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CN113128759A - Regional energy optimization operation method considering demand side response - Google Patents

Regional energy optimization operation method considering demand side response Download PDF

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CN113128759A
CN113128759A CN202110410269.7A CN202110410269A CN113128759A CN 113128759 A CN113128759 A CN 113128759A CN 202110410269 A CN202110410269 A CN 202110410269A CN 113128759 A CN113128759 A CN 113128759A
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王红艳
袁全
周蒙恩
宋国辉
张喜东
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Nanjing Institute of Technology
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Abstract

The invention discloses a regional energy optimization operation method considering demand side response, which mainly comprises the following steps: and constructing a regional energy optimization scheduling model considering demand side response, establishing an objective function of the regional energy optimization scheduling model, giving constraint conditions of the regional energy optimization scheduling model including equality constraint and inequality constraint, and finally solving the regional energy optimization scheduling model by using a multi-objective particle swarm algorithm to obtain an optimal solution of the scheduling model. The invention has the advantages that: meanwhile, demand side response based on price and demand side response based on excitation are considered, peak clipping and valley filling can be effectively realized, unit output among gas-electric systems is coordinated, various energy requirements are efficiently met, and the overall economy and environmental protection of regional energy are realized.

Description

Regional energy optimization operation method considering demand side response
Technical Field
The invention relates to an optimal scheduling method for a regional energy system, in particular to a regional energy optimal operation method considering demand side response, and belongs to the field of optimal scheduling of a power distribution network.
Background
In the current regional energy system, the general loads are mainly divided into an electric load and a gas load, and the electric load and the gas load are coupled to a P2G device through a gas turbine, the gas turbine generates electric energy by consuming natural gas, and the P2G device generates natural gas by consuming excessive electric energy and supplies the natural gas to the gas load. At present, load demand side responses are added into a dispatching model of a regional energy system, the considered demand side responses are generally price type or incentive type, and the two responses are rarely considered simultaneously, for example, a Chinese utility model patent with publication number CN107807523 with publication date of 3, 16 and 2018 discloses a regional energy internet multivariate coordination optimization operation strategy considering time-of-use electricity price, and the shortcoming is that only price-based demand side responses (time-of-use electricity price) are considered.
Disclosure of Invention
Technical purpose
Aiming at the problem that the existing regional energy optimization scheduling method is mostly limited to the consideration of a power generation side or a single demand side, the invention discloses a regional energy optimization operation method considering demand side response.
Technical scheme
In order to achieve the technical purpose, the invention adopts the following technical scheme.
A regional energy optimization operation method taking into account demand side response, comprising the steps of:
s1, constructing a regional energy optimization scheduling model considering demand side response, wherein the regional energy optimization scheduling model considering the demand side response comprehensively considers the output characteristics of each unit and also considers the influence of a demand side response mechanism;
s2, establishing an objective function of the regional energy optimization scheduling model considering the response of the demand side, wherein the method for establishing the objective function is that the objective function is given according to the scheduling model established in the step S1 by considering the operation cost of regional energy and the harmful gas emission condition;
s3, giving a constraint condition of a regional energy optimization scheduling model considering demand side response, considering supply and demand balance among electric loads and supply and demand balance among air loads in the regional energy system and constancy of the total load in a scheduling period in a demand side response mechanism, and establishing equation constraint; considering the output limit of each unit in the regional energy system, the output limit of a storage battery and the capacity limit of the storage battery, and establishing inequality constraints;
and S4, solving the regional energy optimization scheduling model by using a multi-objective particle swarm algorithm to obtain an optimal solution of the scheduling model, wherein the optimal solution comprises a demand side response scheme and output plans of all units.
Further, the scheduling model in step S1 specifically includes a mathematical model of the capacity characteristic of the unit and a mathematical model of the demand-side response mechanism.
The unit mainly comprises a coupling unit, the coupling unit mainly refers to a gas turbine and a P2G device, the gas turbine generates electric energy by consuming natural gas, the P2G device generates natural gas by consuming electric energy, and a mathematical model of the gas turbine is as follows:
Qgt,t＝[h2(Pgt,t)2+h1Pgt,t+h0]/HHV
in the formula, Qgt,tRepresenting the gas consumption of the gas turbine at time t, Pgt,tRepresents the power generation amount, h, of the gas turbine at time t2、h1、h0Is a coefficient of a consumption characteristic curve of the generator set, and HHV represents the high heat value of the natural gas.
The mathematical model of the P2G device is:
Qp2g,t＝ηp2g×Pp2g,t/HHV
in the formula, Qp2g,tIndicating the gas production, η, of the device at time t P2Gp2gDenotes a P2G deviceTransformation efficiency of (2), Pp2g,tIndicating the power consumption at time t of P2G.
