Comprehensive energy service system optimization method and system based on carbon transaction

Abstract

The invention belongs to the field of integrated energy service systems, and discloses an integrated energy service system optimization method and system based on carbon transaction, and the method comprises the steps: obtaining the operation data of an integrated energy service system, and calculating the carbon emission of the integrated energy service system; constructing a double-layer model of the electricity-carbon P2P joint market, wherein the double-layer model comprises an electricity-carbon P2P transaction upper-layer model and a power distribution system operator lower-layer model; inputting the carbon emission into an electricity-carbon P2P transaction upper layer model, and solving to obtain an electricity-carbon P2P transaction result; the power distribution system operator lower-layer model calculates and updates the carbon emission of the integrated energy service system based on an electricity-carbon P2P transaction result; and iteratively solving the double-layer model of the electricity-carbon P2P joint market to obtain an optimization strategy of the comprehensive energy service system. According to the method, real-time calculation of the carbon emission is realized through continuous iterative calculation of the dynamic carbon flow tracking technology, and the problem that data security and global coordination are difficult to consider in traditional centralized optimization is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated energy service systems, and particularly to an optimization method and system for an integrated energy service system based on carbon trading. Background Art

[0002] With the increasing global attention to carbon emissions and the need for energy structure transformation, integrated energy service systems are of great significance in improving energy utilization efficiency and reducing carbon emissions. However, the current integrated energy service systems face many challenges.

[0003] On the one hand, the access of renewable energy brings intermittency and uncertainty, affecting the stable operation of the system and the reliability of energy supply. On the other hand, the traditional centralized energy management mode is difficult to adapt to the development of distributed energy resources, lacking an effective market trading mechanism to coordinate the interest relationships among different energy entities, resulting in the inability to achieve the optimal allocation of energy resources. At the same time, in the carbon trading market, how to accurately measure and allocate carbon emission responsibilities, and how to incentivize integrated energy service providers to actively participate in carbon emission reduction, are still problems to be solved urgently. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides an optimization method and system for an integrated energy service system based on carbon trading to solve the problems that in the carbon trading environment, the integrated energy service system faces the lack of an effective collaborative optimization mechanism for electricity and carbon emission rights trading, resulting in high system operation costs, difficult to maximize social benefits, and difficult to effectively control carbon emissions.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, the present invention provides an optimization method for an integrated energy service system based on carbon trading, including: Obtaining the operation data of the integrated energy service system, and calculating the carbon emissions of the integrated energy service system based on the operation data; Constructing a two-layer model of an electricity-carbon P2P joint market, where the two-layer model includes an upper-layer model of electricity-carbon P2P trading and a lower-layer model of a distribution system operator; Inputting the carbon emissions into the upper-layer model of electricity-carbon P2P trading, and solving the upper-layer model of electricity-carbon P2P trading by the alternating direction method of multipliers to obtain the electricity-carbon P2P trading result; The lower-layer model of the distribution system operator recalculates and updates the carbon emissions of the integrated energy service system based on the electricity-carbon P2P trading result; Iteratively solve the two - layer model of the electricity - carbon P2P joint market to obtain the optimization strategy of the integrated energy service system.

[0007] As a preferred solution of an integrated energy service system optimization method based on carbon trading according to the present invention, wherein: input the carbon emissions into the upper - layer model of electricity - carbon P2P trading, including: Based on the carbon emissions, design the objective function of the upper - layer model of electricity - carbon P2P trading, and the objective function includes the objective function of the integrated energy service system and the objective function of the load aggregator. The objective function of the integrated energy service system is to minimize the operating cost of the integrated energy service system, and the operating cost of the integrated energy service system includes methane purchase cost, carbon sequestration cost, carbon emission cost and methanol production revenue. The objective function of the load aggregator is to maximize social benefits.

[0008] As a preferred solution of an integrated energy service system optimization method based on carbon trading according to the present invention, wherein: solve the upper - layer model of electricity - carbon P2P trading by the alternating direction method of multipliers to obtain the electricity - carbon P2P trading result, including: Introduce auxiliary variables to construct equality constraints. Based on the equality constraints, expand the objective function of the upper - layer model of electricity - carbon P2P trading to construct an augmented Lagrangian function. Divide the augmented Lagrangian function into a sub - problem of the integrated energy service system and a sub - problem of the load aggregator, and independently solve the sub - problem of the integrated energy service system and the sub - problem of the load aggregator to obtain the electricity - carbon P2P trading result.

[0009] As a preferred solution of an integrated energy service system optimization method based on carbon trading according to the present invention, wherein: the lower - layer model of the distribution system operator recalculates and updates the carbon emissions of the integrated energy service system based on the electricity - carbon P2P trading result, including: Based on the electricity - carbon P2P trading result, design the objective function of the lower - layer model of the distribution system operator. The objective function of the lower - layer model of the distribution system operator is to minimize the operating cost of the distribution network, and the operating cost of the distribution network includes the cost of purchasing excess carbon permits, the cost of external power purchase and the cost of forced adjustment penalty.

