A low-carbon collaborative scheduling method for integrated energy suppliers and end users

Abstract

The present invention discloses a low-carbon collaborative scheduling method for integrated energy suppliers and end users. The present invention establishes a ladder carbon emission trading mechanism model through the ladder carbon emission quota and the carbon emission of the integrated energy system, and allows the integrated energy system to participate in the energy market and the carbon trading market as a whole. Then, the energy-carbon price of each park user is formulated by a pricing method based on layered Shapley, and the energy-carbon benefits are allocated to the lower-level end users, and the price information is transmitted to the lower layer; the lower-level park users perform comprehensive load response according to the price formulated by the upper layer, and feed back the energy purchase strategy to the upper-level supplier. The upper-level supplier re-schedules according to the energy purchase strategy updated by the lower-level park. The decision-making information interaction between the upper and lower layers is realized through the ADMM distributed iterative algorithm. This enables the end users of the integrated energy park to indirectly participate in the carbon market, stimulates the enthusiasm of the end users to participate in the carbon market, reduces the cost and carbon emissions of the system, and realizes the low-carbon collaborative scheduling of the system.

Description

Technical Field

[0001] The present invention belongs to the field of integrated energy distributed optimization scheduling, and in particular relates to a low-carbon collaborative scheduling method for integrated energy suppliers and end users. Background Art

[0002] In recent decades, with the rapid development of the global economy and the increasing demand for energy, massive emissions of greenhouse gases such as carbon dioxide have exacerbated global warming and air pollution. To achieve a low-carbon economic transition, many countries and regions have established carbon trading markets. However, participants in these markets generally only include energy producers and energy-intensive industries, such as steel and cement. End-users of energy, the primary contributors to carbon emissions, are too numerous to directly participate in the carbon market. This can lead to a lack of willingness among end-users to reduce carbon emissions, making it difficult to reduce systemic carbon emissions. Currently, there is a lack of research on how to incorporate end-users into the carbon market system. In the two-tier supplier-user model, suppliers and users, as a whole, participate in external energy-carbon market transactions. How to incentivize end-users to save energy and reduce emissions and achieve low-carbon coordinated scheduling between energy suppliers and end-users is an urgent issue. Summary of the Invention

[0003] This paper addresses the problem that end-user energy consumers are currently unable to be included in the existing carbon market system, resulting in a low willingness to save energy and reduce emissions. It proposes a low-carbon collaborative scheduling method for integrated energy suppliers and end-users. The specific implementation process of this method is as follows:

[0004] Step 1: Establish a tiered carbon emissions trading mechanism model based on tiered carbon emissions quotas and carbon emissions of the integrated energy system;

[0005] Step 2: Establish a low-carbon economic dispatch model for integrated energy suppliers: Utilize the tiered carbon emissions trading mechanism model to construct an objective function that minimizes the total energy cost and total carbon cost of the integrated energy system. This is then combined with the model of the CHP unit with thermoelectric coupling elements, the second-order cone model of the AC power flow in the distribution network, and the transient microelement model of the heat pipe network to establish a low-carbon economic dispatch model for integrated energy suppliers.

[0006] Step 3: Allocate the carbon emission benefits to users in the park according to the proportion of user loads in each park, establish Shapley's energy-carbon price allocation model, and calculate the energy price and carbon emission price of rigid load and flexible load;

[0007] Step 4: Based on the final energy price and carbon emission price, establish a comprehensive load response model for lower-level park users to respond to the energy-carbon price issued by the upper-level integrated energy supplier. The comprehensive load response model includes: an objective function that minimizes the sum of the total energy cost, carbon cost, and the penalty coefficient caused by user participation in load response, as well as an electrical load constraint model and a thermal load constraint model.

[0008] Step 5: Create replicated variables to decouple the coupling point between the integrated energy supplier and the user, that is, the interaction power of electricity and thermal energy. Construct the Lagrangian augmented form objective functions of the integrated energy supplier and the park user respectively, construct the Lagrangian multiplier update formula and the penalty factor update formula, and use the variable penalty factor ADMM method to achieve distributed collaborative optimization scheduling.

[0009] Furthermore, in step 1, establishing a tiered carbon emissions trading mechanism model includes the following steps:

[0010] Step 1.1: The carbon trading authority pre-allocates free carbon allowances based on the historical carbon emissions of the integrated energy system or industry benchmarks. When the actual carbon emissions generated by the integrated energy system are lower than the allocated allowances, the excess allowances can be sold on the carbon trading market; otherwise, the excess allowances can be purchased on the carbon emissions market.

[0011] Step 1.2: Divide carbon emissions into several intervals, each corresponding to a different carbon trading price. The formula for the tiered carbon emission quota is as follows:

[0012] E trade =E system -E quota

[0013] The formula for calculating the tiered carbon price for integrated energy suppliers is:

[0014]

[0015] in The carbon price paid by upper-tier integrated energy suppliers, E trade , E quota , E system are the carbon emissions participating in the carbon market, the carbon emission share allocated by the carbon trading authority, and the total carbon emissions of the system, respectively; λ1, λ2, and λ3 are the prices of each carbon emission interval, and λ3>λ2>λ1; σ1 and σ2 are the dividing points of each carbon emission interval, and σ1>σ1;

[0016] Step 1.3: Calculate the carbon emissions of the integrated energy system. Calculate the carbon emissions on the energy supply side of the integrated energy system. Each unit has a certain carbon emission intensity. The carbon emission intensity of all units multiplied by the output of each unit is the total carbon emissions of the integrated energy system. The carbon emissions calculation formula for the integrated energy system is:

[0017]

[0018] Where c is the carbon emission coefficient per unit power of each unit, P is the active power of each unit, G is the gas consumption of the CHP unit, subscripts g, b, CHP, and w represent the thermal power unit, the main power grid, the CHP unit, and the wind turbine, respectively, and t represents the time series of the power system. is the time set for scheduling, and Δt represents the time scale for scheduling.

[0019] Furthermore, the step 2 includes the following steps:

[0020] Step 2.1: Construct the objective function of the integrated energy supplier, that is, to minimize the total energy cost and total carbon cost of the integrated energy system. The optimization objective function is:

[0021]

[0022]

[0023] Among them C I 、 and They represent the total cost, energy cost and carbon cost of the integrated energy supplier respectively. p represents the price per unit power of each unit, p curtail represents the cost of wind curtailment, With P wind They represent the predicted value of wind power and the dispatched wind power active power, g, b, and CHP represent thermal power units, large power grids, and CHP units, respectively. t represents the time series of the power system. is the time set for scheduling;

[0024] Step 2.2: Construct a model of the thermoelectric coupling element CHP unit, and its expression is:

[0025]

[0026]

[0027] Among them, P CHP,t , H CHP,τ is the electrical power and thermal power output of the CHP unit, K e With K hare the power generation efficiency and heating efficiency of the CHP unit, τ and t are the time series of the power system and thermal system respectively;

[0028] Step 2.3: Construct the second-order cone model of the AC power flow of the distribution network, which is expressed as follows:

[0029]

[0030]

[0031]

[0032]

[0033] Among them, P PG,i,t With Q PG,i,t They represent the active power and reactive power of the motor on node i at time t, P L,i,t With Q L,i,t They represent the active power and reactive power consumed by the load on node i at time t, P ij,t With Q ij,t are the active and reactive powers flowing from node i to node j respectively. ij,t is the square of the current in line ij, v i,t is the square of the voltage at node i, r ij with x ij They represent the resistance and reactance of line ij respectively; is the set of all nodes in the line, m and i are midpoint;

[0034] The current, voltage range, and balance node constraints of the distribution network are:

[0035]

[0036]

[0037]

[0038] Among them, v o,t is the square of the voltage at the equilibrium node o, v min With v max are the lower and upper limits of the voltage squared, respectively. max is the upper limit of the square of the current;

[0039] Step 2.4: Construct a transient microelement model of the heat pipe network. This model includes the radial heat dissipation microelement temperature formula for the insulation layer, the water temperature formula within the heat pipe network, the flow mixing and temperature mixing formulas at the pipelines, the heat exchange formula between the heat exchange station and the load, and the temperature constraint formula.

[0040] The radial heat dissipation infinitesimal temperature formula of the insulation layer is:

[0041]

[0042] in c u is the specific heat capacity of the insulation layer, D out With D in are the outer diameter and inner diameter of the pipeline respectively, Δx is the length of the pipeline element, Δτ is the time element of the heating network, ρ u is the density of the insulation layer, R ru With R us The thermal resistance between hot water and insulation layer, and between insulation layer and hot water, T u,l,k,τ With T r,l,k,τ are the temperatures of the insulation layer and hot water at the kth section of line l at time τ, T s is the soil temperature, is the set of segments of pipeline l, is the set of all pipelines;

[0043] The water temperature formula in the heat pipe network is:

[0044]

[0045] Among them, M l is the flow rate of pipeline l, ρ r is the density of hot water, c r is the specific heat capacity of hot water.

[0046] The flow mixing and temperature mixing formulas in the pipeline are:

[0047]

[0048]

[0049] in, and Respectively represent the pipeline sets flowing into / out of node a, if and of represent the pipelines flowing into and out of node a, respectively. r,of,1,τ It represents the hot water temperature of the first infinitesimal element of the outflow pipeline a at time τ.

[0050] The heat exchange formula between the heat exchange station and the load is:

[0051]

[0052]

[0053] Wherein, the subscripts p and q represent the heat exchange station and load respectively, H q,τ represents the thermal power consumed at load node q at time τ, ηex and η load Respectively represent the heat exchange efficiency of the CHP unit and the load.

[0054] The water temperature in the heat pipe network needs to be maintained within a certain temperature range. The temperature constraint formula is as follows:

[0055]

[0056] During a scheduling cycle, the temperature difference at the load inlet needs to be kept within a certain temperature difference:

[0057]

[0058] in, and They represent the water temperature of the last segment of the water inlet pipeline of load node r at the initial and final moments of scheduling respectively.

[0059] Furthermore, the step 3 includes the following steps:

[0060] Step 3.1: Further divide the load of users in each park into flexible load and rigid load based on load transferability, and divide it into peak load, normal load, and valley load based on time dimension;

[0061] Step 3.2: Allocate the initial carbon emission benefits to the park users. When the load of all parks is 0, there will be a certain amount of carbon emission benefits under the tiered carbon trading mechanism. Therefore, it is necessary to first fairly distribute the carbon emission benefits to each park user based on the proportion of the load of each park user.

[0062] Step 3.3: For the rigid load of each park user, the Shapley value method is used to allocate the energy-carbon cost corresponding to the rigid load of each park user. The formula for allocating the energy-carbon price of park users using the Shapley value method is:

[0063]

[0064] Where: x i is the amount of responsibility shared by load member i; S is the sub-alliance composed of existing sub-alliance members before load member i joins; =(i) / ...

[0065]

[0066] Among them, n N Indicates the number of all participating members, n S Represents the number of participating members in the sub-alliance S.

[0067] 3.4 For flexible loads, the Aumann Shapley method is used to allocate the energy-carbon cost of each park user's flexible load in different peak, flat, and valley time periods. Each user load is divided into six small loads based on the six dimensions of peak / flat / valley, rigid load, and flexible load. Each small load is then divided into N segments. The discretized Aumann Shapley method is used to express the contribution of each small load i to the system energy-carbon cost as follows: Where P is a load that includes all users vector.

[0068] Step 3.5: Based on the Shapley allocation results of steps 3.3 and 3.4, calculate the energy price and carbon emission price respectively: When calculating the energy price, the integrated energy supplier sets a fixed profit margin, and the energy costs of the rigid load and flexible load are obtained from steps 3.3 and 3.4 respectively, and then multiplied by the fixed profit margin to obtain the final energy price; the carbon emission price of each park is the carbon emission cost allocation value of the rigid load and flexible load obtained from steps 3.3 and 3.4 respectively; the energy-carbon price corresponding to the CHP unit output is allocated to electrical energy and thermal energy according to the unit's heat-to-electricity ratio; in the final calculation, the energy-carbon price of thermal energy is not distinguished by time scale.

[0069] Furthermore, the step 4 includes the following steps:

[0070] 4.1 Construct the objective function for the end-users in the park; that is, minimize the sum of the total energy cost, carbon cost, and the penalty coefficient caused by the user's participation in load response. The objective function expression is:

[0071]

[0072]

[0073]

[0074]

[0075] in, represents the total cost for user n, They represent the energy cost, carbon cost and penalty coefficient of user n due to user participation in load response, is the initial carbon emission benefit shared by user n in step 3.2. represents the electricity price of user n at time t, represents the hot price of user n. and They represent the electricity carbon price of the rigid load part and the flexible load part of user n at time t, represents the heat carbon price of user n at time t. and Represent the rigid load and flexible load of user n respectively, and The superscripts shift and conv respectively represent the load that can be transferred and the load that can be converted into electricity and heat. shift with d conv is the load response penalty coefficient, where d shift >d conv , and They represent the transferable load and the load that can be converted into electricity and heat after the load response, and Indicates the load change after user n load response, and is the initial transferable load and electric-thermal conversion load of user n before load response.

[0076] in and It can be linearized by the following formula:

[0077]

[0078]

[0079]

[0080]

[0081] in, and Additional variables are added.

[0082] 4.2 Establish an electric load constraint model. Electric loads include rigid loads and flexible loads. The basic load does not participate in the load response; flexible loads include transferable loads and loads that can be converted to electric heat. The formula is:

[0083]

[0084] The constraints on transferable loads are:

[0085]

[0086] The constraints on the electric-to-heat conversion load are:

[0087]

[0088] in, is the original value of the load that can be converted into heat before the load response of user n, It is the heat load participating in the electric-heat conversion after the load response.

[0089] 4.3 Establish a heat load constraint model. Model the heat dissipation, heat radiation, and heat conduction of the building. When the room temperature meets the PMV comfort range, the indoor heat load demand is met.

