CN114676886A - Master-slave game optimization scheduling method for energy hubs based on comprehensive demand response and reward and punishment ladder carbon trading - Google Patents

Abstract

A master-slave game optimal scheduling method for energy hubs based on comprehensive demand response and reward and punishment ladder carbon trading, including: modeling gas turbines, gas boilers, electric chillers, absorption chillers, and batteries included in the energy hub structure, and reacting to the input power relationship with output power; establish a comprehensive demand response model, including user cooling load demand modeling, user heating load demand modeling and user electric load demand response; establish a master-slave game low-carbon model, so that EHO and users participating in the game interaction Pursue the optimal self-interest under their respective operating constraints; solve the master-slave game low-carbon model through differential evolution algorithm and CPLEX solver. The invention can effectively take into account the interests of both parties, give full play to the user's demand response potential, and realize EH economy and low-carbon operation.

Description

Master-slave game optimal scheduling method for energy hubs based on comprehensive demand response and reward and punishment ladder carbon trading

technical field

The invention relates to the technical field of integrated energy system scheduling, in particular to an energy hub master-slave game optimization scheduling method based on comprehensive demand response and reward and punishment ladder carbon trading.

Background technique

With the gradual reform and opening up of the national energy market, seeking a safe, efficient, low-carbon and clean energy operation model has become a hot research topic. The integrated energy system is an efficient, clean, multi-energy coupled energy management system. As a future form of high-efficiency energy, energy hubs play an important role in the study of integrated energy systems IES. Integrated demand response is an extension and extension of traditional power demand response, and plays a key role in the optimal operation of energy hub EH. It is an urgent problem to establish an IDR model that can consider various environmental disturbance factors and reflect the actual energy demand.

The reform of the energy market has caused a large number of emerging players to enter the market to compete fiercely. The application of game theory can well deal with the conflict of interests between different players. Carbon trading is considered to be one of the effective means to enhance the environmental benefits of the system while taking into account the economy. At present, most researches only introduce carbon transaction costs into IES operating costs, and giving full play to the energy-saving and emission-reduction capabilities of demand-side resources is one of the key issues to be solved.

SUMMARY OF THE INVENTION

The invention provides an energy hub master-slave game optimization scheduling method based on comprehensive demand response and reward and punishment ladder carbon trading, which can effectively take into account the interests of both parties, give full play to the user's demand response potential, and realize EH economy and low-carbon operation.

The technical scheme adopted in the present invention is:

The master-slave game optimization scheduling method for energy hubs based on comprehensive demand response and reward and punishment ladder carbon trading includes the following steps:

Step 1: Model the gas turbines, gas boilers, electric refrigerators, absorption refrigerators, and batteries included in the energy hub structure to reflect the relationship between input power and output power;

Step 2: Establish a comprehensive demand response model, including user cooling load demand modeling, user heating load demand modeling and user electric load demand response.

Step 3: Establish a master-slave game low-carbon model, so that the energy hub operators and users participating in the game interaction pursue their own interests optimally under their respective operating constraints.

Step 4: Solve the master-slave game low-carbon model through differential evolution algorithm and CPLEX solver.

In the step 1,

1), the gas turbine model is as follows:

与所消耗的气功率

关系如下所示：Electric power output by gas turbine GT

with the gas power consumed

The relationship looks like this:

为GT的启停状态标记位；a、b为燃耗系数，c为GT启停成本系数。where:

is the start-stop status flag bit of the GT; a and b are the fuel consumption coefficients, and c is the GT start-stop cost coefficient.

In order to accurately reflect the actual operating conditions of GT and facilitate rapid calculation, formula (1) is linearized in three segments. The slopes of the three segments after segmenting are:

In the formula: , , , represent the segmented GT electric power curve parameters, , are the upper and lower limits of the GT output electric power, so formula (1) can be rewritten as:

When GT is running, the exhausted high-temperature flue gas generates heat through the waste heat boiler WHB, and its heating characteristic model is:

和

分别表示GT和WHB输出的热功率；λ_GT和λ_WHB分别表示燃气轮机输出的电热功率比和热回收效率。where:

and

Represent the thermal power output by GT and WHB, respectively; λ _GT and λ _WHB represent the electric-to-heat power ratio and heat recovery efficiency output by the gas turbine, respectively.

2), the gas boiler model is as follows:

Gas boiler GB produces heat by burning natural gas, and the relationship between its output heat power H ^t _GB and input gas power G ^t _GB is:

In the formula: η _GB is the heat production efficiency of GB.

3) The models of electric refrigerators and absorption refrigerators are as follows:

The output cooling powers Q ^t _AC and Q ^t _AR of the electric refrigerator AC (Air Conditioner) and the absorption refrigerator AR (Air Refrigerator) are as follows:

In the formula: η _AR and η _AC represent the cooling efficiency of AR and AC; P ^t _AC and H ^t _AR represent the input electric power of AC and the input thermal power of AR respectively.

4) The battery model is as follows:

The energy storage capacity of the battery BT before and after charging and discharging must meet the following constraints:

表示BT的t时刻容量状态；h_BT.chr、h_BT.dis分别为BT的充、放电效率。where:

Represents the capacity state of BT at time t; h _BT.chr and h _BT.dis are the charge and discharge efficiencies of BT, respectively.

表示BT的t-1时刻容量状态、

表示t时刻的充电功率、

表示t时刻的放电功率、△t表示时间间隔，

表示BT的容量状态下限、

表示t时刻的容量状态、

表示BT的容量状态上限。

Indicates the capacity state of BT at time t-1,

represents the charging power at time t,

represents the discharge power at time t, Δt represents the time interval,

Indicates the lower limit of the capacity state of BT,

represents the capacity state at time t,

Indicates the upper limit of the capacity status of BT.

In addition, BT also needs to meet the charge and discharge frequency constraints and mutual exclusion constraints:

表示t时刻的BT放电功率标志位、

表示t时刻的BT充电功率标志位；

Indicates the BT discharge power flag bit at time t,

Indicates the BT charging power flag bit at time t;

t represents the time interval;

In the step 2:

1): The modeling of user cooling load demand is as follows:

Assuming that the building refrigeration equipment runs continuously during the use time, according to the energy conservation principle, the indoor heat change ΔL _c in the t period is equal to the difference between the cooling capacity L ^t _c and the building heat absorption L _B , and the building heat balance equation is obtained:

为室内温度变化率；V_B为建筑体积。In the formula: ρ _Air is the air density; C _Air is the air specific heat capacity;

is the indoor temperature change rate; _VB is the building volume.

