Optimal dispatch method of regional integrated energy system considering dynamic carbon trading price

Abstract

The present application belongs to the technical field of new energy development, and specifically relates to a regional comprehensive energy system optimal scheduling method considering dynamic carbon trading price, which firstly establishes a regional comprehensive energy system model composed of subsystems such as thermal power units, CHP units, wind power units, storage batteries and electric boilers; introduces a carbon trading mechanism into the regional comprehensive energy system scheduling model, improves the stepped carbon emission mechanism according to the different carbon emissions of different units under multiple energies; establishes a regional comprehensive energy system optimal scheduling model considering dynamic carbon trading price through a regional comprehensive energy system carbon emission right model, an objective function and constraint conditions; and finally takes a regional comprehensive energy system in the Three-North region as a research object to perform example analysis, which is reasonable in structure, can reduce the energy consumption and carbon emission of the regional comprehensive energy system, and improve the economic performance and environmental protection of system operation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of new energy development, in particular to a regional integrated energy system optimal scheduling method considering dynamic carbon trading price. BACKGROUND

[0002] The so-called regional integrated energy system can realize multiple energy complementation and collaborative optimization within and across regions, breaking the existing mode of separate planning and independent operation of each energy, and can promote renewable energy consumption, optimize energy structure and improve energy comprehensive utilization efficiency, and has important significance for building a clean, low-carbon and efficient energy system.

[0003] Although the carbon emission intensity has gradually decreased in recent years, the total carbon emission amount is still on the rise with the continuous increase in the scale of the power industry. In addition, under the requirements of integrated energy systems and low-carbon power, the government and regulatory authorities also pay more and more attention to carbon emission problems. At present, the carbon trading mechanism is considered to be one of the most effective energy-saving and emission-reducing measures. Therefore, in order to effectively reduce carbon emissions and develop a low-carbon energy system, it is necessary to introduce carbon trading into the regional integrated energy system to further exert the advantages brought by the multi-energy system. The essence of carbon trading is that the government allocates free carbon emission quotas to each enterprise through the establishment of indicators, establishes a legal trading system, and allows flexible trading of carbon emission quotas through the market to control carbon dioxide emissions.

[0004] At present, many domestic and foreign researchers have introduced the carbon trading mechanism into the traditional power system and constructed a variety of models. Many literatures have made detailed research on the carbon trading mechanism model and pricing strategy, but have not made further research on different generator groups and carbon trading benchmark prices and other parameters to facilitate carbon trading analysis of multi-energy systems.

[0005] Therefore, we propose a regional integrated energy system optimal scheduling method considering dynamic carbon trading price. SUMMARY

[0006] This section aims to summarize some aspects of the embodiments of the present application and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of the specification to avoid obscuring the purpose of this section, the abstract and the title, and such simplifications or omissions cannot be used to limit the scope of the present application.

[0007] In view of the problems existing in the prior art of new energy development, the present application is proposed.

[0008] Therefore, the application aims to provide a regional integrated energy system optimal scheduling method considering dynamic carbon trading prices, which can reduce the energy consumption and carbon emissions of the regional integrated energy system and improve the economic performance and environmental protection of system operation.

[0009] To solve the above technical problems, according to one aspect of the application, the application provides the following technical solutions.

[0010] The regional integrated energy system optimal scheduling method considering dynamic carbon trading prices specifically comprises the following steps.

[0011] Step 1: establishing a regional integrated energy system model;

[0012] Step 2: introducing a stepped carbon trading mechanism;

[0013] Step 3: establishing a regional integrated energy system optimal scheduling model considering dynamic carbon trading prices:

[0014] Step 4: performing example analysis on the model.

[0015] As a preferred scheme of the regional integrated energy system optimal scheduling method considering dynamic carbon trading prices, in step 1, the regional integrated energy system is composed of thermal power units, CHP units, wind power units, storage batteries, electric boilers and heat storage tanks; and the heat load is flexibly supplied by the CHP units, the electric boilers and the heat storage tanks.