[0090] The heat dissipation process from indoor to outdoor is modeled as:

[0091]

[0092] in, is the heat dissipation power from indoor to outdoor, is the area of the fence, is the thermal conductivity, and are the indoor and outdoor temperatures of user n at time t;

[0093] The thermal radiation power absorbed by solar radiation is modeled as:

[0094]

[0095] in, is the thermal radiation power of sunlight, α n is the solar refraction absorption coefficient, is the window area of user n, is the radiation heat flux density of user n wall.

[0096] The heat conduction model of indoor temperature is modeled as:

[0097]

[0098] in, is the thermal power absorbed by user n in the heating network at time t, is the thermal power of the indoor heat source, F n is the thermal resistance of the wall.

[0099] The PMV formula is used to measure human comfort. According to the ISO7730 standard, when the PMV is between [-0.5, 0.5], the user is in a comfortable state. According to China's "Design Code for Heating, Ventilation and Air Conditioning", when the PMV is between [-1, 1], the indoor thermal comfort requirements can be met. Considering that human sensory sensitivity is higher during the day than at night, the PMV formula is used to measure human comfort. According to the ISO7730 standard, when the PMV is between [-0.5, 0.5], the user is in a comfortable state. According to China's "Design Code for Heating, Ventilation and Air Conditioning", when the PMV is between [-1, 1], the indoor thermal comfort requirements can be met. Considering that human sensory sensitivity is higher during the day than at night, the PMV formula is used to measure human comfort. According to the ISO7730 standard, the PMV is set between [-0.5, 0.5] during the day and [-1, 1] at night. The PMV formula is:

[0100]

[0101] where μ PMV is the value of PMV, I is the user's metabolic rate, which is related to the intensity of physical activity and is set at 70W / m 2 . T comfy R is the average temperature of human skin in a comfortable state, which is set to be approximately 33.5°C. clo is the thermal resistance of the clothing, which is 0.11 (m2·℃) / W.

[0102] Furthermore, the step 5 includes the following steps:

[0103] 5.1 Create replicated variables. Decouple the coupling point between the integrated energy supplier and the user, that is, the interactive power of electric energy and thermal energy; the power sold by the integrated energy supplier to user n at time t and the electric power purchased by user n from the integrated energy supplier at time t are expressed as and The thermal power is expressed as and

[0104] 5.2 Construct the Lagrangian augmented objective functions of the integrated energy supplier and the park user respectively, which are:

[0105]

[0106]

[0107] in, and is the objective function of the integrated energy supplier and user in the augmented Lagrangian form at the k+1th iteration, and are the electricity / heat power sold by the integrated energy supplier to user n and the electricity / heat power purchased by user n at the kth iteration, and is the Lagrange multiplier of the electric power and thermal power of user n at iteration step k, ρ e,n,t and ρ h,n,t are the penalty parameters for electric power and thermal power of user n at iteration step k.

[0108] 5.3 Construct the Lagrange multiplier update formula:

[0109]

[0110]

[0111] 5.4 Construct penalty factor update formula:

[0112]

[0113] Among them, ρ k is a vector containing all penalty factors at the kth iteration, τ incr , τ decr and μ are penalty factor update parameters, both greater than 1, usually μ=10, τ incr =τ decr =2. and They represent the original residual and the dual residual respectively, and their expressions are:

[0114]

[0115]

[0116] 5.5 Use the variable penalty factor ADMM method to achieve distributed collaborative optimization scheduling.

[0117] (1) Initialization: Initialize the Lagrange multiplier and and penalty factor and Users report their energy consumption plans to upper-level integrated energy suppliers.

[0118] (2) When k≤k max and When executing:

[0119] a) The integrated energy supplier performs low-carbon economic dispatch based on the model described in step 2, where the objective function is changed to the augmented Lagrangian objective function of the integrated energy supplier in step 5.2, and the constraints remain unchanged;

[0120] b) The integrated energy supplier provides energy-carbon pricing for users based on the model described in step 3 and passes the energy-carbon pricing to the corresponding park users.

[0121] c) The park performs an integrated load response based on the model described in step 4, where the objective function is changed to the user's augmented Lagrangian objective function in step 5.2, and the updated energy purchase information is transmitted to the integrated energy supplier.

[0122] d) Update the Lagrange multiplier based on step 5.3 and And update the penalty factor based on step 5.4 and

[0123] (3) The iteration ends and the results are output.

[0124] The beneficial effects of the present invention are:

[0125] It provides a way for users to indirectly participate in the energy and carbon market, stimulating their enthusiasm for energy conservation and emission reduction.

[0126] A novel energy-carbon pricing method is designed to accurately guide users to respond to loads, save energy and reduce emissions, and reduce the energy and carbon costs of the system through price signals.

[0127] The variable penalty factor ADMM distributed iterative algorithm fully protects user privacy and only requires the necessary energy exchange volume and price to achieve iteration. The distributed algorithm also helps fully leverage user initiative and achieve optimal energy and carbon costs for the system as a whole.

[0128] Fully exploit the flexibility of source-load relationships. Model the heat network at the source to leverage its heat storage effect; implement comprehensive demand response at the load end. This provides a viable path for energy conservation and emission reduction in the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0129] Figure 1 The integrated energy system of the embodiment of the present invention is an integrated energy system model of a park-level integrated energy user;

[0130] Figure 2 This is a schematic diagram of a tiered carbon emission price according to an embodiment of the present invention;

[0131] Figure 3 This is a schematic diagram of load segmentation in the time dimension and transferability according to an embodiment of the present invention;

[0132] Figure 4 This is a flowchart of the ADMM distributed collaborative interaction according to an embodiment of the present invention;

[0133] Figure 5is a topological diagram of an integrated energy system according to an embodiment of the present invention;

[0134] Figure 6 This is the change in the total cost of the integrated energy supplier and the park users during the iteration process in the embodiment of the present invention. DETAILED DESCRIPTION

[0135] In order to facilitate those skilled in the art to understand and implement the present invention, the present invention is further described in detail below with reference to the accompanying drawings and embodiments.

[0136] It should be understood that the embodiments described herein are only used to illustrate and explain the present invention, and are not intended to limit the present invention.

[0137] In such Figure 1 In the integrated energy system model shown, which combines an integrated energy supplier with a campus-level integrated energy user, the integrated energy supplier considers the entire integrated energy system as a whole and participates in the carbon trading market led by the carbon trading authority. After the integrated energy supplier completes the energy-carbon transaction, the energy and carbon emission costs are allocated to each end user according to the hierarchical Shapley-based energy-carbon pricing method proposed in this invention. This energy-carbon pricing information is used to incentivize each end user to conserve energy and reduce emissions.

[0138] Integrated energy suppliers, as a whole, participate in both the energy trading market and the carbon emissions trading market led by carbon trading authorities, using their entire integrated energy system as a whole. In the energy trading market, integrated energy suppliers pay energy fees to thermal power units, the main grid, CHP units, and other power supply units. They then supply energy to users through the integrated energy network and collect energy fees from them. In the carbon trading market, integrated energy suppliers pay carbon emissions fees or earn carbon emissions revenue to carbon trading authorities based on their carbon emissions. These revenues are then distributed to each user based on their contribution to carbon emissions. This rational price-sharing mechanism allows end users to indirectly participate in the carbon trading market.

