A comprehensive energy service system optimization method and system based on carbon trading

Abstract

This invention belongs to the field of integrated energy service systems and discloses a method and system for optimizing an integrated energy service system based on carbon trading. The method comprises: obtaining operational data of the integrated energy service system to calculate the system's carbon emissions; constructing a two-layer model for a power-carbon P2P joint market, comprising an upper-layer model for power-carbon P2P trading and a lower-layer model for distribution system operators; inputting carbon emissions into the upper-layer model to obtain the results of power-carbon P2P trading; the lower-layer model for distribution system operators calculates and updates the carbon emissions of the integrated energy service system based on the results of power-carbon P2P trading; and iteratively solving the two-layer model to obtain an optimization strategy for the integrated energy service system. This invention utilizes dynamic carbon flow tracking technology to achieve real-time calculation of carbon emissions through continuous iterative calculations, addressing the difficulty of balancing data security and global coordination in traditional centralized optimization.

Description

Technical Field

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

[0002] With the increasing global concern about carbon emissions and the need to transform the energy structure, integrated energy service systems are of great significance in improving energy efficiency and reducing carbon emissions. However, the integrated energy service system currently faces many challenges.

[0003] On the one hand, the integration of renewable energy introduces intermittency and uncertainty, impacting the stable operation of the system and the reliability of energy supply. On the other hand, the traditional centralized energy management model struggles to adapt to the development of distributed energy resources. The lack of an effective market trading mechanism to coordinate the interests of different energy entities results in suboptimal energy resource allocation. Furthermore, in the carbon trading market, how to accurately measure and allocate carbon emission responsibilities, as well as how to incentivize integrated energy service providers to actively participate in carbon reduction, remain pressing challenges. 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 faced by the integrated energy service system in a carbon trading environment, such as the lack of an effective collaborative optimization mechanism for electricity and carbon emission rights trading, which leads to high system operating costs, difficulty in maximizing social benefits, and difficulty in effectively controlling carbon emissions.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a method for optimizing an integrated energy service system based on carbon trading, comprising:

[0008] Obtaining operating data of an integrated energy service system, and calculating carbon emissions of the integrated energy service system based on the operating data;

[0009] Constructing a two-tier model for the electricity-carbon P2P joint market, comprising an upper-tier electricity-carbon P2P trading model and a lower-tier distribution system operator model;

[0010] Inputting the carbon emissions into an upper-level model of electricity-carbon P2P trading, and solving the upper-level model of electricity-carbon P2P trading by an alternating direction multiplication method to obtain an electricity-carbon P2P trading result;

[0011] The distribution system operator's lower-level model recalculates and updates the carbon emissions of the integrated energy service system based on the results of electricity-carbon P2P transactions;

[0012] 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.

[0013] As a preferred solution of the carbon trading-based integrated energy service system optimization method of the present invention, the carbon emissions are input into the upper-layer model of the electricity-carbon P2P trading, including:

[0014] Based on the carbon emissions, designing an objective function of the upper-layer 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;

[0015] The objective function of the integrated energy service system is to minimize the operating costs of the integrated energy service system, which include methane purchase costs, carbon sequestration costs, carbon emission costs, and methanol production revenue;

[0016] The objective function of the load aggregator is to maximize social benefits.

[0017] As a preferred solution of the carbon trading-based integrated energy service system optimization method described in the present invention, the alternating direction multiplier method is used to solve the electricity-carbon P2P transaction upper model to obtain the electricity-carbon P2P transaction results, including:

[0018] Introduce auxiliary variables to construct equality constraints;

[0019] Based on the equality constraint, the objective function of the upper-layer model of the electricity-carbon P2P transaction is expanded to construct an augmented Lagrangian function;

[0020] 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.

[0021] As a preferred embodiment of the carbon trading-based integrated energy service system optimization method of the present invention, 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, including:

[0022] Based on the electricity-carbon P2P trading results, design the objective function of the lower-layer model of the distribution system operator;

[0023] The objective function of the lower-level model of the distribution system operator is to minimize the distribution network operating costs, which include the cost of purchasing excess carbon permits, the cost of external power purchases, and the cost of forced adjustment penalties.

[0024] As a preferred solution of the method for optimizing an integrated energy service system based on carbon trading described in the present invention, it further includes:

[0025] 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;

[0026] The updated carbon emissions are transmitted to the upper-level model of electricity-carbon P2P trading.

[0027] As a preferred solution of the carbon trading-based integrated energy service system optimization method described in the present invention, the two-tier model of the electricity-carbon P2P joint market includes:

[0028] 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.

[0029] As a preferred solution of the integrated energy service system optimization method based on carbon trading described in the present invention, the integrated energy service system includes an energy-to-electricity unit, an electricity-to-energy unit and a carbon capture equipment unit.

