CN114358432A - Multi-energy system optimal scheduling method and device considering demand response and carbon trading - Google Patents

Abstract

The invention discloses a multi-energy system optimal scheduling method considering demand response and carbon trading, which comprises the following steps: determining and acquiring basic operating parameters of an energy system; modeling a heat supply network and a power grid flow considering dynamic characteristics; constructing a heat supply comfort level model of a heating building; constructing a target function with the lowest operation cost and carbon transaction cost of the comprehensive energy system; constructing constraint conditions of energy balance constraint, system network constraint and equipment model constraint; solving the objective function to obtain a day-ahead optimized scheduling scheme of the comprehensive energy system; the method can realize supply and demand balance through the complementation and the coordination of the multi-energy flow and the multi-energy system on the premise of meeting the thermal comfort, reduce the whole carbon emission and the operation cost of the system, and fully play the energy use economy, the flexibility and the low carbon property of the multi-energy system.

Description

Multi-energy system optimal scheduling method and device considering demand response and carbon trading

technical field

The invention relates to a comprehensive energy system optimal dispatching scheme, in particular to a multi-energy system optimal dispatching method and device taking into account demand response and carbon trading.

Background technique

The joint optimal dispatch of multi-energy systems can break down barriers between energy sources, promote energy revolution and low-carbon transformation, and achieve economical and clean energy systems while meeting the diversified energy demands of the economy and society. A comprehensive energy system composed of multiple energy systems such as electricity, cooling, and heat can achieve multi-energy complementarity, mutual assistance and coordination optimization through coupled equipment, effectively improving energy utilization efficiency. Optimizing the dispatch of the integrated energy system is conducive to promoting energy transformation and helping to achieve the goal of dual carbon control.

The existing technologies have the following shortcomings and deficiencies: the steady-state model is mostly used as the analysis object, and the dynamic characteristics are less considered. Some scholars have proved that the thermal system is a reliable dispatching resource, but there are few studies on its interaction with the carbon trading mechanism, and less integration of the demand response considering the comfort evaluation index to optimize the energy consumption behavior.

SUMMARY OF THE INVENTION

The purpose of the present invention is to overcome the defects of the above-mentioned background technology, and propose a multi-energy system optimization scheduling method that takes into account the comfort of demand response and carbon trading. Energy coupling equipment such as heat pumps and gas turbines can build a comprehensive energy system optimization scheduling model that takes into account the demand response of comfort. Energy economy, flexibility and low carbon.

In order to solve the technical problem, the present invention adopts the following technical solutions:

The optimal scheduling method for multi-energy systems considering demand response and carbon trading includes the following steps:

Determine and obtain the basic parameters of energy system operation;

Modeling of thermal and grid power flows considering dynamic characteristics;

Build a heating comfort model for heating buildings;

Construct the objective function with the lowest operating cost and carbon transaction cost of the integrated energy system;

Construct the constraints of energy balance constraints, system network constraints, and equipment model constraints;

The objective function is solved to obtain the day-ahead optimal scheduling scheme of the integrated energy system.

Preferably, the thermal network and the power flow of the power grid are modeled considering the dynamic characteristics, and the formulation is carried out by the following steps:

Thermal network modeling

Taking into account only the heat transfer loss of the pipeline due to the temperature difference between the pipeline and the surrounding environment, the temperature at the end of the pipeline can be expressed as:

表示只计及热损时，管道j在当前时段t的末端温度的加权平均值；

表示管道j在当前时段t的首端温度的加权平均值；T^am为管道周围环境温度；α_j为管道j的损耗常数；k_j为管道热损失系数；c为水的比热容。in,

Represents the weighted average of the temperature at the end of the pipeline j in the current period t when only the heat loss is taken into account;

represents the weighted average of the temperature at the head end of the pipe j in the current period t; T ^am is the ambient temperature around the pipe; α _j is the loss constant of the pipe j; k _j is the heat loss coefficient of the pipe; c is the specific heat capacity of water.

At the same time, there is a time delay in the transmission of hot water, and the delay time of the temperature change at both ends of the heating pipe can be expressed as:

Among them, j is the set of heat supply pipes in the heat network; τ _j is the thermal delay time of pipe j; D _j , L _j , and q _j are the diameter, length and heat medium mass flow of pipe j, respectively;

Further, the length of the pipe is discretized, combined with the time series of the temperature of the heat medium at the head end of the pipe, the temperature at the end of the pipe can be obtained, namely:

表示管道j在当前时段t的末端温度的加权平均值；[t-τ_j]表示不大于t-τ_j的最大整数。in,

Represents the weighted average of the temperature at the end of pipe j in the current period t; [t-τ _j ] represents the largest integer not greater than t-τ _j .

Grid Modeling:

For the traditional radial distribution network, the Dist-Flow power flow equation is used to establish the network model of the distribution network, including the active power flow equation, the reactive power flow equation and the voltage equation, namely:

为节点i处t时刻电源的有功、无功功率；

为节点i处t时刻有功、无功负荷；r_i、x_i为t时刻节点i到节点i+1的线路的电阻、电抗；V_i,t为t时刻节点i的电压。Among them, P _i,t and Q _i,t are the active and reactive power on the line from node i to node i+1 of the grid at time t,

is the active and reactive power of the power supply at time t at node i;

are the active and reactive loads at node _{i at time t; ri and x i are} the resistance and reactance of the line from node i to node i+1 at time t; V _i,t is the voltage at node i at time t.

Further, variables such as equation (8) are introduced, and the second-order cone relaxation is performed on models (5)-(7) to obtain equation (9).

Preferably, in the step 3, the heating comfort model of the heating building is constructed, and its formulation is carried out by the following steps:

Further, heating building modeling:

The heat loss of a building is mainly composed of three parts: the heat loss of the envelope structure, the heat loss of the cold air infiltration and the heat loss of the ventilation. The energy consumption prediction model is as follows:

为t时段节点k对应采暖建筑的建筑围护结构热损失、冷风渗透热损失和通风热损失；

为t时段节点k对应采暖建筑的室内温度、室外温度；S_k、V_k为节点k对应采暖建筑的建筑围护结构面积、围护结构体积；F_k为热传导系数；

为节点k对应采暖建筑的楼层高度修正系数和建筑朝向修正系数；N_coa、V_coa为每小时换气次数和通风量；c_air为室外空气的恒压比热容；ρ为t时段室外空气密度。in,

is the heat loss of the building envelope, the cold air infiltration heat loss and the ventilation heat loss of the heating building corresponding to node k in the t period;

are the indoor temperature and outdoor temperature of the heating building corresponding to node k in t period; S _k and V _k are the building envelope area and volume of the heating building corresponding to node k; F _k is the thermal conductivity;

is the floor height correction coefficient and building orientation correction coefficient of the heating building corresponding to node k; N _coa , V _coa are the number of air changes per hour and ventilation volume; c _air is the constant pressure specific heat capacity of the outdoor air; ρ is the outdoor air density in the t period.