[0016] As a preferred scheme of the regional integrated energy system optimal scheduling method considering dynamic carbon trading prices, in step 2, the stepped carbon trading is divided into intervals on both sides at a distance from the initial carbon emission quota, and a plurality of carbon emission intervals are divided. When the carbon emission is greater or smaller, the carbon trading interval is farther away from the reference carbon trading quota, and the carbon trading price is higher. In this interval, the cost or benefit brought by carbon trading is also more, so it is conducive to controlling the carbon emission. In view of the different carbon emissions of different units under multiple energies and for further controlling the carbon emission intensity of power generation enterprises, the stepped carbon emission mechanism is improved to encourage the coal-fired units to be converted to gas turbines, and small-capacity coal-fired units are shut down. The improved stepped carbon trading is shown in formula (1).

[0017] (1)

[0018] In the formula, F is the cost or benefit of carbon trading; λ is the price of carbon trading on the day; d is the length of each carbon emission interval; σ is the carbon trading price growth rate of each step, that is, the carbon trading price increases σλ for each step; E C is the actual carbon emission of the gas unit of the power generation enterprise; E G is the actual carbon emission of the coal-fired unit of the power generation enterprise; and EC is the actual carbon emission of the coal-fired unit of the power generation enterprise; μ is a dynamic adjustment coefficient of the carbon trading price.

[0019] As a preferred scheme of the method for optimizing dispatch of the regional integrated energy system considering dynamic carbon trading price, in step 3, first, a carbon emission right model of the regional integrated energy system is established, the part of the carbon trading quota obtained by the entire regional integrated energy system is mainly the coal-fired unit, the CHP unit and the wind turbine, and the part of the carbon trading cost to be optimized mainly includes the coal-fired unit and the CHP unit, wherein the coal-fired unit is a coal-fired unit, and the CHP unit is a gas-fired unit.

[0020] (1) Carbon emission quota trading model of the coal-fired unit of the power plant

[0021] The carbon emission quota of the power plant adopts a model proportional to the power generation capacity, as shown in formula (2).

[0022] (2)

[0023] In the formula, E pMT is the carbon emission quota of the coal-fired unit; η e is the carbon emission quota per unit of power generation capacity; P MTi (t) is the power generation capacity of the i th coal-fired unit at the time period t.

[0024] (2) Carbon emission quota trading model of the wind turbine

[0025] Although the wind farm does not have carbon emission, it can still obtain carbon emission quota, and the carbon quota of the wind turbine is similar to the calculation of the carbon quota of the coal-fired unit of the power plant, as shown in formula (3).

[0026] (3)

[0027] In the formula, E pWT is the carbon emission quota of the wind turbine; P WTi (t) is the power generation capacity of the i th wind turbine at the time period t.

[0028] (3) Carbon emission quota trading model of the CHP unit

[0029] The CHP unit can provide heat energy while generating power, which is different from different power generators, so the heat supply power of the unit needs to be considered when calculating the carbon emission quota, and therefore the power generation capacity needs to be converted into heat supply capacity, and the carbon emission quota is allocated according to the total equivalent heat supply capacity, and the carbon emission quota is as shown in formula (4).

[0030] (4)

[0031] In the formula, E pCHPCarbon emission quota for CHP unit; η h Carbon emission quota for unit heat supply; P CHPi (t) is the power generation of the i-th CHP unit at time period t; Q CHPi (t) is the heat supply of the i-th CHP unit at time period t; c eh is the coefficient for converting power generation into heat supply;

[0032] Then, the sum of the operation and maintenance costs of each unit, the operation cost of energy storage equipment and the carbon trading cost in the scheduling period is minimized as the objective function of the regional comprehensive energy system planning model, the scheduling period is 24 hours, and the objective function of the model is shown in formula (5);

[0033] (5);

[0034] In the formula: P Cj (t) = P CHPj + δQ CHPj ; α, β, σ are fuel cost coefficients of CHP units; m1 is the number of CHP units; P CHPj (t) is the power generation of the j-th CHP unit at time period t; Q CHPj (t) is the heat release power of the j-th CHP unit at time period t; δ is the coefficient for converting heat power into electric power; a, b, c are fuel cost coefficients of thermal power units; P MTj (t) is the power generation of the j-th thermal power unit at time period t; m2 is the number of thermal power units; c wt is the operation and maintenance cost per unit of wind turbine power; m3 is the number of wind turbine units; P WTj (t) is the actual on-grid power of the j-th wind turbine unit at time period t; P AWTj (t) is the wind curtailment power of the j-th wind turbine unit at time period t; c bess is the cost of the battery unit per unit of time charging and discharging power; m4 is the number of battery units; P CHAj (t) is the charging power of the j-th battery unit at time period t; P DISj (t) is the discharging power of the j-th battery unit at time period t; C carbon is the cost of carbon trading, i.e. F C in formula (1);

