Network-constraint-free bilateral transaction optimization clearing method and system based on new energy consumption

Abstract

The invention belongs to the field of electric power automation, and discloses a network-constraint-free bilateral transaction optimization clearing method and system based on new energy consumption, which can better identify market supply and demand deviation through accurate comparison between a plan value and a predicted value, thereby optimizing new energy consumption and reducing resource waste caused by supply and demand mismatching. By constructing a detailed market bidding space curve and a quantity-price information matching mechanism, the transmission efficiency of market price signals is enhanced, the demand side can respond to price change more quickly and accurately, and the price elasticity of power demand is released. Various market constraint conditions are considered through the established optimization clearing model, an optimal transaction pair can be found in a complex market environment, high efficiency and fairness of the transaction process are ensured, meanwhile, the market stability is improved, and the risk of market failure is reduced.

Description

Technical Field

[0001] The present invention belongs to the field of electric power automation, and specifically relates to a method and system for optimizing and clearing bilateral transactions without network constraints based on new energy consumption. Background Art

[0002] With the rapid advancement of new power systems, the installed capacity of wind power, photovoltaic power and other power generation has increased significantly. As of October 2023, the total installed capacity of wind, light and hydropower will reach 1.36 billion kilowatts, accounting for 48.4% of the total installed capacity in the country. It is estimated that by 2030, non-fossil energy will account for about 25% of primary energy consumption. With the increase in new energy construction, promoting the consumption of new energy has become a top priority. Now, through the efficient and optimized allocation of power resources, a new way is provided for the large-scale consumption of new energy in the market, and the price advantage of new energy is explored by market means, thereby promoting the consumption of new energy. In the early stage of power market construction, the full purchase of new energy at a fixed on-grid electricity price was mostly adopted, which could not effectively play the role of the market in rationally allocating resources, and could not reasonably plan the consumption of new energy, resulting in a waste of resources. Summary of the invention

[0003] The purpose of the present invention is to overcome the shortcomings of the prior art due to the low transmission efficiency of price signals and the demand side, thereby limiting the release of the price elasticity of electricity demand and causing waste of resources, and to provide a bilateral transaction optimization clearing method and system without network constraints based on new energy consumption.

[0004] In order to achieve the above object, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for optimizing and clearing bilateral transactions without network constraints based on new energy consumption, comprising the following steps: Obtain the planned value, forecast value and cross-section limit value of the new energy market, compare the forecast value with the cross-section limit value, and obtain the market bidding space curve based on the comparison results and the forecast value and planned value; Obtain buyer quantity and price information and seller quantity and price information; Based on the buyer's quantity and price information, the seller's quantity and price information and the market bidding space curve, an optimized clearing model and the constraints of the optimized clearing model are constructed; Based on the optimized clearing model and constraints, combined with the current buyer quantity and price information and seller quantity and price information, the optimized clearing model is solved by using transaction pair matching to obtain the optimized clearing result of bilateral transactions.

[0005] A further improvement of the present invention is that the predicted value is compared with the section limit value. If the comparison result is that the predicted value is greater than the section limit value, the market bidding space curve is obtained through the difference between the predicted value and the planned value.

[0006] A further improvement of the present invention is that the optimization clearing model is as follows:

[0007] in, for t Time Buyer j No. n The transaction power corresponding to the segment quotation; for t Time Buyer j No. n Segment quotation price; for t Time period new energy power plant i No. m The transaction power corresponding to the segment quotation; for t Time period new energy power plant i No. m Segment quotation price; T It is a collection of trading sessions; I Assemble for new energy power plants; M i For new energy power plants i The quotation segment collection of J Aggregate for the buyer; N j For the buyer j The quotation segment collection of β is the penalty factor.

[0008] A further improvement of the present invention is that the constraints of the optimized clearing model include power purchase constraints, power sale constraints, power generation capacity constraints, ramp constraints and power purchase and sale balance constraints.

