CN113256045A

CN113256045A - Park comprehensive energy system day-ahead economic dispatching method considering wind and light uncertainty

Info

Publication number: CN113256045A
Application number: CN202010773898.1A
Authority: CN
Inventors: 何川; 吕祥梅; 刘天琪
Original assignee: Sichuan University
Current assignee: Sichuan University
Priority date: 2020-08-04
Filing date: 2020-08-04
Publication date: 2021-08-13
Anticipated expiration: 2040-08-04
Also published as: CN113256045B

Abstract

The invention discloses a park comprehensive energy system day-ahead economic dispatching method considering wind and light uncertainty. Because the CCG method has better calculation efficiency in processing the robust optimization model, the invention applies the CCG method to solve. And finally, carrying out example simulation by using a commercial solver Gurobi to obtain a two-stage robust optimization scheduling strategy considering the combined thermoelectric demand response, verifying that the method can better process the uncertainty in the system, and simultaneously can reduce the operation cost of the park and promote the consumption of new energy.

Description

Park comprehensive energy system day-ahead economic dispatching method considering wind and light uncertainty

Technical Field

The invention belongs to the technical field of optimization operation of a comprehensive energy system, and particularly relates to a park comprehensive energy system day-ahead economic dispatching method considering wind-light uncertainty.

Background

The variance of the actual output and the predicted output of the wind power is far larger than the traditional load variance, and the running mode of a traditional power system is not enough to ensure the reliability of the system. Due to physical limitations of an electric power system (such as climbing limitation of capacity of a conventional generator and a power transmission line), wind power reduction frequently occurs, so that the wind power utilization rate is low, and the enthusiasm of wind power investment is restrained in the long run. When a large-scale renewable energy source is connected into a power system, the output of the renewable energy source is difficult to be accurately predicted due to strong randomness and intermittence of the renewable energy source, and in planning and scheduling of the power system, the error of the output predicted value of the renewable energy source causes non-negligible influence, in other words, the random fluctuation of renewable energy source power generation reduces the supply flexibility of the energy source system. Therefore, the traditional deterministic optimal scheduling method cannot meet the requirements, and the uncertainty of renewable energy sources needs to be considered on the basis of the original deterministic optimal scheduling method.

Stochastic optimization and robust optimization are typical methods for solving the problem of optimization with uncertainty. Compared with random optimization, the robust optimization has the advantages of simple data acquisition, high solving speed, suitability for solving large-scale uncertainty problems and the like, and is widely applied to the processing of uncertainty problems. At present, many scholars research the application of robust optimization in an integrated energy system, but the existing literature does not fully consider the influence of the energy coupling relation and the load side demand side response of various devices in the system on the uncertainty of renewable energy in the process of processing the wind-light uncertainty of the integrated energy system by using the robust optimization.

The comprehensive energy system model can adaptively adjust the output of the unit to adapt to the change of the generated energy of the renewable energy source through the energy conversion relation among the devices, and the running safety of the system is ensured. The combined thermoelectric demand response can fully utilize the coupling relation among various devices, further improve the capability of the system for dealing with the uncertainty of renewable energy sources, reduce the abandoned wind and abandoned light and increase the permeability of the renewable energy sources. Therefore, on the basis of the existing research, a two-stage adjustable robust optimization model of the park comprehensive energy system based on combined heat and power demand response is further considered, and the two-stage adjustable robust optimization model has important significance for the optimized operation of the comprehensive energy system.

Disclosure of Invention

The technical problem to be solved by the invention is to provide a park comprehensive energy system day-ahead economic dispatching method considering wind and light uncertainty, a two-stage adjustable robust optimization model considering combined thermoelectric demand response is established to process uncertainty problems caused by wind and light output in a park, a dual-layer max-min problem in the model is converted into a single-layer max problem through a dual theory, and a CCG (column and constraint generation) method is adopted to solve, so that the dispatching result is more practical while the solving speed is improved, and the consumption of new energy is further promoted.

In order to solve the technical problems, the invention adopts the technical scheme that: in order to consider the uncertainty of renewable energy sources and aim at the problem of day-ahead economic dispatching of a park integrated energy system, a park integrated energy system day-ahead economic dispatching method considering the uncertainty of wind and light is provided, and a two-stage adjustable robust model considering the combined heat and power demand response is established. The day-ahead economic dispatching of the park comprehensive energy system aims at a basic scene with wind power and photovoltaic output values as predicted values, and when uncertainty occurs in operation, the park comprehensive energy system can adaptively and safely redistribute a generator set, a heat supply unit, P2G equipment, energy storage equipment and energy exchange between a park and a superior network. The optimal scheduling decision completely conforms to the idea of a two-stage adjustable robust model. In other words, the start-stop state of the gas turbine and the cogeneration unit is determined in the first stage under the basic scene, and the worst unsafe scene of the system is searched in the second stage when uncertainty occurs. The two-stage adjustable robust scheduling model provided by the invention assumes that the start-stop state of the unit is a first-stage variable, namely when the uncertain variable of the system changes in the fluctuation interval of the uncertain variable, the start-stop state of the unit is kept unchanged, because the physical characteristics of most generator sets limit that the start-stop state of the unit cannot be changed rapidly under the uncertain condition.

A park comprehensive energy system day-ahead economic dispatching method considering wind and light uncertainty comprises the following steps:

(1) determining the specific composition of the multi-energy park, including the introduced new energy form and the specific equipment composition;

(2) establishing models of various energy conversion equipment in the park;

(3) establishing a demand response model;

(4) on the premise of meeting system safety constraints, establishing a two-stage adjustable robust model considering combined heat and power demand response by taking the minimum running cost of a basic scene as a target function;

(5) obtaining an abstract expression of a two-stage adjustable robust optimization model of the park comprehensive energy system day-ahead economic dispatching;

(6) establishing a maximum and minimum subproblem of worst scene identification;

(7) solving a two-stage adjustable robust model considering combined heat and power demand response by using a CCG method;

(8) inputting energy access of the park comprehensive energy system, new energy output data, equipment parameters and operation parameters, and solving a park comprehensive energy system day-ahead economic dispatching two-stage robust optimization model considering wind-light uncertainty by adopting a commercial solver Gurobi to obtain a dispatching strategy of the park comprehensive energy system.