[0035] Finally, the constraint conditions are added to the model, including basic power balance constraints, output constraints of each unit and carbon emission constraints;

[0036] (1) Power balance constraint

[0037] (6);

[0038] (2) CHP unit output constraint

[0039] (7);

[0040] wherein: X CHP (t) is the working condition state of the CHP unit in the t period; P CHPmax is the upper limit of the power generation of the CHP unit; P CHPmin is the lower limit of the power generation of the CHP unit; L CHPup is the upper limit of the ramp rate of the CHP unit; L CHPdown is the lower limit of the ramp rate of the CHP unit;

[0041] (3) Thermal power unit output constraint

[0042] (8);

[0043] wherein: X MT (t) is the working condition state of the thermal power cogeneration unit in the t period; P MTmax is the upper limit of the power generation of the thermal power unit; P MTmin is the lower limit of the power generation of the thermal power unit; L MTup is the upper limit of the ramp rate of the thermal power unit; L MTdown is the lower limit of the ramp rate of the thermal power unit;

[0044] (4) Electric boiler output constraint

[0045] (9);

[0046] wherein: Q EB (t) is the heat supply power of the electric boiler in the t period; COP EB is the electric-thermal conversion coefficient of the electric boiler; Q EBmax is the upper limit of the heat supply power of the electric boiler; Q EBmin is the lower limit of the heat supply power of the electric boiler; L EBup is the upper limit of the ramp rate of the electric boiler operation; L EBdown is the lower limit of the ramp rate of the electric boiler operation;

[0047] (5) Carbon emission constraint

[0048] (10);

[0049] wherein: E m is the upper limit of the allocated carbon emission quota.

[0050] As a preferred scheme of the method for optimizing scheduling of a regional integrated energy system considering dynamic carbon trading price according to the application, in the step 4, the specific steps are as follows: taking the scheduling period as T=24h and the unit scheduling time as At=1h, the reliability of the proposed dynamic adjustment coefficient considering carbon trading on the operation benefit of the regional integrated energy system is verified, and the influence of different carbon trading mechanism parameters on the carbon trading level is analyzed.

[0051] Compared with the prior art, the application has the following beneficial effects:

[0052] (1) Carbon trading is mainly through the government encouraging carbon emission quota to trade in the market, so that power generation enterprises spontaneously reduce carbon emissions to reduce the cost of carbon trading. After considering the dynamic adjustment of the carbon trading price, compared with the ordinary step-by-step carbon trading mechanism, the total cost and carbon emissions of the regional integrated energy system operation can be further reduced by adjusting the output of different units.

[0053] (2) The step-by-step carbon trading benchmark price and the interval length change will affect the scheduling result of the low-carbon economic scheduling model. With the rise of the carbon trading price, the carbon emissions are continuously reduced, while the carbon trading cost and the total cost are first increased and then decreased. In addition, with the continuous increase of the carbon trading price interval, the carbon emissions are gradually increased, while the carbon trading cost and the total cost are gradually decreased. Therefore, selecting appropriate step-by-step carbon trading related parameters can improve the carbon emissions and the total cost of system operation, and play a role in energy saving and emission reduction. BRIEF DESCRIPTION OF DRAWINGS

[0054] In order to more clearly illustrate the technical scheme of the embodiments of the application, the application will be described in detail below with reference to the drawings and detailed embodiments. Obviously, the drawings in the following description are only some embodiments of the application, and for those skilled in the art, other drawings can also be obtained without creative labor. Among them:

[0055] Figure 1 It is a step flow structure diagram of the application;

[0056] Figure 2 It is a regional integrated energy system model structure diagram of the application;

[0057] Figure 3 It is the relationship between the carbon trading price dynamic adjustment coefficient mu and the carbon emissions of each unit of the application;

[0058] Figure 4 It is the power generation amount of each type of unit in one scheduling period under four modes of the application;

[0059] Figure 5Influence of different benchmark carbon trading prices on the low-carbon scheduling model

[0060] Figure 6 Influence of different carbon trading price interval lengths on the low-carbon scheduling model. DETAILED DESCRIPTION

[0061] In order to make the above objectives, characteristics and advantages of the present application more apparent, specific embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0062] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. However, it will be apparent to one skilled in the art that the present application can be practiced without the specific details given herein, that the present application can be practiced with other different ways, and that the present application is not limited to the details given herein.