Further, the relationship between thermal load and room temperature can be expressed as:

为t时段节点k采暖建筑的建筑围护结构热损失、冷风渗透热损失和通风热损失；

为t时段节点k采暖建筑的室内温度；

为t时段流入节点k采暖建筑的热功率；c_M为室内空气比热容；M_k为节点k采暖建筑的室内空气质量。Among them, k is the set of heat network load nodes;

is the heat loss of the building envelope, the heat loss of cold air infiltration and the heat loss of ventilation of the heating building at node k in period t;

is the indoor temperature of the heating building at node k at time t;

is the thermal power flowing into the heating building at node k during t period; c _M is the specific heat capacity of indoor air; M _k is the indoor air quality of the heating building at node k.

Further, the heating comfort index is established:

The PMV index is used to evaluate the user's comfort to the environmental temperature change. The relationship between PMV value and indoor temperature can be expressed as:

Among them, ψ _PMV is the PMV value, and θ _best is the most comfortable temperature for the user.

In order to ensure user comfort, the PMV value should be within the comfortable range, that is, the indoor temperature of the building should be kept within the comfortable range, which can be expressed as:

为满足用户舒适度的PMV值的最低取值、最高取值，

为满足用户舒适度的室内最低温度、室内最高温度。in,

In order to meet the minimum and maximum values of PMV value for user comfort,

In order to meet the user's comfort level, the indoor minimum temperature and the indoor maximum temperature.

The human body is not sensitive to temperature changes in a small range, so changing the temperature by adjusting the heating supply within a certain range has little effect on the temperature comfort. On the premise of satisfying user comfort, the heating building can be used as a scheduling resource to participate in the optimal scheduling, and the total heat supply in the scheduling period should be the same, namely:

为t时段节点k的热负荷允许可调上下限，其取值与PMV上下限取值有关；

为t时段节点k的标准热负荷。in,

is the allowable upper and lower limits of the heat load of node k in the t period, and its value is related to the upper and lower limits of PMV;

is the standard heat load of node k in period t.

Likewise, cooling loads, like heating loads, have inertia. On the premise of satisfying comfort, it has a certain degree of adjustability in the scheduling period. Therefore, after considering the inertia of the cooling load, the cooling power of the system is:

为t时段节点k的冷负荷允许可调上下限，

为t时段节点k的标准冷负荷。in,

is the allowable upper and lower limits of the cooling load of node k in t period,

is the standard cooling load of node k in period t.

Preferably, an objective function with the lowest operating cost and carbon transaction cost of the integrated energy system is constructed, and its formulation is carried out using the following steps:

The running cost objective function is formulated:

The operating cost includes the cost of equipment energy supply and the cost of purchasing and selling electricity. In order to simplify the model, the cost of starting and stopping the unit is ignored for the sake of simplicity:

为t时段机组n设备运行成本，其中m₃＝{GT,CHP,HP,WT,ESS,TES，AR,AC}，m₃为需要计算运行成本的机组，GT、CHP、HP、WT、ESS、TES、AR、AC分别为燃气轮机、热电联产机组、热泵、风电、电储能、热储能、吸收式制冷机、电制冷机；

为供能机组总数；

为t时段的购售电成本。Among them, C _energy is the energy supply cost of the system,

is the operating cost of unit n equipment in t period, where m ₃ ={GT,CHP,HP,WT,ESS,TES,AR,AC}, m ₃ is the unit whose operating cost needs to be calculated, GT, CHP, HP, WT, ESS , TES, AR, and AC are gas turbines, cogeneration units, heat pumps, wind power, electric energy storage, thermal energy storage, absorption chillers, and electric chillers, respectively;

is the total number of power supply units;

is the cost of purchasing and selling electricity in period t.

分别为t时段与上级电网的购电功率和售电功率，

分别为t时刻购电价格和售电价格；in,

are the power purchase and sale power of the upper power grid and the power grid in the t period, respectively,

are the electricity purchase price and the electricity selling price at time t, respectively;

The carbon trading cost objective function is formulated:

The carbon allowance calculation formula can be expressed as:

为各机组碳配额；

为各机组总数；P_t,n为t时段机组n电出力，在表示CHP机组时，P_t,n为机组在纯凝工况下的折算电出力；σ为单位电量碳排放分配系数；Among them, m ₁ is the set of energy supply units, where m ₁ = {GT, CHP, WT, HP, AR, AC}, m ₁ is the unit that needs to calculate carbon quotas, GT, CHP, WT, HP, AR, AC are gas turbines, cogeneration units, wind power, heat pumps, absorption chillers, and electric chillers; T is the scheduling period;

Carbon quota for each unit;

is the total number of units; P _t,n is the electrical output of unit n in the t period. When representing CHP units, P _t,n is the converted electrical output of the unit under pure condensing conditions; σ is the carbon emission distribution coefficient per unit of electricity;

Wind turbines can be considered to not produce carbon emissions, while other energy supply units will produce carbon emissions during operation, and their carbon emissions can be expressed as:

为各机组碳排放量，其中m₂＝{GT,CHP,HP,AR,AC}m₂为需要计算碳排放的机组，GT、CHP、HP、AR、AC分别为燃气轮机、热电联产机组、热泵、吸收式制冷机、电制冷机；

为碳排机组总数；γ_n为机组n单位出力的碳排放强度；in,

is the carbon emission of each unit, where m ₂ ={GT,CHP,HP,AR,AC}m ₂ is the unit whose carbon emission needs to be calculated, GT, CHP, HP, AR, and AC are the gas turbine, cogeneration unit, Heat pumps, absorption chillers, electric chillers;

is the total number of carbon emission units; γ _n is the carbon emission intensity of unit n output;

The carbon trading volume involved in the integrated energy system is:

In the ladder-type carbon trading model, the carbon trading cost can be expressed as:

Among them, C _carbon is the carbon trading cost, C _b is the carbon trading market price, x ₁ and x ₂ are the reward/punishment coefficients for different carbon emission ranges; h is the length of the value range for different carbon emissions;

The comprehensive optimization objective function is formulated:

The present invention considers that economic goals and low-carbon goals are equally important, so the comprehensive optimization objective function is set as formula (25).

minF=min(C _energy +C _carbon ) (25)

Among them, F is the comprehensive cost of the system.