Further, the specific composition of the park integrated energy system in the step (1) is as follows:

(1.1) the new energy form of the park integrated energy system is as follows: wind power and photovoltaic power generation;

(1.2) energy conversion equipment introduced into a park comprehensive energy system comprises: electricity-to-gas equipment, an electric boiler, a gas turbine, a cogeneration unit and gas/heat storage equipment.

The energy conversion equipment models in the park are as follows;

(2.1) model of electric gas-converting apparatus

In the formula: t is scheduling time; m is an index of the electric-to-gas equipment;

are respectively provided for electric gas (P2G)Reserve gas power, consumed power and electricity-to-gas efficiency, L^HANGTaking 9.7kWh/m as low heating value of natural gas³；

The minimum and maximum pneumatic power of the mth station P2G.

(2.2) electric boiler model

In the formula: t is scheduling time; n is an electric boiler index;

and

respectively the power consumption and the heat production of the nth electric boiler in the time period t;

the electric heat conversion efficiency of the nth electric boiler,

dividing into the minimum and maximum heating power of the nth electric boiler;

the start-stop state of the nth electric boiler in the time period t is shown (1 represents start-up, and 0 represents stop).

(2.3) gas turbine model

In the formula: t is scheduling time; q is a gas turbine index;

and

respectively representing the power generation power and the gas consumption power of the gas turbine; f (-) represents the gas turbine energy consumption curve;

and

respectively representing the consumption of natural gas required by the startup and shutdown of the gas turbine; a is_q、b_qAnd c_qRepresents the gas coefficient of F (-); l is^HANGTaking 9.7kWh/m as low heating value of natural gas³；

And dividing the power into the minimum and maximum power generation power of the q gas turbines.

Representing the power generation power of the qth gas turbine in the t period;

in the start-stop state of the qth gas turbine in the time period t (1 represents start-up, 0 represents stop),

starting and stopping states of a qth gas turbine in a time period t;

the upward climbing rate and the downward climbing rate of the qth gas turbine,

for the continuous startup and shutdown time of the qth gas turbine in the t-1 period,

the minimum startup and shutdown time of the qth gas turbine in the time period t.

(2.4) Combined Heat and Power Unit model

In the formula: t is scheduling time; p is an index of the cogeneration unit;

and

respectively representing heat production power and gas consumption power of a combined heat and power generation unit (CHP);

the generated power and the generated efficiency of the micro-combustion engine in the t period,

in order to achieve a high heat dissipation loss rate,

and

the heating coefficient and the flue gas recovery rate of the bromine refrigerator are respectively; l is^HANGTaking 9.7kWh/m as low heating value of natural gas³；

Dividing into the p-th CHP unit minimum and maximum generating power;

representing the power generation power of the p-th cogeneration unit in the t period;

the starting and stopping states of the pth CHP unit in the period t (1 represents starting, 0 represents stopping),

the starting and stopping state of a P-th cogeneration unit in a time period t;

the climbing rate and the descending rate of the pth CHP unit;

for the continuous startup and shutdown time of the pth CHP unit in the t-1 time period,

the minimum startup and shutdown time of the p-th CHP unit in the time period t is obtained.

(2.5) energy storage device model

ESS(t)＝(1-σ^S)·ESS(t-1)+ESⁱⁿ(t)·ηⁱⁿ-ES^out(t)/η^out

In the formula: t is scheduling time; ESS (t), ESⁱⁿ(t)、ES^out(t) storing energy, stored energy power and released energy power of the energy storage device at a time period t, respectively; ESS (t-1) represents the stored energy of the energy storage device during the t-1 period; sigma^SIs the specific consumption of the energy storage system; etaⁱⁿ、η^outRespectively storing energy and releasing energy efficiency for the energy storage equipment;

for the gas storage and discharge power of t period, GS^in,max、GS^out,maxThe maximum gas storage and gas release power of the gas storage device are respectively;

the gas storage capacity of the gas storage equipment in the time period t,

the gas storage capacity of the gas storage equipment is t-1 time; c^GS,min、C^GS,maxMinimum and maximum gas storage capacity, eta, of the gas storage equipment^CGS、η^GS,in、η^GS,outThe self-consumption rate, the gas storage efficiency and the gas release efficiency of the gas storage equipment are improved.

For heat-storage, heat-release power for period t, HS^in,max、HS^out,maxThe maximum heat storage power and the maximum heat release power of the heat storage equipment are respectively;

the amount of stored gas in the heat storage device for the period t,

the gas storage capacity of the heat storage equipment is t-1 time period; c^HS,min、C^HS,maxMinimum and maximum heat storage capacity, eta, of the heat storage apparatus_CHS、η_HS,in、η_HS,outThe self consumption rate, the heat storage efficiency and the heat release efficiency of the heat storage equipment are obtained.

The demand response model in the step (3) is specifically as follows:

P_t ^DR＝P_t ^DR，inte+P_t ^DR，shif

in the formula: t is scheduling time;

P_t ^DR、P_t ^DR,inte、P_t ^DR,shif、P_t ^LD,maxfor a predicted value of the electric load, a demand side response electric load, a demand side transfer electric load, a maximum electric load allowed by the system, P^inte,maxThe maximum interruptible electrical load during the scheduled time.

The maximum interruptible and transferable electrical load proportion for the period t. P_t ^DR,shifPositive means turning out of translatable load, and vice versa.

In the formula: t is scheduling time;

H_t ^DR、

and

respectively setting a predicted value of the thermal load, a demand side response thermal load proportion, a maximum thermal load allowed by a system and an electric load after considering demand response in a time period t; h^DR,maxThe maximum interruptible thermal load within the scheduling time; n is a radical of_tThe time period is scheduled for the whole time.

The two-stage adjustable robust model considering the combined heat and power demand response in the step (4) is specifically as follows:

(4.1) objective function

In the formula: t is scheduling time;

respectively the electricity and gas purchase cost;

the starting-up and shutdown costs of the pth CHP are respectively;

in order to make a penalty on the cost of wind abandonment,

punishment of cost for light abandonment;

to sell the electricity, C^E,ccA compensation cost to interrupt the electrical load for demand side response; p_t ⁱⁿIn order to purchase the electric power,

in order to obtain the gas purchasing power,

wind abandoning power and light abandoning power of the ith fan and the jth group of photovoltaic cells in the t period are respectively set; p_t ^outTo sell electric power, P_t ^DR,inteThe electrical load is interrupted for the demand side. Δ t is the scheduling time interval, N_tIs the number of scheduling periods.