[0139] After receiving the energy-carbon price set by the upper-level integrated energy supplier, lower-level end users implement a comprehensive load response. This response considers load shifting of electrical loads, flexibility of thermal loads, and the conversion of electrical and thermal loads to achieve energy conservation and emission reduction.

[0140] The present invention provides a low-carbon collaborative scheduling method for integrated energy suppliers and end users. This method is applied to a two-tier model of integrated energy suppliers and end users in a park. The specific process includes:

[0141] Step 1: Establish a tiered carbon emissions trading mechanism model based on tiered carbon emissions quotas and carbon emissions of the integrated energy system;

[0142] Step 2: Establish a low-carbon economic dispatch model for integrated energy suppliers: Utilize the tiered carbon emissions trading mechanism model to construct an objective function that minimizes the total energy cost and total carbon cost of the integrated energy system. This is then combined with the model of the CHP unit with thermoelectric coupling elements, the second-order cone model of the AC power flow in the distribution network, and the transient microelement model of the heat pipe network to establish a low-carbon economic dispatch model for integrated energy suppliers.

[0143] Step 3: Allocate the carbon emission benefits to users in the park according to the proportion of user loads in each park, establish Shapley's energy-carbon price allocation model, and calculate the energy price and carbon emission price of rigid load and flexible load;

[0144] Step 4: Based on the final energy price and carbon emission price, establish a comprehensive load response model for lower-level park users to respond to the energy-carbon price issued by the upper-level integrated energy supplier. The comprehensive load response model includes: an objective function that minimizes the sum of the total energy cost, carbon cost, and the penalty coefficient caused by user participation in load response, as well as an electrical load constraint model and a thermal load constraint model.

[0145] Step 5: Create replicated variables to decouple the coupling point between the integrated energy supplier and the user, that is, the interaction power of electricity and thermal energy. Construct the Lagrangian augmented form objective functions of the integrated energy supplier and the park user respectively, construct the Lagrangian multiplier update formula and the penalty factor update formula, and use the variable penalty factor ADMM method to achieve distributed collaborative optimization scheduling.

[0146] Furthermore, the step 1 includes:

[0147] 1.1 The carbon trading authority pre-allocates free carbon allowances based on the historical carbon emissions of the integrated energy system or industry benchmarks. When the actual carbon emissions generated by the integrated energy system are lower than the allocated allowances, the excess allowances can be sold on the carbon trading market; otherwise, the excess allowances can be purchased on the carbon emissions market.

[0148] 1.2 Carbon emissions are divided into several intervals, each corresponding to a different carbon trading price. When actual carbon emissions are less than the carbon emission quota, the integrated energy supplier can sell excess quotas in the carbon trading market. The less carbon emissions, the greater the difference with the quota, and the higher the quota selling price in the corresponding interval. Conversely, when actual carbon emissions exceed the carbon quota, the integrated energy supplier needs to pay a certain fee to purchase the excess carbon emissions. Accordingly, the more carbon emissions, the greater the difference with the quota, the higher the purchase price of carbon emission rights in the corresponding interval, and the higher the purchase cost of carbon emission rights. The formula for the tiered carbon emission quota is as follows:

[0149] E trade =E system -Equota

[0150] Tiered carbon prices for integrated energy providers Figure 2 As shown in Figure 2, the formula for calculating the tiered carbon price for integrated energy suppliers is:

[0151]

[0152] in The carbon price paid by upper-tier integrated energy suppliers, E trade , E quota， E system are the carbon emissions of participants in the carbon market, the carbon emission share allocated by the carbon trading authority, and the total carbon emissions of the system, respectively. λ1, λ2, and λ3 are the prices of each carbon emission interval, and λ3>λ2>λ1. σ1 and σ2 are the dividing points of each carbon emission interval, and σ1>σ1.

[0153] 1.3 Calculate the carbon emissions of the integrated energy system. To calculate the carbon emissions of the energy supply side of the integrated energy system, each unit has a certain carbon emission intensity. The carbon emission intensity of all units multiplied by the output of each unit is the total carbon emissions of the integrated energy system. The formula for calculating the carbon emissions of the integrated energy system is:

[0154]

[0155] Where c is the carbon emission coefficient per unit power of each unit, P is the active power of each unit, G is the gas consumption of the CHP unit, subscripts g, b, CHP, and w represent the thermal power unit, the main power grid, the CHP unit, and the wind turbine, respectively, and t represents the time series of the power system. is the time set for scheduling, and Δt represents the time scale for scheduling.

[0156] Furthermore, the step 2 includes:

[0157] 2.1 Construct the objective function of the integrated energy supplier, that is, to minimize the total energy cost and total carbon cost of the integrated energy system. The optimization objective function is:

[0158]

[0159]

[0160] Among them C I 、 and They represent the total cost, energy cost and carbon cost of the integrated energy supplier respectively. p represents the price per unit power of each unit, p curtail represents the cost of wind curtailment, With P windThey represent the predicted value of wind power and the dispatched wind power active power respectively.

[0161] 2.2 Construct the model of the thermoelectric coupling element CHP unit, and its expression is:

[0162]

[0163]

[0164] Among them, P CHP,t , H CHP,τ is the electrical power and thermal power output of the CHP unit, K e With K h are the power generation efficiency and heating efficiency of the CHP unit respectively. τ and t are the time series of the power system and thermal system respectively.

[0165] 2.3 Construct the second-order cone model of AC power flow in distribution network, which is expressed as follows:

[0166]

[0167]

[0168]

[0169]

[0170] Among them, P PG,i,t With Q PG,i,t They represent the active power and reactive power of the motor on node i at time t, P L,i,t With Q L,i,t They represent the active power and reactive power consumed by the load on node i at time t, P ij,t With Q ij,t are the active and reactive powers flowing from node i to node j respectively. ij,t is the square of the current in line ij, v i,t is the square of the voltage at node i, r ij with x ij are the resistance and reactance of line ij, is the set of all nodes in the line, m and i are Midpoint.

[0171] The current, voltage range, and balance node constraints of the distribution network are:

[0172]

[0173]

[0174]

[0175] Among them, v o,t is the square of the voltage at the equilibrium node o, v min With v max are the lower and upper limits of the voltage squared, l max is the upper limit of the square of the current, is the set of all nodes in the line.