[0009] A further improvement of the present invention is that the power purchase constraint is:

[0010] in, for t Time Buyer j No. n Section declaration of electricity, for t Time Buyer j No. n The transaction power corresponding to the segment quotation.

[0011] A further improvement of the present invention is that the power selling constraint is:

[0012] in, for t Time period new energy power plant i No. m Section declaration of electricity.

[0013] A further improvement of the present invention is that the power generation capacity constraint is:

[0014]

[0015] in, , , for t Time period new energy power plant i The intraday planning value of for t Time period new energy power plant i The predicted value of for t Time period new energy power plant i Transaction power.

[0016] A further improvement of the present invention is that the climbing constraint is:

[0017]

[0018] in, , , To improve the ramp-up capability of new energy power plants, Improve the climbing capacity of new energy power plants.

[0019] A further improvement of the present invention is that the buying and selling electricity balance constraint includes:

[0020]

[0021]

[0022]

[0023]

[0024]

[0025] in, It is the relationship matrix between transaction paths and new energy power plants; For transaction path h With new energy power plants i The relationship factor between For transaction path h A collection of new energy power plants; for t Session Trading Pathh Electricity transaction at the sending end; H is a set of transaction paths; It is the transaction path and buyer relationship matrix; For transaction path h With the buyer j The relationship factor between For transaction path h Buyer collection.

[0026] In a second aspect, the present invention provides a bilateral transaction optimization clearing system without network constraints based on new energy consumption, including: The data comparison module is used to obtain the planned value, forecast value and section limit value of the new energy market, compare the forecast value with the section limit value, and obtain the market bidding space curve based on the comparison result and the forecast value and the planned value; A data acquisition module is used to obtain buyer quantity and price information and seller quantity and price information; Model building module, used to build an optimized clearing model and the constraints of the optimized clearing model based on the buyer's quantity and price information, the seller's quantity and price information and the market bidding space curve; The model output module is used to solve the optimized clearing model based on the optimized clearing model and constraints, combined with the current buyer quantity and price information and seller quantity and price information, using the transaction pair matching method to obtain the optimized clearing result of bilateral transactions.

[0027] In a third aspect, the present invention provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the steps of a method for optimizing clearing bilateral transactions without network constraints based on new energy consumption.

[0028] In a fourth aspect, the present invention provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a method for optimizing clearing of bilateral transactions without network constraints based on new energy consumption.

[0029] Compared with the prior art, the present invention has the following beneficial effects: The present invention can better identify the deviation of market supply and demand by accurately comparing the planned value with the predicted value, thereby optimizing the consumption of new energy and reducing the waste of resources caused by the mismatch between supply and demand. By constructing a refined market bidding space curve and a quantity-price information matching mechanism, the transmission efficiency of market price signals is enhanced, which enables the demand side to respond to price changes more quickly and accurately, and release the price elasticity of electricity demand. The established optimization clearing model takes into account a variety of market constraints, and can find the optimal transaction pair in a complex market environment, ensuring the efficiency and fairness of the transaction process, while improving the stability of the market and reducing the risk of market failure. By optimizing and solving the clearing optimization goal with the maximization of social welfare of new energy consumption, the market fluctuations caused by the forecast error of new energy supply are reduced, the market's adaptability to changes in supply and demand is enhanced, and the overall market operation efficiency is improved. In summary, the present invention achieves more efficient new energy consumption and more stable market operation by optimizing the trading mechanism of the power market, and overcomes the problem of insufficient release of demand price elasticity in the traditional power market due to low efficiency of price signal transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 is a flow chart of the present invention; Figure 2 is a system diagram of the present invention; Figure 3 is a flow chart of Example 3; Figure 4 This is a system diagram of Example 5. DETAILED DESCRIPTION

[0031] In order to further understand the content of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the embodiments are only for explaining the present invention and are not intended to limit it.

