Virtual power plant block chain cross-chain transaction method and system

Abstract

The invention relates to the technical field of block chain cross-chain transaction, in particular to a virtual power plant block chain cross-chain transaction method and system, and the method comprises the steps: constructing a virtual power plant model, and defining the internal composition and operation constraints of a virtual power plant; carbon, green certificate and electricity transaction platforms are connected through a block chain relay chain technology, and a smart contract is designed to automatically execute cross-chain transaction; establishing a dynamic change model of electricity, carbon and green certificate prices according to a market supply-demand relationship; taking the minimum total cost of the virtual power plant as a target, and solving a target operation scheme by using a mixed integer linear programming algorithm; and outputting the target operation scheme. The method has the advantages that flexible regulation and optimal operation of the virtual power plant are realized, transaction transparency and credibility are improved, transaction cost and risk are reduced, load requirements, carbon emission reduction and green certificate quota requirements are met at the same time, sustainable development of the virtual power plant is promoted, and the method is suitable for popularization and application. And the energy power industry is promoted to be transformed and upgraded to a low-carbon and green direction.

Description

Technical Field

[0001] The present invention relates to the field of blockchain cross-chain transaction technology, and in particular to a method and system for blockchain cross-chain transactions of virtual power plants. Background Art

[0002] As global attention to climate change continues to grow, carbon emission reduction has become a common goal of the international community. In the energy sector, traditional power systems face challenges such as high carbon emissions, low energy utilization efficiency, and difficulties in absorbing new energy. As an innovative energy management model, virtual power plants achieve flexible regulation and optimized operation of power systems by integrating distributed energy, energy storage systems, and controllable loads, becoming an important tool for promoting energy transformation. At the same time, green certificates (Green Certificates), as proof of the environmental rights of renewable energy, play a key role in promoting the trading and development of renewable energy. The improvement of the carbon emissions trading market gives carbon emission reduction indicators a clear economic value, further strengthening the driving force for low-carbon development.

[0003] However, the current operation of virtual power plants faces significant bottlenecks: electricity, carbon, and green certificates transactions are fragmented across different platforms, lacking coordination mechanisms and a trusted trading environment. The independent operations of electricity trading, carbon quota trading, and green certificate circulation lead to inefficient transactions, insufficient information transparency, and the risk of double-counting environmental rights. While blockchain technology, with its decentralized, tamper-proof, and traceable characteristics, offers new solutions for energy trading, existing research remains limited. For example, existing blockchain applications often focus on single scenarios (such as pure electricity trading or standalone carbon trading), failing to achieve multi-market synergy across electricity, carbon, and green certificates. While traditional cross-chain technologies can improve the security of single transactions, they lack support for coupled transactions across multiple markets and are unable to effectively address complex issues such as fluctuations in renewable energy output and market supply and demand dynamics. Furthermore, existing trading frameworks often rely on centralized oversight, struggling to balance the needs of information disclosure and privacy protection, resulting in high transaction costs and weak trust mechanisms. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0005] In a first aspect, the present invention provides a method for cross-chain transactions on a virtual power plant blockchain, comprising constructing a virtual power plant model and defining the internal composition and operating constraints of the virtual power plant;

[0006] Connect carbon, green certificate, and electricity trading platforms through blockchain relay chain technology, and design smart contracts to automatically execute cross-chain transactions;

[0007] Establish a dynamic price change model for electricity, carbon, and green certificates based on market supply and demand;

[0008] With the goal of minimizing the total cost of the virtual power plant, a mixed integer linear programming algorithm is used to solve the target operation plan;

[0009] Output the target operation plan.

[0010] As a preferred solution of the virtual power plant blockchain cross-chain transaction method of the present invention, before constructing the virtual power plant model and defining the internal composition and operation constraints of the virtual power plant, it also includes:

[0011] Collect target data, including but not limited to historical new energy output data and load data;

[0012] The Latin hypercube sampling method is used to generate multiple scenarios of renewable energy output and load, and the K-means clustering algorithm is used to reduce the number of scenarios.

[0013] As a preferred solution of the virtual power plant blockchain cross-chain transaction method of the present invention, wherein: constructing a virtual power plant model and defining the internal composition and operation constraints of the virtual power plant include:

[0014] Define the electrical and thermal output constraints of the gas turbine;

[0015] Define the charging and discharging efficiency, capacity, and power constraints of electrical energy storage devices;

[0016] Define the output constraints of new energy units;

[0017] Define the output constraints of thermal power units;

[0018] Define interaction constraints with the electricity and gas grids;

[0019] Define the power consumption and CO2 capture capacity constraints of the carbon capture device.