The demand side response mechanism in step S1 is divided into a price type demand side response mechanism and an incentive type demand side response mechanism,
the mathematical model of the price type demand side response mechanism is as follows:
Figure BDA0003023906410000021
Figure BDA0003023906410000022
in the formula, PtIndicating the electrical load at time t before the price-based demand-side response was not made, Δ PtRepresenting the amount of change in electrical load after a price-based demand-side response is made, the electrical load at time t, QtAnd Δ QtRespectively represent the initial electricity price and the time-of-use electricity price, epsiloni，jThe method comprises the steps of representing an elasticity coefficient, representing a self elasticity coefficient when i ≠ j, representing a mutual elasticity coefficient when i ≠ j, wherein all the self elasticity coefficients generally take the same value and are negative, all the mutual elasticity coefficients generally take the same value and are negative, the self elasticity coefficient represents the relation between the electricity price of the current time period and the electricity quantity of the current time period, the mutual elasticity coefficient represents the relation between the electricity price of the current time period and the electricity quantity of another time period, and the price type demand response is mainly realized by transferring the load of a peak time period to a valley time period, so that the total load change quantity in one scheduling period is 0.
The mathematical model shows the relationship of electricity price and traction electric quantity.
The incentive type demand side response is divided into an incentive type demand side response with replaceable demand and an incentive type demand side response with reducible load, and the dispatching mechanism selects economic compensation given to the response according to the response degree of the user.
The incentive type demand side response capable of reducing the load refers to that the dispatching center reduces the demand amount of the load during the peak period of electricity utilization, namely Pcut,tLess than or equal to 0, wherein P iscut,tIndicating a reducible electrical load at time t;
the demand-replaceable incentive type demand side response means that according to the transverse distributed distribution condition of energy demand on the same time node, a user can select energy with different cost performance under the same time dimension to meet the own energy demand, namely, the electricity and gas loads can be equivalently realized by using heat values, and the mathematical model is as follows:
Pt,Tran+HHV×Qt,Tran＝0
in the formula Pt,Tran、Qt,TranRespectively representing the transferred electric load and the gas load at the time t;
further, the objective function in step S2 specifically includes an objective function of the operation cost of the regional energy system and an objective function of the harmful gas emission of the regional energy system.
The operation cost of the regional energy system in the step S2 includes the operation cost of the generator set, the gas purchase cost, the compensation cost of the excitation response, and the wind curtailment cost.
The mathematical model of the operation cost of the regional energy system is as follows:
Figure BDA0003023906410000031
F1(Pi,t)＝α(Pi,t)2+βPi,t+γ
in the formula, CTotalRepresents the total cost of electricity generation, NGIndicating the number of generator sets in the regional energy system, Pi,tRepresenting the output of the generator set i at time t, F1(Pi,t) Representing a functional relationship between the generating cost of the generator set and the generating capacity of the generator set, CgasDenotes the unit price of natural gas, StRepresenting the amount of natural gas purchased at time t, i.e. the gas production of the source at time t, CDamRepresenting the economic compensation after the excitation-type demand-side response, and q represents the curtailment penalty, Δ Pw,tIndicates the time tThe air abandoning amount, alpha, beta and gamma are respectively a quadratic coefficient, a primary coefficient and a constant coefficient of the cost function of the generator set.
In step S2, the harmful gas emission of the regional energy system includes emission of harmful gas such as carbon dioxide, sulfur dioxide, and nitrogen dioxide, and the mathematical model of the harmful gas emission of the regional energy system is:
Figure BDA0003023906410000041
in the formula (I), the compound is shown in the specification,
Figure BDA0003023906410000042
respectively representing the generated energy and the discharged CO of the generator set2、SO2And NO2As a function of the quantity of, F2Representing the total harmful gas emission of the power generating unit.
Further, the constraints of the regional energy system optimization scheduling model considering the demand-side response in step S3 include inequality constraints and equality constraints.
The inequality constraints refer to the output limit of each unit in the energy system, the output limit of the storage battery and the capacity limit of the storage battery, and are as follows:
Pi，min≤Pi,t≤Pi,max
PES,min≤PES,t≤PES,max
Emin≤Et≤Emax
in the formula, Pi，min、Pi，maxAnd Pi.tRespectively, the minimum active power, the maximum active power and the actual output power, P, of the distributed generator set i at the moment tES,min、PES，maxAnd PES,tRespectively the minimum active power, the maximum active power and the actual active power of the storage battery at the moment t, Emin、Emax、EtRespectively, the minimum, maximum and actual capacity of the battery capacity.
The equality constraint comprises balance between supply and demand of the regional energy system and constancy of the total load in one scheduling period in the price type demand side response mechanism, and the balance between supply and demand of the regional energy system specifically comprises supply and demand balance between gas load and supply and demand balance between electric load.
The equation is constrained as follows:
Qgt,t+Qt+Qtran,t＝Qp2g,t+St
Pgt,t+Pi,t+PES,t+Ppv,t＝Pp2g,t+Pt+Pcut,t+Pmov,t+Ptran,t
in the formula, QtIndicating the air load at time t, Qtran,tIndicating the alternative gas load at time t, StRepresenting the amount of natural gas purchased at time t, i.e. the gas production from the source at time t, PES,tRepresenting the battery output, P, at time tpv,tIndicating the fan output at time t, PtIndicating the electrical load at time t before the price-based demand-side response was not made, Pcut,tIndicating the electrical load reducible at time t, Pmov,tRepresenting transferable electric load at time t, Ptran,tRepresenting the alternative electrical load at time t.