[0032] Embodiment 1: See also Figure 1 , the optimal clearing method of bilateral transactions without network constraints based on new energy consumption includes the following steps: S1, obtain the planned value, forecast value and section limit value of the new energy market, compare the forecast value with the section limit value, and obtain the market bidding space curve based on the comparison result and combining the forecast value with the planned value.

[0033] S2, obtain the buyer's quantity and price information and the seller's quantity and price information.

[0034] S3, based on the buyer's quantity and price information, the seller's quantity and price information and the market bidding space curve, construct an optimized clearing model and the constraints of the optimized clearing model.

[0035] S4, based on the optimized clearing model and constraints, combined with the current buyer quantity and price information and seller quantity and price information, the optimized clearing model is solved by transaction pair matching to obtain the optimized clearing result of bilateral transactions.

[0036] Embodiment 2: See also Figure 2 , a bilateral transaction optimization clearing system without network constraints based on new energy consumption, including: The data comparison module is used to obtain the planned value, forecast value and section limit value of the new energy market, compare the forecast value with the section limit value, and obtain the market bidding space curve based on the comparison result and the forecast value and the planned value; A data acquisition module is used to obtain buyer quantity and price information and seller quantity and price information; Model building module, used to build an optimized clearing model and the constraints of the optimized clearing model based on the buyer's quantity and price information, the seller's quantity and price information and the market bidding space curve; The model output module is used to solve the optimized clearing model based on the optimized clearing model and constraints, combined with the current buyer quantity and price information and seller quantity and price information, using the transaction pair matching method to obtain the optimized clearing result of bilateral transactions.

[0037] Embodiment 3: See also Figure 3 , the optimal clearing method of bilateral transactions without network constraints based on new energy consumption includes the following steps: Step 1: Obtain the planned value and forecast value of the new energy market, calculate the current new energy consumption situation through the planned value and forecast value of the new energy station, determine whether the market needs to be opened, and compare the forecast value with the section limit value. If the comparison result is that the forecast value is greater than the section limit value, obtain the market bidding space curve through the difference between the forecast value and the planned value.

[0038] Step 2: The market operating organization (usually the dispatching center and the trading center) announces the market opening information such as the market bidding space curve on the bidding day on the operation day.

[0039] Step three, obtain the buyer's quantity and price information and the seller's quantity and price information.

[0040] Step 4: Based on the buyer's quantity and price information and the seller's quantity and price information, combined with the market bidding space curve, construct an optimized clearing model and the constraints corresponding to the optimized clearing model. The specific method is as follows: The first step is to consider the maximization of social welfare of new energy consumption as the clearing optimization goal:

[0041] in, For social welfare, t Total electricity purchase cost during the period; for t The total electricity sales cost during the period, β is the penalty factor. for t Time Buyer j No. n The transaction power corresponding to the segment quotation; for t Time Buyer j No. n Segment quotation price; for t Time period new energy power plant i No. m The transaction power corresponding to the segment quotation; for t Time period new energy power plant i No. m Segment quotation price; T It is a collection of trading sessions; I Assemble for new energy power plants; M i For new energy power plants i The quotation segment collection of J Aggregate for the buyer; N j For the buyer j A collection of quote segments.

[0042] The following are the explanations for the above formula: 1) Buyer cost expression.

[0043] The buyer's fee expression is as follows:

[0044] In the formula: represents N j Total number of buyer's quotes, Indicates the buyer j The report n The energy price corresponding to each output range.

[0045] 2) Seller cost expression.

[0046] The seller's fee expression is as follows:

[0047] In the formula: represents M i The total number of price segments of the seller's offer, Indicates the seller i The report m The energy price corresponding to each output range.

[0048] 3) Expression of new energy consumption

[0049] Where: M i Represents new energy power plant i Total number of quotation segments, for t Time period new energy power plant i Transaction power, Indicates that new energy power plants are in the period t No. m The winning bid electricity in the output range.