[0030] In a second aspect, the present invention provides an integrated energy service system optimization system based on carbon trading, comprising:

[0031] A data acquisition module is used to obtain the operating data of the integrated energy service system;

[0032] a calculation module, configured to calculate the carbon emissions of the integrated energy service system based on the operation data;

[0033] A model design module for constructing a two-tier model for the electricity-carbon P2P joint market, comprising an upper-tier electricity-carbon P2P trading model and a lower-tier distribution system operator model;

[0034] A strategy acquisition module is configured to input the carbon emissions into an upper-level model of the electricity-carbon P2P transaction and solve the upper-level model using an alternating direction multiplication method to obtain the electricity-carbon P2P transaction results; the lower-level 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; and iteratively solve the two-level model of the electricity-carbon P2P joint market to obtain an optimization strategy for the integrated energy service system.

[0035] In a third aspect, the present invention provides a computer device, comprising:

[0036] memory and processor;

[0037] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. 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.

[0038] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the carbon trading-based integrated energy service system optimization method.

[0039] Compared with the existing technology, 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 calculations using dynamic carbon flow tracking technology. Existing studies mostly use static carbon emission factor allocation mechanisms, which are difficult to reflect the impact of grid topology and real-time trends on carbon emissions. 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 coordinate the optimal scheduling between electricity trading and carbon emissions. The upper-layer model of electricity-carbon P2P trading coordinates the purchase and sale of electricity. The lower-layer model of the distribution system operator allocates electricity and updates carbon emissions according to the transaction results of the upper-layer model for the next iterative calculation, solving the problem of balancing data security and global coordination in traditional centralized optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] 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.

[0041] Figure 1 This is a schematic diagram of the general process logic of a method for optimizing an integrated energy service system based on carbon trading according to an embodiment of the present invention;

[0042] Figure 2 This is a specific process logic diagram of a method for optimizing an integrated energy service system based on carbon trading according to an embodiment of the present invention;

[0043] Figure 3 This is a schematic structural diagram of an integrated energy service system in an integrated energy service system optimization method based on carbon trading according to an embodiment of the present invention;

[0044] Figure 4 This is a schematic diagram of the framework of a two-layer model of an electricity-carbon P2P joint market in a method for optimizing an integrated energy service system based on carbon trading according to an embodiment of the present invention;

[0045] Figure 5 The present invention is a flowchart of a delay protocol in a method for optimizing an integrated energy service system based on carbon trading according to an embodiment of the present invention. DETAILED DESCRIPTION

[0046] 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.

[0047] Example 1, with reference to Figure 1-Figure 5 , as an embodiment of the present invention, provides a method for optimizing an integrated energy service system based on carbon trading, comprising:

[0048] S100: Obtaining operating data of the integrated energy service system, and calculating carbon emissions of the integrated energy service system based on the operating data;

[0049] Preferably, the integrated energy service system includes an energy-to-electricity unit, an electricity-to-energy unit, and a carbon capture device unit. Figure 3 As shown;

[0050] In one possible embodiment, the energy-to-electricity unit consists of a coal-fired generator, a photovoltaic array, a wind power generation system, and other components; the power-to-energy unit comprises water electrolysis hydrogen production equipment, methane production equipment, and methanol production equipment. The water electrolysis hydrogen production equipment absorbs the electricity output of photovoltaic power plants and produces hydrogen, addressing the issue of curtailed photovoltaic power output when there is excess photovoltaic power. The hydrogen and captured carbon dioxide are used to synthesize methane and methanol. The methane is used in gas turbines to generate electricity again, and the methanol can be sold to the market. Carbon dioxide emissions reductions achieved through carbon capture, utilization, and storage technology can also be combined with the power-to-energy unit to convert carbon into methane, which becomes a new fuel.

[0051] In a possible embodiment, the operating data may include the generated power, operating time, and fuel consumption of the coal-fired generator in the energy-to-electricity unit, the real-time power output, light intensity, and temperature of the photovoltaic array, the wind speed, wind direction, wind turbine speed, and generated power of the wind power generation system; the input power and hydrogen production of the water electrolysis hydrogen production equipment in the power-to-electricity unit, the raw material input, product output, and operating time of the methane and methanol production equipment; the power consumption and power consumption time of various electrical equipment within the system, the carbon capture amount, carbon sequestration rate, and operating energy consumption of the carbon capture equipment unit; the voltage amplitude, phase angle, active and reactive power injection and output of each node in the power grid, the resistance, reactance, conductance, susceptance and other parameters of the line and its length and rated capacity; the meteorological aspects of temperature, air pressure, and humidity; as well as the carbon trading price, methane and methanol market prices, and the external power purchase price.