Further, in the step S4, the scheduling cycle of the multi-target particle swarm algorithm is 24 hours, the scheduling scale is 1 hour, and the variables are mainly as follows: gas consumption of the gas turbine for 24 hours, 24-hour output of the gas turbine, gas production of the P2G device for 24 hours, power consumption of the P2G device for 24 hours, 24-hour output of the wind turbine, 24-hour output of the conventional unit, 24-hour purchase of natural gas, 24-hour output of the storage battery, 24-hour energy storage of the storage battery, 24-hour output of the power load involved in the price-based demand-side response, 24-hour output of the power load involved in the load-reducible incentive demand-side response, and 24-hour output of the power load involved in the demand-alternative incentive demand-side response, and thus 24 × 12 is a dimension of one particle.
Bringing each particle into two objective functions to obtain two objective function values of the particle, wherein the inequality constraints comprise the output size limit of a wind turbine generator, the output size limit of a storage battery, the output size limit of a gas turbine, the output size limit of a conventional unit, the size limit of an electric load participating in price response, the size limit of an electric load participating in reducible and replaceable electric loads, and the size limit of an air source; and the equality constraints are respectively the constancy of the total load in one scheduling period in the power load conservation mechanism, the gas load conservation mechanism and the price type demand side response mechanism.
For the optimization problem of a plurality of inequalities and a plurality of equations, the invention adopts a penalty function, namely, any dimension of each particle must meet the size limit, then the particle is respectively brought to the gas and electricity equality constraints, the difference value of the left side and the right side of each equality constraint is judged, the difference value is added to the total running cost in the form of the penalty function, and the larger the difference value is, the larger the total running cost is.
Dividing the particle population into a dominant solution set and a non-dominant solution set; storing the non-inferior solution set in an external solution set, updating the speed and the position of the dominant solution set each time, then taking out the non-dominant solution in the dominant solution set, comparing the non-dominant solution with the solution in the external solution set, and keeping the non-inferior solution in the external solution set; and finally, when the iteration times or the search precision is reached, stopping the operation of the algorithm, and taking out a solution in an external solution set, namely the scheduling scheme of the regional energy system.
Advantageous effects
According to the invention, by simultaneously considering the demand side response based on price and the demand side response based on excitation, the regional energy system model is optimized, the economy and the environmental protection are both considered under the condition of ensuring the balance of supply and demand, and the utilization efficiency of regional energy is improved.
Drawings
FIG. 1 is a general process flow diagram of the present invention;
fig. 2 is a schematic diagram of the coupling between the power grid and the gas grid according to the present invention.
Detailed Description
The regional energy optimization operation method considering the demand side response of the present invention will be further described and explained with reference to the drawings and the embodiments, it should be understood that the specific embodiments described herein are only for the purpose of explaining the present invention and are not intended to limit the present invention.
Examples
As shown in fig. 1, a regional energy optimization operation method considering demand-side response specifically includes the following steps:
step one, constructing a regional energy optimization scheduling model considering demand side response, comprehensively considering the output characteristics of each unit by the regional energy optimization scheduling model considering demand side response, and considering the influence of a demand side response mechanism.
The scheduling model specifically comprises a mathematical model of the output characteristic of the unit and a mathematical model of a demand side response mechanism; the unit mainly comprises a coupling unit, the coupling unit mainly comprises a gas turbine and a P2G device, the gas turbine generates electric energy by consuming natural gas, and the P2G device generates natural gas by consuming electric energy.
The mathematical model of the gas turbine is:
Qgt,t＝[h2(Pgt,t)2+h1Pgt,t+h0]/HHV (1)
in the formula, Qgt,tRepresenting the gas consumption of the gas turbine at time t, Pgt,tRepresents the power generation amount, h, of the gas turbine at time t2、h1、h0Is a coefficient of a consumption characteristic curve of the generator set, and HHV represents the high heat value of natural gas;
the mathematical model for the P2G device is:
Qp2g,t＝ηp2g×Pp2g,t/HHV (2)
in the formula, Qp2g,tIndicating the gas production, η, of the device at time t P2Gp2gThe conversion efficiency of the P2G apparatus, Pp2g,tIndicating the power consumption at time t of P2G.
Demand-side response mechanisms can be divided into price-type demand-side response mechanisms and incentive-type demand-side response mechanisms.
The mathematical model of the price type demand side response mechanism is as follows:
Figure BDA0003023906410000071
in the formula, PtIndicating the electrical load at time t before the price-based demand-side response was not made, Δ PtRepresenting the amount of change in electrical load, Q, after a price-based demand-side response is madetAnd Δ QtRespectively represent the initial electricity price and the time-of-use electricity price, epsiloni，jThe method comprises the steps of representing an elasticity coefficient, representing a self-elasticity coefficient when i ≠ j, representing a mutual-elasticity coefficient when i ≠ j, wherein all self-elasticity coefficients generally take the same value and are negative, all mutual-elasticity coefficients generally take the same value and are negative, representing the relation between the electricity price of a current time period and the electricity quantity of the current time period, and representing the relation between the electricity price of the current time period and the electricity quantity of another time period.
The incentive type demand side response is divided into an incentive type demand side response with replaceable demand and an incentive type demand side response with reducible load, and the dispatching mechanism selects economic compensation given to the response according to the response degree of the user.