[0176] 2.4 Construct a transient microelement model of the heat pipe network. This model includes: the radial heat dissipation microelement temperature formula of the insulation layer, the water temperature formula within the heat pipe network, the flow mixing and temperature mixing formulas at the pipeline, the heat exchange formula between the heat exchange station and the load, and the temperature constraint formula;

[0177] The radial heat dissipation infinitesimal temperature formula of the insulation layer is:

[0178]

[0179] in c u is the specific heat capacity of the insulation layer, D out With D in are the outer diameter and inner diameter of the pipeline respectively, Δx is the length of the pipeline element, Δτ is the time element of the heating network, ρ u is the density of the insulation layer, R ru With R us The thermal resistance between hot water and insulation layer, and between insulation layer and hot water, T u,l,k,τ With T r,l,k,τ are the temperatures of the insulation layer and hot water at the kth section of line l at time τ, T s is the soil temperature, is the set of segments of pipeline l, is the collection of all pipelines.

[0180] The water temperature formula in the heat pipe network is:

[0181]

[0182] Among them, M l is the flow rate of pipeline l, ρ r is the density of hot water, c r is the specific heat capacity of hot water.

[0183] The flow mixing and temperature mixing formulas in the pipeline are:

[0184]

[0185]

[0186] in, and Respectively represent the pipeline sets flowing into / out of node a, if and of represent the pipelines flowing into and out of node a, respectively. r,of,1,τ It represents the hot water temperature of the first infinitesimal element of the outflow pipeline a at time τ.

[0187] The heat exchange formula between the heat exchange station and the load is:

[0188]

[0189]

[0190] Wherein, the subscripts p and q represent the heat exchange station and load respectively, H q,τ represents the thermal power consumed at load node q at time τ, η ex and η load Respectively represent the heat exchange efficiency of the CHP unit and the load.

[0191] The water temperature in the heat pipe network needs to be maintained within a certain temperature range. The temperature constraint formula is as follows:

[0192]

[0193] During a scheduling cycle, the temperature difference at the load inlet needs to be kept within a certain temperature difference:

[0194]

[0195] in, and They represent the water temperature of the last segment of the water inlet pipeline of load node r at the initial and final moments of scheduling respectively.

[0196] Furthermore, the step 3 includes:

[0197] 3.1 Further divide the load of users in each park into detailed categories, such as Figure 3 As shown in the figure, loads can be divided into flexible loads / rigid loads according to their transferability, and into peak loads / normal loads / valley loads in terms of time.

[0198] 3.2 Allocate the initial carbon emission benefits to the park users. When the load of all parks is 0, there will be a certain amount of carbon emission benefits under the tiered carbon trading mechanism. Therefore, it is necessary to first fairly distribute the carbon emission benefits to each park user based on the proportion of the load of each park user.

[0199] 3.3 For the rigid load of each park user, the Shapley value method is used to allocate the energy-carbon cost corresponding to the rigid load of each park user. The formula for allocating the energy-carbon price of park users using the Shapley value method is:

[0200]

[0201] Where: x i is the amount of responsibility shared by load member i; S is the sub-alliance composed of existing sub-alliance members before load member i joins; =(i) / ...

[0202]

[0203] Among them, n N Indicates the number of all participating members, n S Represents the number of participating members in the sub-alliance S.

[0204] 3.4 For flexible loads, the Aumann Shapley method is used to allocate the costs of each park user during different peak, flat and valley time periods. The analytical expression of the Aumann Shapley method is:

[0205]

[0206] Where P is a load that includes all users vector.

[0207] When using the Aumann Shapley method to allocate the energy carbon cost of the integrated energy supplier to the end user, the expression needs to be discretized. * When , the marginal impact of user i's newly added sub-segment on the total cost is:

[0208]

[0209] where ΔP i is a system where only the i-th term is ΔP i , and the rest are vectors of 0.

[0210] Each user load is divided into six small loads based on the six dimensions of peak / flat / valley, rigid load / flexible load. Each small load is then divided into N segments. The discretized Aumann Shapley method is used to express the contribution of each small load i to the system energy-carbon cost as follows:

[0211] Considering the need to use energy-carbon prices to guide users' IDRs, it is necessary to classify loads in different dimensions. In terms of transferability, there are two types of loads: base load and flexible load. The former will not be transferred; the latter can be shifted or converted from one energy source to another in the time dimension. The energy-carbon price contribution per unit load is different in different time dimensions. For example, due to the existence of a tiered carbon trading mechanism, energy prices during peak periods are higher than during off-peak periods, and there is a difference between the carbon prices of base load and flexible load. Therefore, in order to accurately allocate energy and carbon costs among park users, the Aumann Shapley method is used to allocate energy-carbon prices for flexible loads. That is, if there are n parks and three time periods: peak, flat, and off-peak, it is considered that there are 3n members in the alliance.

[0212] 3.5 Finally, based on the Shapley allocation results from steps 3.3 and 3.4, the energy price and carbon emission price are calculated, respectively. When calculating the energy price, the integrated energy supplier sets a fixed profit margin. The energy costs for rigid and flexible loads, obtained in steps 3.3 and 3.4, respectively, are then multiplied by the fixed profit margin to obtain the final energy price. Since the carbon emission price is a punitive pricing measure, the integrated energy supplier should not make a profit. Therefore, the carbon emission price for each park is the carbon emission cost allocation for rigid and flexible loads, obtained in steps 3.3 and 3.4, respectively. Since heat and power are coupled through CHP units, the energy-carbon price corresponding to the CHP unit output is allocated to electricity and heat according to the unit's heat-to-power ratio. Given the time delay of the heat pipe network, the energy-carbon price for heat energy is not differentiated by time scale in the final calculation.

[0213] Furthermore, the step 4 includes:

[0214] 4.1 Construct the objective function for the end-users in the park. That is, the sum of the total energy cost, carbon cost, and the penalty coefficient caused by the user's participation in load response should be minimized. The objective function expression is:

[0215]

[0216]

[0217]

[0218]

[0219] in, represents the total cost for user n, They represent the energy cost, carbon cost and penalty coefficient of user n due to user participation in load response, is the initial carbon emission benefit shared by user n in step 3.2. represents the electricity price of user n at time t, represents the hot price of user n. and They represent the electricity carbon price of the rigid load part and the flexible load part of user n at time t, represents the heat carbon price of user n at time t. and Represent the rigid load and flexible load of user n respectively, and The superscripts shift and conv respectively represent the load that can be transferred and the load that can be converted into electricity and heat. shift with d conv is the load response penalty coefficient, where d shift >d conv , and They represent the transferable load and the load that can be converted into electricity and heat after the load response, and Indicates the load change after user n load response, and is the initial transferable load and electric-thermal conversion load of user n before load response.

[0220] in and It can be linearized by the following formula:

[0221]

[0222]

[0223]

[0224]

[0225] in, and Additional variables are added.

[0226] 4.2 Conduct electrical load modeling. Electrical loads include rigid loads and flexible loads. Basic loads do not participate in load response; flexible loads include transferable loads and loads that can be converted to electricity and heat. The formula is:

[0227]

[0228] The constraints on transferable loads are:

[0229]

[0230] The constraints on the electric-to-heat conversion load are:

[0231]

[0232] in, is the original value of the load that can be converted into heat before the load response of user n, It is the heat load participating in the electric-heat conversion after the load response.

[0233] 4.3 Establish a heat load constraint model. Model the heat dissipation, heat radiation, and heat conduction of the building. When the room temperature meets the PMV comfort range, the indoor heat load demand is met.