[0020] As a preferred solution of the virtual power plant blockchain cross-chain transaction method of the present invention, wherein: according to the market supply and demand relationship, a dynamic change model of carbon, green certificate and electricity prices is established, including:

[0021] Establish a dynamic change model of carbon prices based on the supply and demand relationship in the carbon market;

[0022] According to the supply and demand relationship of the green certificate market, a dynamic change model of green certificate prices is established;

[0023] Based on the supply and demand relationship in the electricity market, a dynamic change model of electricity prices is established.

[0024] As a preferred solution of the method for cross-chain transactions of the virtual power plant blockchain of the present invention, the method further includes verifying the output target operation plan.

[0025] As a preferred solution of the virtual power plant blockchain cross-chain transaction method of the present invention, the total cost of the virtual power plant includes power generation cost, carbon trading cost, green certificate trading cost and energy storage maintenance cost.

[0026] As a preferred solution of the virtual power plant blockchain cross-chain transaction method of the present invention, the target operation plan includes the operating cost and the output plan of each unit.

[0027] In a second aspect, the present invention provides a system for cross-chain transactions of a virtual power plant blockchain, comprising: a construction definition module for constructing a virtual power plant model and defining the internal composition and operation constraints of the virtual power plant;

[0028] The connection design module is used to connect electricity, carbon, and green certificate trading platforms by adopting blockchain relay chain technology, and design smart contracts to automatically execute cross-chain transactions;

[0029] Establish a module to build a dynamic price change model for electricity, carbon, and green certificates based on market supply and demand;

[0030] Calculation module: used to solve the target operation plan using the mixed integer linear programming algorithm with the goal of minimizing the total cost of the virtual power plant;

[0031] Output module: used to output the target operation plan.

[0032] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0033] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above method when the computer program is executed by a processor.

[0034] Compared with the existing technology, the beneficial effects of the present invention are: to build a virtual power plant model, integrate distributed energy resources, energy storage systems and controllable loads, use blockchain relay chain technology to connect electricity, carbon and green certificate trading platforms, design smart contracts to automatically execute cross-chain transactions, establish a price dynamic change model, and take the minimum total cost of the virtual power plant as the goal, solve the optimal (target) operation plan through the mixed integer linear programming algorithm, realize the flexible regulation and optimized operation of the virtual power plant, improve the transparency and credibility of the transaction, reduce transaction costs and risks, and at the same time meet the load demand, carbon emission reduction and green certificate quota requirements, promote the sustainable development of virtual power plants, and promote the transformation and upgrading of the energy and power industry to a low-carbon and green direction. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0036] Figure 1 Schematic diagram of the virtual power plant structure.

[0037] Figure 2 Schematic diagram of relay chain cross-chain transactions.

[0038] Figure 3 Schematic diagram of the cross-chain transaction framework for multiple virtual power plants.

[0039] Figure 4 Schematic diagram of the cross-chain transaction process of the virtual power plant blockchain Figure 1 .

[0040] Figure 5 Schematic diagram of the cross-chain transaction process of the virtual power plant blockchain Figure 2 . DETAILED DESCRIPTION

[0041] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0042] Example 1, with reference to Figures 1 to 5 , which is the first embodiment of the present invention, provides a method for cross-chain transactions of a virtual power plant blockchain, including:

[0043] S1. Build a virtual power plant model and define the internal composition and operation constraints of the virtual power plant.

[0044] It should be noted that, with reference to the Figure 1 The virtual power plant model is mainly composed of a carbon capture power plant, a combined heat and power unit (CHP), a new energy output unit, an electricity storage unit and other devices. The carbon capture power plant can effectively reduce carbon dioxide emissions within the virtual power plant.

[0045] Furthermore, before building the virtual power plant model and defining the internal composition and operation constraints of the virtual power plant, the following steps are also required:

[0046] Collect target data, including but not limited to historical renewable energy output data and load data, as well as market transaction data;

[0047] The Latin hypercube sampling method is used to generate multiple scenarios of renewable energy output and load, and the number of scenarios is reduced through the K-means clustering algorithm. Among them, the Latin hypercube sampling method (LHS), as an analysis method based on the stratified sampling principle, has the characteristic of evenly distributing sampling points in the sampling space.