[0052] In the embodiment of the present application, a carbon emission theory of the power system is established, and the carbon emissions are calculated based on the obtained operating data of the integrated energy service system;

[0053] It should be noted that the power system carbon emission theory regards the carbon dioxide produced by generators burning fossil fuels as a virtual carbon flow attached to the flow of electricity, which is transmitted to the load side through the grid topology path. For example, the CO2 emitted by coal-fired units flows with electricity to the load node.

[0054] In the embodiment of the present application, the carbon emission flow rate is the carbon emission flowing through a node or line per unit time, expressed as:

[0055] ,

[0056] in, is the total carbon emissions, is the carbon emission flow rate, in units of , For time.

[0057] In the embodiment of the present application, the carbon emissions calculation includes line carbon emissions and node carbon emissions;

[0058] Specifically, the formula for calculating line carbon emissions is expressed as:

[0059] ,

[0060] in, For the line In the period of carbon emissions, is the square of the line current, is the line resistance, is the carbon emission intensity of the line, for a period of time;

[0061] Line carbon emission intensity Equal to its head node The carbon emission intensity, that is, the carbon emission per unit of electricity transmitted by the line is determined by its power supply end, and the calculation formula is expressed as:

[0062] ,

[0063] in, is the carbon emission intensity of the line, For the line carbon emission flows, For the line exist The actual active power transmitted at any moment.

[0064] Specifically, the calculation formula for node carbon emissions is expressed as:

[0065] ,

[0066] in, For nodes In the period of carbon emissions, is the node carbon intensity, For nodes The load power, for a period of time;

[0067] The calculation formula for node carbon intensity is expressed as:

[0068] ,

[0069] in, is the node carbon intensity, is the square of the line current, is the line resistance, is the carbon emission intensity of the line, For nodes Generator power, For the line exist The actual active power transmitted at all times, is the carbon emission intensity of the generator.

[0070] It should be noted that the numerator of the node carbon intensity calculation formula is the total carbon emissions flowing into the node, 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.

[0071] S102: Construct a two-tier model for the electricity-carbon P2P joint market. The two-tier model includes the electricity-carbon P2P transaction upper-layer model and the distribution system operator lower-layer model. The two-tier model of the electricity-carbon P2P joint market is as follows: Figure 4As shown;

[0072] In the two-tier model of the electricity-carbon P2P joint market, the upper-tier model is the participant, consisting of a decentralized trading network composed of an integrated energy service system and load aggregators. The electricity trading volume and the corresponding carbon emission responsibility transfer are directly negotiated through the P2P protocol, which is called the electricity-carbon P2P trading upper-tier model; among them, the load aggregator participates in the electricity and carbon trading markets on behalf of users and is the market trading entity, not the power network infrastructure.

[0073] The lower-level model is the executor. The distribution system operator supplies electricity based on the upper-level calculation results, and at the same time calculates the node carbon intensity and feeds it back to the upper-level model for calculation in the next iteration of the upper-level model. This is called the distribution system operator lower-level model.

[0074] S104: Inputting the carbon emissions into the upper-level model of the electricity-carbon P2P transaction, and solving the upper-level model of the electricity-carbon P2P transaction by the alternating direction multiplication method to obtain the electricity-carbon P2P transaction result;

[0075] Preferably, based on carbon emissions, an objective function of the upper-level model of electricity-carbon P2P trading is designed, and the objective function includes the objective function of the integrated energy service system and the objective function of the load aggregator.

[0076] Preferably, 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;

[0077] Specifically, the objective function of the integrated energy service system is expressed as follows:

[0078] in, For photovoltaic output scenarios The amount of methane purchased, For photovoltaic output scenarios The amount of carbon dioxide stored, is the methanol production, are the prices of methane, carbon dioxide storage, and methanol, respectively. is the carbon price, for Time Node The carbon emissions generated, For nodes With node exist Point-to-point carbon emissions at all times, is the penalty item, For photovoltaic output scenarios The weight coefficient is used to measure the importance of different photovoltaic output scenarios in cost calculation. The stronger the photovoltaic output, the less energy purchased and the lower the cost. The weight is used to measure the importance of different photovoltaic output scenarios in cost calculation. The stronger the photovoltaic output, the less energy purchased and the lower the cost. is the index upper limit of the photovoltaic output scenario, From 1 to Changes indicate different photovoltaic output scenarios. For example, the photovoltaic output value is low at night. Set to 1, is the upper limit of the time period index, From 1 to Changes indicate different time periods.

[0079] Preferably, the objective function of the load aggregator is to maximize social benefits;

[0080] Specifically, the objective function of the load aggregator is expressed as follows:

[0081] ,

[0082] in, For social benefits, is the carbon price, is the penalty item for carbon P2P trading, For nodes With node exist Point-to-point carbon emissions at all times, For nodes exist The carbon emissions at the moment, the corresponding node carbon emissions calculation formula, The index of photovoltaic output scene is online. From 1 to Changes indicate different photovoltaic output scenarios. For example, the photovoltaic output value is low at night. Set to 1, The time period online index, From 1 to Changes indicate different time periods.