The main factors affecting the heat absorption of the building are: the heat L _Wall and L _Win transmitted by the building exterior walls and exterior windows, the indoor heat source L _In generated by the building due to the absorption of heat such as indoor lighting and human body heat dissipation, and the heat generated by solar radiation L _S , Therefore, _LB can be expressed as:

分别为建筑朝向为j时的建筑外墙、外窗与室外的传热系数；

分别为建筑朝向为j时建筑外墙、外窗面积；j表示建筑朝向；In the formula: L _B represents the heat absorbed by the building;

are the heat transfer coefficients of the building exterior wall, exterior window and outdoor when the building orientation is j;

are the area of the exterior wall and exterior window of the building when the building orientation is j; j represents the building orientation;

分别为建筑外墙、外窗与室外的传热系数；

分别为建筑外墙、外窗面积；T_In、T_Out分别为室内外温度；I为太阳辐射功率；S、C分别为外窗遮阳系数、得热因子；

are the heat transfer coefficients of the building exterior wall, exterior window and exterior, respectively;

are the area of the building exterior wall and exterior window; T _In and T _Out are the indoor and outdoor temperature respectively; I is the solar radiation power; S and C are the shading coefficient and heat gain factor of the exterior window, respectively;

Combining equations (11) and (12), and through differential processing, the discretized building heat balance equation can be obtained:

The relationship between indoor temperature and cooling power can be obtained from formula (11). In order to ensure user comfort, the room temperature should meet the upper and lower limits and room temperature fluctuation constraints:

表示t时刻的室内温度；

represents the indoor temperature at time t;

分别为用户可接受的室内温度上限、下限，

为设定的最适宜室温where:

are the upper and lower limits of indoor temperature acceptable to users, respectively.

the optimum room temperature

分别表示室温波动相对值的上下限；

respectively represent the upper and lower limits of the relative value of room temperature fluctuation;

2): The modeling of user heat load demand is as follows:

The relationship between water supply temperature and heat load is described by a hot water storage model:

分别表示储水总量和替换消耗热水的冷水总量；

表示供应热水所需要的能量。

表示t+1时刻的室内温度；In the formula: C _h is the specific heat capacity of water; T _h and T _C,h represent the temperature of the storage water and the temperature of the cold water entering the water storage tank instead of consuming hot water; V _h and T C,h respectively

Represents the total amount of stored water and the total amount of cold water that replaces the consumed hot water;

Indicates the energy required to supply hot water.

Indicates the indoor temperature at time t+1;

In order to ensure user comfort, the water temperature should meet the upper and lower limit constraints and the water temperature fluctuation constraints:

表示t时刻的水温；

represents the water temperature at time t;

分别为用户可接受的室内温度上限、下限，

为设定的最适宜水温。where:

are the upper and lower limits of indoor temperature acceptable to users, respectively.

is the optimum water temperature set.

分别表示水温波动相对值的上下限；

respectively represent the upper and lower limits of the relative value of the water temperature fluctuation;

3): The model of the user's electric load demand response is as follows:

User electrical load includes fixed electrical load and transferable electrical load. Transferable electrical load refers to the user's transfer according to the electricity price information and user needs, and adjust the electricity consumption strategy without affecting their own comfort. Suppose the expressions of transferable load L ^t _e,k in time period t are as shown in equations (21)-(22).

表示第i个用户未经过电价IDR调节前的可转移电负荷功率；

和

分别为第i个用户经电价IDR调节后转入和转出的电负荷功率，M为参与响应的用户数量where:

Indicates the transferable load power of the i-th user before the electricity price IDR adjustment;

and

are the electricity load power transferred in and out by the i-th user after adjustment by the electricity price IDR, M is the number of users participating in the response

经电价IDR调节后转入和转出的电负荷功率；

Electric load power transferred in and out after adjustment by electricity price IDR;

In step 3, the master-slave game low-carbon model includes:

1): EHO carbon emission quota allocation, as follows:

The baseline method is used to determine the free carbon emission quota of EHO, and the carbon emission quota in EH includes CCHP, GB and conventional units. Convert CCHP power generation into equivalent calorific value and allocate carbon allowances:

E _p = E _Grid + E _GB + E _CCHP (23);

E _Grid = δ _e P _buy (24);

为EHO从外部电网购买的电量；

表示折算系数；δ_e、δ_h为单位电量、热量的碳排放分配额系数，分别取0.728t/(MWh)和0.102t/(GJ)。where E _Grid , E _GB and E _CCHP are the power purchases from external power grids, and the free carbon emission quotas of GB and CCHP, respectively; E _p is the total system carbon emission allocation;

Electricity purchased from external grids for EHO;

Represents the conversion coefficient; δ _e and δ _h are the carbon emission allocation coefficients per unit of electricity and heat, which are taken as 0.728t/(MWh) and 0.102t/(GJ) respectively.

表示t时刻的GB输出热功率、

表示t时刻GT的输出电功率、

t时刻WHB 的输出热功率、

表示t时刻AR的输出冷功率；

Represents the GB output thermal power at time t,

represents the output electric power of GT at time t,

The output thermal power of WHB at time t,

represents the output cold power of AR at time t;

2): Reward and punishment ladder type carbon trading cost calculation model, as follows:

The constructed reward and punishment ladder-type carbon trading cost model is shown in equation (27), when the carbon emission is less than the free carbon allowance, the energy supply enterprise can sell the excess carbon emission allowance and obtain a part of the reward subsidy; otherwise, it needs to purchase the insufficient carbon allowance. carbon emission rights. The larger the carbon emission range, the higher the corresponding carbon trading price.

In the formula: E _co2 is the carbon transaction cost borne by EHO, E _c is the actual total carbon emission of EHO, c is the unit carbon transaction price; Among them, the actual carbon emission of conventional generator sets is 1.08t/(MWh); the actual carbon emission of CCHP and GB is 0.065t/GJ. E _p represents the total carbon emission quota of IES;

3): The energy hub operator model, as follows:

EHO represents an energy hub operator. EHO adjusts the output of the energy coupling equipment in the EH and the internal energy price according to the user's energy consumption strategy, and maximizes the net profit of the EHO as the objective function:

maxE _EHO =E _sale -E _buy -E _co2 -E _K (28);

为相应的用户负荷；

分别为EHO向外部电网购、售电价格，

分别为相应的购、售电功率；λ^gas为天然气价格，

分别为GT、GB所消耗的天然气功率；K_i为设备的单位运行维护费用；

表示各设备输出功率。△t表示时间间隔；In the formula: i∈{e, c, h}, E _sale is EHO’s energy sales revenue; E _buy is EHO’s electricity and gas cost; E _co2 and E _K are carbon transaction costs and equipment operation and maintenance borne by EHO, respectively Cost; λ _ti is the price at which EHO sells the i-th energy to users,

for the corresponding user load;

are the prices of electricity purchased and sold by EHO to external power grids, respectively,

are the corresponding purchasing and selling power respectively; λ ^gas is the price of natural gas,

are the natural gas power consumed by GT and GB respectively; K _i is the unit operation and maintenance cost of the equipment;

Indicates the output power of each device. △t represents the time interval;

In the optimal scheduling of EHO, not only the supply and demand balance of various energy sources in the EH and the upper and lower limit constraints of each energy equipment need to be considered, but also the constraints of internal energy prices:

表示t时刻的第i种能源价格。In the formula: λ _ti,min and λ _ti,max are the upper and lower limits of the price of the i-th energy sold by EHO to users, respectively.