[0063] Secondly, the present application is described in detail in combination with the schematic diagram, and in the detailed description of the embodiments of the present application, the cross-sectional view of the device structure is locally enlarged without the general proportion for the convenience of illustration, and the schematic diagram is only an example, which should not limit the scope of protection of the present application herein. In addition, the three-dimensional spatial dimensions of length, width and depth should be included in the actual manufacture.

[0064] In order to make the objectives, technical solutions and advantages of the present application more apparent, the embodiments of the present application will be further described in detail below in combination with the accompanying drawings.

[0065] The present application provides the following technical solutions: a regional integrated energy system optimal scheduling method considering dynamic carbon trading prices, which can reduce the energy consumption and carbon emissions of the regional integrated energy system, and improve the economic performance and environmental protection of system operation; EMBODIMENT

[0066] Step 1: Establish a regional integrated energy system model, the regional integrated energy system is composed of thermal power units, CHP units, wind power units, batteries, electric boilers and heat storage tanks; the heat load is flexibly supplied by the CHP units, electric boilers and heat storage tanks;

[0067] Step 2: Introduce a stepped carbon trading mechanism. The stepped carbon trading is based on the initial carbon emission quota. Each interval is divided at a certain distance from both sides. When the carbon emission is larger or smaller, the carbon trading interval it is in is farther away from the baseline carbon trading quota, and the carbon trading price is higher. In this interval, the cost or benefit brought by carbon trading will be more, so it is conducive to controlling carbon emission. For different carbon emissions of different units under multiple energy sources and to further control the carbon emission intensity of power generation enterprises, the stepped carbon emission mechanism is improved to encourage coal-fired units to change to gas turbines, and to shut down small-capacity coal-fired units. The improved stepped carbon trading is shown in formula (1);

[0068] (1);

[0069] F = λd + σλd (Ei - E0) + σλd (Ej - E0) (1) C F is the cost or benefit of carbon trading; λ is the price of carbon trading on the day; d is the length of each carbon emission interval; σ is the carbon trading price growth rate of each step, that is, the carbon trading price increases σλ for each step up; E G is the actual carbon emission of the gas turbine unit of the power generation enterprise; E C is the actual carbon emission of the coal-fired unit of the power generation enterprise; μ is the dynamic adjustment coefficient of the carbon trading price;

[0070] Step 3: Establish a regional integrated energy system optimization scheduling model considering dynamic carbon trading price. First, establish a regional integrated energy system carbon emission right model. The entire regional integrated energy system can obtain part of the carbon trading quota, mainly for thermal power units, CHP units and wind power units. The carbon trading cost part that needs to be optimized mainly includes thermal power units and CHP units, wherein the thermal power units are all coal-fired units, and the CHP units are all gas turbine units.

[0071] (1) Thermal power plant unit carbon emission quota trading model

[0072] The carbon emission quota of the thermal power plant adopts a model proportional to its power generation capacity, as shown in formula (2).

[0073] (2);

[0074] Ei = ηiPi (t) (2) pMT Ei is the carbon emission quota of the thermal power unit; η e is the carbon emission quota per unit of power generation capacity; P MTi (t) is the power generation capacity of the i th thermal power unit at time period t;

[0075] (2) Wind power unit carbon emission quota trading model

[0076] Although wind farms have no carbon emissions, they can still obtain carbon emission quotas. The carbon quota of a wind turbine is similar to that of a thermal power plant, as shown in equation (3);

[0077] (3);

[0078] Ei(t) = P pWT i(t) * c WTi where Ei(t) is the carbon emission quota of the ith wind turbine; P pCHP i(t) is the power generation of the ith wind turbine at time period t; and c h is the carbon emission quota per unit of power generation.

