CN113128759A - Regional energy optimization operation method considering demand side response - Google Patents
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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
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:
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:
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:
in the formula (I), the compound is shown in the specification,
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:
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:
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:
in the formula (I), the compound is shown in the specification,
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. 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.
2. The regional energy optimization operation method considering demand-side response according to claim 1, wherein the scheduling model in step S1 specifically includes a mathematical model of the output characteristics of the unit and a mathematical model of the demand-side response mechanism;
the unit includes the coupling unit, and the coupling unit mainly refers to gas turbine and P2G device, and gas turbine generates the electric energy through consuming the natural gas, and the P2G device produces the natural gas through consuming the electric energy, and gas turbine's mathematical model is:
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 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 P2Gp2gThe conversion efficiency of the P2G apparatus, Pp2g,tRepresents the power consumption of P2G device at time t;
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:
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，jRepresenting an elasticity coefficient, wherein the elasticity coefficient represents a self-elasticity coefficient when i ≠ j, the mutual elasticity coefficient when i ≠ j represents a relation between the electricity price of the current time period and the electric quantity of the current time period, and the mutual elasticity coefficient represents a relation between the electricity price of the current time period and the electric 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 and provides corresponding economic compensation 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 a user selects energy with different cost performance according to the transverse distributed distribution condition of energy demand on the same time node, two loads of electricity and gas are equivalently realized by using a heat value, and a mathematical model is as follows:
Pt,Tran+HHV×Qt,Tran＝0
in the formula Pt,Tran、Qt,TranThe transferred electrical load and the gas load at time t are shown, respectively, and HHV represents the high heating value of the natural gas.
3. The regional energy optimization operation method considering the demand-side response according to claim 1, wherein the objective function of the operation cost and the objective function of the emission of the harmful gas in the step S2;
the operation cost in the step S2 comprises the operation cost of the generator set, the gas purchase cost, the compensation cost of the excitation response and the wind abandoning cost;
the mathematical model of the operation cost is as follows:
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,tThe air abandon quantity at the time t is represented, and alpha, beta and gamma are respectively a quadratic coefficient, a primary coefficient and a constant coefficient of a cost function of the generator set;
the harmful gas emission in the step S2 includes emission of carbon dioxide, sulfur dioxide and nitrogen dioxide harmful gas, and the mathematical model of the harmful gas emission is as follows:
in the formula (I), the compound is shown in the specification,
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.
4. The method for regional energy optimization operation considering demand-side response of claim 1, wherein the constraints in step S3 include inequality constraints and equality constraints;
the inequality constraints comprise the output size limitation of each generator set in the energy system, the output size limitation of a storage battery and the capacity limitation 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,tThe minimum active power, the maximum active power and the actual output power P of the generator set i at the moment t respectivelyES,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 balance between supply and demand of the regional energy system and constancy of the total load in a scheduling period in a 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 loads and 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
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, 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.
5. The method for regional energy optimization operation considering demand-side response as claimed in claim 1, wherein the scheduling period of the multi-objective particle swarm algorithm in step S4 is 24 hours, and the scheduling scale is 1 hour;
variables of the multi-target particle swarm algorithm comprise 24-hour gas consumption of a gas turbine, 24-hour power generation of the gas turbine, 24-hour gas production of a P2G device, 24-hour power consumption of a P2G device, 24-hour output of a generating set, 24-hour output of a fan, 24-hour purchase quantity of natural gas, 24-hour output of a storage battery, 24-hour energy storage of the storage battery, 24-hour output of a power load participating in price-based demand side response, 24-hour output of a power load participating in load reducible incentive type demand side response and 24-hour output of a power load participating in demand replaceable incentive type demand side response, and particles are brought into an objective function to obtain a corresponding objective function value;
the inequality constraint comprises the output size limit of a generator set, the output size limit of a storage battery and the capacity size limit of the storage battery, the equality constraint comprises the power load conservation, the gas load conservation and the constancy of the total load in a scheduling period in a price type demand side response mechanism, and particles are brought into the constraint condition to obtain a solution set;
dividing the particle population solution set 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 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 a scheduling scheme of the regional energy system.
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