Represents the i-th energy price at time t.

4) The user model is as follows:

The user's objective function is the sum of energy purchase cost and discomfort cost. Assuming that users in the EH can accept a certain degree of discomfort, the objective function is:

minE _User = C _User + U _User (33);

In the formula: C _User is the user's energy purchase cost; U _User is the user's discomfort cost, and their expressions are:

为用户最舒适的负荷需求；

为用户执行 IDR后的实际负荷；△L_ti表示用户执行IDR前后的负荷变化量。In the formula: i∈{e,c,h}, γ _i corresponds to the user's discomfort coefficient of transferring or reducing the i-th energy, reflecting the user's demand preference for energy; λ _ti is the price of the i-th energy consumed by the user at time t;

The most comfortable load requirement for the user;

It is the actual load after the user performs IDR; ΔL _ti represents the load change before and after the user performs IDR.

For transferable loads, the following constraints need to be satisfied:

表示负荷可转移量的上限值，

表示负荷的可转移负荷总量，

表示需求响应前第i种能源的用能负荷，

表示t时刻第i种能源的用能负荷，

表示t时刻第i种负荷的变化量，△t代表时间间隔，T代表一天24小时；where:

Indicates the upper limit of the load transferable amount,

represents the total transferable load of the load,

represents the energy load of the i-th energy source before demand response,

represents the energy load of the ith energy at time t,

Represents the variation of the i-th load at time t, Δt represents the time interval, and T represents 24 hours a day;

The step 4 includes the following steps:

Step 4.1: Initialize the population, let the number of iterations k=0;

Step 4.2: If k≤k _max , output the optimal result, otherwise let k=k+1;

Step 4.3: EHO sends the internal price to the user;

Step 4.4: The user invokes CPLEX to optimize the load;

Step 4.5: The user sends the optimized afterload to the EHO;

Step 4.6: EHO solves the objective function E;

Step 4.7: Perform mutation operation;

计算子代E′,若E＞E′，则令

且k＝k+1. 若不是，则令

且k＝k+1，回到步骤4.2。Step 4.8: Perform the crossover operation. produce offspring

Calculate the offspring E', if E>E', then let

And k=k+1. If not, let

And k=k+1, go back to step 4.2.

The present invention is an energy hub master-slave game optimization scheduling method based on comprehensive demand response and reward and punishment ladder carbon trading, and the technical effects are as follows:

1): The refined building IDR model of the present invention comprehensively considers various heat disturbance factors such as heat storage capacity, outdoor/indoor temperature and solar radiation, and considers the room temperature fluctuation caused by the user's participation in the IDR, which can describe the user more accurately. The energy consumption characteristics and schedulable characteristics in real life give full play to the response flexibility of demand-side resources.

2): The present invention introduces a reward and punishment ladder-type carbon trading mechanism into the supply and demand game model, and analyzes the impact of the unit carbon trading price and different reward coefficients on the optimal scheduling of EH. The simulation results show that the proposed model can not only effectively reduce the carbon emissions of the system, but also take into account the interests of both parties, achieving a win-win situation of EH economy and environmental protection.

Description of drawings

Figure 1 is a schematic diagram of the EH structure.

Figure 2 is a piecewise linearized GT fuel consumption curve.

Figure 3 is a schematic diagram of the building heat transfer process.

Figure 4 is a master-slave game frame diagram.

Figure 5 is a flowchart of the Stackelberg game solution.

Figure 6 is a load and new energy forecast curve.

Figure 7(a) is a graph of the prediction data of the heat source in the building before the day;

Figure 7(b) is a graph of the prediction data of outdoor temperature and light intensity.

Figure 8(a) is a schematic diagram of the cooling scheme of the residential building;

Figure 8(b) is a schematic diagram of the cooling scheme of the office building;

Figure 8(c) is a schematic diagram of the cooling scheme of the apartment building;

Figure 8(d) is a diagram of the refrigeration scheme of the shopping mall.

Figure 9 is a diagram of an optimization scheme for domestic hot water.

Figure 10(a) is the result of electric load optimization;

Figure 10(b) shows the result of thermal load optimization.

Figure 11(a) is the result of optimization of power dispatching equipment;

Figure 11(b) shows the result of optimization of thermal energy dispatching equipment;

Figure 11(c) shows the result of optimization of cold energy dispatching equipment.

Figure 12 shows the impact of carbon trading prices on carbon emissions.

Figure 13 shows the effect of different reward coefficients on carbon trading costs.

Detailed ways

The master-slave game optimal scheduling method for energy hubs based on comprehensive demand response and reward and punishment ladder carbon trading, including: establishing a refined comprehensive demand response model considering various heat disturbance factors, The ability to integrate the building heat transfer model with the domestic hot water storage model into the building EH model. A room temperature fluctuation constraint model is established to ensure user comfort. Establish a reward and punishment ladder-type carbon trading mechanism to limit EHO's carbon emissions and give full play to users' green regulation capabilities. Based on the Stackelberg game theory, a master-slave game model of energy hub operators and users is constructed, and a reward and punishment ladder-type carbon trading mechanism is introduced into the game model to limit the carbon emissions of EHO and exert users' green regulation capabilities. The proposed model was solved using the differential evolution algorithm combined with the CPLEX toolbox. The simulation results show that the proposed method can effectively take into account the interests of both parties, give full play to the user's demand response potential, and achieve EH economical and low-carbon operation. Include the following steps:

Step 1: Model the gas turbine, gas boiler, electric refrigerator, absorption refrigerator, and battery included in the EH structure, and reflect the relationship between input power and output power;

Step 2: Establish a comprehensive demand response model, including user cooling load demand modeling, user heating load demand modeling and user electric load demand response.

Step 3: Establish a master-slave game low-carbon model, so that the EHO and users participating in the game interaction can pursue their own interests optimally under their respective operating constraints.

Step 4: Solve the proposed game model through differential evolution algorithm and CPLEX solver.

Step 5: Carry out example analysis considering the actual situation to verify the correctness of the proposed scheme and model.