[0234] The heat dissipation process from indoor to outdoor is modeled as:

[0235]

[0236] in, is the heat dissipation power from indoor to outdoor, is the area of the fence, is the thermal conductivity, and are the indoor and outdoor temperatures of user n at time t;

[0237] The thermal radiation power absorbed by solar radiation is modeled as:

[0238]

[0239] in, is the thermal radiation power of sunlight, α n is the solar refraction absorption coefficient, is the window area of user n, is the radiation heat flux density of user n wall.

[0240] The heat conduction model of indoor temperature is modeled as:

[0241]

[0242] in, is the thermal power absorbed by user n in the heating network at time t, is the thermal power of the indoor heat source, F n is the thermal resistance of the wall.

[0243] The PMV formula is used to measure human comfort. According to the ISO7730 standard, users are comfortable when the PMV is between [-0.5, 0.5]. According to China's "Heating, Ventilation, and Air Conditioning Design Code," indoor thermal comfort requirements are met when the PMV is between [-1, 1]. Considering that human sensory sensitivity is higher during the day than at night, the PMV is set between [-0.5, 0.5] during the day and [-1, 1] at night. The PMV formula is:

[0244]

[0245] where μ PMV is the value of PMV, I is the user's metabolic rate, which is related to the intensity of physical activity and is set at 70W / m 2 . T comfy R is the average temperature of human skin in a comfortable state, which is set to be approximately 33.5°C. clo is the thermal resistance of the clothing, which is 0.11 (m2·℃) / W.

[0246] Furthermore, the step 5 includes:

[0247] 5.1 Create replicated variables. Decouple the coupling point between the integrated energy supplier and the user, that is, the interactive power of electric energy and thermal energy. The power sold by the integrated energy supplier to user n at time t and the electric power purchased by user n from the integrated energy supplier at time t are expressed as and The thermal power is expressed as and

[0248] 5.2 Construct the Lagrangian augmented objective functions of the integrated energy supplier and the park user respectively, which are:

[0249]

[0250]

[0251] in, and is the objective function of the integrated energy supplier and user in the augmented Lagrangian form at the k+1th iteration, and are the electricity / heat power sold by the integrated energy supplier to user n and the electricity / heat power purchased by user n at the kth iteration, and is the Lagrange multiplier of the electric power and thermal power of user n at iteration step k, ρ e,n,t and ρ h,n,t are the penalty parameters for electric power and thermal power of user n at iteration step k.

[0252] 5.3 Construct the Lagrange multiplier update formula:

[0253]

[0254]

[0255] 5.4 Construct penalty factor update formula:

[0256]

[0257] Among them, ρ k is a vector containing all penalty factors at the kth iteration, τ incr , τ decr and μ are penalty factor update parameters, both greater than 1, usually μ=10, τ incr =τ decr =2. and They represent the original residual and the dual residual respectively, and their expressions are:

[0258]

[0259]

[0260] The basic idea of ADMM with variable penalty factor is to keep the original residual and the dual residual within the same order of magnitude, which can greatly improve the convergence speed.

[0261] 5.5 Using variable penalty factor ADMM method to realize distributed collaborative optimization scheduling, such as Figure 4 As shown, the steps are as follows:

[0262] (1) Initialization: Initialize the Lagrange multiplier and and penalty factor and Users report their energy consumption plans to upper-level integrated energy suppliers.

[0263] (2) When k≤k max and When executing:

[0264] a) The integrated energy supplier performs low-carbon economic dispatch based on the model described in step 2, where the objective function is changed to the augmented Lagrangian objective function of the integrated energy supplier in step 5.2, and the constraints remain unchanged;

[0265] b) The integrated energy supplier provides energy-carbon pricing for users based on the model described in step 3 and passes the energy-carbon pricing to the corresponding park users.

[0266] c) The park performs an integrated load response based on the model described in step 4, where the objective function is changed to the user's augmented Lagrangian objective function in step 5.2, and the updated energy purchase information is transmitted to the integrated energy supplier.

[0267] d) Update the Lagrange multiplier based on step 5.3 and And update the penalty factor based on step 5.4 and

[0268] (3) The iteration ends and the results are output.

[0269] The effects of the present invention will be further described below with reference to simulation examples:

[0270] The integrated energy system studied includes an integrated energy supplier and three end users. The topology of the integrated energy system is as follows: Figure 5 As shown in Figure 1, it includes an IEEE-33 node standard distribution network and a 6-node heat network. The wind turbine is located at node 17, the thermal power unit is located at node 32, the large power grid is at 33, and the CHP unit couples the power grid and the heat network. The model parameters involved in the simulation are shown in Table 1-4. The optimized scheduling results obtained by simulation are shown in Figure 6 As shown, after the iteration, park users can indirectly participate in the carbon market through price signals set by upper-tier integrated energy suppliers, achieving energy conservation and emission reduction through integrated demand response. The iterative energy-carbon prices are shown in Table 5-6. The energy-carbon price of the heat load is related to the heat load's distance from the heat exchange station. The farther away from the heat exchange station, the greater the heat loss of hot water flowing through the heat pipe network, resulting in a corresponding increase in the energy price and unit heat load. The energy-carbon price of the electricity load is related to the topology of each park in the power grid. Park 3, which is closer to the wind turbines, has a lower electricity-carbon price, demonstrating the rationality of the proposed pricing mechanism. This demonstrates that the proposed pricing mechanism can encourage park users to establish local wind power, encourage the use of cleaner energy, and achieve energy conservation and emission reduction.

[0271] Table 1 Model parameters in simulation

[0272]

[0273] Table 2 Tiered prices for large power grids and thermal power units

[0274] Time interval Large power grid / thermal power unit price ($ / MWh) Valley: 1-5, 22-24 38 / 30 Draw:6-7,11-15,21 62 / 50 Peak:8-10,16-20 132 / 106

[0275] Table 3 Unit operating parameters

[0276]

[0277] Table 4 Parameters of the step-by-step carbon emission mechanism

[0278] Carbon quota Split Point Tiered carbon pricing <![CDATA[E quota =58]]> <![CDATA[σ1=0.2,σ2=0.5]]> <![CDATA[λ1=50,λ1=80,λ3=120]]>

[0279] Table 5 Carbon prices for end users

[0280]

[0281] Table 6 Energy prices for end users

[0282]

[0283] It should be understood that the above description of the embodiments is relatively detailed and cannot be regarded as limiting the scope of protection of the patent of the present invention. Under the guidance of the present invention, ordinary persons in this field can make substitutions or modifications without departing from the scope of protection of the claims of the present invention, which shall fall within the scope of protection of the present invention. The scope of protection requested by the present invention shall be based on the attached claims.