Respectively the unit purchase electricity, gas and electricity prices phi_t ^WT、φ_t ^PVRespectively the punishment price of wind abandoning and light abandoning,

is a compensation price per unit demand side response to an interrupted electrical load.

The starting and stopping states of the pth CHP in the period t (1 represents starting, 0 represents stopping),

the cost of starting up and stopping the p-th CHP is respectively. N is a radical of^WT、N^PV、N^CHPThe number of the fan, the photovoltaic cell, the electric boiler and the CHP are respectively.

(4.2) constraint Condition

v_1t,v_2t,v_3t,v_4t≥0

P_t ^u,out≤P^out,min,P_t ^u,out≤P^out,max:(λ_{9_3,t},λ_{9_4,t})

P_t ^u,DR＝P_t ^u,DR,inte+P_t ^u,DR,shif:(λ_{10_6,t})

In the formula: t is scheduling time; (.)^uThe wind and light output is a corresponding adjusted variable under real-time change; v. of_1,tAnd v_2,tA power balance constraint relaxation variable; v. of_3,tAnd v_4,tConstraint relaxation variables for thermal balance; omega^WT、Ω^PVRespectively are wind power and photovoltaic output uncertain sets;

respectively the deviation between the wind power output and the photovoltaic output and the predicted value,

the ratio of the wind power and photovoltaic output deviation value to the predicted value is obtained;

is a variable from 0 to 1 in the uncertain convergence; delta_i、Δ_jRespectively calculating uncertainty precalculated values of wind power output and photovoltaic output;

correcting the climbing and descending of the pth CHP unit;

correcting the climbing and descending of the qth gas turbine unit; and lambda (-) is a dual variable corresponding to the constraint condition.

The abstract expression of the two-stage adjustable robust optimization model of the park comprehensive energy system economic dispatch in the past in the step (5) is as follows:

s.t. Ax+By≤b

in the formula: x represents the starting and stopping states of the units related to the CHP and the gas turbine, y and z represent the basic scene and the rest unit adjustment of the system adjusted according to the wind-light output transformation respectivelyDegree output u is an uncertain variable related to wind power and photovoltaic output uncertainty, c_b、c_gA, B, B, F, h, C, D, E, F, G can be derived by the objective function and constraint conditions in (4).

The maximum and minimum subproblems identified in the worst scene in the step (6) are specifically as follows:

s.t. λ_{9_1,t}≤0,λ_{9_2,t}≤0,λ_{9_3,t}≤0,λ_{9_4,t}≤0,λ_{9_5,t}≤0,λ_{9_6,t}≤0,t∈N^T

λ_{6_2,q,t}≤0,λ_{6_3,q,t}≤0,λ_{6_4,q,t}≤0,λ_{6_6,q,t}≤0,λ_{6_7,q,t}≤0,q∈N^GT,t∈N^T

λ_{7_2,p,t}≤0,λ_{7_3,p,t}≤0,λ_{7_4,p,t}≤0,λ_{7_6,p,t}≤0,λ_{7_7,p,t}≤0,p∈N^CHP,t∈N^T

λ_{8_1,t}≤0,λ_{8_2,t}≤0,λ_{8_3,t}≤0,λ_{8_4,t}≤0,t∈N^T

λ_{13_1,t}≤0,λ_{13_2,t}≤0,λ_{13_3,t}≤0,λ_{13_4,t}≤0,t∈N^T

λ_{4_2,m,t}≤0,λ_{4_3,m,t}≤0,t∈N^T,m∈N^P2G,t∈N^T

λ_{5_2,n,t}≤0,λ_{5_3,n,t}≤0,t∈N^T,n∈N^EB,t∈N^T

λ_{10_1,t}≤0,λ_{10_2,t}≤0,λ_{10_3,t}≤0,λ_{10_4,t}≤0,t∈N^T

λ_{11_3,t}≤0,λ_{11_3,t}≤0,λ_{12_1,t}≤0,λ_{12_2,t}≤0,t∈N^T

λ_{10_7}≤0,λ_{10_8}≤0,λ_{11_4}≤0,λ_{11_5}≤0,t∈N^T

s.t. -λ_1,t-λ_{6_1,q,t}·b_q-λ_{6_2,q,t}+λ_{6_3,q,t}-λ_{6_4,q,t+1}+

λ_{6_4,q,t}+λ_{6_5,p,t+1}-λ_{6_5,p,t}-λ_{6_6,q,t}+λ_{6_7,q,t}≤0

-λ_1,t-λ_{9_1,t}+λ_{9_2,t}≤0,t∈N^T

λ_1,t-λ_{9_3,t}+λ_{9_4,t}≤0,t∈N^T

λ_1,t+λ_{10_5,t}≤0,t∈N^T

-λ_{10_1,t}-λ_{10_2,t}+λ_{10_3,t}+λ_{10_5,t}-λ_{10_7}+λ_{10_8}＝0,t∈N^T

-λ_{10_1,t}+λ_{10_4,t}+λ_{10_5,t}+λ_{10_5,t}+λ_{10_6}＝0,t∈N^T

λ_1,t+λ_{12_1,i,t}≤0,t∈N^T,i∈N^WT

λ_1,t+λ_{12_2,j,t}≤0,t∈N^T,j∈N^PV

-λ_2,t+λ_{7_1,p,t}≤0,t∈N^T,p∈N^CHP

-λ_2,t+λ_{6_1,q,t}≤0,t∈N^T,q∈N^GT

-λ_2,t+λ_{4_1,m,t}-λ_{4_2,m,t}+λ_{4_3,m,t}≤0,t∈N^T,m∈N^P2G

λ_2,t-λ_{9_5,t}+λ_{9_6,t}≤0,t∈N^T

λ_2,t+λ_{8_2,t}+λ_{8_5,t}/η^GS,out≤0,t∈N^T

-λ_2,t+λ_{8_1,t}-λ_{8_5,t}·η^GS,in≤0,t∈N^T

-λ_{8_3,t}+λ_{8_4,t}-λ_{8_5,t+1}·η^GS,in+λ_{8_5,t}≤0,t∈N^T-λ_3,t·η^h+λ_{13_2,t}+λ_{13_5,t}/η^HS,out≤0,t∈N^T