Constraints of energy balance constraints, system network constraints, and equipment model constraints are constructed using the following methods:

Further, the constructed system energy balance constraint is:

In the operation of the system, the energy balance constraints of electricity, heat and cold need to be satisfied respectively:

分别为t时段电网节点i处燃气轮机、风电发出的电功率；

为t时段电网节点i处与外网交换的电功率；

为t时段电网节点i处的电负荷；

分别为t时段连接在电网节点i处和热网节点k处的CHP机组的电出力、热出力；

分别为t时段连接在电网节点i处和热网节点k处的热泵消耗的电功率和发出的热功率；

分别为t时段连接在电网节点i处和热网节点k处的储电装置、储热装置的功率；

分别为t时段连接在电网节点i处和热网节点k处的电制冷机和吸收式制冷机消耗的电功率和热功率；

分别为t时段电制冷机和吸收式制冷机发出的冷功率。in,

are the electric powers generated by the gas turbine and wind power at the grid node i in the period t, respectively;

is the electric power exchanged with the external network at the grid node i in the t period;

is the electrical load at grid node i in period t;

are the electrical output and thermal output of the CHP units connected at grid node i and heat grid node k in time t, respectively;

are the electric power consumed and the thermal power emitted by the heat pump connected at the grid node i and the heat grid node k in the t period, respectively;

are the power of the power storage device and the heat storage device connected at the grid node i and the heat grid node k in the t period, respectively;

are the electric power and thermal power consumed by the electric refrigerator and the absorption refrigerator connected at the grid node i and the heat grid node k in the t period, respectively;

are the cooling powers emitted by the electric refrigerator and the absorption refrigerator in the t period, respectively.

Further, the constructed device model constraints are:

Unit output constraints, namely:

分别为机组n电出力上下限，其中m₄＝{GT,WT,CHP,AC}，m₄为需要满足电出力约束的机组，GT、WT、CHP、AC分别为燃气轮机、风电、热电联产机组、电制冷机；

分别为机组n热出力上下限，其中m₅＝{HP,AR}，m₅为需要满足热出力约束的机组，HP、AR分别为热泵、吸收式制冷机；

为t时段机组n的热出力；in,

are the upper and lower limits of power output of unit n, where m ₄ ={GT,WT,CHP,AC}, m ₄ is the unit that needs to meet the power output constraints, GT, WT, CHP, and AC are gas turbine, wind power, cogeneration, respectively Units, electric refrigerators;

are the upper and lower limits of the heat output of unit n, where m ₅ ={HP, AR}, m ₅ is the unit that needs to meet the heat output constraints, and HP and AR are the heat pump and absorption chiller, respectively;

is the heat output of unit n in period t;

Crew climbing constraints, namely:

和

为机组n滑坡速率和爬坡速率，其中m₆＝{GT,CHP,AC}，m₆为需要满足爬坡约束的机组，GT、CHP、AC分别为燃气轮机、热电联产机组、电制冷机；

和

为机组n的滑坡速率和爬坡速率；in,

and

are the landslide rate and ramp rate of unit n, where m ₆ ={GT,CHP,AC}, m ₆ is the unit that needs to meet the climbing constraints, GT, CHP, AC are gas turbine, cogeneration unit, electric refrigerator, respectively ;

and

are the landslide rate and climbing rate of unit n;

Energy storage device constraints, namely:

The constraints of electric energy storage equipment are shown in formula (33). In order to ensure the continuity of dispatching, electric energy storage equipment needs to ensure that the storage capacity is the same at the beginning and end time; to ensure that charging and discharging cannot be performed at the same time in each period; the principle of thermal energy storage is similar ,No longer.

为t时段电储能设备的充、放电功率；

为t时段电储能设备的储电量；P^ESS,max、P^ESS,min为电储能设备储电功率上下限；

和

分别为

和

的功率上限；

和

分别为放电因子和充电因子。in,

is the charging and discharging power of the electric energy storage device in t period;

is the stored power of the electric energy storage device in the t period; P ^ESS,max and P ^ESS,min are the upper and lower limits of the storage power of the electric energy storage device;

and

respectively

and

power limit;

and

are the discharge factor and the charge factor, respectively.

Energy Conversion Constraints:

The energy conversion constraints of the extraction-condensing thermal power unit are:

为机组n在凝工况下最小、最大电出力；

为机组n热出力上限；c_m,n、K_m,n、c_v,n为机组常数。in,

is the minimum and maximum electrical output of unit n under condensing condition;

is the upper limit of heat output of unit n; c _m,n , K _m,n , and _cv,n are unit constants.

Heat pump energy conversion constraints, namely

Among them, η _HP is the electric heat conversion coefficient of the heat pump.

Absorption chiller energy conversion constraints, namely

Among them, η _HP is the conversion coefficient of absorption chiller.

Electric refrigerator energy conversion constraints, namely

Among them, η _HP is the conversion coefficient of the electric refrigerator.

Further, the constructed system network constraints are:

For the traditional radial distribution network, the classic Dist-Flow power flow equation is used to establish the distribution network model, and the second-order cone relaxation is performed as the network constraint.

Considering the dynamic characteristics of the heat medium in the transmission process, a dynamic heat supply network model is established.

为t时段流入节点k的热媒质量流量；

为热源节点的热出力和换热站节点的热交换量；q_t,k为t时段流入节点k的热媒质量流量；

为t时段节点k的供回水温度；

为供回水温度上下限；

为将节点k作为首端和末端的管道集合；T_t,k为t时段节点k混合温度和下级管道入口温度。in,

is the mass flow of heat medium flowing into node k during t period;

is the heat output of the heat source node and the heat exchange amount of the heat exchange station node; q _t,k is the mass flow of the heat medium flowing into the node k in the t period;

is the supply and return water temperature of node k in period t;

The upper and lower limits of the supply and return water temperature;

is the pipeline set with node k as the head end and the end; T _t,k is the mixing temperature of node k and the inlet temperature of the lower pipeline in t period.

Preferably, the day-ahead optimal scheduling scheme of the integrated energy system is obtained by solving the following methods:

Combining the objective function and constraints, it can be seen that the optimal scheduling model of the integrated energy system is a mixed integer linear programming problem, so it can be modeled based on the YALMIP toolbox, and the commercial solver CPLEX can be called to solve the model.