[0050] The social welfare mentioned in the optimization goal refers to the income generated by the total cost of electricity purchased by all buyers minus the total cost of electricity sold by sellers and the transmission cost. This optimization goal is based on the declared electricity price and declared electricity of the buyer and seller, and takes the transaction electricity as the decision variable to obtain the transaction electricity under the maximum social benefit goal.

[0051] The second step is constraints: 1) Constraints on purchased electricity

[0052] Where: N j Indicates the buyer j Total number of quotation segments; for t Time period new energy power plant i The intraday planning value of for t Time Buyer j No. n The transaction power corresponding to the segment quotation; for t Time Buyer j No. n Section declaration of electricity.

[0053] 2) Constraints on electricity sales

[0054] Where: for t Time period new energy power plant i No. m Section declaration of electricity.

[0055] 3) Power generation capacity constraints

[0056] Where: , , for t Time period new energy power planti The intraday planning value of for t Time period new energy power plant i The predicted value of for t Time period new energy power plant i Transaction power.

[0057] 4) Climbing constraints

[0058]

[0059] in, , , To improve the ramp-up capability of new energy power plants, Improve the climbing capacity of new energy power plants.

[0060] 5) Constraints on the balance of buying and selling electricity Because the transaction path capacity constraint is not considered, it is assumed that there are buying point nodes and new energy power plant virtual paths, I is the set of new energy power plants, J is a set of buyers, a virtual trading network is formed by doing Cartesian product between new energy power plants and buyers, and H is a set of trading paths.

[0061]

[0062]

[0063]

[0064] It is the relationship matrix between transaction paths and new energy power plants; For transaction path h With new energy power plants i The relationship factor between For transaction path h A collection of new energy power plants; for t Session Trading Path h Electricity transaction at the sending end; H A collection of transaction paths.

[0065]

[0066]

[0067]

[0068] It is the transaction path and buyer relationship matrix; For transaction pathh With the buyer j The relationship factor between For transaction path h Buyer collection.

[0069] Step 5. Through the above expression, combined with the current buyer quantity and price information and seller quantity and price information, the optimized clearing model is solved by using transaction pair matching to obtain the bilateral transaction optimized clearing result.

[0070] Step six: The optimized clearing results of bilateral transactions solved by the model are released to the buyer and seller market entities after security verification.

[0071] Embodiment 4: The specific method of the optimal clearing method of bilateral transactions without network constraints based on new energy consumption is as follows: Assumptions and initial data: 1. Time period (T): Assuming one day's trading is considered, the time period is 24 hours (T=24).

[0072] 2. Buyer (J): Assume there are two buyers J1 and J2, each with a different quantity and price quotation segment.

[0073] 3. Seller (I): Assume there are two sellers I1 and I2, representing two new energy power plants respectively.

[0074] 4. Bidding space curve: S1: Obtain the planned value and predicted value, and calculate the bidding space; The section limit of new energy power plants I1 and I2 is 750MW. The planned power generation capacity of the new energy power plant I1 is 400MW and the forecast value is 600MW.

[0075] The planned power generation capacity of the new energy power plant I2 is 350MW, and the predicted value is 550MW.

[0076] The comparison between the planned value and the section limit is as follows: (I1+I2) predicted value>section limit, the market needs to be opened.

[0077] Therefore, the market bidding space is: Buyer I1: 200MW, Buyer I2: 200MW.

[0078] S2: Obtain quantity and price information of buyers and sellers; Buyer J1: The first paragraph of quotation: Power: (0,50] Price: 0.1 yuan / MW.

[0079] The second quotation: Power: (50,150] Price: 0.2 yuan / MW.

[0080] The third section of the quotation: Power: (150,200] Price: 0.4 yuan / MW.

[0081] Buyer J2: The first paragraph of the quotation: Power: (0,50] Price: 0.2 yuan / MW.

[0082] The second quotation: Power: (50,150] Price: 0.3 yuan / MW.