λ_3,t·η^h+λ_{13_1,t}-λ_{13_5,t}·η^HS,in≤0,t∈N^T-λ_{13_3,t}+λ_{13_4,t}-λ_{13_5,t+1}·η^HS,in+λ_{13_5,t}≤0,t∈N^T

-λ_3,t·η^h+λ_{5_1,n,t}-λ_{5_2,n,t}+λ_{5_3,n,t}≤0,t∈N^T,n∈N^EB-λ_3,t·η^h+λ_{7_8,n,t}≤0,t∈N^T,n∈N^EB

λ_3,t+λ_{11_1,t}≤0,t∈N^Tλ_{11_1,t}-λ_{11_2,t}+λ_{11_3,t}-λ_{11_4}+λ_{11_5,t}≤0,t∈N^T

-1≤λ_1,t≤1,-1≤λ_3,t≤1,t∈N^T

the two-stage adjustable robust model set for solving and considering combined heat and power demand response by using the CCG method in the step (7) is as follows:

(7.1) Main problem

Ax+By≤b

(7.2) sub-problem

(7.3) CCG solving step

Step 1: setting the iteration counter s to 0 and setting the maximum allowed value epsilon of the system for violating the safety regulations^RO。

Step 2: solving the main problem, if the main problem is solved, obtaining a system unit start-stop state x and a unit output arrangement y, and performing the step 3; otherwise, stopping iteration and outputting no solution.

And step 3: and (3) solving the maximum and minimum subproblems according to the x and y obtained by the solution in the step (2), and finding out the wind power and photovoltaic output under the worst scene which causes the maximum possibility of violating the safety specified value.

And 4, step 4: if the maximum possible violation safety-specifying value solved in step 3 is less than epsilon^ROThen x and y are the final optimization solution and the iteration is stopped; otherwise, let s be s +1, and according to the wind power and photovoltaic output value under the worst scene solved in step 3

The CCG constraint shown below is added to the main question, returning to step 2.

f^Tv_s≤ε^RO

And (8) the park integrated energy system data further comprises the specific composition of the park integrated energy system, the energy price, the equipment parameters and values of each energy conversion equipment, the demand response proportion, the new energy output fluctuation situation under the basic scene and the worst scene, the maximum violation safety specified value and the predicted value of the new energy output and load.

Compared with the prior art, the invention has the beneficial effects that:

(1) the optimization problem containing uncertainty is solved by robust optimization, and a two-stage adjustable robust optimization model is established, so that a result close to reality can be obtained by only acquiring simple data in the scheduling process of the park comprehensive energy system, and the solving speed is high. The improved two-stage adjustable robust optimization model considers the wind and light uncertainty of the integrated energy system and simultaneously fully considers the influence of the energy coupling relation of various devices in the system and the response of the load side on the uncertainty of renewable energy. The day-ahead economic dispatching of the park comprehensive energy system aims at basic scenes taking wind power and photovoltaic output values as predicted values, and when uncertainty occurs in operation, the park comprehensive energy system can adaptively and safely redistribute a generator set, a heat supply unit, P2G equipment, energy storage equipment and energy exchange between a park and a superior network. The two-stage robust optimization ensures that the system can meet the safety constraint under any condition, minimizes the running cost of a basic scene, and meets the requirements of the system on safety and economy.

(2) After the combined heat and power demand response is considered, the functions of gas energy storage, heat energy storage and electric heat load demand response are fully exerted, the coupling relation among various devices can be fully utilized by the combined heat and power demand response, the capability of the system for coping with the uncertainty of renewable energy sources is further improved, the wind and light abandon is reduced, and the permeability of the renewable energy sources is increased. The comprehensive energy system model can adaptively adjust the output of the unit to adapt to the change of the generated energy of the renewable energy source through the energy conversion relation among the devices, thereby ensuring the safety of the operation of the system, promoting the consumption of the renewable energy source, improving the robustness of the system and improving the economic benefit of the park.

Drawings

FIG. 1 is a flow chart of the steps of the method of the present invention;

FIG. 2 is a diagram of the specific components of the park integrated energy system;

FIG. 3 is a CCG method solving flow chart;

FIG. 4 is a forecast of wind turbines, photovoltaic output, electrical load and thermal load for a typical winter day for a park multi-energy integrated system.

Detailed Description

In order to explain the technical solutions disclosed in the present invention in detail, the present invention will be further described with reference to the accompanying drawings and specific examples.

The invention discloses a park comprehensive energy system day-ahead economic dispatching method considering wind and light uncertainty. The specific implementation step flow is shown in fig. 1, and the technical scheme of the invention comprises the following steps:

step 1: the specific composition of the park integrated energy system is determined, including the new energy forms introduced and the specific equipment composition.

Step 2: and establishing various energy conversion equipment models in the park.

(2.1) the technology of the electric-to-gas equipment model P2G can realize the conversion of electric energy to natural gas, the natural gas is transmitted in a large capacity, long time and long distance through a natural gas pipeline, powerful technical support is provided for the consumption of renewable energy, the large-range and long-distance space-time transfer of wind power can be realized, meanwhile, the P2G response is rapid, and the application prospect is strong. The relationship between the device gas production power and the power consumption and the gas production power limit of the P2G are as follows:

respectively gas making power, consumed power and electricity-to-gas efficiency, L, of an electricity-to-gas (P2G) facility^HANGTaking 9.7kWh/m as low heating value of natural gas³；

The minimum and maximum pneumatic power of the mth station P2G.