Load shedding incentivized demand-side response refers to a dispatch center reducing the demand for load during peak demand periods, i.e., Pcut,tLess than or equal to 0, wherein P iscut,tIndicating a reducible electrical load at time t;
the demand-replaceable incentive type demand side response means that according to the transverse distributed distribution condition of energy demand on the same time node, a user can select energy with different cost performance under the same time dimension to meet the own energy demand, namely, the electricity and gas loads can be equivalently realized by using heat values, and the mathematical model is as follows:
Pt,Tran+HHV×Qt,Tran＝0 (4)
in the formula Pt,Tran、Qt,TranThe transferred electrical load and the gas load at time t are shown, respectively.
Step two, an objective function of the regional energy optimization scheduling model considering the response of the demand side is established, and the method for establishing the objective function is that the objective function is given according to the scheduling model established in the step S1 by considering the operation cost of regional energy and the harmful gas emission condition.
The objective function specifically comprises an objective function of the operation cost of the regional energy system and an objective function of the harmful gas emission of the regional energy system, wherein the operation cost comprises the operation cost of the generator set, the gas purchase cost, the compensation cost of the excitation response and the wind curtailment cost.
The mathematical model of the operating cost of the regional energy system is as follows:
Figure BDA0003023906410000081
F1(Pi,t)＝α(Pi,t)2+βPi,t+γ
in the formula, CTotalRepresents the total cost of electricity generation, NGIndicating the number of generator sets in the regional energy system, Pi,tRepresenting the output of the generator set i at time t, F1(Pi,t) Representing a functional relationship between the generating cost of the generator set and the generating capacity of the generator set, CgasDenotes the unit price of natural gas, StRepresenting the amount of natural gas purchased at time t, i.e. the gas production of the source at time t, CDamRepresenting the economic compensation after the excitation-type demand-side response, and q represents the curtailment penalty, Δ Pw,tAnd alpha, beta and gamma are respectively a quadratic coefficient, a primary coefficient and a constant coefficient of the cost function of the generator set.
When the price type and excitation type demand responses are considered simultaneously by the dispatching model, in the low-valley period of the load, the existence of the price type response mechanism can enable the power load in the peak period to be transferred to the low-valley period, and the existence of the excitation type demand response mechanism can convert the demand of the air load into the power load, so that the total power demand in the low-valley period is increased, the output of the wind turbine generator in the low-valley period is increased, namely, the consumption rate of the wind turbine generator is improved, and the operation load of the wind turbine generator is reduced.
During peak hours of the load, on the one hand the presence of the price-type mechanism reduces the electrical load, and on the other hand the load during peak hours is reduced due to the presence of the excitation response, while the electrical load is converted into a gas load.
In addition, the excitation type demand side response mechanism can improve economic compensation and reduce the running cost of the system. Thus, after the scheduling model considers both demand responses, the load curve after optimization is more gradual than before optimization.
The harmful gas emission of the regional energy system comprises the emission of carbon dioxide, sulfur dioxide, nitrogen dioxide and other harmful gases, and the mathematical model of the harmful gas emission of the regional energy system is as follows:
Figure BDA0003023906410000091
in the formula (I), the compound is shown in the specification,
Figure BDA0003023906410000092
respectively representing the generated energy and the discharged CO of the generator set2、SO2And NO2As a function of the quantity of, F2Representing the total harmful gas emission of the power generating unit.
Giving a constraint condition of a regional energy optimization scheduling model considering demand side response, considering supply and demand balance among electric loads and supply and demand balance among air loads in the regional energy system and constancy of total load in a scheduling period in a demand side response mechanism, and establishing equation constraint; and (4) considering the output limit of each unit in the regional energy system, the output limit of the storage battery and the capacity limit of the storage battery, and establishing inequality constraints.
The inequality constraint refers to the limit of the output of each unit in the energy system, the limit of the output of the storage battery and the limit of the capacity of the storage battery, and the inequality constraint is as follows:
Pi，min≤Pi,t≤Pi,max
PES,min≤PES,t≤PES,max
Emin≤Et≤Emax (7)
in the formula, Pi，min、Pi，maxAnd Pi,tRespectively, the minimum active power, the maximum active power and the actual output power, P, of the distributed generator set i at the moment tES,min、PES，maxAnd PES,tRespectively the minimum active power, the maximum active power and the actual active power of the storage battery at the moment t, Emin、Emax、EtMinimum, maximum and actual battery capacity, respectively;
the equality constraint comprises the balance between the supply and the demand of the regional energy system and the constancy of the load total amount in a scheduling period in a price type demand side response mechanism, and the balance between the supply and the demand of the regional energy system specifically comprises the supply and demand balance between gas loads and the supply and demand balance between electric loads;
the equation is constrained as follows:
Qgt,t+Qt+Qtran,t＝Qp2g,t+St
Pgt,t+Pi,t+PES,t+Ppv,t＝Pp2g,t+Pt+Pcut,t+Pmov,t+Ptran,t (8)
in the formula, Qgt,tRepresenting the gas consumption of the gas turbine at time t, QtIndicating the air load at time t, Qtran,tRepresenting the alternative gas load at time t, Qp2g,tDenotes the gas production of the device at time t P2G, StRepresenting the amount of natural gas purchased at time t, i.e. the gas production from the source at time t, Pgt,tRepresenting the power generation of the gas turbine at time t, Pi,tRepresenting the output of the generator set i at time t, PES,tRepresenting the battery output, P, at time tpv,tIndicating the fan output at time t, Pp2g,tRepresents the power consumption at time t of P2G device, PtRepresenting the electrical load at time t, Pcut,tIndicating that time t is availableReduced electrical load, Pmov,tRepresenting transferable electric load at time t, Ptran,tRepresenting the alternative electrical load at time t.