[0010] As a preferred solution of an integrated energy service system optimization method based on carbon trading according to the present invention, wherein: it further includes: Solve the objective function of the lower - layer model of the distribution system operator to obtain the updated carbon emissions of the integrated energy service system. Transfer the updated carbon emissions to the upper layer model of the electricity-carbon P2P trading.

[0011] As a preferred solution of an integrated energy service system optimization method based on carbon trading according to the present invention, wherein: the two-layer model of the electricity-carbon P2P joint market includes: Introduce a delay protocol to coordinate the iterative process of the two-layer model of the electricity-carbon P2P joint market, and synergistically optimize the integrated energy service system and the distribution network through the power trading deviation and the carbon intensity convergence criterion.

[0012] As a preferred solution of an integrated energy service system optimization method based on carbon trading according to the present invention, wherein: the integrated energy service system includes an energy-to-electricity unit, an electricity-to-energy unit, and a carbon capture equipment unit.

[0013] In a second aspect, the present invention provides an integrated energy service system optimization system based on carbon trading, including: A data acquisition module for acquiring the operation data of the integrated energy service system; A calculation module for calculating the carbon emissions of the integrated energy service system based on the operation data; A model design module for constructing a two-layer model of the electricity-carbon P2P joint market, the two-layer model including an upper layer model of electricity-carbon P2P trading and a lower layer model of the distribution system operator; A strategy acquisition module for inputting the carbon emissions into the upper layer model of the electricity-carbon P2P trading, and solving the upper layer model of the electricity-carbon P2P trading by the alternating direction multiplier method to obtain the electricity-carbon P2P trading result; the lower layer model of the distribution system operator recalculates and updates the carbon emissions of the integrated energy service system based on the electricity-carbon P2P trading result; iteratively solve the two-layer model of the electricity-carbon P2P joint market to obtain the integrated energy service system optimization strategy.

[0014] In a third aspect, the present invention provides a computer device, including: A memory and a processor; The memory is used for storing computer-executable instructions, and the processor is used for executing the computer-executable instructions, and when the computer-executable instructions are executed by the processor, the steps of the integrated energy service system optimization method based on carbon trading are implemented.

[0015] In a fourth aspect, the present invention provides a computer-readable storage medium, which stores computer-executable instructions, and when the computer-executable instructions are executed by the processor, the steps of the integrated energy service system optimization method based on carbon trading are implemented.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention proposes a multi - energy collaborative optimization framework, which realizes the accurate calculation of carbon emissions through continuous iterative calculation of dynamic carbon flow tracking technology. Most existing studies adopt static carbon emission factor allocation mechanisms, which are difficult to reflect the impact of the power grid topology and real - time power flow on carbon emissions. However, the present invention realizes the real - time calculation of carbon emissions by establishing a dynamic coupling model of node carbon intensity and line carbon flow rate. The present invention proposes a two - layer model to coordinately achieve the optimal scheduling between power trading and carbon emissions. The upper - layer model of electricity - carbon P2P trading coordinates the electricity purchase and sale volumes, and the lower - layer model of the distribution system operator distributes power according to the trading results of the upper - layer model and updates the carbon emissions for the next iterative calculation, solving the problem that it is difficult to balance data security and global coordination in traditional centralized optimization. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0018] Figure 1 It is a schematic diagram of the general process logic of an optimization method for an integrated energy service system based on carbon trading according to an embodiment of the present invention; Figure 2 It is a schematic diagram of the specific process logic of an optimization method for an integrated energy service system based on carbon trading according to an embodiment of the present invention; Figure 3 It is a schematic diagram of the structure of an integrated energy service system in an optimization method for an integrated energy service system based on carbon trading according to an embodiment of the present invention; Figure 4 It is a schematic diagram of the framework of a two - layer model of an electricity - carbon P2P joint market in an optimization method for an integrated energy service system based on carbon trading according to an embodiment of the present invention; Figure 5 It is a schematic diagram of the delay protocol process in an optimization method for an integrated energy service system based on carbon trading according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] In order to make the above - mentioned objects, features, and advantages of the present invention more obvious and understandable, the following will make a detailed description of the specific embodiments of the present invention in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0020] Example 1, referring to Figures 1-5 , which is an embodiment of the present invention, provides an optimization method for an integrated energy service system based on carbon trading, including: S100: Obtain the operation data of the integrated energy service system, and calculate the carbon emissions of the integrated energy service system based on the operation data; Preferably, the integrated energy service system includes an energy-to-electricity unit, an electricity-to-energy unit, and a carbon capture equipment unit, as shown in Figure 3 . In a possible embodiment, the energy-to-electricity unit is composed of a coal-fired generator, a photovoltaic array, a wind power generation system, etc.; the electricity-to-energy unit is composed of a water electrolysis hydrogen production device, a methane production device, and a methanol production device, etc. The water electrolysis hydrogen production device is used to absorb the electric energy output by the photovoltaic and produce hydrogen to address the problem of abandoned light when the photovoltaic output is excessive. The hydrogen and the captured carbon dioxide are used to synthesize methane and methanol. The methane is used by the gas turbine to generate electricity again, and the methanol can be sold to the market. The carbon dioxide emission reduction achieved by using carbon capture, utilization, and storage technology can be combined with the electricity-to-energy unit to convert carbon into methane, becoming a new fuel.