[0083] In the embodiment of the present application, in order to ensure the stable operation of the integrated energy service system, achieve the optimization goals, and comply with actual physical and economic laws, constraints are set on the behavior of the integrated energy service system and load aggregators from different aspects to regulate and restrict them;

[0084] Constraints include:

[0085] (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 coal-fired generators, as shown in the following formula:

[0086] ,

[0087] ,

[0088] The power and reserve capacity provided by coal-fired generators should meet the next-generation and previous-generation constraints in the first three equations. For the Coal-fired generators in photovoltaic output scenarios hour, The power generation at the moment, For the coal-fired generators in The lower limit of power generation at any moment, For the Coal-fired generators in photovoltaic output scenarios hour, The upper limit of power generation at any moment, For the Coal-fired generators in photovoltaic output scenarios hour, The spare capacity at all times, For the coal-fired generators in The lower limit of spare capacity at all times, For the coal-fired generators in The upper limit of the reserve capacity at any time; in the above, the last equation is the gas consumption of the coal-fired generator, where For the Coal-fired generators in photovoltaic output scenarios hour, The amount of methane purchased at a given moment, For the Coal-fired generators in photovoltaic output scenarios hour, The amount of carbon dioxide captured by carbon capture at any given moment, for The corresponding fuel cost coefficient is that the unit fuel cost changes nonlinearly, with high efficiency in the initial stage and rising marginal costs in the later stage. for Fixed cost coefficient under power.

[0089] (2) The integrated energy service system has the following restrictions on load aggregators:

[0090] ,

[0091] in, For nodes With node exist Point-to-point carbon emissions at all times, For nodes exist The lower limit of carbon emissions at any time, For nodes exist The upper limit of carbon emissions at any time, A collection of nodes connecting load aggregators and integrated energy service systems, For nodes With node Point-to-point power, For nodes exist The lower limit of power at the moment, For nodes exist The upper limit of power at any moment, is the set of nodes connected to the load aggregator, which only includes nodes connected to the load aggregator's line pipe; in the above, the last equation limits the carbon emissions of the load aggregator, which is calculated by the carbon emission flow, where For the line exist Equivalent carbon emissions of nodes corresponding to the time and line, Represents the value of the previous iteration. Since carbon emissions in the integrated energy service system will continue to change with transactions and energy flows, the carbon intensity obtained in the previous iteration can reflect the carbon emission characteristics of the system state at the previous moment. Combined with the current electricity trading situation, it can more accurately calculate the current carbon emissions and provide an accurate basis for judging carbon emission constraints. The contribution of carbon emissions generated by power transmission lines to the carbon emissions of the integrated energy service system. For nodes exist The carbon emission intensity of the last iteration at the moment. Carbon intensity is used to measure the carbon emissions corresponding to a unit of electricity. When calculating carbon emissions, it reflects the relationship between power consumption and carbon emissions at the node. is the time interval, It reflects the carbon emissions caused by the participation of the integrated energy service system in electricity P2P trading activities. The carbon emission relaxation coefficient is set slightly larger than 1 to provide a certain space for carbon P2P transactions. For nodes exist The initial carbon emissions at the time are the basic carbon emissions for maintaining the integrated energy service system.

[0092] (3) At any time, the power in the grid needs to be balanced, and the nodes The power input and output must be balanced, and the balance limit relationship is shown as follows:

[0093] ,

[0094] The left side of the above formula is the electric injection power, where For nodes In photovoltaic output scenario 、 The power generated by photovoltaics at any moment, For nodes In photovoltaic output scenario 、 The power generated by the wind at any moment, For nodes In photovoltaic output scenario 、 The power is always obtained from the grid. When local power generation cannot meet the demand, it is necessary to purchase power from the external grid. For nodes In photovoltaic output scenario 、 Moments and other nodes The total power consumed in P2P (peer-to-peer) electricity transactions; the right side of the above equation is the power consumption, where For nodes In photovoltaic output scenario 、 The power consumption related to carbon capture and storage is always used. Carbon capture and storage technology is used to reduce carbon emissions and requires a certain amount of power. For nodes In photovoltaic output scenario 、 The power used to produce hydrogen at any given time, For nodes In photovoltaic output scenario 、 The power used to produce methane at any given moment, For nodes In photovoltaic output scenario 、 The power used to produce methanol at any given time.