Claims

1. A low-carbon collaborative scheduling method for integrated energy suppliers and end users, characterized in that: The following steps are involved: Step 1: Establish a tiered carbon emissions trading mechanism model based on tiered carbon emissions quotas and carbon emissions of the integrated energy system; Step 2: Establish a low-carbon economic dispatch model for integrated energy suppliers: Utilize the tiered carbon emissions trading mechanism model to construct an objective function that minimizes the total energy cost and total carbon cost of the integrated energy system. This is then combined with the model of the CHP unit with thermoelectric coupling elements, the second-order cone model of the AC power flow in the distribution network, and the transient microelement model of the heat pipe network to establish a low-carbon economic dispatch model for integrated energy suppliers. Step 3: Allocate the carbon emission benefits to users in the park according to the proportion of user loads in each park, establish Shapley's energy-carbon price allocation model, and calculate the energy price and carbon emission price of rigid load and flexible load; Step 4: Based on the final energy price and carbon emission price, establish a comprehensive load response model for the lower-level park users to respond to the energy-carbon price issued by the upper-level integrated energy supplier; The load response model includes: an objective function that minimizes the sum of total energy cost, carbon cost, and penalty coefficients resulting from user participation in load response, an electric load constraint model, and a thermal load constraint model; Step 5: Create replicated variables to decouple the coupling point between the integrated energy supplier and the user, that is, the interaction power of electricity and thermal energy. Construct the Lagrangian augmented form objective functions of the integrated energy supplier and the park user respectively, construct the Lagrangian multiplier update formula and the penalty factor update formula, and use the variable penalty factor ADMM method to achieve distributed collaborative optimization scheduling.

2. A low-carbon collaborative scheduling method for integrated energy suppliers and end users according to claim 1, characterized in that: In step 1, establishing a tiered carbon emissions trading mechanism model includes the following steps: Step 1.1: The carbon trading authority pre-allocates free carbon allowances based on the historical carbon emissions of the integrated energy system or industry benchmarks. When the actual carbon emissions generated by the integrated energy system are lower than the allocated allowances, the excess allowances can be sold on the carbon trading market; otherwise, the excess allowances can be purchased on the carbon emissions market. Step 1.2: Divide carbon emissions into several intervals, each corresponding to a different carbon trading price. The formula for the tiered carbon emission quota is as follows: AND trade =And system -AND quota The formula for calculating the tiered carbon price for integrated energy suppliers is: in The carbon price paid by upper-tier integrated energy suppliers, E trade , E quota , E system are the carbon emissions participating in the carbon market, the carbon emission share allocated by the carbon trading authority, and the total carbon emissions of the system, respectively; λ1, λ2, and λ3 are the prices of each carbon emission interval, and λ3>λ2>λ1; σ1 and σ2 are the dividing points of each carbon emission interval, and σ1>σ1; Step 1.3: Calculate the carbon emissions of the integrated energy system. Calculate the carbon emissions on the energy supply side of the integrated energy system. Each unit has a certain carbon emission intensity. The carbon emission intensity of all units multiplied by the output of each unit is the total carbon emissions of the integrated energy system. The carbon emissions calculation formula for the integrated energy system is: Where c is the carbon emission coefficient per unit power of each unit, P is the active power of each unit, G is the gas consumption of the CHP unit, subscripts g, b, CHP, and w represent the thermal power unit, the main power grid, the CHP unit, and the wind turbine, respectively, and t represents the time series of the power system. is the time set for scheduling, and Δt represents the time scale for scheduling.

3. A low-carbon collaborative scheduling method for integrated energy suppliers and end users according to claim 1, characterized in that: The step 2 includes the following steps: Step 2.1: Construct the objective function of the integrated energy supplier, that is, to minimize the total energy cost and total carbon cost of the integrated energy system. The optimization objective function is: Among them C I 、 and They represent the total cost, energy cost and carbon cost of the integrated energy supplier respectively; p represents the price per unit power of each unit, p curtail represents the cost of wind curtailment, With P wind They represent the predicted value of wind power and the dispatched wind power active power, g, b, and CHP represent thermal power units, large power grids, and CHP units, respectively. t represents the time series of the power system. is the time set for scheduling; Step 2.2: Construct a model of the thermoelectric coupling element CHP unit, and its expression is: Among them, P CHP,t , H CHP,τ is the electrical power and thermal power output of the CHP unit, K e With K h are the power generation efficiency and heating efficiency of the CHP unit, τ and t are the time series of the power system and thermal system respectively; Step 2.3: Construct the second-order cone model of the AC power flow of the distribution network, which is expressed as follows: Among them, P PG,i,t With Q PG,i,t They represent the active power and reactive power of the motor on node i at time t, P L,i,t With Q L,i,t They represent the active power and reactive power consumed by the load on node i at time t, P ij,t With Q ij,t are the active and reactive powers flowing from node i to node j respectively; l ij,t is the square of the current in line ij, v i,t is the square of the voltage at node i, r ij with x ij They represent the resistance and reactance of line ij respectively; is the set of all nodes in the line, m and i are midpoint; The current, voltage range, and balance node constraints of the distribution network are: Among them, v o,t is the square of the voltage at the equilibrium node o, v min With v max are the lower and upper limits of the voltage squared, respectively. max is the upper limit of the square of the current; Step 2.4: Construct a transient microelement model of the heat pipe network. This model includes the radial heat dissipation microelement temperature formula for the insulation layer, the water temperature formula within the heat pipe network, the flow mixing and temperature mixing formulas at the pipelines, the heat exchange formula between the heat exchange station and the load, and the temperature constraint formula. The radial heat dissipation infinitesimal temperature formula of the insulation layer is: in c u is the specific heat capacity of the insulation layer, D out With D in are the outer diameter and inner diameter of the pipeline respectively, Δx is the length of the pipeline element, Δτ is the time element of the heating network, ρ u is the density of the insulation layer, R ru With R us The thermal resistance between hot water and insulation layer, and between insulation layer and hot water, T u,l,k,τ With T r,l,k,τ are the temperatures of the insulation layer and hot water at the kth section of line l at time τ, T s is the soil temperature, is the set of segments of pipeline l, is the set of all pipelines; The water temperature formula in the heat pipe network is: Among them, M l is the flow rate of pipeline l, ρ r is the density of hot water, c r is the specific heat capacity of hot water; The flow mixing and temperature mixing formulas in the pipeline are: in, and Respectively represent the pipeline sets flowing into / out of node a, if and of represent the pipelines flowing into and out of node a, respectively. r,of,1,τ represents the hot water temperature of the first infinitesimal element of the outflow pipeline a at time τ; The heat exchange formula between the heat exchange station and the load is: Wherein, the subscripts p and q represent the heat exchange station and load respectively, H q,τ represents the thermal power consumed at load node q at time τ, η ex and η load They represent the heat exchange efficiency between the CHP unit and the load respectively; The water temperature in the heat pipe network needs to be maintained within a certain temperature range. The temperature constraint formula is as follows: During a scheduling cycle, the temperature difference at the load inlet needs to be kept within a certain temperature difference: in, and They represent the water temperature of the last segment of the water inlet pipeline of load node r at the initial and final moments of scheduling respectively.