[0021] In a possible embodiment, the operation data may include the power generation power, operation duration, fuel consumption of the coal-fired generator in the energy-to-electricity unit, the real-time power output, light intensity, temperature of the photovoltaic array, the wind speed, wind direction, fan speed, power generation power of the wind power generation system; the input power of the water electrolysis hydrogen production device in the electricity-to-energy unit, the hydrogen production amount, the raw material input amount, product output amount, operation time of the methane and methanol production devices; the power consumption and power consumption duration of various electrical equipment within the system, the carbon capture amount, carbon sequestration rate, operation energy consumption of the carbon capture equipment unit; the voltage amplitude, phase angle, active and reactive power injection and output of each node of the power grid, the resistance, reactance, conductance, susceptance and other parameters of the line, as well as the length and rated capacity; the temperature, air pressure, humidity in terms of meteorology; and the carbon trading price, methane and methanol market prices, external power purchase price, etc.

[0022] In the embodiment of the present application, a power system carbon emission theory is established, and the carbon emissions are calculated based on the obtained operation data of the integrated energy service system; It should be noted that the power system carbon emission theory regards the carbon dioxide generated by the generator burning fossil fuels as a virtual carbon flow attached to the power flow, which is transmitted to the load side through the power grid topology path. For example, the CO2 emitted by the coal-fired unit flows to the load node along with the power.

[0023] In the embodiment of the present application, the carbon emission flow rate is the carbon emission amount flowing through a node or line per unit time, expressed as: , where, is the total carbon emissions, is the carbon emission flow rate, with the unit of , is the time.

[0024] In the embodiments of the present application, the carbon emission calculation includes line carbon emissions and node carbon emissions; Specifically, the calculation formula for line carbon emissions is expressed as: , where, is the carbon emissions of line in the time period , is the square current of the line, is the line resistance, is the line carbon emission intensity, is a period of time; The line carbon emission intensity is equal to the carbon emission intensity of its head node , that is, the carbon emissions per unit of electricity transmitted by the line are determined by its power supply end, and the calculation formula is expressed as: , where, is the line carbon emission intensity, is the carbon emission flow of line , is the line at the actual active power transmitted at the moment.

[0025] Specifically, the calculation formula for node carbon emissions is expressed as: , where, is the carbon emissions of node in the time period , is the node carbon intensity, is the node load power, is a period of time; The calculation formula for node carbon intensity is expressed as: , where, is the node carbon intensity, is the square current of the line, is the line resistance, is the line carbon emission intensity, is the node generator power at, is the line At the actual active power transmitted at the moment, is the carbon emission intensity of the generator.

[0026] It should be noted that the numerator of the node carbon intensity calculation formula is the total carbon emissions flowing into the node, including line carbon emissions and local generator carbon emissions, and the denominator is the total active power flowing into the node, line active power, and local generator active power.

[0027] S102: Construct a two-layer model of the electricity-carbon P2P joint market. The two-layer model includes the upper-layer model of electricity-carbon P2P trading and the lower-layer model of the distribution system operator; the two-layer model of the electricity-carbon P2P joint market is as Figure 4 shown; In the two-layer model of the electricity-carbon P2P joint market, the upper-layer model is the participant. A decentralized trading network is formed by the integrated energy service system and the load aggregator. Through the P2P protocol, the electricity trading volume and the corresponding carbon emission responsibility transfer are directly negotiated, which is called the upper-layer model of electricity-carbon P2P trading; among them, the load aggregator represents users to participate in the electricity and carbon trading markets, and is the market trading entity, rather than the power network infrastructure.

[0028] The lower-layer model is the executor. The distribution system operator supplies electricity according to the upper-layer calculation results, and at the same time calculates the node carbon intensity and feeds it back to the upper-layer model for calculation in the next iteration of the upper-layer model, which is called the lower-layer model of the distribution system operator.

[0029] S104: Input the carbon emissions into the upper-layer model of electricity-carbon P2P trading, and solve the upper-layer model of electricity-carbon P2P trading by the alternating direction method of multipliers to obtain the electricity-carbon P2P trading result; Preferably, based on the carbon emissions, design the objective function of the upper-layer model of electricity-carbon P2P trading. The objective function includes the objective function of the integrated energy service system and the objective function of the load aggregator.

[0030] Preferably, the objective function of the integrated energy service system is to minimize the operating cost of the integrated energy service system. The operating cost of the integrated energy service system includes methane purchase cost, carbon sequestration cost, carbon emission cost, and methanol production revenue; Specifically, the objective function of the integrated energy service system is expressed by the formula: Among them, is the methane purchase volume in the photovoltaic output scenario at is the carbon dioxide sequestration volume in the photovoltaic output scenario at is the methanol production volume, The prices of methane, carbon dioxide sequestration, and methanol respectively, is the carbon price, is the time node at which the carbon emissions are generated, is the node and the node at the point-to-point carbon emissions at the time, is the penalty term, is the weight coefficient for the photovoltaic output scenario when used to measure the importance of different photovoltaic output scenarios in cost calculation. The stronger the photovoltaic output, the less energy is purchased and the lower the cost, and the lower the weight. is the upper limit of the index of the photovoltaic output scenario, ranging from 1 to changing to represent different photovoltaic output scenarios. For example, the photovoltaic output value is low at night, and at this time is set to 1, is the upper limit of the time period index, ranging from 1 to changing to represent different time periods.