[0048] It should be noted that the Latin Hypercube Sampling (LHS) method is used to perform random sampling operations on the renewable energy output (wind and solar power generation) data and load data at different times of the day: First, assume that X1 = [π1, ..., π T ],X2=[σ1,...,σ T ],X3=[ξ1,...,ξ T ] random variables as input, where π1,...,π T ,σ1,...,σ T ,ξ1,...,ξ T The forecast data of wind and solar power generation and load in T periods of one day; at the same time, set X n1 (n1=1,2,...T), X n2 (n2=1,2,...T), X n3 (n3=1,2,...T) is any random variable input, and the corresponding cumulative probability distribution is:

[0049] Y n1 =F n1 (X n1 ) Formula (1)

[0050] Y n2 =F n2 (X n2 ) Formula (2)

[0051] Y n3 =F n3 (X n3 ) Formula (3)

[0052] Where: Y n1 、Y n2 and Y n3 They represent the cumulative probability distribution functions of wind turbine power generation, photovoltaic power generation and electric load, respectively, and X n1 、X n2 、X n3 They represent the historical load data of wind turbine power generation, photovoltaic power generation and electric load respectively.

[0053] The cumulative probability distribution function is divided into N equally spaced and non-overlapping spaces in the interval [0, 1]. In this embodiment, N is set to 1000 for analysis, and the inverse function is obtained as follows:

[0054] X n1 =F -1 (Y n1 ),n1=1,2,…,T Formula (4)

[0055] X n2 =F -1 (Y n2 ),n2=1,2,…,T Formula (5)

[0056] X n3 =F -1 (Y n3 ),n3=1,2,…,T Formula (6)

[0057] Finally, a set of wind and solar power output and load scenarios can be obtained.

[0058] It should be further explained that since the number of scenarios generated by the Latin Hypercube Sampling (LHS) method is quite large, in order to reduce the complexity of the model calculation and improve the accuracy of the calculation, the scene reduction work is performed by using the K-means clustering method. The K-means method has the significant characteristics of low complexity and fewer required input parameters. s (s=1,…,N), where n s Represents the sth scene, where s = 1, ..., N, indicating that there are N scenes. At the same time, the target scene tree is set to C. The specific steps are as follows:

[0059] 1. First, select C scenes as cluster centers and set the cluster center set to Ω = {n s Ω}(s=1,…,C), where n s Ω is the cluster center of the sth initial scene.

[0060] 2. Determine the number of remaining scenes K = {n s' C}(s'=1,…,NC), and calculate the distance from the remaining scenes to the cluster center in, The remaining scene data.

[0061] 3. According to the distance from the scene to the cluster center, the remaining cluster centers are classified with the nearest cluster center, and clustered again to obtain a set where Ω i A collection of similar scenes.

[0062] 4. Update the cluster center and assume the scene cluster Ω i There is O s scenes, calculate the sum of the distances between each scene and other scenes as s=1,2,…,O s , where n s′ For other scene data, select DL S =min(DL S ) scene k l is the new cluster center.

[0063] 5. Repeat steps 2 and 3 until the cluster center and cluster center no longer change, and the scene reduction stops.

[0064] Furthermore, a virtual power plant model is constructed, and the internal composition and operation constraints of the virtual power plant are defined, including:

[0065] Define the electrical and thermal output constraints of the gas turbine;

[0066] It should be noted that the mathematical expression of the gas turbine-related constraints is as follows:

[0067]

[0068] Where: P l,CHP,t and Q l,CHP,t are the electrical output and thermal output of the CHP unit of the virtual power plant l at time t; η l,e and η l,q are the electrical efficiency and thermal efficiency of the CHP unit of virtual power plant l; H l,CHP,t is the natural gas consumed by the CHP unit of virtual power plant l at time t; P l,CHP,min and P l,CHP,max are the minimum and maximum values of CHP power of virtual power plant l; Q l,CHP,min and Q l,CHP,max are the lower and upper limits of the thermal power generated by the CHP of the virtual power plant l; ΔP l,CHP,min and ΔP l,CHP,max are the upper and lower limits of the CHP unit power ramp of virtual power plant l; ΔQ l,CHP,max and ΔQ l,CHP,min They are the upper and lower limits of the CHP thermal power ramp of virtual power plant 1.