And fourthly, solving the regional energy optimization scheduling model by using a multi-objective particle swarm algorithm to obtain an optimal solution of the scheduling model, wherein the optimal solution comprises a demand side response scheme and output plans of all the units.
The scheduling cycle adopted by the invention is 24 hours, the scheduling scale is 1 hour, and the variables are mainly as follows: gas consumption of the gas turbine for 24 hours, 24-hour output of the gas turbine, gas production of the P2G device for 24 hours, power consumption of the P2G device for 24 hours, 24-hour output of the wind turbine, 24-hour output of the conventional unit, 24-hour purchase of natural gas, 24-hour output of the storage battery, 24-hour energy storage of the storage battery, 24-hour output of the power load involved in the price-based demand-side response, 24-hour output of the power load involved in the load-reducible incentive demand-side response, and 24-hour output of the power load involved in the demand-alternative incentive demand-side response, and thus 24 × 12 is a dimension of one particle.
Bringing each particle into two objective functions to obtain two objective function values of the particle, wherein the inequality constraints comprise the output size limit of a wind turbine generator, the output size limit of a storage battery, the output size limit of a gas turbine, the output size limit of a conventional unit, the size limit of an electric load participating in price response, the size limit of an electric load participating in reducible and replaceable electric loads, and the size limit of an air source; and the equality constraints are respectively the constancy of the total load in one scheduling period in the power load conservation mechanism, the gas load conservation mechanism and the price type demand side response mechanism. For the optimization problem of a plurality of inequalities and a plurality of equations, the invention adopts a penalty function, namely, any dimension of each particle must meet the size limit, then the particle is respectively brought to the gas and electricity equality constraints, the difference value of the left side and the right side of each equality constraint is judged, the difference value is added to the total running cost in the form of the penalty function, and the larger the difference value is, the larger the total running cost is.
Dividing the particle population into a dominant solution set and a non-dominant solution set; storing the non-inferior solution set in an external solution set, updating the speed and the position of the dominant solution set each time, then taking out the non-dominant solution in the dominant solution set, comparing the non-dominant solution with the solution in the external solution set, and keeping the non-inferior solution in the external solution set; and finally, when the iteration times or the search precision is reached, stopping the operation of the algorithm, and taking out a solution in an external solution set, namely the scheduling scheme of the regional energy system.
As shown in fig. 2, a wind turbine, a conventional generator and an electric storage device are connected to a power grid to output an electric load, the power grid and the power grid are coupled with a P2G device through a gas turbine, a demand side response mechanism based on price and a demand side response mechanism based on excitation influence on a regional energy system, after a scheduling scheme is obtained according to a particle swarm algorithm, excess natural gas is consumed through the work of the gas turbine to generate electricity to supply the electric load or energy storage is completed through an energy storage device when the gas load is excessive, and excess electricity is consumed through the work of the P2G device to generate gas such as methane and hydrogen to supply the gas load when the electric load is excessive.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.

Claims (5)

