaser From the electricity seller During the period From the passage Purchased renewable energy electricity;

[0117] Maximum price constraint for electricity purchasers: For electricity purchasers, their weighted average landed electricity price for the entire day shall not exceed the maximum electricity price they can accept. The specific formula is as follows:

[0118]

[0119] in, For electricity sellers During the period The price of thermal power, For electricity sellers During the period The price of new energy This represents the total electricity demand of the electricity purchaser.

[0120] Furthermore, the optimization module also includes:

[0121] Based on the maximum price limit constraint for electricity purchasers, by introducing auxiliary integer variables, the constraint is transformed into a set of equivalent constraints, and the original nonlinear optimization problem is transformed into a hybrid solution model.

[0122] The solution adopts a branch-and-bound approach as the main framework, traversing the feasible solution space to find the globally optimal quantity-price matching solution; at the same time, heuristic rules are added to generate high-quality solutions for key processes.

[0123] The solved quantity-price matching parameters are analyzed and merged. The solved electricity purchaser, electricity seller, energy type, channel, time and period index, as well as electricity quantity and price parameters are organized to construct a data structure that meets the specifications for medium and long-term electricity transaction data. At the same time, process calculation information is added for subsequent verification.

[0124] Based on the same inventive concept, this embodiment also provides a computing device, including: one or more processors, one or more memories, and one or more programs, wherein the programs are stored in the memory and configured to be executed by the processor, and when the programs are loaded onto the processor, they implement the steps of the inter-provincial medium- and long-term power transaction quantity-price matching optimization method according to any of the above claims.

[0125] Based on the same inventive concept, this embodiment also provides a storage medium storing a computer program, the computer program including program instructions, which, when executed by a processor, cause the processor to perform the steps of the inter-provincial medium- and long-term power transaction quantity-price matching optimization method according to any one of the above claims.

[0126] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0127] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0128] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0129] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0130] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for optimizing the matching of quantity and price in inter-provincial medium- and long-term electricity transactions, characterized in that, include: Mathematical modeling is performed on inter-provincial medium- and long-term power trading scenarios to construct objective functions that minimize the total landing cost for the purchaser and maximize the consumption of new energy. The physical and policy constraints in power transmission are analyzed and used as constraints for the optimization model. Based on the objective function and constraints, the optimal inter-provincial medium- and long-term power transaction volume-price matching parameters are obtained, and the results of the optimized execution are output.

2. The inter-provincial medium- and long-term power trading volume-price matching optimization method according to claim 1, characterized in that, The objective function specifically includes: minimizing the buyer's total implementation cost. The expression is: ； in, This is an index for electricity purchasers, representing a single electricity purchaser. This is an index for electricity retailers, representing a single electricity retailer. It is a date index, representing a start and end date; This is a time-segment index, representing the time period from 0 to 23 of a day; This is a power source type index, indicating the selection of thermal power and new energy sources; Indicates the inter-regional power transmission route; Indicates the electricity seller Power type During the period The on-grid electricity price is expressed in yuan / MWh; This indicates that electricity is supplied by the electricity seller. via path Power is delivered to the electricity purchaser The equivalent transmission price including line loss, in yuan / MWh; For decision variables, representing the date Time period , electricity purchaser From the electricity seller via path Purchase power type The amount of electricity, expressed in MWh.

3. The inter-provincial medium- and long-term power trading volume-price matching optimization method according to claim 1, characterized in that, The constraints include: Electricity purchaser demand balance constraint: The total electricity purchased by the electricity purchaser in each time period is equal to its electricity demand in that time period. The specific formula is as follows: ； in, For thermal power during time periods From the electricity seller Purchased electricity For new energy during the period From the electricity seller Purchased electricity For the electricity purchaser during the time period The electricity demand; Minimum Renewable Energy Ratio Constraint: The proportion of renewable energy purchased by the power purchaser to the total purchased electricity during the entire transaction cycle shall not be less than a preset value. The specific formula is as follows: ； Province-sending balance limit constraint: Each province that sends electricity must meet certain time periods. The total electricity generated from thermal power and renewable energy sold through all channels must not exceed the available transmission quota of the sending province for that period. The specific formula is as follows: ； in, For the time period Through the passage Electricity sold from thermal power plants For the time period Through the passage The electricity sold is from energy sources; Power sellers' power transmission capacity constraints for each power type: Each power seller's power transmission capacity for each time period Thermal power sold through all channels The power transmission limit for thermal power generation during that period cannot be exceeded. or the sale of new energy electricity The power transmission limit for renewable energy sources during that period cannot be exceeded. The specific formula is as follows: ； Transmission channel quota constraints: in each time period Under these conditions, flow through each channel The total amount of electricity generated must not exceed the transmission capacity limit of the channel during that time period. The specific formula is as follows: ； in, For the electricity purchaser From the electricity seller During the period From the passage Purchased thermal power generation For the electricity purchaser From the electricity seller During the period From the passage Purchased renewable energy electricity; Maximum price constraint for electricity purchasers: For electricity purchasers, their weighted average landed electricity price for the entire day shall not exceed the maximum electricity price they can accept. The specific formula is as follows: ； in, For electricity sellers During the period The price of thermal power, For electricity sellers During the period The price of new energy This represents the total electricity demand of the electricity purchaser.