Multi-energy system optimization dispatching devices that take into account demand response and carbon trading, including:

The parameter acquisition module is used to determine and acquire the basic operating parameters of the energy system;

Thermal network and grid power flow modeling module for modeling thermal network and grid power flow considering dynamic characteristics;

The heating comfort model building module is used to construct the heating comfort model of the heating building;

The objective function establishment module is used to construct the objective function with the lowest operating cost and carbon transaction cost of the integrated energy system;

Constraint building module, used to construct constraints of energy balance constraints, system network constraints, and device model constraints;

The objective function solving module is used to solve the objective function and obtain the day-ahead optimal dispatch plan of the integrated energy system.

A computing device comprising:

one or more processing units;

storage unit for storing one or more programs,

Wherein, when the one or more programs are executed by the one or more processing units, the one or more processing units cause the one or more processing units to execute the above-mentioned optimal scheduling method for a multi-energy system that takes into account demand response and carbon trading.

A computer-readable storage medium having a non-volatile program code executable by a processor, when the computer program is executed by the processor, the computer program realizes the above-mentioned optimal scheduling method for a multi-energy system considering demand response and carbon trading. step.

Compared with the existing technology, this scheme adopts the multi-energy system optimization scheduling method of demand response and carbon trading considering comfort, and achieves the following beneficial effects:

(1) Based on the dynamic characteristics of the energy system, the flexibility of the integrated energy system is improved under the consideration of the user's thermal comfort demand response, and the heat load can be flexibly adjusted to better ensure the energy consumption needs. To achieve multi-energy complementarity;

(2) The introduction of a ladder-type carbon trading mechanism can improve the absorption capacity of wind power and reduce the overall carbon emissions of the units. The interaction between the dynamic characteristics of the thermal network and the carbon trading mechanism can reduce the operating cost of the system and increase the cost of carbon trading. profit, so as to realize the operation of a low-carbon economy.

Description of drawings

FIG. 1 is a flowchart of a multi-energy system optimization scheduling method that takes into account demand response and carbon trading provided by Embodiment 1 of the present invention;

2 is a network diagram of an integrated energy system provided by Embodiment 1 of the present invention;

3 is a load, wind power output and outdoor temperature curve diagram provided by Embodiment 1 of the present invention;

FIG. 4 is a comparison before and after the heat load demand response provided by Embodiment 1 of the present invention;

FIG. 5 is a comparison before and after the cooling load demand response provided by Embodiment 1 of the present invention;

FIG. 6 is a scheduling power balance optimization result provided by Embodiment 1 of the present invention;

FIG. 7 is a scheduling thermal balance optimization result provided by Embodiment 1 of the present invention;

FIG. 8 is an optimization result of scheduling cooling load balance provided by Embodiment 1 of the present invention.

Detailed ways

Example 1

The invention provides a multi-energy system optimization scheduling method that takes into account demand response and carbon trading in consideration of comfort. The method takes into account the dynamic characteristics of the network, and constructs a combination of heat and power units, heat pumps, gas turbines and other energy coupling equipment. The optimal dispatching model of the integrated energy system is developed. Firstly, the refined modeling is carried out for the dynamic characteristics of the thermal network and power flow; secondly, the heating comfort model of the heating building is constructed; finally, the objective function is to minimize the operating cost of the integrated energy system and the carbon transaction cost, and the energy balance constraints , the network constraints are the constraints, and a day-ahead optimal scheduling scheme for the integrated energy system is proposed. The simulation results of an example show that the proposed optimal scheduling method can achieve the balance of supply and demand through the complementary and collaborative multi-energy system of multi-energy flow under the premise of satisfying thermal comfort.

The formula involved in this scheme is as follows:

表示只计及热损时，管道j在当前时段t的末端温度的加权平均值；

表示管道j在当前时段t的首端温度的加权平均值；T^am为管道周围环境温度；α_j为管道j的损耗常数；k_j为管道热损失系数；c为水的比热容。in,

Represents the weighted average of the temperature at the end of the pipeline j in the current period t when only the heat loss is taken into account;

represents the weighted average of the temperature at the head end of the pipe j in the current period t; T ^am is the ambient temperature around the pipe; α _j is the loss constant of the pipe j; k _j is the heat loss coefficient of the pipe; c is the specific heat capacity of water.

Among them, j is the set of heat supply pipes in the heat network; τ _j is the thermal delay time of pipe j; D _j , L _j , and q _j are the diameter, length and mass flow of heat medium of pipe j, respectively.

表示管道j在当前时段t的末端温度的加权平均值；[t-τ_j]表示不大于t-τ_j的最大整数。in,

Represents the weighted average of the temperature at the end of pipe j in the current period t; [t-τ _j ] represents the largest integer not greater than t-τ _j .

为节点i处t时刻电源的有功、无功功率；

为节点i处t时刻有功、无功负荷；r_i、x_i为t时刻节点i到节点i+1的线路的电阻、电抗；V_i,t为t时刻节点i的电压。Among them, P _i,t and Q _i,t are the active and reactive power on the line from node i to node i+1 of the grid at time t,

is the active and reactive power of the power supply at time t at node i;

are the active and reactive loads at node _{i at time t; ri and x i are} the resistance and reactance of the line from node i to node i+1 at time t; V _i,t is the voltage at node i at time t.

为t时段节点k对应采暖建筑的建筑围护结构热损失、冷风渗透热损失和通风热损失；

为t时段节点k对应采暖建筑的室内温度、室外温度；S_k、V_k为节点k对应采暖建筑的建筑围护结构面积、围护结构体积；F_k为热传导系数；

为节点k对应采暖建筑的楼层高度修正系数和建筑朝向修正系数；N_coa、V_coa为每小时换气次数和通风量；c_air为室外空气的恒压比热容；ρ为t时段室外空气密度。in,

is the heat loss of the building envelope, the cold air infiltration heat loss and the ventilation heat loss of the heating building corresponding to node k in the t period;

are the indoor temperature and outdoor temperature of the heating building corresponding to node k in t period; S _k and V _k are the building envelope area and volume of the heating building corresponding to node k; F _k is the thermal conductivity;

is the floor height correction coefficient and building orientation correction coefficient of the heating building corresponding to node k; N _coa , V _coa are the number of air changes per hour and ventilation volume; c _air is the constant pressure specific heat capacity of the outdoor air; ρ is the outdoor air density in the t period.

为t时段节点k采暖建筑的建筑围护结构热损失、冷风渗透热损失和通风热损失；

为t时段节点k采暖建筑的室内温度；

为t时段流入节点k采暖建筑的热功率；c_M为室内空气比热容；M_k为节点k采暖建筑的室内空气质量。Among them, k is the set of heat network load nodes;

is the heat loss of the building envelope, the heat loss of cold air infiltration and the heat loss of ventilation of the heating building at node k in period t;

is the indoor temperature of the heating building at node k at time t;

is the thermal power flowing into the heating building at node k during t period; c _M is the specific heat capacity of indoor air; M _k is the indoor air quality of the heating building at node k.