1. The energy hub structure model is described as follows:

The EH structure studied in the present invention is shown in FIG. 1 . The new energy equipment includes wind turbines and photovoltaic units; the energy supply equipment includes gas turbines and gas boilers; the energy conversion equipment includes waste heat boilers, electric refrigerators, and absorption refrigerators; and the energy storage equipment is batteries.

1) The gas turbine model is described as follows:

与所消耗的气功率

关系如下所示：Electric power output by GT

with the gas power consumed

The relationship looks like this:

为GT的启停状态标记位；a、b为燃耗系数，c为GT启停成本系数。where:

is the start-stop status flag bit of the GT; a and b are the fuel consumption coefficients, and c is the GT start-stop cost coefficient.

In order to accurately reflect the actual operating conditions of the GT and facilitate rapid calculation, the present invention performs three-piece linearization processing on formula (1), as shown in FIG. 2 . The three segment slopes after segmentation are:

In the formula: , , , represent the segmented GT electric power curve parameters, , are the upper and lower limits of the GT output electric power, so formula (1) can be rewritten as:

When GT is running, the exhausted high-temperature flue gas generates heat through WHB, and its heating characteristic model is:

和

分别表示GT和WHB输出的热功率；λ_GT和λ_WHB分别表示燃气轮机输出的电热功率比和热回收效率。where:

and

Represent the thermal power output by GT and WHB, respectively; λ _GT and λ _WHB represent the electric-to-heat power ratio and heat recovery efficiency output by the gas turbine, respectively.

2) The gas boiler model is described as follows:

GB produces heat by burning natural gas, and the relationship between its output heat power H ^t _GB and input gas power G ^t _GB is:

In the formula: η _GB is the heat production efficiency of GB.

3) The electric refrigerator and absorption refrigerator models are described as follows:

The output cold powers Q ^t _AC and Q ^t _AR of AC and AR are as follows:

In the formula: η _AR and η _AC represent the cooling efficiency of AR and AC; P ^t _AC and H ^t _AR represent the input electrical power of AC and the input thermal power of AR, respectively.

4) The battery model is described as follows:

The energy storage capacity before and after BT charging and discharging needs to meet the following constraints:

In the formula: SOC ^t _x is the capacity state of BT; h _BT.chr and h _BT.dis are the charge and discharge efficiencies of BT, respectively. In addition, BT also needs to meet the charge and discharge frequency constraints and mutual exclusion constraints:

2. The integrated demand response model is described as follows:

Through the refined modeling of user load demand, the energy consumption strategy can be adjusted within the range of temperature comfort and room temperature fluctuation, which can effectively improve the economical and environmental protection of EH operation.

1) The modeling of user cooling load demand is described as follows:

Assuming that the building cooling equipment operates continuously during the usage time, according to the energy conservation principle, the indoor heat change ΔL _c in the t period is equal to the difference between the cooling capacity L ^t _c and the building heat absorption L _B , and the building heat balance equation can be obtained:

为室内温度变化率；V_B为建筑体积。In the formula: ρ _Air is the air density; C _Air is the air specific heat capacity;

is the indoor temperature change rate; _VB is the building volume.

Figure 3 shows the building heat transfer process. The main factors affecting the heat absorption of the building are: the heat L _Wall and L _Win transmitted by the building exterior walls and external windows, the indoor heat source L _In generated by the building due to the absorption of heat such as indoor lighting and human body heat dissipation, and the heat generated by solar radiation L _S , So _LB can be expressed as:

In the formula: j represents the building orientation; kWall and kWin are the heat transfer coefficients of the building exterior wall, exterior window and outdoor, respectively; fWall and fWin are the area of the building exterior wall and exterior window, respectively; TIn and TOut are the indoor and outdoor temperatures, respectively; I is the Solar radiation power; S and C are the shading coefficient and heat gain factor of the exterior window, respectively;

Combining equations (11) and (12), and through differential processing, the discretized building heat balance equation can be obtained:

The relationship between indoor temperature and cooling power can be obtained from formula (11). In order to ensure user comfort, the room temperature should meet the upper and lower limits and the room temperature fluctuation constraints:

分别为用户可接受的室内温度上限、下限，

为设定的最适宜室温。 2)用户热负荷需求建模描述如下：where:

are the upper and lower limits of indoor temperature acceptable to users, respectively.

The optimum room temperature is set. 2) The user heat load demand modeling is described as follows:

The relationship between water supply temperature and heat load is described by a hot water storage model:

In the formula: C _h is the specific heat capacity of water; T _h and T ^t _C,h represent the temperature of the storage water and the temperature of the cold water that enters the water storage tank instead of consuming hot water; V _h and V ^t _C,h represent the total amount of water stored, respectively and the total amount of cold water that replaces the consumption of hot water; L _th represents the energy required to supply hot water.

In order to ensure user comfort, the water temperature should meet the upper and lower limit constraints and the water temperature fluctuation constraints:

分别为用户可接受的室内温度上限、下限，

为设定的最适宜水温。 3)用户电负荷需求建模描述如下：where:

are the upper and lower limits of indoor temperature acceptable to users, respectively.

is the optimum water temperature set. 3) The modeling description of the user's electric load demand is as follows:

User electrical load includes fixed electrical load and transferable electrical load. Transferable electrical load refers to the user's transfer according to the electricity price information and user needs, and adjust the electricity consumption strategy without affecting their own comfort. Assume that the expressions of transferable load L ^t _e,k in time period t are as shown in equations (21)-(22).

表示第i个用户未经过电价IDR调节前的可转移电负荷功率；

和

分别为第i个用户经电价IDR调节后转入和转出的电负荷功率，M为参与响应的用户数量。where:

Indicates the transferable load power of the i-th user before the electricity price IDR adjustment;

and

are the electricity load power transferred in and out by the i-th user after adjustment by the electricity price IDR, and M is the number of users participating in the response.

3. Establish a master-slave game low-carbon model:

The master-slave game framework of the present invention is shown in FIG. 4 . The market entities participating in the game interaction are EHO and users, and both parties pursue their own best interests under their respective operating constraints.

1): The allocation of EHO carbon emissions is described as follows:

The present invention adopts the baseline method to determine the gratuitous carbon emission quota of EHO, and considers that the distribution of carbon emission rights in EH mainly includes CCHP, GB and conventional units. Convert CCHP power generation into equivalent calorific value and allocate carbon allowances:

E _p = E _Grid + E _GB + E _CCHP (23);

E _Grid = δ _e P _buy (24);

为EHO从外部电网购买的电量；

表示折算系数；δ_e、δ_h为单位电量、热量的碳排放分配额系数，分别取0.728t/(MWh)和0.102t/(GJ)。where E _Grid , E _GB and E _CCHP are the power purchases from external power grids, and the free carbon emission quotas of GB and CCHP, respectively; E _p is the total system carbon emission allocation;

Electricity purchased from external grids for EHO;

Represents the conversion coefficient; δ _e and δ _h are the carbon emission allocation coefficients per unit of electricity and heat, which are taken as 0.728t/(MWh) and 0.102t/(GJ) respectively.