1.一种考虑需求侧响应的区域能源优化运行方法，其特征在于，包括以下步骤：1. A district energy optimization operation method considering demand side response, is characterized in that, comprises the following steps: S1.构建考虑需求侧响应的区域能源优化调度模型，所述考虑需求侧响应的区域能源优化调度模型综合考虑了各个机组的出力特性，还考虑了需求侧响应机制的影响；S1. Construct a regional energy optimal scheduling model considering demand-side response, the regional energy optimal scheduling model considering demand-side response comprehensively considers the output characteristics of each unit, and also considers the impact of the demand-side response mechanism; S2.建立考虑需求侧响应的区域能源优化调度模型的目标函数，建立所述目标函数的方法为——根据所述步骤S1中建立的调度模型考虑区域能源的运行成本和有害气体排放情况，给出所述目标函数；S2. establish the objective function of the regional energy optimal scheduling model considering the demand side response, and the method for establishing the objective function is: according to the scheduling model established in the step S1, considering the operating cost of the regional energy and the emission of harmful gases, give out the objective function; S3.给定考虑需求侧响应的区域能源优化调度模型的约束条件，考虑所述区域能源系统中电负荷之间的供需平衡和气负荷之间的供需平衡以及所述需求侧响应机制中一个调度周期内负荷总量的恒定，建立等式约束；考虑所述区域能源系统中各个机组的出力大小限制、蓄电池出力大小限制和蓄电池容量大小的限制，建立不等式约束；S3. Given the constraints of the regional energy optimal dispatch model considering demand-side response, consider the supply-demand balance between electrical loads and the supply-demand balance between gas loads in the regional energy system and a dispatch cycle in the demand-side response mechanism The total internal load is constant, and an equation constraint is established; considering the output size limit of each unit in the regional energy system, the battery output size limit, and the battery capacity limit, an inequality constraint is established; S4.利用多目标粒子群算法对所述区域能源优化调度模型进行求解，得到调度模型的最优解，包括需求侧响应方案和各个机组的出力计划。S4. Use the multi-objective particle swarm algorithm to solve the regional energy optimization scheduling model, and obtain the optimal solution of the scheduling model, including the demand side response plan and the output plan of each unit. 2.如权利要求1所述的一种考虑需求侧响应的区域能源优化运行方法，其特征在于，所述步骤S1中的调度模型具体包括所述机组的出力特性的数学模型和所述需求侧响应机制的数学模型；2. A regional energy optimization operation method considering demand-side response according to claim 1, wherein the scheduling model in the step S1 specifically includes a mathematical model of the output characteristics of the unit and the demand-side Mathematical model of the response mechanism; 所述机组包括耦合机组，耦合机组主要指的是燃气轮机与P2G装置，燃气轮机通过消耗天然气生成电能，P2G装置通过消耗电能产生天然气，燃气轮机的数学模型为：The unit includes a coupled unit. The coupled unit mainly refers to a gas turbine and a P2G device. The gas turbine generates electricity by consuming natural gas, and the P2G device generates natural gas by consuming electricity. The mathematical model of the gas turbine is: Qgt,t＝[h2(Pgt,t)2+h1Pgt,t+h0]/HHVQ gt,t =[h 2 (P gt,t ) 2 +h 1 P gt,t +h 0 ]/HHV 式中,Qgt,t表示燃气轮机在t时刻的耗气量，Pgt,t表示燃气轮机在t时刻的发电量，h2、h1、h0是发电机组耗量特性曲线的系数，HHV表示天然气的高热值；In the formula, Q gt,t represents the gas consumption of the gas turbine at time t, P gt,t represents the power generation of the gas turbine at time t, h 2 , h 1 , and h 0 are the coefficients of the power consumption characteristic curve of the generator set, and HHV represents the natural gas high calorific value; 所述P2G装置的数学模型为：The mathematical model of the P2G device is: Qp2g,t＝ηp2g×Pp2g,t/HHVQ p2g,t = η p2g ×P p2g,t /HHV 式中，Qp2g,t表示t时刻P2G装置的产气量，ηp2g表示P2G装置的转化效率，Pp2g,t表示P2G装置t时刻的耗电量；In the formula, Q p2g,t represents the gas production of the P2G device at time t, η p2g represents the conversion efficiency of the P2G device, and P p2g,t represents the power consumption of the P2G device at time t; 所述步骤S1中的需求侧响应机制分成价格型需求侧响应机制与激励型需求侧响应机制，The demand-side response mechanism in the step S1 is divided into a price-type demand-side response mechanism and an incentive-type demand-side response mechanism, 价格型需求侧响应机制的数学模型为：The mathematical model of the price-based demand-side response mechanism is:
Figure FDA0003023906400000021
Figure FDA0003023906400000021
Figure FDA0003023906400000022
Figure FDA0003023906400000022
 式中，Pt表示未做出基于价格的需求侧响应前t时刻的电负荷，ΔPt表示做出基于价格的需求侧响应后电负荷变化量，Qt和ΔQt分别表示初始电价与分时电价，εi，j表示弹性系数，当i＝j时表示自弹性系数，当i≠j时表示互弹性系数，自弹性系数表示的是当前时段的电价与当前时段的电量之间的关系，而互弹性系数则表示当前时段的电价与另一个时段的电量之间的关系；In the formula, P t represents the electrical load at time t before the price-based demand-side response is made, ΔP t represents the change in the electrical load after the price-based demand-side response is made, and Q t and ΔQ t represent the initial electricity price and price, respectively. electricity price, ε i, j represents the elastic coefficient, when i=j, it represents the self-elastic coefficient, when i≠j, it represents the mutual elastic coefficient, and the self-elastic coefficient represents the relationship between the electricity price in the current period and the electricity in the current period , and the mutual elasticity coefficient represents the relationship between the electricity price in the current period and the electricity in another period; 激励型需求侧响应分为需求可替代的激励型需求侧响应与负荷可削减的激励型需求侧响应，调度机构根据用户的响应程度选择给予相应的经济补偿；Incentive demand-side response is divided into incentive-type demand-side response with substitutable demand and incentive-type demand-side response with load reduction. The dispatching agency chooses to give corresponding economic compensation according to the user's response degree; 