[0083] The third section of the quotation: Power: (150,200] Price: 0.4 yuan / MW.

[0084] Seller I1: The first paragraph of the quotation: Power: (0,100] Price: 0.5 yuan / MW.

[0085] The second quotation: Power: (100,200] Price: 0.35 yuan / MW.

[0086] The third section of quotation: Power: (200,300] Price: 0.1 yuan / MW.

[0087] Seller I2: The first paragraph of the quotation: Power: (0,50] Price: 0.6 yuan / MW.

[0088] The second quotation: Power: (50,100] Price: 0.3 yuan / MW.

[0089] The third section of the quotation: Power: (100,300] Price: 0.2 yuan / MW.

[0090] S3: Construct an optimal clearing model and its constraints; The optimization goal is to maximize social welfare:

[0091] Assume the penalty factor = 0.01.

[0092] S4: Solve the optimized clearing model by matching trading pairs; Based on the quotation information and bidding curves of buyers and sellers, an optimization problem is constructed and solved.

[0093] When section constraints are not considered, Buyer J1: 150MW; Buyer J2: 150MW Seller I1: 200MW; Seller I2: 100MW Marginal electricity price: 0.4 yuan / MW.

[0094] S5: Check and publish the optimization and clearing results; The solved results are security-checked to ensure that the transaction meets the requirements for safe and stable operation of the power system, and then the results are released to buyers and sellers.

[0095] Final result: Buyer J1 purchases 150MW of electricity, and buyer J2 purchases 150MW of electricity, at a price of 0.4 yuan / MW.

[0096] Seller I1 sold 200MW of electricity and seller I2 sold 100MW of electricity at a price of 0.4 yuan / MW.

[0097] The above process demonstrates a simplified specific solution process and obtains the optimal clearing result of bilateral transactions.

[0098] Embodiment 5: See also Figure 4 As shown, the present invention also provides an electronic device 100 for an optimized clearing method of bilateral transactions without network constraints based on new energy consumption; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.

[0099] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the method for optimizing the clearing of bilateral transactions without network constraints based on the consumption of new energy in Example 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data (such as audio data) created according to the use of the electronic device 100, etc. In addition, the memory 101 can include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices.

[0100] The at least one processor 102 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor, etc. The processor 102 is the control center of the electronic device 100, and uses various interfaces and lines to connect various parts of the entire electronic device 100.

[0101] The memory 101 in the electronic device 100 stores a plurality of instructions to implement a bilateral transaction optimization clearing method without network constraints based on new energy consumption, and the processor 102 can execute the plurality of instructions to implement: Obtain the planned value and forecast value of the new energy market, compare the planned value with the forecast value, and obtain the market bidding space curve based on the comparison results; Obtain buyer quantity and price information and seller quantity and price information; Based on the buyer's quantity and price information, the seller's quantity and price information and the market bidding space curve, an optimized clearing model and the constraints of the optimized clearing model are constructed; Based on the optimized clearing model and constraints, combined with the current buyer quantity and price information and seller quantity and price information, the optimized clearing model is solved by using transaction pair matching to obtain the optimized clearing result of bilateral transactions.

[0102] Embodiment 6: If the module / unit integrated in the electronic device 100 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory and read-only memory (ROM, Read-Only Memory).

[0103] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented 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.

[0104] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0105] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0106] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0107] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A bilateral transaction optimization clearing method without network constraints based on new energy consumption, characterized by: The following steps are involved: Obtain the planned value, forecast value and cross-section limit value of the new energy market, compare the forecast value with the cross-section limit value, and obtain the market bidding space curve based on the comparison results and the forecast value and planned value; Obtain buyer quantity and price information and seller quantity and price information; According to the buyer's quantity and price information, the seller's quantity and price information and the market bidding space curve, an optimized clearing model and the constraints of the optimized clearing model are constructed; Based on the optimized clearing model and constraints, combined with the current buyer quantity and price information and seller quantity and price information, the optimized clearing model is solved by using transaction pair matching to obtain the optimized clearing result of bilateral transactions.