[0079] (3) CHP unit carbon emission quota trading model

[0080] CHP units can provide heat energy using waste heat while generating electricity, which is different from different generating units. Therefore, when calculating the carbon emission quota, the heat supply power of the unit should be considered. Therefore, the power generation needs to be converted into heat supply, and the carbon emission quota is allocated according to the total equivalent heat supply. The carbon emission quota is shown in equation (4);

[0081] (4);

[0082] Ei(t) = η pCHP i(t) * c h where Ei(t) is the carbon emission quota of the ith CHP unit; η CHPi i(t) is the carbon emission quota per unit of heat supply; P CHPi i(t) is the power generation of the ith CHP unit at time period t; Q eh i(t) is the heat supply of the ith CHP unit at time period t; and c Cj is the coefficient for converting power generation into heat supply.

[0083] Then, the regional comprehensive energy system planning model takes the sum of the operating and maintenance costs of each unit, the operating cost of energy storage equipment, and the carbon trading cost in the dispatching period as the objective function, with the dispatching period being 24 hours. The objective function of the model is shown in equation (5);

[0084] (5);

[0085] P(t) = ∑(P CHPj i(t) + δQ CHPj i(t)) + ∑(P CHPj j(t) + δQ CHPj j(t)) + ∑(P MTj k(t) + δQ wt k(t)) Cj where P(t) is the total power generation at time period t; P CHPj i(t) is the power generation of the ith CHP unit at time period t; δQ CHPj i(t) is the heat supply of the ith CHP unit at time period t; P CHPj j(t) is the power generation of the jth CHP unit at time period t; δQ CHPj j(t) is the heat supply of the jth CHP unit at time period t; P MTj k(t) is the power generation of the kth thermal power unit at time period t; and δQ wt k(t) is the heat supply of the kth thermal power unit at time period t.is the operation and maintenance cost of the wind turbine per unit of power generation; m3 is the number of wind turbines; P WTj is the actual power output of the jth wind turbine at time t; P AWTj is the curtailed power of the jth wind turbine at time t; c bess is the cost of charging and discharging per unit of power of the battery; m4 is the number of battery units; P CHAj is the charging power of the jth battery unit at time t; P DISj is the discharging power of the jth battery unit at time t; C carbon is the cost of carbon trading, i.e., F in equation (1) C ;

[0086] Finally, constraints are added to the model, including basic power balance constraints, output constraints of various units, and carbon emission constraints;

[0087] (1) Power balance constraint

[0088] (6);

[0089] (2) CHP unit output constraint

[0090] (7);

[0091] In the formula: X CHP is the operating state of the CHP unit at time t; P CHPmax is the upper limit of the power generation of the CHP unit; P CHPmin is the lower limit of the power generation of the CHP unit; L CHPup is the upper limit of the ramp rate of the CHP unit; L CHPdown is the lower limit of the ramp rate of the CHP unit;

[0092] (3) Thermal power unit output constraint

[0093] (8);

[0094] In the formula: X MT is the operating state of the CHP unit at time t; P MTmax is the upper limit of the power generation of the CHP unit; P MTmin is the lower limit of the power generation of the CHP unit; L MTup is the upper limit of the ramp rate of the CHP unit; L MTdown is the lower limit of the ramp rate of the CHP unit;

[0095] (4) Electric boiler output constraint

[0096] (9);

[0097] In the formula: Q EB (t) is the heating power of the electric boiler at time t; COP EB is the electric heating conversion coefficient of the electric boiler; Q EBmax is the upper limit of the heating power of the electric boiler; Q EBmin is the lower limit of the heating power of the electric boiler; L EBup is the upper limit of the climbing rate of the electric boiler operation; L EBdown is the lower limit of the climbing rate of the electric boiler operation;

[0098] (5) Carbon emission constraint

[0099] (10);

[0100] In the formula: E m is the upper limit of the allocated carbon emission quota:

[0101] Step 4: Example analysis of the model, taking a certain regional integrated energy system in the Three Norths region as the research object, the system consisting of CHP units, wind turbine units, thermal power units, and electric boilers, the carbon trading related parameters as shown in Table 1, wherein the carbon trading price interval is divided into 7 intervals according to formula (1) and Figure 3 (2). Taking the dispatching period as T=24h and the unit dispatching time as At=1h, the reliability of the proposed carbon trading dynamic adjustment coefficient considering carbon trading on the operation benefit of the regional integrated energy system is verified, and the influence of different carbon trading mechanism parameters on the carbon trading level is analyzed.