[0031] Preferably, the objective function of the load aggregator is to maximize social benefits; Specifically, the objective function of the load aggregator is expressed by the formula: , where, is the social benefit, is the carbon price, is the carbon P2P trading penalty term, is the node and the node at the point-to-point carbon emissions at the time, is the node at the carbon emissions at the time, corresponding to the node carbon emission calculation formula, is the upper limit of the index of the photovoltaic output scenario, ranging from 1 to changing to represent different photovoltaic output scenarios. For example, the photovoltaic output value is low at night, and at this time is set to 1, is the upper limit index of the time period, ranging from 1 to changing to represent different time periods.

[0032] In the embodiments of the present application, to ensure the stable operation of the integrated energy service system, achieve the optimization goal, and comply with the actual physical and economic laws, constraint conditions are set for the behaviors of the integrated energy service system and the load aggregator from different aspects for standardization and restriction; The constraint conditions include: (1) Considering the actual operation of the internal equipment of the integrated energy service system, it is necessary to limit the power, capacity, and power generation of the coal-fired generator, as shown in the following formula: , , The power and reserve capacity provided by the coal-fired generator should meet the next-generation and previous-generation restrictions in the first three equations. Among them, is the th coal-fired generator at the time in the photovoltaic output scenario , is the th coal-fired generator at the time, is the th coal-fired generator at the time in the photovoltaic output scenario , is the th coal-fired generator at the time in the photovoltaic output scenario , is the th coal-fired generator at the time, is the th coal-fired generator at the time; in the above, the last equation is the gas consumption of the coal-fired generator, where is the th coal-fired generator at the time in the photovoltaic output scenario , is the th coal-fired generator at the time in the photovoltaic output scenario , is corresponding fuel cost coefficient, because the unit fuel cost changes non-linearly, with high efficiency in the initial stage and rising marginal cost in the later stage, is fixed cost coefficient at the

[0033] (2)The limitations on the load aggregator in the integrated energy service system are as follows: , where is the point-to-point carbon emission between node and node at time . is the lower limit of the carbon emission of node at time . is the upper limit of the carbon emission of node at time . is the set of nodes connecting the load aggregator and the integrated energy service system, is the point-to-point power between node and node . is the lower limit of the power of node at time . is the upper limit of the power of node at time . is the set of nodes connected to the load aggregator, only including the nodes related to the load aggregator; in the above, the last equation limits the carbon emission of the load aggregator. Through the calculation of carbon emission flow, where is the equivalent carbon emission of the line corresponding to the nodes of line at time . represents the value of the previous iteration. Since the carbon emission situation in the integrated energy service system changes continuously with transactions and energy flows, the carbon intensity obtained from the previous iteration can reflect the carbon emission characteristics under the system state at the previous moment. Combining with the current power trading situation, the current carbon emission can be calculated more accurately, providing an accurate basis for the judgment of carbon emission constraints. is the contribution of the carbon emission generated by the line during power transmission to the carbon emission of the integrated energy service system, is the carbon emission intensity of node at time in the previous iteration. The carbon intensity is used to measure the carbon emission corresponding to unit electricity consumption. When calculating carbon emissions, it reflects the relationship between power consumption and carbon emission at the node. is the time interval, represents the carbon emission brought about by the participation of the integrated energy service system in the power P2P trading activity, is the carbon emission relaxation coefficient, set slightly greater than 1, aiming to provide a certain space for carbon P2P trading, is node at time The initial carbon emissions at a moment are the basic carbon emissions to maintain the integrated energy service system.

[0034] (3) At any time, the power in the power grid needs to be kept balanced, and the power input and output at node should be balanced. The balance constraint relationship is shown in the following formula: , The left side of the above formula is the power injection. Among them, is the power generated by photovoltaic at node in the photovoltaic output scenario , moment; is the power generated by wind at node in the photovoltaic output scenario , moment; is the power obtained from the power grid by node in the photovoltaic output scenario , moment. When the local power generation cannot meet the demand, power needs to be purchased from the external power grid. is the total power of the P2P (peer-to-peer) power transaction between node and other nodes , in the photovoltaic output scenario moment; The right side of the above formula is the power consumption. Among them, is the power consumption related to carbon capture and storage at node in the photovoltaic output scenario , moment. Carbon capture and storage technology is used to reduce carbon emissions and requires a certain amount of power consumption. is the power used for hydrogen production at node in the photovoltaic output scenario , moment; is the power used for methane production at node in the photovoltaic output scenario , moment; is the power used for methanol production at node in the photovoltaic output scenario , moment.

[0035] (4) Considering the carbon emission capacity, the equivalent carbon emissions under the emission theory are less than the emission limit, as shown in the following formula: , Among them, is the node In the photovoltaic output scenario , the power generated by the photovoltaic at a certain moment, is the node In the photovoltaic output scenario , the power generated by the wind at a certain moment, is the node In the photovoltaic output scenario , the power obtained from the power grid at a certain moment. When the local power generation cannot meet the demand, power needs to be purchased from the external power grid, is the sum of the power consumption of the internal loads of the integrated energy service system, is the node at the initial carbon emissions at a certain moment, which is the basic carbon emissions to maintain the integrated energy service system, is the carbon emission relaxation coefficient, set slightly greater than 1, aiming to provide a certain space for carbon P2P trading, is the node and the node at the point-to-point carbon emissions at a certain moment, is the line at the equivalent carbon emissions of the line and its corresponding nodes at a certain moment.