Among them, ψ _PMV is the PMV value, and θ _best is the most comfortable temperature for the user.

为满足用户舒适度的PMV值的最低取值、最高取值，

为满足用户舒适度的室内最低温度、室内最高温度。in,

In order to meet the minimum and maximum values of PMV value for user comfort,

In order to meet the user's comfort level, the indoor minimum temperature and the indoor maximum temperature.

为t时段节点k的热负荷允许可调上下限，其取值与PMV上下限取值有关，

为t时段节点k的标准热负荷。in,

is the allowable upper and lower limits of the heat load of node k in the t period, and its value is related to the upper and lower limits of PMV,

is the standard heat load of node k in period t.

为t时段节点k的冷负荷允许可调上下限，

为t时段节点k的标准冷负荷。in,

is the allowable upper and lower limits of the cooling load of node k in t period,

is the standard cooling load of node k in period t.

为t时段机组n设备运行成本，其中m₃＝{GT,CHP,HP,WT,ESS,TES，AR,AC}，m₃为需要计算运行成本的机组，GT、CHP、HP、WT、ESS、TES、AR、AC分别为燃气轮机、热电联产机组、热泵、风电、电储能、热储能、吸收式制冷机、电制冷机；

为供能机组总数；

为t时段的购售电成本。Among them, C _energy is the energy supply cost of the system,

is the operating cost of unit n equipment in t period, where m ₃ ={GT,CHP,HP,WT,ESS,TES,AR,AC}, m ₃ is the unit whose operating cost needs to be calculated, GT, CHP, HP, WT, ESS , TES, AR, and AC are gas turbines, cogeneration units, heat pumps, wind power, electric energy storage, thermal energy storage, absorption chillers, and electric chillers, respectively;

is the total number of power supply units;

is the cost of purchasing and selling electricity in period t.

分别为t时段与上级电网的购电功率和售电功率，

分别为t时刻购电价格和售电价格。in,

are the power purchase and sale power of the upper power grid and the power grid in the t period, respectively,

are the electricity purchase price and the electricity selling price at time t, respectively.

为各机组碳配额；

为各机组总数；P_t,n为t时段机组n电出力，在表示CHP机组时，P_t,n为机组在纯凝工况下的折算电出力；σ为单位电量碳排放分配系数；Among them, m ₁ is the set of energy supply units, where m ₁ = {GT, CHP, WT, HP, AR, AC}, m ₁ is the unit that needs to calculate carbon quotas, GT, CHP, WT, HP, AR, AC are gas turbines, cogeneration units, wind power, heat pumps, absorption chillers, and electric chillers; T is the scheduling period;

Carbon quota for each unit;

is the total number of units; P _t,n is the electrical output of unit n in the t period. When representing CHP units, P _t,n is the converted electrical output of the unit under pure condensing conditions; σ is the carbon emission distribution coefficient per unit of electricity;

为各机组碳排放量，其中m₂＝{GT,CHP,HP,AR,AC}，m₂为需要计算碳排放的机组，GT、CHP、HP、AR、AC分别为燃气轮机、热电联产机组、热泵、吸收式制冷机、电制冷机；

为碳排机组总数；γ_n为机组n单位出力的碳排放强度。in,

is the carbon emission of each unit, where m ₂ ={GT,CHP,HP,AR,AC}, m ₂ is the unit whose carbon emission needs to be calculated, GT, CHP, HP, AR, and AC are the gas turbine, cogeneration unit, respectively , heat pump, absorption chiller, electric chiller;

is the total number of carbon emission units; γ _n is the carbon emission intensity of unit n output.

Among them, C _carbon is the carbon trading cost, C _b is the carbon trading market price, x ₁ , x ₂ are the reward/punishment coefficients for different carbon emission ranges; h is the length of the value range for different carbon emissions.

minF=min(C _energy +C _carbon ) (25)

Among them, F is the comprehensive cost of the system.

分别为t时段电网节点i处燃气轮机、风电发出的电功率；

为t时段电网节点i处与外网交换的电功率；

为t时段电网节点i处的电负荷；

分别为t时段连接在电网节点i处和热网节点k处的CHP机组的电出力、热出力；

分别为t时段连接在电网节点i处和热网节点k处的热泵消耗的电功率和发出的热功率；

分别为t时段连接在电网节点i处和热网节点k处的储电装置、储热装置的功率；

分别为t时段连接在电网节点i处和热网节点k处的电制冷机和吸收式制冷机消耗的电功率和热功率；

分别为t时段电制冷机和吸收式制冷机发出的冷功率。in,

are the electric powers generated by the gas turbine and wind power at the grid node i in the period t, respectively;

is the electric power exchanged with the external network at the grid node i in the t period;

is the electrical load at grid node i in period t;

are the electrical output and thermal output of the CHP units connected at grid node i and heat grid node k in time t, respectively;

are the electric power consumed and the thermal power emitted by the heat pump connected at the grid node i and the heat grid node k in the t period, respectively;

are the power of the power storage device and the heat storage device connected at the grid node i and the heat grid node k in the t period, respectively;

are the electric power and thermal power consumed by the electric refrigerator and the absorption refrigerator connected at the grid node i and the heat grid node k in the t period, respectively;

are the cooling powers emitted by the electric refrigerator and the absorption refrigerator in the t period, respectively.

分别为机组n电出力上下限，其中m₄＝{GT,WT,CHP,AC}，m₄为需要满足电出力约束的机组，GT、WT、CHP、AC分别为燃气轮机、风电、热电联产机组、电制冷机；

分别为机组n热出力上下限，其中m₅＝{HP,AR}，m₅为需要满足热出力约束的机组，HP、AR分别为热泵、吸收式制冷机；

为t时段机组n的热出力。in,

are the upper and lower limits of power output of unit n, where m ₄ ={GT,WT,CHP,AC}, m ₄ is the unit that needs to meet the power output constraints, GT, WT, CHP, and AC are gas turbine, wind power, cogeneration, respectively Units, electric refrigerators;

are the upper and lower limits of the heat output of unit n, where m ₅ ={HP, AR}, m ₅ is the unit that needs to meet the heat output constraints, and HP and AR are the heat pump and absorption chiller, respectively;

is the heat output of unit n in period t.