2): The reward and punishment ladder type carbon trading cost calculation model is described as follows:

The reward and punishment ladder type carbon trading cost model constructed by the present invention is shown in formula (27). When the carbon emission is less than the free carbon allowance, the energy supply enterprise can sell the excess carbon emission allowance and obtain a part of the reward subsidy, otherwise, it needs to purchase Insufficient carbon emission rights. The larger the carbon emission range, the higher the corresponding carbon trading price.

In the formula: E _co2 is the carbon transaction cost borne by EHO, E _c is the actual total carbon emission of EHO, c is the unit carbon transaction price; Among them, the actual carbon emission of conventional generator sets is 1.08t/(MWh); the actual carbon emission of CCHP and GB is 0.065t/GJ.

3): The energy hub operator model is described as follows:

EHO adjusts the output of energy coupling equipment and the price of internal energy in the EH according to the user's energy consumption strategy, and maximizes the net profit of EHO as the objective function:

maxE _EHO =E _sale -E _buy -E _co2 -E _K (28);

为相应的用户负荷；λ_tbGrid/λ_tsGrid为EHO向外部电网购/售电价格，P_tb/Grid/P_tsGrid为相应的购/售电功率；λ^gas为天然气价格，

为GT/GB所消耗的天然气功率；K_i为设备的单位运行维护费用；

表示各设备输出功率。In the formula: i∈{e, c, h}, E _sale is EHO’s energy sales revenue; E _buy is EHO’s electricity and gas cost; E _co2 and E _K are carbon transaction costs and equipment operation and maintenance borne by EHO, respectively Cost; λ _ti is the price at which EHO sells the i-th energy to users,

is the corresponding user load; λ _tbGrid /λ _tsGrid is the electricity purchase/selling price of the EHO to the external power grid, P _tb/Grid /P _tsGrid is the corresponding electricity purchasing/selling power; λ ^gas is the price of natural gas,

is the natural gas power consumed by GT/GB; K _i is the unit operation and maintenance cost of the equipment;

Indicates the output power of each device.

EHO not only needs to consider the supply and demand balance of multiple energy sources in the EH and the upper and lower limit constraints of each energy equipment in the optimal scheduling, but also needs to consider the constraints of internal energy prices:

In the formula: λ _ti,min and λ _ti,max are the upper and lower limits of the price of the i-th energy sold by EHO to users, respectively.

4): The user model is described as follows:

The user's objective function is the sum of energy purchase cost and discomfort cost. Assuming that users in the EH can accept a certain degree of discomfort, the objective function is:

minE _User = C _User + U _User (33);

In the formula: C _User is the user's energy purchase cost; U _User is the user's discomfort cost, and its expressions are:

为用户最舒适的负荷需求；

为用户执行IDR后的实际负荷；△L_ti表示用户执行IDR前后的负荷变化量。In the formula: i∈{e,c,h}, γ _i corresponds to the user's discomfort coefficient of transferring or reducing the i-th energy, reflecting the user's demand preference for energy; λ _ti is the price of the i-th energy consumed by the user at time t;

The most comfortable load requirement for the user;

It is the actual load after the user performs IDR; ΔL _ti represents the load change before and after the user performs IDR.

For transferable loads, the following constraints need to be satisfied:

表示负荷可转移量的上限值，W_ti,表示负荷的可转移负荷总量。where:

Indicates the upper limit of the transferable amount of load, W _ti , represents the total amount of transferable load of the load.

4. The differential evolution algorithm combined with cplex software is used to solve the proposed model. The solution process is shown in Figure 5. The steps are as follows:

Step 4.1: Initialize the population, let the number of iterations k=0;

Step 4.2: If k≤k _max , output the optimal result, otherwise let k=k+1;

Step 4.3: EHO sends the internal price to the user;

Step 4.4: The user invokes CPLEX to optimize the load;

Step 4.5: The user sends the optimized afterload to the EHO;

Step 4.6: EHO solves the objective function E;

Step 4.7: Perform mutation operation;

计算子代E′,若E＞E′，则令

且k＝k+1. 若不是，则令

且k＝k+1，回到步骤4.2。Step 4.8: Perform the crossover operation. produce offspring

Calculate the offspring E', if E>E', then let

And k=k+1. If not, let

And k=k+1, go back to step 4.2.

In the above steps, E is the objective function, k is the number of iterations, and λ _ti is the price that EHO sells the i-th energy to users.

5. Example analysis and verification:

1) The basic data are as follows:

Taking a commercial building EH as an example, the simulation analysis is carried out. The typical daily predicted load of users and the predicted output of new energy are shown in Figure 6, and the building-related parameters are shown in Table 1; the equipment and related constraint parameters in the EH are shown in Table 2; Show. The price of natural gas is 3.24 yuan/m3; the upper and lower limits of the cooling and heating prices of EHO are [0.15, 0.4] yuan; the heat source, outdoor temperature and solar radiation curves in the building are shown in Figure 7(a) and Figure 7(b). The user's optimal room temperature is set to 22.5°C, the best water temperature is set to 70°C, and the user's discomfort coefficients γ _e and γc _/h to electricity, heat/cold energy are 0.008 and 0.016, respectively; the carbon trading price is 0.268 yuan /kg, μ is 0.2.

The refrigeration opening times of the four commercial buildings studied in the present invention are as follows. Building A: Residential building, cooling time is 00:00-09:00 and 18:00-23:00; Building B: Office building, cooling time is 08:00-20:00; Building C: Apartment, cooling time is full Day; Building D: shopping mall, cooling time is 10:00-22:00.

Table 1 Building parameters

Table 1Parameters of the buildings

Table 2 Energy Hub Parameters

Table 2Parameters of the EH

Table 3 Purchase and sale price of electricity

Table 3Prices for the purchase and sale of electricity

2) Comparative analysis of different schemes:

The present invention sets four schemes for comparative analysis with the inventive scheme:

Option 1: In the carbon trading model, the IDR model and the supply and demand game model of carbon trading costs are not considered;

Option 2: In the carbon trading model, consider a simplified IDR model and a supply-demand game model that does not consider carbon trading costs;

Option 3: Consider a simplified IDR model and a supply-demand game model with traditional carbon transaction costs;

Option 4: Consider refined IDR models and traditional carbon trading costs.

The comparison results under the five schemes are shown in Table 4.