[0095] (4) Taking into account the capacity of carbon emissions, the equivalent carbon emissions under the emission theory are less than the emission limit, as shown in the following formula:

[0096] ,

[0097] in, For nodes In photovoltaic output scenario 、 The power generated by photovoltaics at any moment, For nodes In photovoltaic output scenario 、 The power generated by the wind at any moment, For nodes In photovoltaic output scenario 、 The power is always obtained from the grid. When local power generation cannot meet the demand, it is necessary to purchase power from the external grid. is the sum of the internal load power consumption of the integrated energy service system, For nodes exist The initial carbon emissions at the moment are the basic carbon emissions to maintain the integrated energy service system. The carbon emission relaxation coefficient is set slightly larger than 1 to provide a certain space for carbon P2P transactions. For nodes With node exist Point-to-point carbon emissions at all times, For the line exist Equivalent carbon emissions of nodes corresponding to the time and line.

[0098] Preferably, the process of solving the electricity-carbon P2P transaction problem of the upper model of electricity-carbon P2P transaction using the alternating direction method of multipliers (ADMM) algorithm includes:

[0099] Introduce auxiliary variables to construct equality constraints;

[0100] Based on equality constraints, the objective function of the upper-level model of electricity-carbon P2P trading is expanded to construct an augmented Lagrangian function.

[0101] The augmented Lagrangian function is divided into an integrated energy service system subproblem and a load aggregator subproblem. The integrated energy service system subproblem and the load aggregator subproblem are solved independently to obtain the electricity-carbon P2P trading results.

[0102] The electricity-carbon P2P trading problem involves trading electricity and carbon emission rights between an integrated energy service system (IESS) and a load aggregator. Both the IESS and the load aggregator have their own optimization objectives, which can conflict with each other. To achieve global optimization while protecting the privacy of all participants, the alternating direction method of multipliers (ADMM) algorithm is used to solve the electricity-carbon P2P trading problem.

[0103] The core idea of ​​the ADMM algorithm is to decompose a global optimization problem into multiple subproblems, each of which is solved independently by different participants. The algorithm then iteratively updates local and global variables to ultimately reach a global optimal solution. The advantage of the ADMM algorithm lies in its ability to achieve distributed optimization while protecting the privacy of each participant.

[0104] Specifically, the original problem can be expressed as:

[0105] ,

[0106] in, is the objective function of the subproblem, is a local variable, is a constant, is a linear operator associated with the subproblem, which converts the local variable By linear transformations related to the constants on the right side of the equality constraints, It represents the number of variables involved in P2P transactions and constitutes a constraint.

[0107] The ADMM algorithm first expands the objective function of the upper model of electricity-carbon P2P transactions and introduces auxiliary variables , and add equality constraints , which 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. It is also called a global variable;

[0108] Constructing the augmented Lagrangian function , expressed as:

[0109] ,

[0110] in, is the objective function of the upper-level model of electricity-carbon P2P trading, For auxiliary variables Related constraints, etc. The price of electricity trading and carbon emission rights trading, is the penalty parameter, and is an appropriate coefficient matrix.

[0111] The expanded original problem is transformed into:

[0112] ,

[0113] in, is the number of iterations, is the objective function of the subproblem, is a local variable, is a constant, is a linear operator associated with the subproblem, which converts the local variable By linear transformations related to the constants on the right side of the equality constraints, represents the number of variables involved in P2P transactions, constituting constraints. is the penalty parameter.

[0114] Then 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 expressions are:

[0115] ,

[0116] Then, the complete expression of the objective function of the upper-level model of electricity-carbon P2P trading is:

[0117] ,

[0118] In order to ensure that the transaction results of all participants are consistent, the ADMM algorithm introduces a global consistency constraint. Specifically, the sum of electricity transactions and carbon emission rights transactions must be zero, that is:

[0119] , ,

[0120] The ADMM algorithm gradually approaches the global optimal solution by iteratively updating local variables and dual variables. The specific iterative steps are as follows:

[0121] Local variable update, each integrated energy service system and load aggregator updates its local variables according to the current global variables, namely the prices of electricity trading and carbon emission rights trading, which can be expressed as:

[0122] ,

[0123] Global variable update: Update global variables based on the local variables of all participants to ensure that global consistency constraints are met:

[0124] ,

[0125] Dual variables are updated according to and Update the value and substitute it into the following equation to get the updated dual variable. The dual variable can reflect the difference between the local variable and the global variable:

[0126] ,

[0127] The iteration stops when the following conditions are met:

[0128] ,

[0129] in, and represents the original remainder, and represents the dual residue, and represent the tolerance of the primal residual and the dual residual, respectively.