[0036] Preferably, the process of solving the electricity-carbon P2P trading problem in the upper layer of the electricity-carbon P2P trading model by using the alternating direction method of multipliers (ADMM) algorithm includes: Introduce auxiliary variables to construct equality constraints; Based on the equality constraints, expand the objective function of the upper layer of the electricity-carbon P2P trading model to construct an augmented Lagrangian function; Divide the augmented Lagrangian function into the integrated energy service system sub-problem and the load aggregator sub-problem, and independently solve the integrated energy service system sub-problem and the load aggregator sub-problem to obtain the electricity-carbon P2P trading results.

[0037] The electricity-carbon P2P trading problem involves the electricity and carbon emission rights trading between the integrated energy service system and the load aggregator. Both the integrated energy service system and the load aggregator have their own optimization goals, and there are conflicts between these goals. In order to achieve global optimization while protecting the privacy of each participating party, the alternating direction method of multipliers (ADMM) algorithm is used to solve the electricity-carbon P2P trading problem.

[0038] The core idea of the ADMM algorithm is to decompose a global optimization problem into multiple sub-problems, each of which is independently solved by different participants. By iteratively updating the local variables and global variables, the global optimal solution is finally achieved. The advantage of the ADMM algorithm is that it can achieve distributed optimization while protecting the privacy of each participant.

[0039] Specifically, the original problem can be expressed as: , where, is the objective function of the sub-problem, is the local variable, is a constant, is a linear operator related to the sub-problem, which associates the local variable with the constant on the right side of the equality constraint through a linear transformation, represents the number of variables participating in the P2P transaction and constitutes the constraint condition.

[0040] The ADMM algorithm first expands the objective function of the upper-layer model of the electricity-carbon P2P transaction, introduces an auxiliary variable , and adds an equality constraint . This is an auxiliary variable introduced to achieve global consistency. It needs to be coordinated and updated at the entire system level to ensure that the values of each local variable meet the global requirements and constraints, also known as the global variable; Construct the augmented Lagrangian function , expressed as: , where, is the objective function of the upper-layer model of the electricity-carbon P2P transaction, is the constraint term related to the auxiliary variable , etc., are the prices of electricity trading and carbon emission rights trading, is the penalty parameter, and are appropriate coefficient matrices.

[0041] The expanded original problem is transformed into: , where, is the number of iterations, is the objective function of the sub-problem, is the local variable, is a constant, is a linear operator related to the sub-problem, which associates the local variable with the constant on the right side of the equality constraint through a linear transformation, Represents the number of variables participating in the P2P transaction, which constitutes a constraint condition. Is the penalty parameter.

[0042] Next, the expanded original problem is divided into multiple sub-problems, namely the integrated energy service system sub-problem and the load aggregator sub-problem. The sub-problem expression is: , Then, the complete expression of the objective function of the upper-layer model of the electricity-carbon P2P transaction is: , To ensure the consistency of the transaction results of all participating parties, the ADMM algorithm introduces global consistency constraints. Specifically, the sum of the electricity transaction and the carbon emission rights transaction must be zero, that is: , , The ADMM algorithm gradually approaches the global optimal solution by iteratively updating the local variables and the dual variables. The specific iterative steps are as follows: Local variable update. Each integrated energy service system and load aggregator updates its local variables according to the current global variables, that is, the prices of the electricity transaction and the carbon emission rights transaction, which is expressed as: , Global variable update. Update the global variables according to the local variables of all participating parties to ensure that the global consistency constraint is satisfied: , Dual variable update. Based on and Update values, substitute them into the following equation to obtain the updated dual variables. The dual variables can reflect the difference between the local variables and the global variables: , When the following conditions are met, the iteration stops: , Among them, and Represent the primal residual, and Represent the dual residual, and Represent the tolerances of the primal and dual residuals respectively.

[0043] S106: The lower-layer model of the distribution system operator recalculates and updates the carbon emissions of the integrated energy service system based on the electricity-carbon P2P transaction results; Preferably, based on the electricity-carbon P2P transaction results, design the objective function of the lower-layer model of the distribution system operator; Preferably, the objective function of the lower-layer model of the distribution system operator is to minimize the operation cost of the distribution network, and the operation cost of the distribution network includes the cost of purchasing excess carbon permits, the cost of external power purchase, and the penalty cost of forced adjustment; Specifically, the calculation formula of the objective function of the lower-layer model of the distribution system operator is expressed as: , Among them, is the carbon emission price coefficient at time , which is used to measure the cost of carbon emissions and reflects the influence weight of carbon emissions on the total cost. is the excess carbon emission at node at time is the active power price coefficient at time is the active power input from the external power grid at time is the reactive power price coefficient at time is the reactive power input from the external power grid at time is the penalty function.