Comparing scheme 1 and scheme 2, in scheme 2, the user energy cost and system carbon emissions decreased by 3.81% and 4.76% respectively. This is because after considering the IDR strategy, users can reasonably adjust the load demand according to the price signal, effectively smoothing the user load. The peak-to-valley difference reduces the cost of energy consumption and the carbon emissions generated by purchasing electricity. But EHO's net profit fell by 2.84% as users shifted and slashed some loads.

Comparing scheme 2 and scheme 3, in scheme 3, the user energy cost and system carbon emissions decrease by 1.40% and 5.98% respectively, and the net profit of EHO increases by 3.43%. It can be seen that after considering the carbon trading mechanism in the optimization model, the EHO can reasonably adjust the output of the equipment and reduce the carbon emissions generated by the purchased electricity. Obtain carbon revenue in the carbon trading market. In addition, users can also benefit from the adjustment of EHO.

Comparing scheme 3 and scheme 4, in scheme 4, the user energy cost and system carbon emissions decreased by 1.59% and 1.79% respectively. This is because when the refined IDR model is adopted, factors such as temperature comfort and room temperature fluctuations are taken into account, which can reflect the user's energy consumption in the actual situation, so that the user can more actively participate in the IDR response and ensure the user's energy consumption. comfort.

Comparing scheme 4 with the strategy of the present invention, in the strategy of the present invention, the user's energy cost and system carbon emissions decrease by 0.88% and 2.32%, respectively, and the EHO net profit increases by 2.01%. This is because after the introduction of the stepped carbon trading cost and reward coefficient, EHO can not only sell excess carbon allowances to obtain carbon income, but also obtain a certain reward income, so EHO is further encouraged to increase the output of each equipment and reduce the purchased electricity, thereby Effectively reduce the total carbon emissions of the system.

Table 4 Comparison results under different schemes

Table 4 Comparison results under different schemes

3) Analysis of scheduling results:

Firstly, the influence of flexible cooling load on EH is analyzed. Assuming that when the flexible cooling load is not introduced, the temperature of the building remains constant at 22.5°C during its respective opening hours, and there is no requirement for non-opening time, and the cooling load at this time will be regarded as the original cooling load; when the flexible cooling load is introduced, the temperature inside the building It can fluctuate during its opening time, and the cooling load at this time takes it as the actual cooling load. It can be seen from the building cooling results shown in Figures 8(a)-8(d) that the cooling load will increase rapidly in the first period of time when the building is open to meet the room temperature demand in the house, and in the subsequent period of time, the cooling load will increase rapidly. Its cooling load basically changes with the change of the outside temperature and solar radiation to maintain the indoor temperature.

In addition, after the introduction of flexible cooling load, its building cooling capacity is related to electricity price. For residential buildings, the actual cooling load is also larger when the cooling price is lower from 00:00 to 7:00, that is, the peak cooling load is transferred to the valley cooling load. This can not only ensure the comfort of users, but also store heat in advance to reduce the load demand during peak cooling prices; since the opening hours of office buildings and shopping malls are at 08:00-20:00 when energy prices are high and the outdoor temperature is relatively high, appropriate Increasing the indoor temperature can reduce cooling load demand and energy costs; apartments that are open all day can also improve system economics by reducing or shifting some of the cooling load.

Figure 9 shows the results of daily domestic hot water scheduling. Similar to the building cooling results, the system can reduce energy costs by reducing water temperature, transferring and reducing part of the heat load, such as 06:00-10:00 and 17:00-20:00. During the period of 00, in order to reduce the cost, the user transfers and reduces the heat load, and during the period from 15:00 to 16:00, the hot water in the water storage tank is additionally heated to meet the heat in the time period when the heat price is higher in the future. water demand.

Figure 10(a) and Figure 10(b) are the results of internal energy price and user energy consumption strategy formulated by EHO after game equilibrium optimization. It can be seen from Figure 10(a) that the energy sales price set by the EHO is within the time-of-use price of the external power grid, and its price fluctuation trend is consistent with the time-of-use price. Users perform load shifting and shedding. For example, when the electricity price is higher from 08:00 to 12:00 and 16:00 to 22:00, the user will transfer the electricity load during these time periods to 00:00 to 7:00 and 23:00 to 24:00, where the electricity price is lower. 00 period, reducing its own energy purchase cost.

Similarly, the optimization results of heating load and cooling load are shown in Fig. 10(b). In order to simplify the model, the heating price and cooling price of the present invention are optimized using the same variables, so the corresponding price change trend is related to the total cooling and heating load. The change trend of the quantity is similar, similar to the analysis in Fig. 10(a), and the optimization results of the heating load and cooling load are shown in Fig. 10(b). In order to simplify the model, the heat selling price and the cooling selling price of the present invention are optimized by using the same variable, so the corresponding price change trend is similar to the change trend of the total cooling and heating loads. From 10(b), it can be seen that the cooling and heating loads are higher in the periods of 12:00-15:00 and 17:00-20:00, and the heating/cooling prices are higher at this time, so there are different degrees of reduction. , and transfer to time periods such as 10:00-11:00 and 21:00-24:00. It has the advantage of good peak shaving and valley filling.

Figures 11(a) to 11(c) show the equipment scheduling results after the Stackelberg game optimization of the present invention. Considering environmental protection, EHO prioritizes the consumption of renewable energy PV and WT. First of all, for the period of 00:00-6:00, the electricity price is in the valley, at this time GT stops starting, EHO mainly uses WT power generation and purchased power to meet the user's electricity load; heat load is satisfied by GB heat generation; cooling load is satisfied by AC cooling Satisfy. For the period from 07:00 to 12:00, PV starts to output power. At this time, the electricity load is mainly provided by GT, PV and WT. In order to make profits, EHO has more output from GT, and the rich electricity can be supplied to AC or sold to external power grids; The heat load is met by WHB and GB, and the surplus heat is cooled by AR and combined with AC to meet the cooling load demand. During the normal electricity price period from 13:00 to 18:00, due to the high heating and cooling loads at this time, in order to use the waste heat of power generation to meet the thermal load, the GT output is relatively high, and the rich electricity load is stored through BT to cope with the next stage of electricity. load peak period. During the peak period of electricity price from 19:00 to 22:00, the working conditions are similar to those in the period from 08:00 to 12:00, but due to the lack of PV power generation, the insufficient electricity needs to be supplemented by purchased electricity and BT. From 23:00 to 24:00, during the normal electricity price period, all kinds of loads gradually decrease, and the power generation of GT gradually decreases. WHB basically meets the heat load demand, and then provides the surplus heat to AR for cooling.