[0130] S106: 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;

[0131] Preferably, based on the results of electricity-carbon P2P trading, the objective function of the lower-level model of the distribution system operator is designed;

[0132] Preferably, the objective function of the lower-level model of the distribution system operator is to minimize the distribution network operating costs, which include the cost of purchasing excess carbon permits, the cost of external power purchases, and the cost of mandatory adjustment penalties;

[0133] Specifically, the objective function calculation formula of the lower-level model of the distribution system operator is expressed as:

[0134] ,

[0135] in, for The carbon emission price coefficient at the moment is used to measure the cost of carbon emissions and reflects the weight of the impact of carbon emissions on the total cost. for node Excess carbon emissions at any given moment, for Active power price coefficient at each moment, for Active power input from the external grid at all times, for Reactive power price coefficient at the moment, for The reactive power input from the external grid at all times, is the penalty function.

[0136] To ensure the safe, stable and efficient operation of the power distribution network and that carbon emissions comply with relevant regulations, constraints are set. The constraints include:

[0137] (1) Constraints of the power distribution network:

[0138] To consider the active power balance relationship at the node, the formula is as follows:

[0139] ,

[0140] in, For Time outflow node ( Indicates line The starting point is The sum of the active power flows of all lines in the transmission network. The active power flow here reflects the transmission of active power on the line. for node The active power consumed at each moment, For nodes exist At the current iteration, the node The total active power of electricity P2P (peer-to-peer) transactions, For Time outflow node ( Indicates line The end node is ) minus the active power flow of all lines in Active power loss at time for Time flows through the line The square of the average current, is the internal resistance of the line.

[0141] To consider the reactive power balance relationship at the node, the formula is as follows:

[0142] ,

[0143] in, For Time outflow node ( Indicates line The starting point 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, For Time outflow node ( Indicates line The end node is ) minus the reactive power flow of all lines in Reactive power loss at time For the line The above two equations are the active power and reactive power balance constraints for each node in the distribution network.

[0144] To limit the line transmission power, ensure that the line operates within a safe power carrying range, and avoid problems such as overheating and damage caused by overload, the constraint formula is set as follows:

[0145] ,

[0146] in, According to the apparent power and active power , reactive power relationship , the left side of the equation represents the line exist The calculation formula of the square of the apparent power at any moment reflects the circuit The combined situation of active and reactive power transmitted, For the line The square of the rated apparent power. The rated apparent power is the maximum apparent power carrying capacity that can be safely and long-term operated when the line is designed.

[0147] Considering the limitations of line resistance and current-related factors on line transmission power, the constraints on line transmission power are further refined. The impact of line resistance and current on power transmission is taken into account, which more accurately reflects the power carrying capacity of the line in actual operation, helping to more accurately evaluate and ensure the safety and reliability of line operation. The constraint formula is set as follows:

[0148] ,

[0149] in, After considering the active power loss, the line The square of the active part related quantity, After considering the reactive power loss, the line The square of the reactive part related quantity, the left side of the equation reflects the comprehensive situation of the actual transmission power and loss of the line. The above formula shows that after considering the active and reactive power losses of the line, the line exist The actual apparent power at any moment cannot exceed its rated apparent power.

[0150] In order to ensure the power supply quality and equipment safety of the power system, the node voltage amplitude is limited, which can be expressed as follows:

[0151] ,

[0152] in, , Represents nodes respectively The upper and lower limits of the voltage amplitude.

[0153] In order to comprehensively consider the relationship between voltage, current and power, the relationship between line transmission power and the voltage and current related quantities at the starting end of the line is established, which can be expressed as follows:

[0154] ,

[0155] in, According to the apparent power and active power , reactive power relationship , the left side of the equation represents the line exist The calculation formula of the square of the apparent power at any moment reflects the circuit The combined situation of active and reactive power transmitted, for Time flows through the line The square of the average current, For the line exist The square of the voltage at that moment.

[0156] It should be noted that the constraints of the distribution network include active and reactive power balance at the nodes, the power of each branch in the network not exceeding the limit, the voltage at each node remaining within a specified range, and AC power flow constraints.

[0157] (2) Constraints of integrated energy service systems and load aggregators

[0158] Considering the carbon emission constraints of each node in the integrated energy service system, the formula is as follows:

[0159] ,

[0160] in, for time Stage Route of carbon emissions, 、 、 、 Flow through the lines Node exist Stage The power generated by the grid, photovoltaic, wind power, and load at all times, is the correlation coefficient of power to carbon emissions, Contains all nodes of the integrated energy service system, Includes all nodes of the integrated energy service system and all nodes of the load aggregator.

[0161] Considering the carbon emission constraints of each node in the load aggregator, the formula is as follows:

[0162] ,

[0163] In the above formula, the left side is the equivalent carbon emissions of the integrated energy service system and load aggregator, including branch carbon emissions and internal carbon emissions, and the right side is the total carbon emissions; for time Stage Route of carbon emissions, For the In the iterations, the node exist Sell ​​to nodes at any time of electricity ( ).