[0069] Define the charging and discharging efficiency, capacity, and power constraints of electrical energy storage devices;

[0070] It should be noted that the mathematical expression of the constraints related to electric energy storage devices is as follows:

[0071]

[0072] Where: E l,e,t is the energy stored in the electrical storage device of the virtual power plant l at time t; σ l,e is the loss rate of the virtual power plant's energy storage itself; η l,ec ,ηl,ed are the charging and discharging efficiency of the virtual power plant l energy storage; P l,ec,t 、P l,ed,t are the charging and discharging power of the virtual power plant l energy storage at time t respectively; The state variables of charging and discharging of the energy storage device of the virtual power plant l at time t are {0,1}; E l,e,min 、E l,e,max are the upper and lower limits of the energy storage capacity of the virtual power plant respectively; P l,ec,max 、P l,ed,max They are the upper and lower limits of the charging and discharging power of the virtual power plant's energy storage, respectively.

[0073] Define the output constraints of new energy units, including wind turbines and photovoltaic power stations;

[0074] It should be noted that the mathematical expression of the relevant constraints on new energy units is as follows:

[0075] 0≤P l,WT,t ≤P l,WT,pre,t Formula (9)

[0076] 0≤P l,PV,t ≤P l,PV,pre,t Formula (10)

[0077] Where: P l,WT,t is the actual power output value of wind turbine group l in virtual power plant at time t; l,WT,pre,t is the predicted power output value of wind turbine group l in virtual power plant at time t; P l,PV,t is the actual power output value of the virtual power plant l photovoltaic power station at time t; P l,PV,pre,t To predict the power output value of the virtual power plant l photovoltaic power station at time t.

[0078] Define the output constraints of thermal power units;

[0079] It should be noted that the mathematical expression of the relevant constraints of thermal power units is as follows:

[0080] P l,G,min ≤P l,G,t ≤P l,G,max Formula (11)

[0081] ΔP l,G,min ≤P l,G,t+1 -P l,G,t ≤ΔP l,G,max Formula (12)

[0082] Where: P l,G,t is the power output of the thermal power unit l of the virtual power plant at time t; P l,G,max and P l,G,minare the upper and lower limits of the output of the thermal power unit of the virtual power plant l; ΔP l,G,max and ΔP l,G,min They are respectively the upper and lower limits of the ramp output of the thermal power units in the virtual power plant.

[0083] It's important to note that in this example, the thermal power units play a dual role: they are both major contributors to carbon emissions and the energy source driving the carbon capture system. The electricity they generate is diverted into two channels: one directly feeds the advanced carbon capture device, helping it capture and process CO2 and reduce its environmental impact; the other provides power for the other modules of the virtual power plant.

[0084] P l,G,t =P l,GN,t +P l,CCS,t Formula (13)

[0085] Where: P l,GN,t To maximize the power output of the virtual power plant l thermal power unit at time t, P l,CCS,t is the total power consumption of the virtual power plant l by the carbon capture device at time t.

[0086] Define interaction constraints with the electricity and gas grids;

[0087] It should be noted that the mathematical expression of the interaction constraints with the electricity and gas grids is as follows:

[0088]

[0089] Where: P l,buy,t and P l,so,t The electric power purchased and sold by the virtual power plant l on the electric energy trading platform at time t; P l,buy,max and P l,so,max are the upper limits of electric power that can be purchased and sold by virtual power plant l on the electric energy trading platform; and are the state variables of the interactive power of the virtual power plant l in the electric energy trading platform, and their values are {0,1}. is the natural gas purchased by the virtual power plant l from the gas grid at time t; The upper limit of natural gas that a virtual power plant can purchase from the gas grid.

[0090] Define the power consumption and CO2 capture capacity constraints of the carbon capture device.

[0091] It should be noted that the relevant mathematical expression for the power consumption of the carbon capture device (consisting of fixed power consumption and operating power consumption) is as follows:

[0092]

[0093] Where: e l,o E is the energy consumption per unit of carbon dioxide captured; l,g,tis the amount of carbon dioxide captured by the virtual power plant l at time t, P l,CCS,op,t is the power consumption of the carbon capture device in the virtual power plant l at time t, P l,o is the fixed energy consumption of the carbon capture device, P l,CCS,t is the total power consumption of the virtual power plant l by the carbon capture device at time t.