(2.2) electric boiler model

The introduction of the electric boiler can break the electric-thermal coupling hard constraint of the CHP unit and change the traditional 'heat-fixed-electricity' scheduling mode. The electric boiler can coordinate the peak valley of the electric heating load, and the relation between the heating capacity and the power consumption and the constraint of the heating capacity are as follows:

in the formula: t is scheduling time; n is an electric boiler index;

and

the electric heat conversion efficiency of the nth electric boiler,

dividing into the minimum and maximum heating power of the nth electric boiler;

(2.3) gas turbine model

The gas turbine converts chemical energy in natural gas into electric energy, and the relation between the consumed natural gas power and the generated power, the generated power limit, the climbing constraint and the minimum on-off time constraint are as follows:

in the formula: t is scheduling time; q is a gas turbine index;

and

and

respectively representing the consumption of natural gas required by the startup and shutdown of the gas turbine; a is_q、b_qAnd c_qGas system represented by F (-) orCounting; l is^HANGTaking 9.7kWh/m as low heating value of natural gas³；

And dividing into the q gas turbine minimum and maximum generating power.

Representing the power generation power of the qth gas turbine in the t period;

the start-stop state of the qth gas turbine in the time period t (1 represents start-up, 0 represents stop),

starting and stopping states of a qth gas turbine in a time period t;

the upward climbing rate and the downward climbing rate of the qth gas turbine,

(2.4) Combined Heat and Power Unit model

Neglecting the influence of external environment change on the power generation and fuel combustion efficiency, the mathematical relationship of the thermoelectric relationship, the gas-electricity relationship, the relationship between the power generation power, the power generation power limit, the climbing constraint and the minimum on-off time constraint are as follows:

in the formula: t is scheduling time; p is an index of the cogeneration unit;

and

in order to achieve a high heat dissipation loss rate,

and

the heating coefficient and the flue gas recovery rate of the bromine cooler are respectively; l is^HANGTaking 9.7kWh/m as low heating value of natural gas³；

Dividing into the p-th CHP unit minimum and maximum generating power;

the starting and stopping state of a P-th cogeneration unit in a time period t;

the climbing rate and the descending rate of the pth CHP unit;

(2.5) energy storage device model

The relationship among the energy storage device model, the gas storage/release of the heat storage device, the constraint of the stored/released power and the gas storage at the time t, the constraint of the gas storage and the heat storage capacity of the heat storage device, the gas storage and the heat storage capacity of the heat storage device and the gas storage at the time t-1, the relationship among the stored heat quantity and the stored/released power at the time t and the stored/released power are as follows:

ESS(t)＝(1-σ^S)·ESS(t-1)+ESⁱⁿ(t)·ηⁱⁿ-ES^out(t)/η^out

in the formula: t is scheduling time; ESS (t), ESⁱⁿ(t)、ES^out(t) stored energy, stored energy power and released energy power of the energy storage device are respectively in a period of t; ESS (t-1) represents the energy storage capacity of the energy storage device during the t-1 period; sigma^SIs the self-consumption rate of the energy storage system; etaⁱⁿ、η^outRespectively storing energy and releasing energy efficiency for the energy storage equipment;

gas storage, gas discharge power, GS, for a period of tⁱⁿ ^,max、GS^out,maxThe maximum gas storage and gas release power of the gas storage device are respectively;

the gas storage capacity of the gas storage device for the period t,

the gas storage capacity of the gas storage equipment is t-1 time; c^GS,min、C^GS,maxRespectively the minimum and maximum gas storage capacity, eta, of the gas storage device^CGS、η^GS,in、η^GS,outThe self-consumption rate, the gas storage efficiency and the gas release efficiency of the gas storage equipment are improved.

the amount of stored gas of the heat storage device for the period t,

the gas storage capacity of the heat storage equipment is t-1 time period; c^HS,min、C^HS,maxMinimum and maximum heat storage capacity, eta, of the heat storage apparatus^CHS、η^HS,in、η^HS,outThe self consumption rate, the heat storage efficiency and the heat release efficiency of the heat storage equipment are obtained.

And step 3: and establishing a demand response model.

Dividing the demand side electric load capable of responding into a translatable electric load and an interruptible electric load, considering the interrupting cost of the interruptible load, wherein the sum of the demand side electric load capable of responding and the current time interval electric load needs to be less than the maximum electric load allowed in the current time interval, and the relation between the predicted value of the electric load and the electric load after the demand response is as follows:

P_t ^DR＝P_t ^DR，inte+P_t ^DR，shif

in the formula: t is scheduling time;

P_t ^DR、P_t ^DR,inte、P_t ^DR,shif、P_t ^LD,maxfor a preset value of the electric load, a response electric load at a demand side, a transfer electric load at the demand side, and a maximum electric load allowed by a system in a period of t, P^inte,maxThe maximum interruptible electrical load within the scheduled time.

The demand side may respond to thermal load constraints as follows:

in the formula: t is scheduling time;

H_t ^DR、α_t ^HLD、

and

respectively obtaining a predicted value of the thermal load, a demand side response thermal load proportion, a maximum thermal load allowed by a system and an electric load after considering demand response in a time period t; h^DR,maxMaximum interruptible thermal load within the scheduling time; n is a radical of_tThe time period is scheduled for the whole time.

And 4, step 4: on the premise of meeting the system safety constraint, a two-stage adjustable robust model considering the combined heat and power demand response is established by taking the minimum running cost of a basic scene as a target function.

(4.1) objective function

The two-stage robust optimization model provided by the invention aims to minimize the operation cost of a basic scene on the premise of meeting the system safety constraint, and because no load loss is allowed in the basic scene, a target function does not contain a load loss related variable, and the target function and a related equation are constrained as follows:

in the formula: t is scheduling time;

respectively the electricity and gas purchase cost;

the starting-up and shutdown costs of the pth CHP are respectively;

in order to make a penalty on the cost of wind abandonment,

penalizing costs for abandoning light;

to sell the electricity, C^E,ccA compensation cost to interrupt the electrical load for demand side response;

in order to purchase the electric power,

in order to obtain the gas purchasing power,

Respectively the unit price for electricity, gas and electricity,

respectively the punishment price of wind abandoning and light abandoning,

is the price compensated per unit of demand side response to the interrupted electrical load.

the cost of starting up and stopping the p-th CHP is respectively. N is a radical of^WT、 N^PV、N^CHPThe number of the fan, the photovoltaic cell, the electric boiler and the CHP are respectively.