[0164] Preferably, based on the set constraints, the objective function of the lower-level 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-level model of the electricity-carbon P2P transaction.

[0165] 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;

[0166] The 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 power transaction deviation and carbon intensity convergence criteria; the delay protocol process is as follows Figure 5 As shown;

[0167] Oscillations may occur during the iterative process of calculation of the upper-level model of electricity-carbon P2P trading and execution of the lower-level model by the distribution system operator. For example, the transaction results of the upper-level model of electricity-carbon P2P trading, such as the amount of electricity sold by node A to B, may exceed the line capacity executed by the lower level. Therefore, the transaction volume will be forced to be adjusted, such as reducing A's electricity sales. The adjusted transaction volume will then cause the upper level to repricing, forming an oscillating cycle that cannot converge or converges slowly.

[0168] Therefore, the delay protocol dynamically adjusts the penalty function to solve the oscillation cycle problem in the two-layer model iteration and ensure the convergence of power trading deviation and carbon intensity. The core idea is:

[0169] (1) Convergence conditions

[0170] When the two-layer model of the electricity-carbon P2P joint market is strictly converged, the electricity trading volume must meet the following requirements:

[0171] ,

[0172] in, For the In the iterations, the node exist Sell ​​to nodes at any time of electricity ( ). For the grid node The forced adjustment amount of the previous round of trading volume ( ), the formula means that the current transaction volume = the previous round of transaction volume - the adjustment amount required by the power grid.

[0173] (2) Punishment mechanism

[0174] Trading bias Penalty imposed on the difference between actual transaction volume and grid adjustment target:

[0175] ,

[0176] in, It is a penalty function used to adjust the objective function in the ADMM algorithm and force the trading parties to reduce the deviation. is the delay price, i.e. the cost of transaction deviation, is the penalty coefficient; the greater the deviation, the heavier the penalty.

[0177] (3) Dynamically adjust parameters

[0178] Delayed price updates:

[0179] ,

[0180] in, is the delay price, i.e. the cost of transaction deviation, is the penalty coefficient, Trading deviation.

[0181] Adjust the unit price of fines according to the deviation, while ensuring that the unit price of fines is non-negative and gradually increases as the deviation accumulates.

[0182] Penalty coefficient update:

[0183] ,

[0184] in, It is a smoothing parameter, ranging from 0 to 1, which controls the speed of penalty growth.

[0185] (4) Integration with upper and lower layer models

[0186] The penalty Add the objective function of the upper model of electricity-carbon P2P trading:

[0187] ,

[0188] in, is the original objective function of the upper-level model of electricity-carbon P2P trading.

[0189] The penalty Add the distribution system operator lower model objective function:

[0190] ,

[0191] in, is the original objective function of the lower-level model of the distribution system operator.

[0192] (5) Convergence judgment

[0193] ,

[0194] ,

[0195] in, is the carbon intensity change rate, that is, the difference in node carbon intensity between two adjacent iterations, The transaction volume change rate is the difference between transaction volumes between two consecutive iterations. When both the carbon intensity change rate and the transaction volume change rate are less than 1%, the iterative calculation of the two-tier model of the electricity-carbon P2P joint market is terminated. This paper dynamically adjusts the penalty function and links it with the carbon price to achieve low-carbon economic operation of the two-tier model of the electricity-carbon P2P joint market.

[0196] In one possible embodiment, the optimization strategy of the integrated energy service system may be to prioritize the allocation of clean energy such as photovoltaics and wind power based on the results of electricity-carbon P2P transactions and real-time system data, convert and store excess electricity, and improve energy recycling efficiency; dynamically adjust the electricity trading volume and the amount of carbon emission responsibility transfer based on market price fluctuations and its own supply and demand to reduce costs; continuously monitor carbon emissions, increase carbon capture and storage efforts when approaching limits, and reduce carbon emission intensity; strengthen collaboration with distribution system operators and optimize its own strategies based on the information they provide; use delay protocols and penalty mechanisms to adjust system operating parameters in real time based on electricity trading deviations and carbon intensity changes to achieve low-carbon economic operation.

[0197] The above is a schematic diagram of a carbon trading-based integrated energy service system optimization method according to this embodiment. It should be noted that the technical solution of this carbon trading-based integrated energy service system optimization system and the technical solution of the carbon trading-based integrated energy service system optimization method described above are based on the same concept. For details not described in detail in the technical solution of the carbon trading-based integrated energy service system optimization system according to this embodiment, please refer to the description of the technical solution of the carbon trading-based integrated energy service system optimization method described above.

[0198] The integrated energy service system optimization system based on carbon trading in this embodiment includes:

[0199] A data acquisition module is used to obtain the operating data of the integrated energy service system;

[0200] A calculation module for calculating the carbon emissions of the integrated energy service system based on operation data;

[0201] A model design module is used to build a two-tier model for the electricity-carbon P2P joint market, which includes an upper-tier electricity-carbon P2P trading model and a lower-tier distribution system operator model.