[0094] Furthermore, power-to-gas (P2G) plants are a key component of CO2 processing, efficiently converting CO2 into methane, a feedstock for gas turbine power generation. In the methanation reaction, the volume ratio of CO2 consumed to methane generated is 1:1. This requires the addition of equilibrium constraints and constraints on the amount of CO2 captured. The mathematical expression for this is as follows:

[0095]

[0096] Where: P l,P2G,t is the energy consumption of the P2G device of the virtual power plant l when generating methane gas at time t, η l,P2G is the P2G conversion efficiency of the virtual power plant l; H l,P2G,out,t is the methane power generated by the virtual power plant l at time t, is the amount of carbon dioxide consumed by methanogenesis in the virtual power plant l at time t, is the density of carbon dioxide, is the unit calorific value of methane, h e is the calorific value of unit electrical energy conversion, E l,cs,t is the amount of carbon dioxide stored by the virtual power plant l at time t.

[0097] It should be further explained that the electricity of the virtual power plant is mainly supplied by CHP cogeneration units, carbon capture power plants, new energy output, etc., while the CHP units also supply heat energy.

[0098] P l,CHP,t +P l,ed,t +P l,WT,t +P l,PV,t +P l,GN,t +P l,buy,t =P l,ec,t +P l,so,t +P l,L,t Formula (20)

[0099] Where: P l,L,t is the electrical load of the virtual power plant l.

[0100] H l,CHP,t =H l,L,t Formula (21)

[0101] S2. Connect electricity, carbon, and green certificate trading platforms through blockchain relay chain technology, and design smart contracts to automatically execute cross-chain transactions.

[0102] It should be noted that in the relay chain cross-chain transaction, cross-chain transactions are mainly achieved through the central hub chain and the cross-chain gateway to connect different blockchains. In the cross-chain transaction process, all information and transfers need to be carried out from the relay chain, which enables the relay chain to record all cross-chain transaction information and achieve cross-chain transaction security. Figure 3 As shown, in the green certificate trading and carbon trading process, the virtual power plant trading platform includes distributed new energy and various distributed resources. First, the virtual power plant platform provides forecast information to the green certificate trading and carbon trading blockchain code. The blockchain trading platform will provide quota feedback based on this information, and government regulatory agencies will review and verify the quota. Virtual power plants can achieve cross-chain transactions through the carbon trading relay chain and the green certificate trading relay chain through the trading platform.

[0103] It should be further explained that in the process of trading value, the carbon trading platform, green certificate trading platform and electricity energy trading platform will display the supply and demand information on the blockchain. Green certificates, carbon trading and trading electricity information will form new chain codes on their respective relay chains. The information mainly includes supply and demand information. When the virtual power plant conducts transactions from various platforms, it will trigger a transaction smart contract.

[0104] S3. Based on the market supply and demand relationship, establish a dynamic change model for the prices of electricity, carbon and green certificates.

[0105] Furthermore, based on the market supply and demand relationship, a dynamic change model of carbon, green certificate and electricity prices is established, including:

[0106] Establish a dynamic change model of carbon prices based on the supply and demand relationship in the carbon market;

[0107] It should be noted that carbon trading mainly exchanges carbon quotas through carbon trading platforms to obtain corresponding profits. This method uses blockchain technology to realize cross-chain transactions of virtual power plants, which not only ensures the interaction between virtual power plants, but also improves transaction security.

[0108] In virtual power plants, the main sources of carbon emissions are traditional thermal power units and gas turbines, which produce carbon dioxide during the power generation process. The present invention uses the government baseline method to allocate carbon quotas.

[0109]

[0110] Where: is the carbon emission quota of virtual power plant l at time t, Obtain carbon emission quotas for powering CHP units, Obtain carbon emission quotas for heating CHP units, Obtain carbon emission quotas for supplying power to thermal power units in carbon capture power plants.

[0111] The actual carbon emissions of a virtual power plant can be expressed as:

[0112]

[0113] Where: is the actual carbon emissions of the virtual power plant l at time t, is the actual carbon emissions per unit of CHP electricity generation, is the actual carbon emission per unit heat of CHP, E l,CCG,t is the actual carbon emissions of the virtual power plant and carbon capture power plant, The carbon emissions per unit of electricity purchased from the grid.