所述负荷可削减的激励型需求侧响应指的是调度中心在用电高峰时期减少负荷的需求量，即Pcut,t≤0，式中Pcut,t表示的是t时刻的可削减的电负荷；The load-cutable incentive demand-side response refers to the demand of the dispatch center to reduce the load during the peak period of electricity consumption, that is, P cut,t ≤ 0, where P cut,t represents the cuttable at time t. electrical load; 所述需求可替代的激励型需求侧响应指用户根据同一时间节点上能源需求的横向分散式分布情况选择不同性价比的能源，将电、气两种负荷以热值等效实现，数学模型为：The demand-replaceable incentive demand-side response means that the user selects energy sources with different cost-effectiveness according to the horizontally distributed distribution of energy demand at the same time node, and realizes the two loads of electricity and gas equivalently by calorific value. The mathematical model is: Pt,Tran+HHV×Qt,Tran＝0P t,Tran +HHV×Q t,Tran =0 式中Pt,Tran、Qt,Tran分别表示的是t时刻的转移电负荷、气负荷，HHV表示天然气的高热值。In the formula, P t,Tran and Q t,Tran respectively represent the transferred electricity load and gas load at time t, and HHV represents the high calorific value of natural gas. 3.如权利要求1所述的一种考虑需求侧响应的区域能源优化运行方法，其特征在于，所述步骤S2中的运行成本的目标函数和有害气体排放的目标函数；3. A district energy optimization operation method considering demand-side response as claimed in claim 1, characterized in that the objective function of the operation cost and the objective function of harmful gas emission in the step S2; 所述步骤S2中的运行成本包括发电机组的运行成本、购气成本、激励响应的补偿成本以及弃风成本；The operation cost in the step S2 includes the operation cost of the generator set, the gas purchase cost, the compensation cost of the incentive response, and the wind abandonment cost; 所述运行成本的数学模型为：The mathematical model of the running cost is:
Figure FDA0003023906400000031
Figure FDA0003023906400000031
 F1(Pi,t)＝α(Pi,t)2+βPi,t+γF 1 (P i,t )=α(P i,t ) 2 +βP i,t +γ 式中，CTotal表示总的发电成本，NG表示区域能源系统中的发电机组的数目，Pi,t表示发电机组i在t时刻的出力，F1(Pi,t)表示发电机组的发电成本与发电机组发电量的函数关系，Cgas表示天然气的单价，St表示t时刻购买的天然气的量，即气源在t时刻的产气量，CDam表示激励型需求侧响应之后的经济补偿，q表示的是弃风惩罚系数，ΔPw,t表示t时刻的弃风量，α、β、γ分别为发电机组成本函数的二次系数、一次系数与常数系数；In the formula, C Total represents the total power generation cost, N G represents the number of generator sets in the district energy system, P i,t represents the output of generator set i at time t, and F 1 (P i,t ) represents the output of the generator set. The functional relationship between the power generation cost and the power generation capacity of the generator set, C gas represents the unit price of natural gas, S t represents the amount of natural gas purchased at time t, that is, the gas production volume of the gas source at time t, and C Dam represents the economic value after the incentive-type demand-side response Compensation, q represents the wind abandonment penalty coefficient, ΔP w,t represents the abandoned wind volume at time t, α, β, γ are the quadratic coefficient, first-order coefficient and constant coefficient of the generator’s cost function; 所述步骤S2中的有害气体排放包括二氧化碳、二氧化硫和二氧化氮有害气体气体的排放，所述的有害气体排放的数学模型为：The harmful gas emission in the step S2 includes the emission of carbon dioxide, sulfur dioxide and nitrogen dioxide harmful gas, and the mathematical model of the harmful gas emission is:
Figure FDA0003023906400000032
Figure FDA0003023906400000032
 式中，
Figure FDA0003023906400000033
分别表示的是发电机组的发电量与排放的CO2、SO2及NO2的量的函数关系，F2表示发电机组的总的有害气体排放量。In the formula,
Figure FDA0003023906400000033
Respectively represent the functional relationship between the power generation of the generator set and the amount of CO 2 , SO 2 and NO 2 emitted, and F 2 represents the total harmful gas emissions of the generator set. 4.如权利要求1所述的一种考虑需求侧响应的区域能源优化运行方法，其特征在于，所述步骤S3中的约束条件包括不等式约束和等式约束；4. A regional energy optimization operation method considering demand-side response according to claim 1, wherein the constraints in the step S3 include inequality constraints and equality constraints; 所述不等式约束包括能源系统中各个发电机组的出力大小限制、蓄电池的出力大小限制和蓄电池容量大小的限制，所述不等式约束如下：The inequality constraints include the output size limit of each generator set in the energy system, the output power size limit of the battery and the battery capacity size limit, and the inequality constraints are as follows: Pi，min≤Pi,t≤Pi,max P i,min ≤P i,t ≤P i,max PES,min≤PES,t≤PES,max P ES,min ≤P ES,t ≤P ES,max Emin≤Et≤Emax E min ≤E t ≤E max 式中，Pi，min、Pi，max与Pi,t分别是发电机组i在t时刻最小有功功率、最大有功功率及实际输出功率，PES,min、PES，max及PES,t分别是t时刻蓄电池最小的有功功率、最大的有功功率及实际有功功率，Emin、Emax、Et分别是蓄电池容量的最小值、最大值和实际容量；In the formula, P i,min , P i,max and P i,t are the minimum active power, maximum active power and actual output power of generator set i at time t respectively, P ES,min , P ES,max and P ES, t are the minimum active power, maximum active power and actual active power of the battery at time t respectively, E min , E max , and E t are the minimum, maximum and actual capacity of the battery capacity, respectively; 