和

为机组n滑坡速率和爬坡速率，其中m₆＝{GT,CHP,AC}，m₆为需要满足爬坡约束的机组，GT、CHP、AC分别为燃气轮机、热电联产机组、电制冷机；

和

为机组n的滑坡速率和爬坡速率。in,

and

are the landslide rate and ramp rate of unit n, where m ₆ ={GT,CHP,AC}, m ₆ is the unit that needs to meet the climbing constraints, GT, CHP, AC are gas turbine, cogeneration unit, electric refrigerator, respectively ;

and

are the landslide rate and climbing rate of unit n.

为t时段电储能设备的充、放电功率；P_t ^ESS为t时段电储能设备的储电量；P^ESS,max、P^ESS,min为电储能设备储电功率上下限；

和

分别为

和

的功率上限；

和

分别为放电因子和充电因子。in,

is the charging and discharging power of the electric energy storage device in the t period; P _t ^ESS is the stored power of the electric energy storage device in the t period; P ^ESS,max and P ^ESS,min are the upper and lower limits of the electric energy storage device storage power;

and

respectively

and

power limit;

and

are the discharge factor and the charge factor, respectively.

为机组n在凝工况下最小、最大电出力；

为机组n热出力上限；c_m,n、K_m,n、c_v,n为机组常数；in,

is the minimum and maximum electrical output of unit n under condensing condition;

is the upper limit of heat output of unit n; c _m,n , K _m,n , _cv,n are unit constants;

Wherein, η _HP is the electric heat conversion coefficient of the heat pump;

Among them, η _HP is the conversion coefficient of absorption chiller;

Among them, η _HP is the conversion coefficient of the electric refrigerator;

为t时段流入节点k的热媒质量流量；

为热源节点的热出力和换热站节点的热交换量；q_t,k为t时段流入节点k的热媒质量流量；

为t时段节点k的供回水温度；

为供回水温度上下限；

为将节点k作为首端和末端的管道集合；T_t,k为t时段节点k混合温度和下级管道入口温度。in,

is the mass flow of heat medium flowing into node k during t period;

is the heat output of the heat source node and the heat exchange amount of the heat exchange station node; q _t,k is the mass flow of the heat medium flowing into the node k during the t period;

is the supply and return water temperature of node k in period t;

The upper and lower limits of the supply and return water temperature;

is the pipeline set with node k as the head end and the end; T _t,k is the mixing temperature of node k and the inlet temperature of the lower pipeline in t period.

This plan includes the following:

(1) Composition of the integrated energy system

The system generates electricity and heat energy through a combined heat and power unit (CHP); electricity and heat conversion is realized through a heat pump (HP). In addition, wind power (WT), gas turbine (GT) and the upper-level grid together supply electricity to electrical load users. Electric energy storage (ESS) and thermal energy storage (TES) can store or release energy when there is excess or shortage of energy. Cooling loads can be provided by electric chillers (AC) and absorption chillers (AR).

(2) Modeling of dynamic characteristics of thermal system

Different from the characteristics of small inertia and fast adjustment of the power system, the thermal system has a large system inertia in dispatching. The dynamic characteristics of the central heating system are mainly reflected in the heating pipe network and heating buildings. In the process of heat medium transmission, the dynamic characteristics of the heat supply pipe network have a direct impact on the temperature of the heat medium, mainly manifested as heat loss and heat delay. In the processing of heat loss, the heat transfer loss of the pipeline caused by the temperature difference between the pipeline and the surrounding environment is calculated, and the temperature at the end of the pipeline is obtained. In the processing of thermal delay, the node method is used to describe the delay process of thermal energy transmission, and the length of the pipeline is discretized to obtain the thermal delay time.

(3) Heating comfort model for heating buildings

Considering the demand response of user comfort, based on the building cluster model at the heat exchange station, on the premise that the heat obtained by the building cluster remains unchanged during the dispatch period, combined with the adjustable range of the total heat output of the heat source in the system, it can be calculated by adjusting each dispatch period. The heat output is adjusted flexibly, so that the heating building can participate in the optimal scheduling as a scheduling resource.

(4) Optimal dispatch model of integrated energy system

Introducing a carbon trading mechanism, aiming at the optimization goal of minimizing the comprehensive cost of system economy and carbon emissions, considering system energy balance constraints, equipment model constraints, network constraints, etc., a multi-energy system with demand response and carbon trading considering comfort is proposed. Optimize the scheduling method.

Figure 1 shows the development process of the optimal scheduling method for multi-energy systems with demand response and carbon trading considering comfort, and its principles and steps are as follows:

1) Initialization 101, initializing basic parameters such as network structure, device access location and maximum power;

2) Obtaining the day-ahead forecast data 102 of wind power and load;

3) Modeling 103 the thermal network and power flow considering the dynamic characteristics;

4) Construct the objective function 104 with the lowest operating cost and carbon transaction cost of the integrated energy system;

5) Constraints 105 for constructing energy balance constraints, system network constraints, and equipment model constraints;

6) Construct an optimization problem 106;

7) Solve the optimization problem 107;

8) Determine whether the comfort index 108 is met, if so, output the integrated energy system prior-day optimal dispatch plan 109 , otherwise modify the load interval 110 that can participate in demand response, and return to 106 .

As an example, in this embodiment, the multi-energy system optimization scheduling method of demand response and carbon trading considering comfort level proposed in this example is based on the modified IEEE 33-node power distribution system and 6-node thermal system to form an integrated energy system calculation example ,as shown in picture 2. The power supply of the system includes cogeneration units, wind turbines, and pure gas turbine generators. The heat network performs energy conversion with the grid through heat pumps, and the cooling load can be provided by electric chillers and absorption chillers. The predicted electric load, standard heating load, standard cooling load, wind power output curve and outdoor temperature curve of the system are shown in Figure 4. The relevant parameters of each device are shown in Table 1. The characteristics of heating building clusters are shown in Table 2.

Table 1 Equipment parameters

Table 2 Characteristic parameters of heating building clusters

architecture Area/m² Volume/m³ maximum temperature lowest temperature Hot network node 1 3.15*10⁵ 1.26*10⁶ 26 twenty one 4 2 2.85*10⁵ 1.12*10⁶ 26 twenty one 5 3 2.45*10⁵ 0.98*10⁶ 26 twenty one 6

This scheme is modeled by the YALMIP toolbox, and the above problems are solved by Cplex12.8.0. Based on the above-mentioned comparison simulation analysis for the four types of scenarios, as shown in Table 3. In the table, "√" and "×" indicate that the influence factor is considered and not considered, respectively.