[0114]

[0115] The carbon trading cost of the virtual power plant can be expressed as:

[0116]

[0117] Where: is the carbon trading cost of virtual power plant l, The carbon trading price.

[0118] The carbon trading price is mainly determined by the supply and demand relationship in the trading market and can be expressed as:

[0119]

[0120] Where: and Represent the coefficient of change of carbon trading electricity price respectively.

[0121] According to the supply and demand relationship of the green certificate market, a dynamic change model of green certificate prices is established;

[0122] It should be noted that to encourage renewable energy generation, governments around the world have introduced new energy quotas, which, on the one hand, facilitate renewable energy consumption, and on the other, renewable energy generation. This concept has led to the idea of green certificate trading. Assume that for every kilowatt-hour of electricity produced by a renewable energy generator, one green certificate is earned.

[0123] The number of green certificates that can be sold for new energy output units is:

[0124]

[0125] Where: δ is the green certificate quota coefficient.

[0126] The number of green certificates that traditional generators need to purchase can be expressed as:

[0127]

[0128] Since the trading price of green certificates is affected by the trading price of green certificates, the price trend of green certificates is expressed linearly.

[0129] F lv =E com +a lv -b lv p lv Formula (29)

[0130] Where: a lv and b lv Indicates the coefficient of change of green certificate transaction price, p lv It is the transaction price of green certificate trading market.

[0131] Based on the supply and demand relationship in the electricity market, a dynamic change model of electricity prices is established.

[0132] Similar to the carbon market and green certificate trading market, electricity market prices are affected by market fluctuations and show a linear trend of change, as shown below.

[0133]

[0134] Where: a e and b e represents the coefficient of change of electricity transaction price, p e Indicates the electricity transaction price.

[0135] S4. With the goal of minimizing the total cost of the virtual power plant, a mixed integer linear programming algorithm is used to solve the target operation plan.

[0136] Furthermore, the total cost of a virtual power plant includes power generation costs, carbon trading costs, green certificate trading costs and energy storage maintenance costs.

[0137] It should be noted that the present invention aims to minimize the operating costs of virtual power plants, which include carbon trading costs, green certificate trading costs, electricity trading costs and internal operating and maintenance costs of virtual power plants.

[0138]

[0139] Where: C is the maintenance cost of the virtual power plant energy storage; l,WP is the maintenance cost of new energy units in virtual power plants; C l,CHP is the cost of purchasing natural gas for the virtual power plant l; C l,G is the fuel cost of the thermal power unit in the virtual power plant.

[0140] 1) Green Certificate Transaction Costs:

[0141] C l,lv =p lv (E com -Flv ) Formula (32)

[0142] 2) Energy storage maintenance costs

[0143]

[0144] Where: is the maintenance cost coefficient of the virtual power plant energy storage.

[0145] 3) Fuel cost of thermal power units

[0146]

[0147] Where: a, b, c are the fuel cost coefficients of thermal power units.

[0148] 4) Gas purchase cost

[0149]

[0150] Where: For natural gas prices.

[0151] 5) Maintenance costs of new energy units

[0152]

[0153] Where: and are the maintenance cost coefficients of photovoltaic and wind turbines in virtual power plant l, respectively.

[0154] 6) Electricity transaction costs

[0155]

[0156] S5. Output the target operation plan.

[0157] Furthermore, the target operation plan includes the operating cost and the output plan of each unit.

[0158] Furthermore, the method also includes verifying the output target operation plan.

[0159] It should be noted that verifying the output target operation plan refers to determining whether the target operation plan meets the convergence conditions. If so, the target operation plan is directly output, otherwise it returns to step S4 to continue solving. It should be noted that the convergence conditions can be formulated by oneself.

[0160] In summary, the beneficial effects of the method for cross-chain transactions of virtual power plants on the blockchain of the present invention are to build a virtual power plant model, integrate distributed energy resources, energy storage systems and controllable loads, use blockchain relay chain technology to connect electricity, carbon and green certificate trading platforms, design smart contracts to automatically execute cross-chain transactions, establish a price dynamic change model, and take the minimum total cost of the virtual power plant as the goal, and solve the optimal (target) operation plan through a mixed integer linear programming algorithm, thereby realizing flexible regulation and optimized operation of the virtual power plant, improving the transparency and credibility of the transaction, reducing transaction costs and risks, and meeting the load demand, carbon emission reduction and green certificate quota requirements at the same time, promoting the sustainable development of virtual power plants, and promoting the transformation and upgrading of the energy and power industry towards a low-carbon and green direction.