(4.2) constraint Condition

In order to ensure the safe operation of the system, the section adopts a double-layer max-min model to identify the worst scene causing the least safe operation of the system, namely the maximum security violation specified value (security operation), wherein the maximum security violation specified value of the system in the worst scene is required to be smaller than a preset value epsilon^RO，ε^ROThe value is set in relation to a predetermined system security level to ensure safe operation of the campus energy complex. The maximum value violating the system safety regulation, the uncertainty set of wind power output and photovoltaic output, the system energy balance constraint, the slack variable constant larger than zero constraint, the unit output constraint and the unit error correction climbing constraint under the condition of uncertain renewable energy output, the energy storage constraint, the power exchange constraint with a superior network, the demand side response constraint and the wind and light abandoning constraint are as follows in the worst scene under uncertain conditions:

v_1t,v_2t,v_3t,v_4t≥0

-P_t ^u,in≤-P^in,min,P_t ^u,in≤P^in,max:(λ_{9_1,t},λ_{9_2,t})

P_t ^u,DR＝P_t ^u,DR,inte+P_t ^u,DR,shif:(λ_{10_6,t})

in the formula: t is scheduling time;

P_t ^u,E,out、

respectively adopting wind abandoning, light abandoning, electric load after demand response, electric power consumed by P2G, electric power consumed by an electric boiler, power sold in a garden, CHP set output and gas turbine output according to different wind power outputs

And photovoltaic output

An adjusted real-time value; (.)^uThe wind and light output is a corresponding adjusted variable under real-time change; v. of_1,tAnd v_2,tA power balance constraint relaxation variable; v. of_3,tAnd v_4,tConstraint relaxation variables for thermal balance; omega^WT、Ω^PVRespectively representing wind power output uncertain collection and photovoltaic output uncertain collection;

is a variable from 0 to 1 in the uncertain convergence;_Δi. delta j is wind power output uncertainty precalculated values and photovoltaic output uncertainty precalculated values respectively;

correcting the climbing and descending of the pth CHP unit;

And 5, obtaining an image extraction expression of the two-stage adjustable robust optimization model of the park comprehensive energy system day-ahead economic dispatching.

The proposed two-stage robust optimization model relates to three systems of electricity, gas and heat, involves more constraints, contains uncertain parameters, and is a nonlinear mixed integer programming problem. For ease of discussion, the two-stage robust optimization scheduling model proposed in this section may take the form of an abstract robust optimization model as follows:

s.t. Ax+By≤b

in the formula: x represents the starting and stopping states of the unit related to the CHP and the gas turbine, and y and z represent bases respectivelyThe dispatching output of other units of the system is adjusted according to the variation of the wind and light output, u is an uncertain variable related to the uncertainty of the wind power and photovoltaic output, and c_b、c_gA, B, B, F, h, C, D, E, F, G can be derived from the objective function and constraint conditions in step 4.

And 6, establishing the maximum and minimum subproblems of the worst scene identification.

And 4, the sub-problem is the problem of identifying the worst scene, the scene causing the maximum violation of the safety specified value of the system is found through the double-layer max-min problem shown in the step 4, namely specific values of the uncertain quantity in the worst scene are determined, and then the double-layer max-min problem is converted into a single-layer bilinear maximum value optimization sub-problem shown below through a dual theory.

ΔD＝ΔD₁+ΔD₂

λ_{8_1,t}≤0,λ_{8_2,t}≤0,λ_{8_3,t}≤0,λ_{8_4,t}≤0,t∈N^T

λ_{13_1,t}≤0,λ_{13_2,t}≤0,λ_{13_3,t}≤0,λ_{13_4,t}≤0,t∈N^T

λ_{4_2,m,t}≤0,λ_{4_3,m,t}≤0,t∈N^T,m∈N^P2G,t∈N^T

λ_{5_2,n,t}≤0,λ_{5_3,n,t}≤0,t∈N^T,n∈N^EB,t∈N^T

λ_{10_1,t}≤0,λ_{10_2,t}≤0,λ_{10_3,t}≤0,λ_{10_4,t}≤0,t∈N^T

λ_{11_3,t}≤0,λ_{11_3,t}≤0,λ_{12_1,t}≤0,λ_{12_2,t}≤0,t∈N^T

λ_{10_7}≤0,λ_{10_8}≤0,λ_{11_4}≤0,λ_{11_5}≤0,t∈N^T

s.t. -λ_1,t-λ_{6_1,q,t}·b_q-λ_{6_2,q,t}+λ_{6_3,q,t}-λ_{6_4,q,t+1}+

λ_{6_4,q,t}+λ_{6_5,p,t+1}-λ_{6_5,p,t}-λ_{6_6,q,t}+λ_{6_7,q,t}≤0

-λ_1,t-λ_{9_1,t}+λ_{9_2,t}≤0,t∈N^T

λ_1,t-λ_{9_3,t}+λ_{9_4,t}≤0,t∈N^T

λ_1,t+λ_{10_5,t}≤0,t∈N^T

-λ_{10_1,t}+λ_{10_4,t}+λ_{10_5,t}+λ_{10_5,t}+λ_{10_6}＝0,t∈N^T

λ_1,t+λ_{12_1,i,t}≤0,t∈N^T,i∈N^WT

λ_1,t+λ_{12_2,j,t}≤0,t∈N^T,j∈N^PV

-λ_2,t+λ_{7_1,p,t}≤0,t∈N^T,p∈N^CHP

-λ_2,t+λ_{6_1,q,t}≤0,t∈N^T,q∈N^GT

-λ_2,t+λ_{4_1,m,t}-λ_{4_2,m,t}+λ_{4_3,m,t}≤0,t∈N^T,m∈N^P2G

λ_2,t-λ_{9_5,t}+λ_{9_6,t}≤0,t∈N^T

λ_2,t+λ_{8_2,t}+λ_{8_5,t}/η^GS,out≤0,t∈N^T

-λ_2,t+λ_{8_1,t}-λ_{8_5,t}·η^GS,in≤0,t∈N^T

-1≤λ_1,t≤1,-1≤λ_3,t≤1,t∈N^T

And 7, solving a two-stage adjustable robust model considering the combined heat and power demand response by utilizing a CCG (column and constraint generation) method.

(7.1) Main problem

Ax+By≤b

(7.2) sub-problem

(7.3) CCG solving step

Step (7.3.1): setting the iteration counter s to 0, and setting the maximum value epsilon allowed by the system to violate the safety regulation^RO。

Step (7.3.2): solving the main problem, if the main problem is solved, obtaining a system unit start-stop state x and a unit output arrangement y, and performing the step (7.3.3); otherwise, stopping iteration and outputting no solution.