所述等式约束包括区域能源系统的供需之间的平衡和价格型需求侧响应机制中一个调度周期内负荷总量的恒定，所述区域能源系统的供需之间的平衡具体包括气负荷之间的供需平衡与电负荷之间的供需平衡；The equality constraints include the balance between the supply and demand of the regional energy system and the constant total load in a dispatch period in the price-based demand-side response mechanism. The balance between the supply and demand of the regional energy system specifically includes the gas load. The supply and demand balance between the electricity load and the supply and demand balance; 所述等式约束如下：The equality constraints are as follows: Qgt,t+Qt+Qtran,t＝Qp2g,t+St Q gt,t +Q t +Q tran,t =Q p2g,t +S t Pgt,t+Pi,t+PES,t+Ppv,t＝Pp2g,t+Pt+Pcut,t+Pmov,t+Ptran,t P gt,t +P i,t +P ES,t +P pv,t =P p2g,t +P t +P cut,t +P mov,t +P tran,t 式中，Qgt,t表示燃气轮机在t时刻的耗气量，Qt表示t时刻的气负荷，Qtran,t表示t时刻可替代的气负荷，Qp2g,t表示t时刻P2G装置的产气量，St表示t时刻购买的天然气的量，即气源在t时刻的产气量，Pgt,t表示燃气轮机在t时刻的发电量，Pi,t表示发电机组i在t时刻的出力，PES,t表示t时刻的蓄电池出力，Ppv,t表示t时刻的风机出力，Pp2g,t表示P2G装置t时刻的耗电量，Pt表示未做出基于价格的需求侧响应前t时刻的电负荷，Pcut,t表示t时刻可削减的电负荷，Pmov,t表示t时刻可转移的电负荷，Ptran,t表示t时刻可替代的电负荷。In the formula, Q gt,t is the gas consumption of the gas turbine at time t, Q t is the gas load at time t, Q tran,t is the alternative gas load at time t, Q p2g,t is the gas production of the P2G unit at time t , S t represents the amount of natural gas purchased at time t, that is, the gas production of the gas source at time t, P gt,t represents the power generation of the gas turbine at time t, P i,t represents the output of generator set i at time t, P ES,t is the battery output at time t, P pv,t is the fan output at time t, P p2g,t is the power consumption of the P2G device at time t, and P t is time t before the price-based demand-side response is made P cut,t represents the electrical load that can be cut at time t, P mov,t represents the electrical load that can be transferred at time t, and P tran,t represents the electrical load that can be replaced at time t. 5.如权利要求1所述的一种考虑需求侧响应的区域能源优化运行方法，其特征在于，所述步骤S4中多目标粒子群算法的调度周期为24小时，调度的尺度是1小时；5. A regional energy optimization operation method considering demand-side response according to claim 1, wherein the scheduling period of the multi-objective particle swarm algorithm in the step S4 is 24 hours, and the scheduling scale is 1 hour; 所述多目标粒子群算法的变量包括燃气轮机24小时的耗气量、燃气轮机24小时的发电量、P2G装置24小时的产气量、P2G装置24小时的耗电量、发电机组24小时的出力大小、风机的24小时出力，天然气24小时的购买量、蓄电池24小时的出力大小、蓄电池24小时的储能量、参与基于价格的需求侧响应的电力负荷的24小时出力、参与负荷可削减的激励型需求侧响应的电力负荷的24小时出力及参与需求可替代的激励型需求侧响应的电力负荷的24小时出力，将粒子带入目标函数得到对应的目标函数值；The variables of the multi-objective particle swarm algorithm include the gas consumption of the gas turbine in 24 hours, the power generation of the gas turbine in 24 hours, the gas production of the P2G device in 24 hours, the power consumption of the P2G device in 24 hours, the output of the generator set in 24 hours, and the fan. 24-hour output, 24-hour purchase of natural gas, 24-hour output of batteries, 24-hour storage energy of batteries, 24-hour output of electricity loads participating in price-based demand-side response, and incentive-type demand-side participation in load reduction The 24-hour output of the responding electric load and the 24-hour output of the electric load participating in the demand-replaceable incentive demand-side response, bring the particles into the objective function to obtain the corresponding objective function value; 不等式约束中包括发电机组的出力大小限制、蓄电池的出力大小限制和蓄电池容量大小限制，等式约束包括电力负荷守恒、气负荷守恒和价格型需求侧响应机制中一个调度周期内负荷总量的恒定，将粒子带入约束条件，获得解集；The inequality constraints include the output size limit of the generator set, the output size limit of the battery and the battery capacity size limit. The equality constraints include the power load conservation, the gas load conservation and the constant total load in a dispatch period in the price-based demand-side response mechanism. , bring the particles into the constraints, and obtain the solution set; 将粒子种群解集分为支配解集与非支配解集，将非劣解集存放在外部解集中，每一次对支配解集进行速度与位置的更新，然后取出支配解集中的非支配解，将其与外部解集中的解进行比较，保留外部解集中的非劣解；当达到迭代次数或者搜索精度时，算法停止运行，取出外部解集中的解，即区域能源系统的调度方案。Divide the particle population solution set into a dominated solution set and a non-dominated solution set, store the non-inferior solution set in the external solution set, update the speed and position of the dominated solution set each time, and then take out the non-dominated solution in the dominated solution set, It is compared with the solutions in the external solution set, and the non-inferior solutions in the external solution set are retained; when the number of iterations or the search accuracy is reached, the algorithm stops running, and the solutions in the external solution set are taken out, that is, the dispatching scheme of the regional energy system.
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