4. The inter-provincial medium- and long-term power trading volume-price matching optimization method according to claim 1, characterized in that, The process of solving for the optimal inter-provincial medium- and long-term power transaction volume-price matching parameters based on the objective function and the constraints, and outputting the results of the optimization execution, also includes: Based on the maximum price limit constraint for electricity purchasers, by introducing auxiliary integer variables, the constraint is transformed into a set of equivalent constraints, and the original nonlinear optimization problem is transformed into a hybrid solution model. The solution adopts a branch-and-bound approach as the main framework, traversing the feasible solution space to find the globally optimal quantity-price matching solution; at the same time, heuristic rules are added to generate high-quality solutions for key processes. The solved quantity-price matching parameters are analyzed and merged. The solved electricity purchaser, electricity seller, energy type, channel, time and time period index, as well as electricity quantity and price parameters are organized to construct a data structure that meets the specifications for medium and long-term electricity transaction data. At the same time, process calculation information is added for subsequent verification.

5. A device for optimizing the matching of quantity and price in inter-provincial medium- and long-term electricity transactions, characterized in that, include: The objective function module is used to mathematically model inter-provincial medium- and long-term power trading scenarios and construct objective functions that minimize the total landing cost for the purchaser and maximize the consumption of new energy. The constraint module is used to analyze the physical and policy constraints in power transmission as constraints on the optimization model. The optimization module is used to solve for the optimal inter-provincial medium- and long-term power transaction volume-price matching parameters based on the objective function and constraints, and output the results of the optimization execution.

6. The inter-provincial medium- and long-term power trading volume-price matching optimization device according to claim 5, characterized in that, The objective function specifically includes: minimizing the buyer's total implementation cost. The expression is: ； in, This is an index for electricity purchasers, representing a single electricity purchaser. This is an index for electricity retailers, representing a single electricity retailer. It is a date index, representing a start and end date; This is a time-segment index, representing the time period from 0 to 23 of a day; This is a power source type index, indicating the selection of thermal power and new energy sources; Indicates the inter-regional power transmission route; Indicates the electricity seller Power type During the period The on-grid electricity price is expressed in yuan / MWh; This indicates that electricity is supplied by the electricity seller. via path Power is delivered to the electricity purchaser The equivalent transmission price including line loss, in yuan / MWh; For decision variables, representing the date Time period , electricity purchaser From the electricity seller via path Purchase power type The amount of electricity, expressed in MWh.

7. The inter-provincial medium- and long-term power trading volume-price matching optimization device according to claim 5, characterized in that, The constraints include: Electricity purchaser demand balance constraint: The total electricity purchased by the electricity purchaser in each time period is equal to its electricity demand in that time period. The specific formula is as follows: ； in, For thermal power during time periods From the electricity seller Purchased electricity For new energy during the period From the electricity seller Purchased electricity For the electricity purchaser during the time period The electricity demand; Minimum Renewable Energy Ratio Constraint: The proportion of renewable energy purchased by the power purchaser to the total purchased electricity during the entire transaction cycle shall not be less than a preset value. The specific formula is as follows: ； Province-sending balance limit constraint: Each province that sends electricity must meet certain time periods. The total electricity generated from thermal power and renewable energy sold through all channels must not exceed the available transmission quota of the sending province for that period. The specific formula is as follows: ； in, For the time period Through the passage Electricity sold from thermal power plants For the time period Through the passage The electricity sold is from energy sources; Power sellers' power transmission capacity constraints for each power type: Each power seller's power transmission capacity for each time period Thermal power sold through all channels The power transmission limit for thermal power generation during that period cannot be exceeded. or the sale of new energy electricity The power transmission limit for renewable energy sources during that period cannot be exceeded. The specific formula is as follows: ； Transmission channel quota constraints: in each time period Under these conditions, flow through each channel The total amount of electricity generated must not exceed the transmission capacity limit of the channel during that time period. The specific formula is as follows: ； in, For the electricity purchaser From the electricity seller During the period From the passage Purchased thermal power generation For the electricity purchaser From the electricity seller During the period From the passage Purchased renewable energy electricity; Maximum price constraint for electricity purchasers: For electricity purchasers, their weighted average landed electricity price for the entire day shall not exceed the maximum electricity price they can accept. The specific formula is as follows: ； in, For electricity sellers During the period The price of thermal power, For electricity sellers During the period The price of new energy This represents the total electricity demand of the electricity purchaser.

8. The inter-provincial medium- and long-term power trading quantity-price matching optimization device according to claim 5, characterized in that, The optimization module further includes: Based on the maximum price limit constraint for electricity purchasers, by introducing auxiliary integer variables, the constraint is transformed into a set of equivalent constraints, and the original nonlinear optimization problem is transformed into a hybrid solution model. The solution adopts a branch-and-bound approach as the main framework, traversing the feasible solution space to find the globally optimal quantity-price matching solution; at the same time, heuristic rules are added to generate high-quality solutions for key processes. The solved quantity-price matching parameters are analyzed and merged. The solved electricity purchaser, electricity seller, energy type, channel, time and time period index, as well as electricity quantity and price parameters are organized to construct a data structure that meets the specifications for medium and long-term electricity transaction data. At the same time, process calculation information is added for subsequent verification.

9. A computing device, characterized in that, include: One or more processors, one or more memories, and one or more programs, said programs being stored in the memory and configured to be executed by the processor, said programs being loaded onto the processor to implement the steps of the inter-provincial medium- and long-term power trading quantity-price matching optimization method according to any one of claims 1 to 4.

10. A storage medium, characterized in that, The storage medium stores a computer program, which includes program instructions that, when executed by a processor, cause the processor to perform the steps of the inter-provincial medium- and long-term power trading quantity-price matching optimization method according to any one of claims 1 to 4.

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