Step (7.3.3): and (4) solving the maximum and minimum subproblems according to the x and y obtained by the solution in the step (7.3.2), and finding out the wind power and photovoltaic output under the worst scene which causes the maximum possibility of violating the safety specified value.

Step (7.3.4): if the maximum possible violation safety regulation value solved in step (7.3.3) is less than epsilon^ROThen x and y are the final optimization solution and the iteration is stopped; otherwise, let s be equal to s +1, and according to the wind power and photovoltaic output value in the worst scene solved in step (7.3.3)

The CCG constraint shown below is added to the main question, returning to step (7.3.2).

f^Tv_s≤ε^RO

And 8, inputting the data of the park comprehensive energy system, and further comprising the specific composition, energy price, equipment parameters and values of each energy conversion equipment, demand response proportion, new energy output fluctuation conditions under a basic scene and a worst scene, maximum violation safety specified values, predicted values of new energy output and load and the like of the park comprehensive energy system, and solving the two-stage adjustable robust optimization operation model of the park comprehensive energy system by adopting a commercial solver Gurobi to obtain a robust optimization scheduling result.

The effects of the present invention will be described in detail below with reference to specific examples.

(1) Introduction to the examples.

Fig. 2 shows the specific components of the park energy system. And (4) selecting a multi-energy complementary park system model containing wind, light, gas, storage and consideration of electricity-to-gas and electricity-to-heat technologies in a simulation mode. The system comprises a gas turbine, a wind driven generator, an electric boiler, P2G equipment, heat storage equipment, gas storage equipment, two CHPs and a group of photovoltaic cells. Heating coefficient of bromine refrigerator

And the recovery rate of flue gas

Respectively 0.9 and 1.2, and the starting-up and shutdown costs of the gas turbine, the CHP and the electric boiler are respectively as follows: 3.5, 1.94, 2.74 yuan. Assuming that the initial state of the CHP and the gas turbine is the off-stream state, the initial gas storage capacity of the gas storage equipment is 10m³The initial heat storage capacity of the heat storage equipment is 100 kW.h, and the self-consumption rate of the gas storage and heat storage equipment is 0.01. The predicted values of the fan, the photovoltaic output, the electric load and the heat load of the park multi-energy integrated system in the typical winter day are shown in fig. 4.

(2) Description of embodiment scenarios.

In order to verify the effectiveness of the proposed two-stage robust adjustable optimization model considering the response of the combined heat and power demand, 7 scheduling operation modes shown in table 1 are set.

Table 17 scheduling operation modes

The wind power and photovoltaic predicted output is adjusted to be 2 times of the original output, and meanwhile uncertainty of wind and photovoltaic output is considered, and a new operation scheme 1-7 is obtained. The threshold value of the safety regulation violation is set to be 0, namely the system does not allow to lose load and overload under any scene, and the uncertain budget of the wind power output and the photovoltaic output is assumed to be 24.

And increasing the electrical load to 2.4 times of the original electrical load, and simultaneously considering the uncertainty of wind-solar output to obtain a new operation scheme 8-14, wherein the violation safety specified value is set to be 0.

(3) Examples analysis of results.

Table 2 gives the operating costs and violation of safety regulations for operating scenarios 1-7 at different uncertainty ratios, from which can be derived: along with the increase of the uncertain proportion of the wind power output and the photovoltaic output, the operation cost of the park is increased, and the value of the maximum violation of the safety regulation which possibly occurs in the park is also larger. However, with the continuous addition of energy conversion equipment such as heat storage equipment, P2G and gas storage equipment thereof, the park can continuously adjust the energy storage state in real time to cope with the real-time change of wind power photovoltaic output, the running risk of the system is reduced, the uncertainty of the system can be flexibly coped with by considering demand response, the combined thermoelectric demand response is considered, and the running cost under the basic scene of the park is further reduced.

TABLE 2 running costs and violation of safety regulations for running scenarios 1-7 at different uncertainty ratios

Table 3 gives the iteration number and violation safety specified value of the operating scheme 1 under different wind power and photovoltaic uncertainty budgets, from which can be obtained: the larger the uncertainty budget value of the renewable energy source is, the fewer the number of iterations required for obtaining an optimization scheme meeting the system requirements is, and the larger the violation of the safety regulation value which may occur in the system is. The robustness of the system can be adjusted by adjusting the uncertainty budget value.

TABLE 3 iteration count and violation of safety regulations for operating scenario 1 under different wind power and photovoltaic uncertainty budgets

Table 4 gives the operating costs and violations of safety regulations for operating scenarios 8-14 at different uncertainty ratios, from which one can derive: when the system load demand is obviously greater than the renewable energy output, the renewable energy output can be completely absorbed by the system, the gas-to-electricity conversion is superior to the electricity-to-gas conversion, the P2G equipment does not work, and the introduction of the energy storage equipment can not improve the robustness of the system. When the uncertain ratio of the output of the renewable energy source is 0.2, the comparison scheme of 8-10 shows that the system has more comprehensive equipment and lower operation cost of the system in a basic scene, and the comparison scheme of 10-14 shows that the introduction of the response at the demand side can further reduce the operation cost of the system. Given the operating costs and violation of safety regulations for the comparison schemes 11-14 at uncertain ratios of different renewable energy sources, the combined heat and power demand response can reduce power load losses by reducing thermal loads, greatly enhancing the robustness, flexibility and economy of the campus integrated energy system.

TABLE 4 running costs and violations of safety regulations for running scenarios 8-14 at different uncertainty ratios

The above description is only an embodiment of the present invention, but not intended to limit the scope of the present invention, and all equivalent changes or substitutions made by using the contents of the present specification and the drawings, which are directly or indirectly applied to other related arts, should be included within the scope of the present invention.