2. The method for optimizing and clearing bilateral transactions without network constraints based on new energy consumption according to claim 1 is characterized in that: The predicted value is compared with the section limit. If the comparison result is that the predicted value is greater than the section limit, the market bidding space curve is obtained through the difference between the predicted value and the planned value.

3. The method for optimizing and clearing bilateral transactions without network constraints based on new energy consumption according to claim 1 is characterized in that: The optimized clearing model is as follows: in, for t Time Buyer j No. n The transaction power corresponding to the segment quotation; for t Time Buyer j No. n Segment quotation price; for t Time period new energy power plant i No. m The transaction power corresponding to the segment quotation; for t Time period new energy power plant i No. m Segment quotation price; T It is a collection of trading sessions; I Assemble for new energy power plants; M i For new energy power plants i The quotation segment collection of J Aggregate for the buyer; N j For the buyer j The quotation segment collection of β is the penalty factor.

4. The method for optimizing and clearing bilateral transactions without network constraints based on new energy consumption according to claim 1 or 3 is characterized in that: The constraints of the optimal clearing model include power purchase constraints, power sale constraints, power generation capacity constraints, ramp constraints, and power purchase and sale balance constraints.

5. The method for optimizing and clearing bilateral transactions without network constraints based on new energy consumption according to claim 4 is characterized in that: The power purchase constraints are: in, for t Time Buyer j No. n Section declaration of electricity, for t Time Buyer j No. n The transaction power corresponding to the segment quotation.

6. The method for optimizing and clearing bilateral transactions without network constraints based on new energy consumption according to claim 4 is characterized in that: The power selling constraints are: in, for t Time period new energy power plant i No. m Section declaration of electricity.

7. The method for optimizing and clearing bilateral transactions without network constraints based on new energy consumption according to claim 4 is characterized in that: The power generation capacity constraint is: in, , , for t Time period new energy power plant i The intraday planning value of for t Time period new energy power plant i The predicted value of for t Time period new energy power plant i Transaction power.

8. The method for optimizing and clearing bilateral transactions without network constraints based on new energy consumption according to claim 4 is characterized in that: The climbing constraint is: in, , , To improve the ramp-up capability of new energy power plants, Improve the climbing capacity of new energy power plants.

9. The method for optimizing and clearing bilateral transactions without network constraints based on new energy consumption according to claim 4 is characterized in that: The constraints on the balance of buying and selling electricity include: in, It is the relationship matrix between transaction paths and new energy power plants; For transaction path h With new energy power plants i The relationship factor between For transaction path h A collection of new energy power plants; for t Session Trading Path h Electricity transaction at the sending end; H is a set of transaction paths; It is the transaction path and buyer relationship matrix; For transaction path h With the buyer j The relationship factor between For transaction path h Buyer collection.

10. A bilateral trading optimization clearing system without network constraints based on new energy consumption, characterized by: include: The data comparison module obtains the planned value, forecast value and cross-section limit value of the new energy market, compares the forecast value with the cross-section limit value, and obtains the market bidding space curve based on the comparison result and the forecast value and the planned value; A data acquisition module is used to obtain buyer quantity and price information and seller quantity and price information; Model building module, used to build an optimized clearing model and the constraints of the optimized clearing model based on the buyer's quantity and price information, the seller's quantity and price information and the market bidding space curve; The model output module is used to solve the optimized clearing model based on the optimized clearing model and constraints, combined with the current buyer quantity and price information and seller quantity and price information, using the transaction pair matching method to obtain the optimized clearing result of bilateral transactions.

11. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for optimizing and clearing bilateral transactions without network constraints based on new energy consumption described in any one of claims 1 to 9 are implemented.

12. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for optimizing and clearing bilateral transactions without network constraints based on new energy consumption described in any one of claims 1 to 9 are implemented.

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