[0102] Table 1 Carbon trading related parameters

[0103]

[0104] Dispatching results under the carbon trading mechanism

[0105] Mode 1: without considering the carbon emission of the system, only considering the dispatching cost of the system operation;

[0106] Mode 2: under the traditional carbon trading mechanism, considering the carbon trading cost and the dispatching cost of the system;

[0107] Mode 3: under the step-type carbon trading mechanism, considering the carbon trading cost and the dispatching cost of the system;

[0108] Mode 4: under the step-type carbon trading mechanism, introducing the step-type carbon trading dynamic adjustment price, considering the carbon trading cost and the dispatching cost of the system.

[0109] From Figure 4It can be seen that with the implementation of the stepped carbon trading, the output of low-carbon or new energy units such as wind turbine generators and CHP units is continuously increased, and the output of high-carbon units such as thermal power units is continuously reduced, thereby effectively reducing carbon emissions. Meanwhile, the power generation capacity of CHP units is continuously increased. Due to the thermal-electric coupling characteristics of CHP units, in order to meet the thermal load balance constraint, the power consumption of the electric boiler will decrease, the total power generation capacity will decrease, thereby reducing the energy loss in the electric-to-thermal process and reducing the operation cost of the system.

[0110] Although the present application has been described with reference to the embodiments above, various changes and modifications can be suggested to one skilled in the art and it is intended that the present application encompass such changes and modifications as fall within the scope of the appended claims. Particularly, each of the features recited in the disclosed embodiments of the present application can be used in any combination, unless the context explicitly states otherwise. The description herein is presented for the purpose of illustration and description and is not intended to limit the present application to the form disclosed. Therefore, the present application is not limited to the specific embodiments disclosed in this specification, but encompasses all technical solutions falling within the scope of the claims.