4. A low-carbon collaborative scheduling method for integrated energy suppliers and end users according to claim 1, characterized in that: The step 3 includes the following steps: Step 3.1: Further divide the load of users in each park into flexible load and rigid load based on load transferability, and divide it into peak load, normal load, and valley load based on time dimension; Step 3.2: Allocate the initial carbon emission benefits to the park users. When the load of all parks is 0, there will be a certain amount of carbon emission benefits under the tiered carbon trading mechanism. Therefore, it is necessary to first fairly distribute the carbon emission benefits to each park user based on the proportion of the load of each park user. Step 3.3: For the rigid load of each park user, the Shapley value method is used to allocate the energy-carbon cost corresponding to the rigid load of each park user. The formula for allocating the energy-carbon price of park users using the Shapley value method is: Where: x i is the amount of responsibility shared by load member i; S is the sub-alliance composed of existing sub-alliance members before load member i joins; =(i) / ... Among them, n N Indicates the number of all participating members, n S Indicates the number of participating members in the sub-alliance S; 3.4 For flexible loads, the Aumann Shapley method is used to allocate the energy-carbon cost of each park user's flexible load in different peak, flat, and valley time periods. Each user load is divided into six small loads based on the six dimensions of peak / flat / valley, rigid load, and flexible load. Each small load is then divided into N segments. The discretized Aumann Shapley method is used to express the contribution of each small load i to the system energy-carbon cost as follows: Where P is a load that includes all users vector of Step 3.5: Based on the Shapley allocation results of steps 3.3 and 3.4, calculate the energy price and carbon emission price respectively: When calculating the energy price, the integrated energy supplier sets a fixed profit margin, and the energy costs of the rigid load and flexible load are obtained from steps 3.3 and 3.4 respectively, and then multiplied by the fixed profit margin to obtain the final energy price; the carbon emission price of each park is the carbon emission cost allocation value of the rigid load and flexible load obtained from steps 3.3 and 3.4 respectively; the energy-carbon price corresponding to the CHP unit output is allocated to electrical energy and thermal energy according to the unit's heat-to-electricity ratio; in the final calculation, the energy-carbon price of thermal energy is not distinguished by time scale.

5. A low-carbon collaborative scheduling method for integrated energy suppliers and end users according to claim 1, characterized in that: The step 4 includes the following steps: 4.1 Construct the objective function for the end-users in the park; that is, minimize the sum of the total energy cost, carbon cost, and the penalty coefficient caused by the user's participation in load response. The objective function expression is: in, represents the total cost for user n, They represent the energy cost, carbon cost and penalty coefficient of user n due to user participation in load response, is the initial carbon emission benefit allocated to user n in step 3.2; represents the electricity price of user n at time t, represents the hot price of user n; and They represent the electricity carbon price of the rigid load part and the flexible load part of user n at time t, represents the heat carbon price for user n at time t; and Represent the rigid load and flexible load of user n respectively, and They represent the electrical load and thermal load used by user n respectively; the superscripts shift and conv represent the transferable load and the load that can be converted into electricity and heat respectively. shift with d conv is the load response penalty coefficient, where d shift >d conv , and They represent the transferable load and the load that can be converted into electricity and heat after the load response, and Indicates the load change after user n load response, and is the initial transferable load and electric-thermal conversion load of user n before load response; in and Linearize it by the following formula: in, and Additional variables are added; 4.2 Establish an electric load constraint model; the electric load includes rigid load and flexible load; the basic load does not participate in the load response; the flexible load includes transferable load and electric heat conversion load; the formula is: The constraints on transferable loads are: The constraints on the electric-to-heat conversion load are: in, is the original value of the load that can be converted into heat before the load response of user n, is the heat load involved in electric-heat conversion after load response; 4.3 Establish a heat load constraint model; model the heat dissipation, heat radiation, and heat conduction of the building. When the room temperature meets the PMV comfort range, the indoor heat load demand is met. The heat dissipation process from indoor to outdoor is modeled as: in, is the heat dissipation power from indoor to outdoor, is the area of the fence, is the thermal conductivity, and are the indoor and outdoor temperatures of user n at time t; The thermal radiation power absorbed by solar radiation is modeled as: in, is the thermal radiation power of sunlight, α n is the solar refraction absorption coefficient, is the window area of user n, is the radiation heat flux density of the wall of user n; The heat conduction model of indoor temperature is modeled as: in, is the thermal power absorbed by user n in the heating network at time t, is the thermal power of the indoor heat source, F n is the thermal resistance of the wall; The PMV is set between [-0.5, 0.5] during the day and [-1, 1] at night. The PMV formula is: where μ PMV is the value of PMV, I is the user's metabolic rate, which is related to the intensity of physical activity and is set at 70W / m 2 ;T comfy is the average temperature of human skin in a comfortable state, which is set to 33.5℃; R clo is the thermal resistance of the clothing, which is 0.11 (m2·℃) / W.

6. A low-carbon collaborative scheduling method for integrated energy suppliers and end users according to claim 1, characterized in that: The step 5 includes the following steps: 5.1 Create replicated variables; decouple the coupling point between the integrated energy supplier and the user, that is, the interactive power of electric energy and thermal energy; the power sold by the integrated energy supplier to user n at time t and the electric power purchased by user n from the integrated energy supplier at time t are expressed as and The thermal power is expressed as and 5.2 Construct the Lagrangian augmented objective functions of the integrated energy supplier and the park user respectively, which are: in, and is the objective function of the integrated energy supplier and user in the augmented Lagrangian form at the k+1th iteration, and are the electricity / heat power sold by the integrated energy supplier to user n and the electricity / heat power purchased by user n at the kth iteration, and is the Lagrange multiplier of the electric power and thermal power of user n at iteration step k, ρ e,n,t and ρ h,n,t are the penalty parameters of electric power and thermal power of user n at iteration step k; 5.3 Construct the Lagrange multiplier update formula: 5.4 Construct penalty factor update formula: Among them, ρ k is a vector containing all penalty factors at the kth iteration, τ incr , τ decr and μ are penalty factor update parameters, both greater than 1, μ = 10, τ incr =τ decr =2; and They represent the original residual and the dual residual respectively, and their expressions are: 5.5 Using variable penalty factor ADMM method to realize distributed collaborative optimization scheduling; (1) Initialization: Initialize the Lagrange multiplier and and penalty factor and Users report their energy consumption plans to upper-level integrated energy suppliers; (2) When k≤k max and When executing: a) The integrated energy supplier performs low-carbon economic dispatch based on the model described in step 2, where the objective function is changed to the augmented Lagrangian objective function of the integrated energy supplier in step 5.2, and the constraints remain unchanged; b) The integrated energy supplier provides energy-carbon pricing for users based on the model described in step 3 and passes the energy-carbon pricing to the corresponding park users; c) The park performs a comprehensive load response based on the model described in step 4, where the objective function is changed to the user's augmented Lagrangian objective function in step 5.2, and the updated energy purchase information is transmitted to the comprehensive energy supplier; d) Update the Lagrange multiplier based on step 5.3 and And update the penalty factor based on step 5.4 and (3) The iteration ends and the results are output.

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