Table 3 Comparison of the conditions of each scheduling scheme

Figure 5 shows the comparison before and after considering the demand response for the heating load, and Figure 6 shows the comparison before and after considering the demand response for the cooling load. On the premise that the comfort index of the heating building cluster is satisfied, the total heat output of each dispatch period is adjusted, and a part of the heat output is shifted from the electric load valley period to the electric load peak period. During the peak heat output period, the heat output of the heat pump decreases, and the power consumption of the heat pump decreases accordingly, and the power system stores the excess power into the power storage device; during the peak period of the electrical load, the power storage device releases electricity, reducing the output of CHP. It is verified that this method can improve the operating economy of the system while reducing carbon emissions.

After considering the carbon trading mechanism and demand response, Figure 6 shows the optimization results of the power subsystem in the day-ahead scheduling, and Figure 7 shows the optimization results of the thermal subsystem in the day-a-day scheduling. Figure 8 shows the optimization results of cooling load balance in day-ahead scheduling. It can be seen from the figure that during the flat electricity price period and the valley electricity price period, the price of electricity purchased from the external network decreases. When the carbon emission of CHP units is too high, carbon allowances need to be purchased from the carbon market, thereby increasing the carbon emission cost of the system, so CHP units are restricted. In turn, it tends to increase the purchase of electricity to meet the load demand; during the period of high electricity price, the price of electricity purchased from the external grid is higher than the cost of carbon emission, which will tend to increase the output of CHP to reduce the purchase of electricity. It is verified that the method effectively achieves multi-energy complementarity.

Table 4 Cost comparison of each scheduling scheme

Program running cost/$ Carbon trading cost/$ Comprehensive target cost/$ 1 54303.7 -4959.7 49344.0 2 54399.4 -6290.2 48109.2 3 52019.9 -5426.2 46593.7 4 52118.7 -6873.1 45245.6

Table 4 shows the cost comparison of each scheduling scheme. It can be seen from Table 4 that, by comparing the energy consumption cost, carbon transaction cost and comprehensive target cost of each dispatching scheme, the method proposed in the present invention effectively utilizes the low-carbon emission units in the system and improves the gain of wind power in the carbon trading market. Considering the demand response under the comfort index, the flexibility of the integrated energy system is improved, the heat load is flexibly adjusted, and the multi-energy complementation is better achieved on the premise of ensuring the energy consumption, so as to achieve economical efficiency , Low-carbon optimal scheduling. To sum up, the multi-energy system optimal scheduling method of demand response and carbon trading considering comfort level proposed by the present invention is effective and reasonable.

Example 2

This embodiment provides a multi-energy system optimization scheduling device that takes into account demand response and carbon trading, including:

The parameter acquisition module is used to determine and acquire the basic operating parameters of the energy system;

Thermal network and grid power flow modeling module for modeling thermal network and grid power flow considering dynamic characteristics;

The heating comfort model building module is used to construct the heating comfort model of the heating building;

The objective function establishment module is used to construct the objective function with the lowest operating cost and carbon transaction cost of the integrated energy system;

Constraint building module, used to construct constraints of energy balance constraints, system network constraints, and device model constraints;

The objective function solving module is used to solve the objective function and obtain the day-ahead optimal dispatch plan of the integrated energy system.

A computing device comprising:

one or more processing units;

storage unit for storing one or more programs,

Wherein, when the one or more programs are executed by the one or more processing units, the one or more processing units execute the above-mentioned optimal scheduling method for a multi-energy system that takes into account demand response and carbon trading; It should be noted that a computing device may include, but is not limited to, a processing unit and a storage unit; those skilled in the art can understand that the inclusion of a processing unit and a storage unit in a computing device does not constitute a limitation on the computing device, and may include more components, or Combining certain components, or different components, for example, computing devices may also include input and output devices, network access devices, buses, and the like.

A computer-readable storage medium having a non-volatile program code executable by a processor, when the computer program is executed by the processor, the computer program realizes the above-mentioned optimal scheduling method for a multi-energy system considering demand response and carbon trading. It should be noted that the readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above; The program may be transmitted using any suitable medium, including, but not limited to, wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing. For example, program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C++, etc., as well as conventional A procedural programming language, such as C or a similar programming language. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, or entirely on a remote computing device or server. Where remote computing devices are involved, the remote computing devices may be connected to the user computing device over any kind of network, including a local area network (LAN) or wide area network (WAN), or may be connected to an external computing device (eg, using an Internet service provider business via an Internet connection).

It should be understood that the embodiments and examples discussed here are only for illustration, and for those skilled in the art, improvements or changes can be made, and all these improvements and changes should belong to the protection scope of the appended claims of the present invention.

Claims (9)

1. The optimal scheduling method of the multi-energy system considering the demand response and the carbon transaction is characterized by comprising the following steps of:

determining and acquiring basic operating parameters of an energy system;

modeling a heat supply network and a power grid flow considering dynamic characteristics;

constructing a heat supply comfort level model of a heating building;

constructing a target function with the lowest operation cost and carbon transaction cost of the comprehensive energy system;

constructing constraint conditions of energy balance constraint, system network constraint and equipment model constraint;

and solving the objective function to obtain a day-ahead optimized scheduling scheme of the comprehensive energy system.

2. The optimal scheduling method of multi-energy system considering demand response and carbon trading according to claim 1, wherein modeling the heat supply network and the power grid load flow considering dynamic characteristics comprises the following steps:

modeling a heat supply network:

taking into account only the heat transfer loss of the pipe due to the temperature difference between the pipe and the surrounding environment, the pipe end temperature can be expressed as:

wherein,

represents a weighted average of the end temperature of the pipe j over the current time period t, taking into account only heat losses;

represents a weighted average of the head end temperature of the pipeline j over the current time period t; t is^amIs the ambient temperature around the pipeline; alpha is alpha_jIs the loss constant of pipe j; k is a radical of_jIs the heat loss coefficient of the pipeline; c is the specific heat capacity of water;

meanwhile, there is a time lag in hot water delivery, and the delay time of temperature change at both ends of the heat supply pipeline can be expressed as:

j is a set of heat supply pipelines of the heat supply network; tau is_jIs the thermal delay time of pipe j; d_j、L_j、q_jThe diameter and the length of the pipeline j and the mass flow of the heating medium are respectively;

discretizing the length of the pipeline, combining the time sequence of the temperature of the heat medium at the head end of the pipeline to obtain the tail end temperature of the pipeline, namely:

wherein,

a weighted average value representing the end temperature of the pipe j over the current time period t; [ t-T ]_j]Denotes a value not greater than t-tau_jThe largest integer of (a);

modeling a power grid:

aiming at the traditional radial power distribution network, a Dist-Flow power Flow equation is adopted to establish a network model of the power distribution network, wherein the network model comprises an active power Flow equation, a reactive power Flow equation and a voltage equation, namely:

wherein, P_i,t、Q_i,tFor the active and reactive power on the line from node i to node i +1 of the grid at time t,

the active power and the reactive power of the power supply at the t moment at the node i are obtained;

is a nodei, active and reactive loads at the moment t; r is_i、x_iThe resistance and reactance of a line from a node i to a node i +1 at the moment t; v_i,tIs the voltage at node i at time t;

introducing variables as formula (8), and performing second-order cone relaxation on the models (5) to (7) to obtain formula (9):