[0044] To ensure the safe, stable, and efficient operation of the distribution network and the carbon emissions comply with relevant regulations, constraint conditions are set, and the constraint conditions include: (1) Constraints of the distribution network: To consider the active power balance relationship at the node, the formula is as follows: , Among them, is the sum of the active power flows of all lines flowing out of node at time ( represents that the starting node of line is ), and the active power flow here reflects the transmission of active power on the line. is the active power consumed at node at time is the sum of the active power of the power P2P (peer-to-peer) transactions between node and other nodes at time in the current iteration. is the sum of the active power flows of all lines flowing out of node at time ( represents that the end node of line is ) The active power flow of all lines minus the active power loss of all lines at the moment, is the square of the average current flowing through line at the moment, and

[0045] is the internal resistance of the line through which the current flows. To consider the reactive power balance relationship at the node, the formula is as follows: , where is the sum of the reactive power flows of all lines flowing out of node at ( indicating that the starting node of line is ). The reactive power flow here reflects the transmission of reactive power on the line. is the influence coefficient of active power on reactive power, is the sum of the reactive power flows of all lines flowing out of node at ( indicating that the ending node of line is ) minus the reactive power loss of all its lines at the moment, is the reactance of line . The above two equations are the active power and reactive power balance constraints for each node in the distribution network.

[0046] To limit the line transmission power and ensure that the line operates within a safe power carrying range to avoid problems such as overheating and damage caused by overload, the constraint formula is set as follows: , where According to the relationship between the apparent power and the active power , reactive power , the left side of the equation represents the calculation formula of the square of the apparent power of line at the moment, reflecting the comprehensive situation of the active power and reactive power transmitted by line . is the square of the rated apparent power of line . The rated apparent power is the maximum apparent power carrying capacity determined during the line design to ensure safe and long-term operation.

[0047] ​Considering the limitations of line resistance and current-related factors on line transmission power, the constraint conditions of line transmission power are further refined, taking into account the impact of line resistance and current on power transmission, more accurately reflecting the power-carrying capacity of the line in actual operation, and helping to more precisely evaluate and ensure the safety and reliability of line operation. The constraint formula is set as follows: , where, is the square of the relevant quantity of the active part of the line after considering the active power loss, is the square of the relevant quantity of the reactive part of the line after considering the reactive power loss. The left side of the equation reflects the comprehensive situation of the actual transmission power and loss of the line. The above formula indicates that after considering the active and reactive power losses of the line, the actual apparent power of the line at moment cannot exceed its rated apparent power.

[0048] To ensure the power supply quality and equipment safety of the power system, the node voltage amplitude is restricted, which is expressed by the formula: , where, , respectively represent the upper and lower limits of the voltage amplitude of node .

[0049] To comprehensively consider the relationship between voltage, current and power, the connection between line transmission power and the relevant quantities of the voltage and current at the starting end of the line is established, which is expressed by the formula: , where, According to the relationship between apparent power and active power , reactive power , the left side of the equation represents the calculation formula of the square of the apparent power of the line at moment, reflecting the comprehensive situation of the active power and reactive power transmitted by the line , is the square of the average current flowing through the line at moment, is the square of the voltage value of the line at

[0050] ​It should be noted that the constraints of the distribution network include active and reactive power balance at nodes, the power of each branch in the network not exceeding the limit, the voltage at each node being maintained within a specified range, and AC power flow constraints.

[0051] (2) Constraints of the integrated energy service system and the load aggregator Considering the carbon emission constraints of each node in the integrated energy service system, the formula is as follows: , where, is at time stage line carbon emissions, , , , are respectively the power generated by the power grid, photovoltaic, wind power, and load at node flowing through line at time in stage , is the power-to-carbon emission correlation coefficient, contains all nodes of the integrated energy service system, contains all nodes of the integrated energy service system and all nodes of the load aggregator.

[0052] Considering the carbon emission constraints of each node of the load aggregator, the formula is as follows: , In the above formula, the left side is the equivalent carbon emission of the integrated energy service system and the load aggregator, including branch carbon emissions and internal carbon emissions, and the right side is the total carbon emission; among them, is at time stage line carbon emissions, is the electricity quantity ( ) sold by node to node at time in the iteration.

[0053] Preferably, based on the set constraint conditions, the objective function of the lower-layer model of the distribution system operator is solved to obtain the updated carbon emissions of the integrated energy service system; the updated carbon emissions are transmitted to the upper-layer model of the electricity-carbon P2P transaction.

[0054] S108: Iteratively solve the two-layer model of the electricity-carbon P2P joint market to obtain the optimization strategy of the integrated energy service system; Introduce a delay protocol to coordinate the iterative process of the two - layer model of the electricity - carbon P2P joint market. Through the electricity trading deviation and carbon intensity convergence criteria, synergistically optimize the integrated energy service system and the distribution network. The delay protocol process is as Figure 5 shown; During the iterative process of calculating the upper - layer model of the electricity - carbon P2P transaction and executing the lower - layer model of the distribution system operator, oscillations may occur. For example, the trading result of the upper - layer model of the electricity - carbon P2P transaction, such as the electricity quantity sold by node A to B, may exceed the line capacity of the lower - layer execution. Therefore, the trading volume will be forced to be adjusted. For example, the electricity sales volume of A will be reduced, and the adjusted trading volume will cause the upper - layer to re - price, forming an oscillating cycle that cannot converge or converges slowly.