Claims

1. A regional integrated energy system optimization scheduling method considering dynamic carbon trading prices, characterized by: Specifically, the steps include the following: Step 1: Establish a regional integrated energy system model; Step 2: Introduce a tiered carbon trading mechanism; The tiered carbon trading mechanism uses the initial carbon emission allowance as a benchmark and divides the carbon emission range into several intervals at regular intervals on both sides. When the carbon emission is larger or smaller, the carbon trading range it is in is further away from the benchmark carbon trading allowance, and the carbon trading price is higher. The cost or benefit brought by carbon trading in this range will also be greater, so it is conducive to controlling carbon emissions. In view of the different carbon emissions of different units under multiple energy sources and in order to further control the carbon emission intensity of power generation enterprises, the tiered carbon emission mechanism is improved to encourage the conversion of coal-fired units to gas turbines and shut down small-capacity coal-fired units. The improved tiered carbon trading mechanism is shown in formula (1). （1）； In the formula: F C λ represents the cost or benefit of carbon trading; d represents the length of each carbon emission range; σ represents the price increase for each tier of carbon trading, i.e., the price increases by σλ for each tier; E represents the cost or benefit of carbon trading. G E represents the actual carbon emissions of gas turbine units in power generation companies. C represents the actual carbon emissions of coal-fired power plants; μ is the dynamic adjustment coefficient for carbon trading prices. Step 3: Establish a regional integrated energy system optimization scheduling model that considers dynamic carbon trading prices: First, establish a regional integrated energy system carbon emission rights model. The parts of the entire regional integrated energy system that can obtain carbon trading quotas are mainly thermal power units, CHP units and wind power units. The carbon trading cost parts that need to be optimized mainly include thermal power units and CHP units. Among them, thermal power units are all coal-fired units and CHP units are all gas-fired units. (1) Carbon emission quota trading model for thermal power plant units The carbon emission quotas for thermal power plants are modeled in proportion to their power generation, as shown in formula (2); （2）； In the formula: E pMT Carbon emission quotas for thermal power units; η e Carbon emission allowance per unit of electricity generated; P MTi (t) represents the power generation of the i-th thermal power unit during time period t; (2) Carbon emission quota trading model for wind turbine units Although wind farms do not emit carbon, they can still obtain carbon emission allowances. The calculation of carbon allowances for wind turbines is similar to that for thermal power plants, as shown in formula (3). （3）； In the formula: E pWT Carbon emission allowances for wind turbine generators; P WTi (t) represents the power generation of the i-th wind turbine in time period t; (3) CHP unit carbon emission quota trading model CHP units can generate electricity while using waste heat to provide thermal energy, unlike other generator sets. Therefore, when calculating carbon emission allowances, the heating power of the unit must be taken into account. Thus, its power generation needs to be converted into heat supply, and carbon emission allowances are allocated according to the total equivalent heat output. The carbon emission allowance is shown in formula (4). （4）； In the formula: E pCHP Carbon emission allowances for CHP units; η h Carbon emission allowance per unit of heating supply; P CHPi (t) represents the power generation of the i-th CHP unit during time period t; Q CHPi (t) represents the heat supply of the i-th CHP unit during time period t; c eh The coefficient used to convert electricity generation into heat supply; Then, the objective function is to minimize the sum of the operating and maintenance costs of each unit, the operating costs of energy storage equipment, and the carbon trading costs within the scheduling cycle of the regional integrated energy system planning model. The scheduling cycle is 24 hours, and the objective function of the model is shown in formula (5). （5）； In the formula: P Cj (t)=P CHPj +δQ CHPj ; α, β, σ are the fuel cost coefficients of CHP units; m1 is the number of CHP units; P CHPj (t) represents the power generation of the j-th CHP unit during time period t; Q CHPj (t) represents the heat release power of the j-th CHP unit during time period t; δ is the heat power converted to electrical power coefficient; a, b, and c are the fuel cost coefficients of the thermal power unit; P MTj (t) represents the power generation of the j-th thermal power unit in time period t; m2 represents the number of thermal power units; c wt The operation and maintenance cost per unit power generated by the wind turbine is m3; the number of wind turbines is P. WTj (t) represents the actual grid-connected power of the j-th wind turbine in time period t; P AWTj (t) represents the wind curtailment power of the j-th wind turbine unit during time period t; c bess The cost per unit time of charge / discharge power of the battery; m4 is the number of battery cells; P CHAj (t) represents the charging power of the j-th battery unit during time period t; P DISj (t) represents the discharge power of the j-th battery cell during time period t; C carbon The cost of carbon trading, i.e., F in equation (1) C ; Finally, constraints were added to the model, including basic power balance constraints, output constraints for each unit, and carbon emission constraints. (1) Power balance constraint （6）； (2) Output constraints of CHP units （7）； In the formula: X CHP (t) represents the operating status of the CHP unit during time period t; P CHPmax This refers to the upper limit of the generating capacity of the CHP unit; P CHPmin This represents the lower limit of the generating capacity of the CHP unit; L CHPup This is the upper limit of the ramp-up rate for CHP units; L CHPdown This is the lower limit of the ramp rate for CHP units; (3) Output constraints of thermal power units （8）； In the formula: X MT (t) represents the operating status of the cogeneration unit during time period t; P MTmax This represents the upper limit of the generating capacity of thermal power units; P MTmin This represents the lower limit of the generating capacity of thermal power units; L MTup This represents the upper limit of the ramp rate for thermal power units; L MTdown This is the lower limit of the ramp rate for thermal power units. (4) Output constraints of electric boilers （9）； In the formula: Q EB (t) represents the heating power of the electric boiler during time period t; COP EB Q is the electrothermal conversion coefficient of the electric boiler; EBmax Q represents the upper limit of the heating capacity of an electric boiler. EBmin This refers to the lower limit of the heating capacity of an electric boiler; L EBup This is the upper limit of the ramp rate for electric boiler operation; L EBdown This is the lower limit of the ramp rate for electric boiler operation; (5) Carbon emission constraints （10）； In the formula: E m This exceeds the upper limit of the allocated carbon emission allowance; Step 4: Perform case studies on the model.

2. The regional integrated energy system optimization scheduling method considering dynamic carbon trading prices according to claim 1, characterized in that: In step 1, the regional integrated energy system consists of thermal power units, CHP units, wind power units, batteries, electric boilers, and thermal storage tanks; the heat load is flexibly supplied by CHP units, electric boilers, and thermal storage tanks.

3. The regional integrated energy system optimization scheduling method considering dynamic carbon trading prices according to claim 1, characterized in that: The specific steps of step 4 are as follows: with a scheduling cycle of T=24h and a unit scheduling time of ∆t=1h, the reliability of the proposed dynamic adjustment coefficient for carbon trading on the operational efficiency of the regional integrated energy system was verified by comparison, and the impact of different carbon trading mechanism parameters on the carbon trading level was analyzed.

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