3. the optimal scheduling method of the multi-energy system considering the demand response and the carbon trading according to claim 2, wherein the building of the heating comfort model of the heating building comprises the following steps:

modeling a heating building:

the heat loss of the building comprises the heat loss of an enclosure structure, the heat loss of cold air infiltration and the heat loss of ventilation, and the energy consumption prediction model is as follows:

wherein,

the node k corresponds to the building envelope heat loss, cold air infiltration heat loss and ventilation heat loss of the heating building at the time period t;

the node k corresponds to the indoor temperature and the outdoor temperature of the heating building in the time period t; s_k、V_kThe node k corresponds to the building envelope area and the envelope volume of the heating building; f_kIs the coefficient of thermal conductivity;

the node k corresponds to a floor height correction coefficient and a building orientation correction coefficient of a heating building; n is a radical of_coa、V_coaThe number of air changes per hour and the ventilation volume; c. C_airIs the constant pressure specific heat capacity of outdoor air; ρ is the outdoor air density for the period t.

The thermal load versus room temperature can be expressed as:

wherein k is a set of heat supply network load nodes;

heating power flowing into the node k for heating the building for a period t; c. C_MThe specific heat capacity of indoor air; m_kIndoor air quality of a building is heated for node k;

establishing a heat supply comfort level index:

the comfort degree of the user to the change of the environmental temperature is evaluated by adopting the PMV index, and the relation between the PMV value and the indoor temperature can be expressed as follows:

wherein psi_PMVIs the PMV value, θ_bestThe most comfortable temperature for the user;

to ensure user comfort, the PMV value should be within a comfort range, i.e. the building indoor temperature should be kept within the comfort range, which can be expressed as:

wherein,

in order to meet the minimum value and the maximum value of the PMV value of the comfort level of the user,

the indoor minimum temperature and the indoor maximum temperature which meet the comfort level of a user;

on the premise of meeting the comfort level of a user, the heating building can participate in optimized scheduling as scheduling resources, and the total heat supply in the scheduling period is the same, namely:

wherein,

the upper and lower limits are allowed to be adjusted for the thermal load of the node k in the time period t, the values of the upper and lower limits are related to the values of the upper and lower limits of the PMV,

the standard thermal load of node k for time period t;

after considering the inertia of the cooling load, the cooling power of the system is as follows:

wherein,

the cooling load of node k allows adjustable upper and lower limits for a period t,

the standard cooling load of node k for time period t.

4. The optimal scheduling method of multi-energy system considering demand response and carbon trading according to claim 3, wherein constructing the objective function with lowest operation cost and carbon trading cost of the integrated energy system comprises the following steps:

and (3) making an operation cost objective function:

the operating costs include equipment energy supply costs and electricity purchase and sale costs:

wherein, C_energyThe cost of energy supply to the system is reduced,

for a time t period of n equipment operating costs of the unit, wherein m₃GT, CHP, HP, WT, ESS, TES, AR, AC, GT, CHP, HP, WT, ESS, TES, AR, AC are gas turbine, cogeneration unit, heat pump, wind power, electrical energy storage, thermal energy storage, absorption chiller, electrical chiller, respectively;

the total number of the energy supply units;

the electricity purchasing and selling cost is t time period;

wherein,

the t time period and the electricity purchasing power and the electricity selling power of the superior power grid are respectively,

respectively obtaining the electricity purchasing price and the electricity selling price at the time t;

carbon transaction cost objective function formulation:

acquiring a carbon quota expression:

wherein m is₁For a collection of energy supply units, in which m₁GT, CHP, WT, HP, AR, AC }; t is a scheduling period;

carbon quota for each unit;

the total number of each unit; p_t,nN electric output of the unit in the period of t, and P when the CHP unit is represented_t,nThe converted electric output of the unit under the pure condensing working condition is obtained; sigma is a carbon emission distribution coefficient of unit electric quantity;

the other energy supply units except the wind turbine generator set generate carbon emission in the operation process, and the carbon emission can be expressed as:

wherein,

for each unit carbon emission, where m₂＝{GT,CHP,HP,AR,AC}；

The total number of carbon emission units; gamma ray_nThe carbon emission intensity of the unit n output is obtained;

the carbon trading volume participated by the comprehensive energy system is as follows:

in the ladder-type carbon transaction model, the carbon transaction cost can be expressed as:

wherein, C_carbonCost for carbon transaction, C_bFor carbon trading market price, x₁、x₂Reward/penalty factors for different step carbon emission intervals; h is the length of the value interval of different carbon emissions;

and (3) making a comprehensive optimization objective function:

minF＝min(C_energy+C_carbon) (25)

wherein F is the comprehensive cost of the system.

5. The method for multi-energy system optimized scheduling with consideration of demand response and carbon trading of claim 4, wherein the plant model constraints comprise unit output constraints, unit ramp constraints, energy storage device constraints, energy conversion constraints.

6. The optimal scheduling method of multi-energy system considering demand response and carbon trading according to claim 5, wherein the objective function is solved by a commercial solver CPLEX.

7. The multi-energy system optimization scheduling device for considering demand response and carbon transaction is characterized by comprising the following components:

the parameter acquisition module is used for determining and acquiring basic operating parameters of the energy system;

the heat supply network and power grid power flow modeling module is used for modeling the heat supply network and the power grid power flow considering the dynamic characteristics;

the heating comfort level model building module is used for building a heating comfort level model of the heating building;

the target function establishing module is used for establishing a target function with the lowest operation cost and carbon transaction cost of the comprehensive energy system;

the constraint condition construction module is used for constructing constraint conditions of energy balance constraint, system network constraint and equipment model constraint;

and the objective function solving module is used for solving the objective function to obtain the day-ahead optimized scheduling scheme of the comprehensive energy system.

8. A computing device, characterized by: the method comprises the following steps:

one or more processing units;

a storage unit for storing one or more programs,

wherein the one or more programs, when executed by the one or more processing units, cause the one or more processing units to perform the method of any of claims 1-6.

9. A computer-readable storage medium with non-volatile program code executable by a processor, characterized in that the computer program realizes the steps of the method according to any one of claims 1 to 6 when executed by the processor.

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