[0202] The strategy acquisition module is used to input carbon emissions into the upper-level model of electricity-carbon P2P trading and solve the upper-level model through the alternating direction multiplication method to obtain the electricity-carbon P2P trading results. The lower-level 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 results. The two-level model of the electricity-carbon P2P joint market is iteratively solved to obtain the optimization strategy of the integrated energy service system.

[0203] This embodiment further provides a computer device suitable for optimizing an integrated energy service system based on carbon trading, including:

[0204] Memory and processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the optimization method of the integrated energy service system based on carbon trading as proposed in the above embodiment.

[0205] This embodiment also provides a storage medium having a computer program stored thereon. When the program is executed by a processor, the method for optimizing an integrated energy service system based on carbon trading as proposed in the above embodiment is implemented.

[0206] 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. For technical details not described in detail in this embodiment, please refer to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0207] From the above description of the embodiments, those skilled in the art will clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This software product can be stored on a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk, or optical disk, and includes instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of various embodiments of the present invention.

[0208] 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 optimizing an integrated energy service system based on carbon trading, characterized in that: include: Obtaining operating data of an integrated energy service system, and calculating carbon emissions of the integrated energy service system based on the operating data; Constructing a two-tier model for the electricity-carbon P2P joint market, comprising an upper-tier electricity-carbon P2P trading model and a lower-tier distribution system operator model; Inputting the carbon emissions into an electricity-carbon P2P transaction upper model, and solving the electricity-carbon P2P transaction upper model by an alternating direction multiplication method to obtain an 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; Iteratively solve the two-layer model of the electricity-carbon P2P joint market to obtain the optimization strategy of the integrated energy service system; Calculation of carbon emissions of the integrated energy service system includes: Calculate the carbon emission flow rate through the integrated energy service system's carbon emissions, i.e., the carbon emissions flowing through a node or line per unit time; The carbon emissions calculation includes line carbon emissions and node carbon emissions. The line carbon emissions are calculated based on the square current of the line within a certain period of time and the line carbon emission intensity. The line carbon emission intensity is equal to the carbon emission intensity of the node at the head end of the line. That is, the carbon emissions per unit of electricity transmitted by the line are determined by the power supply end. The node carbon emissions are calculated by the node carbon intensity and the node load power within a certain period of time; Input the carbon emissions into the upper-level model of the electricity-carbon P2P transaction, including: Based on the carbon emissions, designing an objective function of the upper-layer 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 costs of the integrated energy service system, which include methane purchase costs, carbon sequestration costs, carbon emission costs, and methanol production revenue; The objective function of the load aggregator is to maximize social benefits; The distribution system operator's lower-level model recalculates and updates the carbon emissions of the integrated energy service system based on the results of electricity-carbon P2P transactions, including: Based on the electricity-carbon P2P trading results, design the objective function of the lower-layer 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 the cost of purchasing excess carbon permits, the cost of external power purchases, and the cost of forced adjustment penalties.

2. The method for optimizing an integrated energy service system based on carbon trading according to claim 1, characterized in that: The upper-level model of the electricity-carbon P2P transaction is solved by the alternating direction multiplication 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-layer 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 the electricity-carbon P2P transaction result.

3. The method for optimizing an integrated energy service system based on carbon trading according to claim 1, characterized in that: 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.

4. The method for optimizing an integrated energy service system based on carbon trading according to claim 3, wherein: 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.

5. The method for optimizing an integrated energy service system based on carbon trading according to claim 4, 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.

6. A carbon trading-based integrated energy service system optimization system, applying the carbon trading-based integrated energy service system optimization method according to any one of claims 1 to 5, characterized in that: include: A data acquisition module is used to obtain the operating data of the integrated energy service system; a calculation module, configured to calculate the carbon emissions of the integrated energy service system based on the operation data; A model design module for constructing a two-tier model for the electricity-carbon P2P joint market, comprising an upper-tier electricity-carbon P2P trading model and a lower-tier distribution system operator model; A strategy acquisition module is used to input the carbon emissions into the upper-level model of the electricity-carbon P2P transaction, and solve the upper-level model of the electricity-carbon P2P transaction through the alternating direction multiplication method to obtain the electricity-carbon P2P transaction results; the lower-level 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; and iteratively solve the two-level model of the electricity-carbon P2P joint market to obtain the optimization strategy of the integrated energy service system.

7. A computer device comprising: memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the method for optimizing an integrated energy service system based on carbon trading as described in any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of a method for optimizing an integrated energy service system based on carbon trading as described in any one of claims 1 to 5.

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