Figure 12 shows the impact of carbon trading price changes on system carbon emissions under scenario 4 and the scenario of the present invention. It can be seen from Figure 12 that with the increase of the unit carbon trading price, the proportion of carbon trading cost in the total cost will increase, which will make the system strengthen the constraints on carbon emissions and gradually reduce carbon emissions. In addition, the carbon emissions under the strategy of the present invention are lower than scenario 4, because the reward and punishment ladder carbon trading mechanism has more emission reduction advantages than the traditional carbon trading mechanism, and can better limit the carbon emissions of the system.

Changes in transaction prices. It can be seen from Figure 13 that when the system carbon transaction cost is greater than 0, that is, when the EHO needs to bear the carbon transaction fee, the reward coefficient has no effect on the carbon transaction cost. On the contrary, when EHO starts to obtain carbon trading benefits, the larger the reward coefficient, the more carbon trading benefits, that is, the more significant the reduction in system carbon emissions. The output of CCHP units and GB increased, further reducing the purchased electricity. In addition, when the unit carbon trading price increases to about 450 yuan/t, the downward trend of carbon trading cost becomes slow, indicating that the CCHP unit has basically reached a constant value. If the unit carbon trading price continues to increase, the system carbon emissions will no longer decrease significantly. . Based on the Stackelberg game theory, the invention proposes an EH master-slave game optimization strategy considering a refined IDR model and a reward and punishment ladder-type carbon trading mechanism. The refined building IDR model comprehensively considers various heat disturbance factors such as heat storage capacity, outdoor/indoor temperature, and solar radiation, and considers the room temperature fluctuation caused by the user's participation in the IDR, which can more accurately describe the user's actual life. The energy consumption and schedulable characteristics give full play to the response flexibility of demand-side resources. The reward and punishment ladder-type carbon trading mechanism is introduced into the supply and demand game model, and the impact of the unit carbon trading price and different reward coefficients on the optimal scheduling of EH is analyzed. The results show that the proposed model can not only effectively reduce the carbon emissions of the system, but also take into account the interests of both parties, achieving a win-win situation of EH economy and environmental protection.

Claims (6)

1. An energy hub master-slave game optimization scheduling method based on comprehensive demand response and reward and punishment ladder carbon trading, which is characterized by comprising the following steps: Step 1: Model the gas turbines, gas boilers, electric refrigerators, absorption refrigerators, and batteries included in the energy hub structure to reflect the relationship between input power and output power; Step 2: Establish a comprehensive demand response model, including user cooling load demand modeling, user heating load demand modeling and user electric load demand response; Step 3: Establish a master-slave game low-carbon model, so that the energy hub operators and users participating in the game interaction can pursue their own interests optimally under their respective operating constraints; Step 4: Solve the master-slave game low-carbon model through differential evolution algorithm and CPLEX solver. 2. The master-slave game optimization scheduling method for energy hubs based on comprehensive demand response and reward and punishment ladder carbon trading according to claim 1, characterized in that: in step 1, 1), the gas turbine model is as follows: Electric power output by gas turbine GT

with the gas power consumed

The relationship looks like this:

where:

is the start-stop status flag bit of the GT; a and b are the fuel consumption coefficients, and c is the GT start-stop cost coefficient; In order to accurately reflect the actual operating conditions of GT, Equation (1) is linearized in three segments, and the slopes of the three segments after segmentation are:

In the formula: d ₁ , d ₂ , d ₃ , and d ₄ represent the segmented GT electric power curve parameters, and d ₁ and d ₄ are the upper and lower limits of the GT output electric power, so formula (1) can be rewritten as:

When GT is running, the exhausted high-temperature flue gas generates heat through the waste heat boiler WHB, and its heating characteristic model is:

where:

and

Represent the thermal power output by GT and WHB, respectively; λ _GT and λ _WHB represent the electric-to-heat power ratio and heat recovery efficiency output by the gas turbine, respectively; 2), the gas boiler model is as follows: Gas boiler GB produces heat by burning natural gas, and the relationship between its output heat power H ^t _GB and input gas power G ^t _GB is:

In the formula: η _GB is the heat production efficiency of GB; 3) The models of electric refrigerators and absorption refrigerators are as follows: The output cooling powers Q ^t _AC and Q ^t _AR of the electric refrigerator AC and the absorption refrigerator AR are as follows:

In the formula: η _AR and η _AC represent the cooling efficiency of AR and AC; P ^t _AC and H ^t _AR represent the input electrical power of AC and the input thermal power of AR, respectively; 4) The battery model is as follows: The energy storage capacity of the battery BT before and after charging and discharging must meet the following constraints:

where:

Represents the capacity state of BT at time t; h _BT.chr and h _BT.dis are the charging and discharging efficiencies of BT, respectively;

Indicates the capacity state of BT at time t-1,

represents the charging power at time t,

represents the discharge power at time t, Δt represents the time interval,

Indicates the lower limit of the capacity state of BT,

represents the capacity state at time t,

Indicates the upper limit of the capacity state of BT; In addition, BT also satisfies charge and discharge frequency constraints and mutual exclusion constraints:

Indicates the BT discharge power flag bit at time t,

Indicates the BT charging power flag bit at time t;

t represents the time interval. 3. The master-slave game optimization scheduling method for energy hubs based on comprehensive demand response and reward and punishment ladder carbon trading according to claim 1, characterized in that: in step 2, 1): The modeling of user cooling load demand is as follows: Assuming that the building refrigeration equipment runs continuously during the use time, according to the energy conservation principle, the indoor heat change ΔL _c in the t period is equal to the difference between the cooling capacity L ^t _c and the building heat absorption L _B , and the building heat balance equation is obtained:

In the formula: ρ _Air is the air density; C _Air is the air specific heat capacity;

is the indoor temperature change rate; V _B is the building volume; The main factors affecting the heat absorption of the building include: the heat L _Wall and L _Win transmitted by the building exterior walls and external windows, the indoor heat source L _In generated by the building due to the absorption of heat such as indoor lighting and human body heat dissipation, and the heat generated by solar radiation L _S , Therefore, _LB can be expressed as:

In the formula: L _B represents the heat absorbed by the building;

are the heat transfer coefficients of the building exterior wall, exterior window and outdoor when the building orientation is j;

are the area of the building exterior wall and exterior window when the building orientation is j; j is the building orientation; k _Wall and k _Win are the heat transfer coefficients of the building exterior wall, exterior window and outdoor, respectively; f _Wall and f _Win are the building exterior walls, respectively , the area of the exterior window; T _In and T _Out are the indoor and outdoor temperatures, respectively; I is the solar radiation power; S and C are the shading coefficient and heat gain factor of the exterior window, respectively; Combining equations (11) and (12), and through differential processing, the discretized building heat balance equation is obtained:

The relationship between indoor temperature and cooling power can be obtained from formula (11). In order to ensure user comfort, the room temperature should meet the upper and lower limits and room temperature fluctuation constraints:

represents the indoor temperature at time t;

where:

are the upper and lower limits of indoor temperature acceptable to users, respectively.

the optimum room temperature

respectively represent the upper and lower limits of the relative value of room temperature fluctuation; 2): The modeling of user heat load demand is as follows: The relationship between water supply temperature and heat load is described by a hot water storage model:

In the formula: C _h is the specific heat capacity of water; T _h and T _C,h represent the temperature of the storage water and the temperature of the cold water entering the water storage tank instead of consuming hot water; V _h and T C,h respectively

Represents the total amount of stored water and the total amount of cold water that replaces the consumed hot water;

Indicates the energy required to supply hot water;

Indicates the indoor temperature at time t+1; The water temperature satisfies the upper and lower limit constraints and the water temperature fluctuation constraints:

represents the water temperature at time t;

where:

are the upper and lower limits of indoor temperature acceptable to users, respectively.

is the optimum water temperature set;

respectively represent the upper and lower limits of the relative value of the water temperature fluctuation; 3): The model of the user's electric load demand response is as follows: User electric load includes fixed electric load and transferable electric load. Transferable electric load refers to the user's transfer according to electricity price information and user demand, and adjusts the power consumption strategy without affecting their own comfort; set the transferable load L within the time period t The expressions of ^t _e,k are shown in formulas (21)-(22);

where:

Indicates the transferable load power of the i-th user before the electricity price IDR adjustment;

and

are the electricity load power transferred in and out by the i-th user after adjustment by the electricity price IDR, M is the number of users participating in the response,

The power of the electrical load transferred in and out after the adjustment of the electricity price IDR. 4. The master-slave game optimization scheduling method for energy hubs based on comprehensive demand response and reward and punishment ladder carbon trading according to claim 1, characterized in that: in step 3, the master-slave game low-carbon model comprises: 1): EHO carbon emission quota allocation, as follows: The baseline method is used to determine the free carbon emission quota of EHO. The carbon emission rights allocation in EH includes CCHP, GB and conventional units; the CCHP power generation is converted into equivalent calorific value and the carbon quota is allocated: E _p = E _Grid + E _GB + E _CCHP (23); E _Grid = δ _e P _buy (24);

where E _Grid , E _GB and E _CCHP are the power purchases from external power grids, the free carbon emission allowances of GB and CCHP, respectively; E _p is the total system carbon emission allocation;

Electricity purchased from external grids for EHO;

Represents the conversion coefficient; δ _e and δ _h are the carbon emission allocation coefficients per unit of electricity and heat;

Represents the GB output thermal power at time t,

represents the output electric power of GT at time t,

The output thermal power of WHB at time,

represents the output cold power of AR at time t; 2): Reward and punishment ladder type carbon trading cost calculation model, as follows: The constructed reward and punishment ladder-type carbon trading cost model is shown in Equation (27). When the carbon emission is less than the free carbon allowance, the energy supply enterprise can sell the excess carbon emission allowance and obtain a part of the reward subsidy. carbon emission rights; the larger the carbon emission range, the higher the corresponding carbon trading price;

where E _co2 is the carbon transaction cost borne by EHO, E _c is the actual total carbon emission of EHO, c is the unit carbon transaction price; E _p represents the total carbon emission allowance of IES; 3): The energy hub operator model, as follows: EHO represents an energy hub operator. EHO adjusts the output of the energy coupling equipment in the EH and the internal energy price according to the user's energy consumption strategy, and maximizes the net profit of the EHO as the objective function: maxE _EHO =E _sale -E _buy -E _co2 -E _K (28);

In the formula: i∈{e, c, h}, E _sale is EHO’s energy sales revenue; E _buy is EHO’s electricity and gas cost; E _co2 and E _K are carbon transaction costs and equipment operation and maintenance borne by EHO, respectively Cost; λ _ti is the price at which EHO sells the i-th energy to users,

for the corresponding user load;

are the prices of electricity purchased and sold by EHO to external power grids, respectively,

are the corresponding purchasing and selling power respectively; λ ^gas is the price of natural gas,

are the natural gas power consumed by GT and GB respectively; K _i is the unit operation and maintenance cost of the equipment;

Represents the output power of each device; △t represents the time interval; In the optimal scheduling of EHO, not only the supply and demand balance of various energy sources in the EH and the upper and lower limit constraints of each energy equipment need to be considered, but also the constraints of internal energy prices:

In the formula: λ _ti,min and λ _ti,max are the upper and lower limits of the price of the i-th energy sold by EHO to users, respectively;

represents the i-th energy price at time t; 4) The user model is as follows: The user's objective function is the sum of the cost of purchasing energy and the cost of discomfort. Assuming that users in the EH can accept a certain degree of discomfort, the objective function is: minE _User = C _User + U _User (33); In the formula: C _User is the user's energy purchase cost; U _User is the user's discomfort cost, and their expressions are:

In the formula: i∈{e,c,h}, γ _i corresponds to the user's discomfort coefficient of transferring or reducing the i-th energy, reflecting the user's demand preference for energy; λ _ti is the price of the i-th energy consumed by the user at time t;

The most comfortable load requirement for the user;

is the actual load after the user performs IDR; △L _ti represents the load change before and after the user performs IDR; For transferable loads, the following constraints need to be satisfied:

where:

Indicates the upper limit of the load transferable amount,

represents the total transferable load of the load,

represents the energy load of the i-th energy source before demand response,

represents the energy load of the ith energy at time t,

Indicates the variation of the i-th load at time t, Δt represents the time interval, and T represents 24 hours a day. 5. The master-slave game optimization scheduling method for energy hubs based on comprehensive demand response and reward and punishment ladder carbon trading according to claim 1, wherein the step 4 comprises the following steps: Step 4.1: Initialize the population, let the number of iterations k=0; Step 4.2: If k≤k _max , output the optimal result, otherwise let k=k+1; Step 4.3: EHO sends the internal price to the user; Step 4.4: The user invokes CPLEX to optimize the load; Step 4.5: The user sends the optimized afterload to the EHO; Step 4.6: EHO solves the objective function E; Step 4.7: Perform mutation operation; Step 4.8: Perform crossover operation; produce offspring

Calculate the offspring E', if E>E', then let

And k=k+1. If not, let

And k=k+1, go back to step 4.2. 6. A reward and punishment ladder type carbon trading cost model, characterized in that the model is as follows:

where E _co2 is the carbon transaction cost borne by EHO, E _c is the actual total carbon emission of EHO, c is the unit carbon transaction price; E _p represents the total carbon emission allowance of IES.

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