[0055] Therefore, a delay protocol is used to dynamically adjust the penalty function to solve the oscillating cycle problem in the two - layer model iteration, ensuring the convergence of electricity trading deviation and carbon intensity. The core idea is: (1)Convergence condition When the two - layer model of the electricity - carbon P2P joint market converges strictly, the electricity trading volume needs to satisfy: , where, is the electricity quantity ( ) sold by node to node at time in the th iteration. is the forced adjustment amount ( ) of the grid for the previous - round trading volume of node . The meaning of its formula is that the current trading volume = the previous - round trading volume - the adjustment amount required by the grid.

[0056] (2)Penalty mechanism Impose a penalty on the trading deviation (the difference between the actual trading volume and the grid adjustment target): , where, is the penalty function, which is used to adjust the objective function in the ADMM algorithm to force the trading party to reduce the deviation, is the delay price, that is, the cost of the trading deviation, is the penalty coefficient; the greater the deviation, the heavier the penalty.

[0057] (3)Dynamic adjustment parameters Delay price update: , where, is the delay price, that is, the cost of the trading deviation, is the penalty coefficient, is the trading deviation.

[0058] Adjust the penalty unit price according to the deviation, while ensuring that the penalty unit price is non - negative and gradually increases with the accumulation of the deviation.

[0059] Penalty coefficient update: , where is the smoothing parameter, taking values between 0 and 1, which controls the growth rate of the penalty.

[0060] (4)Integration with upper and lower layer models Add the penalty term to the objective function of the upper - layer model of the electricity - carbon P2P transaction: , where is the original objective function of the upper - layer model of the electricity - carbon P2P transaction.

[0061] Add the penalty term to the objective function of the lower - layer model of the distribution system operator: , where is the original objective function of the lower - layer model of the distribution system operator.

[0062] (5)Convergence judgment , , where is the carbon intensity change rate, that is, the difference in node carbon intensity between two adjacent iterations, is the trading volume change rate, that is, the difference in trading volume between two adjacent iterations. When both the carbon intensity change rate and the trading volume change rate are less than 1%, stop the iterative calculation of the two - layer model of the electricity - carbon P2P joint market. The present invention realizes the low - carbon economic operation of the two - layer model of the electricity - carbon P2P joint market through dynamic adjustment of the penalty function and carbon price linkage.

[0063] In a possible embodiment, the optimization strategy of the integrated energy service system can be based on the results of electricity-carbon P2P transactions and the real-time data of the system. It preferentially allocates clean energy such as photovoltaic and wind power, converts and stores excess electric energy to improve the energy recycling utilization rate; dynamically adjusts the electricity trading volume and the carbon emission responsibility transfer volume according to market price fluctuations and its own supply and demand to reduce costs; continuously monitors the carbon emission volume, and increases the intensity of carbon capture and storage when approaching the limit to reduce the carbon emission intensity; strengthens cooperation with the distribution system operator and optimizes its own strategy based on the information provided by the operator; uses the delay protocol and penalty mechanism to adjust the system operation parameters in real time according to the power trading deviation and the change of carbon intensity to achieve low-carbon economic operation.

[0064] The above is a schematic solution of an optimization method for an integrated energy service system based on carbon trading in this embodiment. It should be noted that the technical solution of the system for optimizing the integrated energy service system based on carbon trading belongs to the same concept as the above-mentioned technical solution of the optimization method for the integrated energy service system based on carbon trading. For the details not described in detail in the technical solution of the system for optimizing the integrated energy service system based on carbon trading in this embodiment, reference can be made to the description of the technical solution of the above-mentioned optimization method for the integrated energy service system based on carbon trading.

[0065] The system for optimizing the integrated energy service system based on carbon trading in this embodiment includes: A data acquisition module, configured to acquire the operation data of the integrated energy service system; A calculation module, configured to calculate the carbon emission volume of the integrated energy service system based on the operation data; A model design module, configured to construct a two-layer model of the electricity-carbon P2P joint market, and the two-layer model includes an upper-layer model of electricity-carbon P2P transactions and a lower-layer model of the distribution system operator; A strategy acquisition module, configured to input the carbon emission volume into the upper-layer model of electricity-carbon P2P transactions, solve the upper-layer model of electricity-carbon P2P transactions by the alternating direction method of multipliers to obtain the electricity-carbon P2P transaction results; the lower-layer model of the distribution system operator recalculates and updates the carbon emission volume of the integrated energy service system based on the electricity-carbon P2P transaction results; iteratively solve the two-layer model of the electricity-carbon P2P joint market to obtain the optimization strategy of the integrated energy service system.

[0066] This embodiment also provides a computer device applicable to the situation of optimizing the integrated energy service system based on carbon trading, including: A memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the method for optimizing the integrated energy service system based on carbon trading as proposed in the above embodiment.

[0067] This embodiment also provides a storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the method for optimizing the integrated energy service system based on carbon trading proposed in the above embodiment.