[0161] Example 2 is the second embodiment of the present invention. This embodiment provides a system for cross-chain transactions of a virtual power plant blockchain, including a construction definition module for constructing a virtual power plant model and defining the internal composition and operation constraints of the virtual power plant;

[0162] The connection design module is used to connect electricity, carbon, and green certificate trading platforms by adopting blockchain relay chain technology, and design smart contracts to automatically execute cross-chain transactions;

[0163] Establish a module to build a dynamic price change model for electricity, carbon, and green certificates based on market supply and demand;

[0164] Calculation module: used to solve the target operation plan using the mixed integer linear programming algorithm with the goal of minimizing the total cost of the virtual power plant;

[0165] Output module: used to output the target operation plan.

[0166] Example 3 is the third embodiment of the present invention, which differs from the first two embodiments in that:

[0167] If the function 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 technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0168] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0169] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0170] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0171] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for cross-chain transactions on a virtual power plant blockchain, characterized by: include, Build a virtual power plant model and define its internal composition and operating constraints; Connect carbon, green certificate, and electricity trading platforms through blockchain relay chain technology, and design smart contracts to automatically execute cross-chain transactions; Establish a dynamic price change model for electricity, carbon, and green certificates based on market supply and demand; With the goal of minimizing the total cost of the virtual power plant, a mixed integer linear programming algorithm is used to solve the target operation plan; The target operation plan is output.

2. The method for cross-chain transactions of a virtual power plant blockchain according to claim 1, characterized in that: Before constructing the virtual power plant model and defining the internal composition and operation constraints of the virtual power plant, the following steps are also included: Collect target data, wherein the target data includes but is not limited to historical new energy output data and load data; The Latin hypercube sampling method is used to generate multiple scenarios of renewable energy output and load, and the K-means clustering algorithm is used to reduce the number of scenarios.

3. The method for cross-chain transactions of a virtual power plant blockchain according to claim 2, characterized in that: The construction of the virtual power plant model and the definition of the internal composition and operation constraints of the virtual power plant include: Define the electrical and thermal output constraints of the gas turbine; Define the charging and discharging efficiency, capacity, and power constraints of electrical energy storage devices; Define the output constraints of new energy units; Define the output constraints of thermal power units; Define interaction constraints with the electricity and gas grids; Define the power consumption and CO2 capture capacity constraints of the carbon capture device.

4. The method for cross-chain transactions of a virtual power plant blockchain according to claim 3, characterized in that: The dynamic change model of carbon, green certificate and electricity prices established based on market supply and demand includes: Establish a dynamic change model of carbon prices based on the supply and demand relationship in the carbon market; According to the supply and demand relationship of the green certificate market, a dynamic change model of green certificate prices is established; Based on the supply and demand relationship in the electricity market, a dynamic change model of electricity prices is established.

5. The method for cross-chain transactions of a virtual power plant blockchain according to claim 4, characterized in that: The method further includes verifying the output target operation plan.

6. The method for cross-chain transactions of a virtual power plant blockchain according to claim 5, characterized in that: The total cost of the virtual power plant includes power generation cost, carbon trading cost, green certificate trading cost and energy storage maintenance cost.

7. The method for cross-chain transactions on a virtual power plant blockchain according to any one of claims 1 to 6, characterized in that: The target operation plan includes the operation cost and the output plan of each unit.

8. A system for cross-chain transactions of virtual power plant blockchain, characterized in that: include: Build a definition module, which is used to build a virtual power plant model and define the internal composition and operation constraints of the virtual power plant; The connection design module is used to connect electricity, carbon, and green certificate trading platforms by adopting blockchain relay chain technology, and design smart contracts to automatically execute cross-chain transactions; Establish a module to build a dynamic price change model for electricity, carbon, and green certificates based on market supply and demand; Calculation module: used to solve the target operation plan using the mixed integer linear programming algorithm with the goal of minimizing the total cost of the virtual power plant; Output module: used to output the target operation plan.

9. A computer 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 according to any one of claims 1 to 7 are implemented.

10. A computer-readable 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 according to any one of claims 1 to 7 are implemented.

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