[0068] The storage medium proposed in this embodiment and the method for optimizing the integrated energy service system based on carbon trading proposed in the above embodiment belong to the same inventive concept. Technical details not described in detail in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0069] From the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software and the necessary general-purpose hardware, and of course, it can also be implemented by hardware. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as a floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk, or optical disc of a computer, etc., including several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of various embodiments of the present invention.

[0070] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. An optimization method for an integrated energy service system based on carbon trading, characterized in that include: Acquire operation data of the integrated energy service system, and calculate the carbon emissions of the integrated energy service system based on the operation data; Constructing a two-layer model of the electricity-carbon P2P joint market, the two-layer model includes an upper-layer model of electricity-carbon P2P transactions and a lower-layer model of distribution system operators; Inputting the carbon emissions into the upper model of the electricity-carbon P2P transaction, and solving the upper model of the electricity-carbon P2P transaction by the alternating direction multiplication method to obtain the electricity-carbon P2P transaction result; The lower-level model of the distribution system operator recalculates and updates the carbon emissions of the integrated energy service system based on the results of the electricity-carbon P2P transaction; The two-layer model of the electricity-carbon P2P joint market is solved iteratively to obtain the optimization strategy of the integrated energy service system.

2. The optimization method of an integrated energy service system based on carbon trading according to claim 1, characterized in that Input the carbon emissions into the upper-level model of the electricity-carbon P2P transaction, including: Based on the carbon emissions, design an objective function of the upper model of the electricity-carbon P2P transaction, wherein the objective function includes an objective function of the integrated energy service system and an objective function of the load aggregator; The objective function of the integrated energy service system is to minimize the operating cost of the integrated energy service system, and the operating cost of the integrated energy service system includes methane purchase cost, carbon sequestration cost, carbon emission cost and methanol production income; The objective function of the load aggregator is to maximize social benefits.

3. The optimization method of an integrated energy service system based on carbon trading according to claim 2, characterized in that, The upper-level model of the electricity-carbon P2P transaction is solved by the alternating direction multiplier method to obtain the electricity-carbon P2P transaction results, including: Introduce auxiliary variables to construct equality constraints; Based on the equality constraint, the objective function of the upper model of the electricity-carbon P2P transaction is expanded to construct an augmented Lagrangian function; The augmented Lagrangian function is divided into an integrated energy service system subproblem and a load aggregator subproblem, and the integrated energy service system subproblem and the load aggregator subproblem are solved independently to obtain an electricity-carbon P2P transaction result.

4. The optimization method of an integrated energy service system based on carbon trading according to claim 3, wherein The lower-level model of the distribution system operator recalculates and updates the carbon emissions of the integrated energy service system based on the results of the electricity-carbon P2P transaction, including: Based on the electricity-carbon P2P trading results, design the objective function of the lower-level model of the distribution system operator; The objective function of the lower-level model of the distribution system operator is to minimize the distribution network operating costs, which include excess carbon permit purchase costs, external power purchase costs, and mandatory adjustment penalty costs.

5. The optimization method of an integrated energy service system based on carbon trading according to claim 1, wherein, Also includes: Solving the objective function of the lower-level model of the distribution system operator to obtain the updated carbon emissions of the integrated energy service system; The updated carbon emissions are transmitted to the upper-level model of electricity-carbon P2P trading.

6. The optimization method of an integrated energy service system based on carbon trading according to claim 5, characterized in that, The two-tier model of the electricity-carbon P2P joint market includes: A delay protocol is introduced to coordinate the iterative process of the two-layer model of the electricity-carbon P2P joint market, and the integrated energy service system and distribution network are collaboratively optimized through the electricity trading deviation and carbon intensity convergence criteria.

7. The optimization method of an integrated energy service system based on carbon trading according to claim 6, characterized in that, The integrated energy service system includes an energy-to-electricity unit, an electricity-to-energy unit and a carbon capture equipment unit.

8. An optimization system for an integrated energy service system based on carbon trading, which applies an optimization method for an integrated energy service system based on carbon trading as described in any one of claims 1-7, characterized in that, include: A data acquisition module, used to acquire the operation data of the integrated energy service system; A calculation module, configured to calculate the carbon emission of the integrated energy service system based on the operation data; A model design module, configured to construct a two-layer model of the electricity-carbon P2P joint market, where the two-layer model includes an upper-layer model of electricity-carbon P2P trading and a lower-layer model of the distribution system operator; A strategy acquisition module, configured to input the carbon emission into the upper-layer model of electricity-carbon P2P trading, and solve the upper-layer model of electricity-carbon P2P trading by the alternating direction multiplier method to obtain the electricity-carbon P2P trading result; the lower-layer model of the distribution system operator recalculates and updates the carbon emission of the integrated energy service system based on the electricity-carbon P2P trading result; iteratively solve the two-layer model of the electricity-carbon P2P joint market to obtain the optimization strategy of the integrated energy service system.

9. A computer device, comprising: A memory and a processor; The memory is configured to store computer-executable instructions, and the processor is configured to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of an optimization method for an integrated energy service system based on carbon trading according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are executed by a processor, the steps of an optimization method for an integrated energy service system based on carbon trading according to any one of claims 1 to 7 are implemented.

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