Claims

1. The method for day-ahead economic dispatching of the park comprehensive energy system considering wind and light uncertainty is characterized by comprising the following steps of:

step 1: determining the specific composition of the park comprehensive energy system, including the introduced new energy form and the specific equipment composition;

step 2: establishing models of various energy conversion equipment in the park;

and step 3: establishing a demand response model;

and 4, step 4: on the premise of meeting system safety constraints, establishing a two-stage adjustable robust model considering combined heat and power demand response by taking the minimum running cost of a basic scene as a target function;

step 5, obtaining an abstract expression of a two-stage adjustable robust optimization model of the park comprehensive energy system day-ahead economic dispatching;

step 6, establishing the maximum and minimum subproblems of the worst scene identification;

step 7, solving a two-stage adjustable robust model considering combined heat and power demand response by using a CCG method;

and 8: inputting energy access of the park comprehensive energy system, new energy output data, equipment parameters and operation parameters, and solving a park comprehensive energy system day-ahead economic dispatching two-stage robust optimization model considering wind-light uncertainty by adopting a commercial solver Gurobi to obtain a dispatching strategy of the park comprehensive energy system.

2. The method for the day-ahead economic dispatch of the park integrated energy system taking into account the uncertainty of wind and light according to claim 1, wherein the park integrated energy system of step 1 is specifically composed of:

(1) the new energy form of the park integrated energy system is as follows: wind power and photovoltaic power generation;

(2) the energy conversion equipment for introducing the park comprehensive energy system comprises: electricity-to-gas equipment, an electric boiler, a gas turbine, a cogeneration unit and gas/heat storage equipment.

3. The method for the day-ahead economic dispatch of the campus integrated energy systems considering the wind and photovoltaic uncertainty as claimed in claim 1, wherein the models of the energy conversion devices in step 2 are as follows;

(1) electric gas conversion equipment model

respectively gas making power, consumed power and electricity conversion efficiency, L of the electricity conversion (P2G) equipment^HANGTaking 9.7kWh/m as low heating value of natural gas³；

The minimum and maximum pneumatic power of the mth station P2G.

(2) Electric boiler model

In the formula: t is scheduling time; n is an electric boiler index;

and

respectively n electric boilers during time tPower consumed and power produced;

the electric heat conversion efficiency of the nth electric boiler,

dividing into the minimum and maximum heating power of the nth electric boiler;

(3) Gas turbine model

In the formula: t is scheduling time; q is a gas turbine index;

and

and

Representing the power generation power of the qth gas turbine in the t period;

starting and stopping states of a qth gas turbine in a time period t;

the upward climbing rate and the downward climbing rate of the qth gas turbine,

(4) Combined heat and power generation unit model

In the formula: t is scheduling time; p is an index of the cogeneration unit;

and

in order to achieve a high heat dissipation loss rate,

and

Dividing into the p-th CHP unit minimum and maximum generating power;

the starting and stopping state of a P-th cogeneration unit in a time period t;

the climbing rate and the descending rate of the pth CHP unit;

(5) Energy storage equipment model

ESS(t)＝(1-σ^S)·ESS(t-1)+ESⁱⁿ(t)·ηⁱⁿ-ES^out(t)/η^out

In the formula: t is scheduling time; ESS (t), ESⁱⁿ(t)、ES^out(t) storing energy, stored energy power and released energy power of the energy storage device at a time period t, respectively; ESS (t-1) represents the stored energy of the energy storage device during the t-1 period; sigma^SIs the self-consumption rate of the energy storage system; etaⁱⁿ、η^outRespectively storing energy and releasing energy efficiency for the energy storage equipment;

gas storage, gas discharge power, GS, for a period of t^in,max、GS^out,maxThe maximum gas storage and gas release power of the gas storage device are respectively;

the gas storage capacity of the gas storage device for the period t,

the gas storage capacity of the gas storage equipment is t-1 time; c^GS,min、C^GS,maxRespectively the minimum and maximum gas storage capacity, eta, of the gas storage device^CGS、η^GS,in、η^GS,outFor storing gasSelf consumption rate, gas storage efficiency and gas release efficiency.

the amount of stored gas in the heat storage device for the period t,

the gas storage capacity of the heat storage equipment is t-1 time period;

C^HS,min、C^HS,maxminimum and maximum heat storage capacity, eta, of the heat storage apparatus^CHS、η^HS,in、η^HS,outThe self consumption rate, the heat storage efficiency and the heat release efficiency of the heat storage equipment are obtained.

4. The method for the day-ahead economic dispatch of the campus integrated energy systems considering wind and photovoltaic uncertainty as claimed in claim 1, wherein the demand response model of step 3 is as follows:

P_t ^DR＝P_t ^DR,inte+P_t ^DR,shif

in the formula: t is scheduling time;

P_t ^DR、P_t ^DR,inte、P_t ^DR,shif、P_t ^LD,maxpredicted value of electric load, demand side response electric load, demand side transfer electric load, maximum electric load allowed by system, P^inte,maxThe maximum interruptible electrical load within the scheduled time.

The maximum interruptible and extractable electrical load proportion for the t period. P_t ^DR,shifPositive means turning out of translatable load, and vice versa.

In the formula: t is scheduling time;

and

5. The method for the day-ahead economic dispatch of a campus integrated energy system considering wind and photovoltaic uncertainty as claimed in claim 1, wherein the two-stage tunable robust model considering combined heat and power demand response of step 4 is as follows:

(1) objective function

In the formula: t is scheduling time;

respectively the electricity and gas purchase cost;

the starting-up and shutdown costs of the pth CHP are respectively;

in order to make a penalty on the cost of wind abandonment,

punishment of cost for light abandonment;

in order to purchase the electric power,

in order to obtain the gas purchasing power,

Respectively the unit price for electricity, gas and electricity,

respectively the punishment price of wind abandoning and light abandoning,

(2) Constraint conditions

In the formula: t is scheduling time; (.)^uThe wind and light output is a corresponding adjusted variable under real-time change; v. of_1,tAnd v₂,_tA power balance constraint relaxation variable; v. of_3,tAnd v_4,tConstraint relaxation variables for thermal balance; omega^WT、Ω^PVRespectively representing uncertain collections of wind power output and photovoltaic output;

is a variable from 0 to 1 in the uncertain convergence; delta_i、△_jRespectively calculating uncertainty precalculated values of wind power output and photovoltaic output;

correcting the climbing and descending of the pth CHP unit;

6. The method for the day-ahead economic dispatch of the park integrated energy system considering the wind and light uncertainty as claimed in claim 1, wherein the abstract expression of the two-stage adjustable robust optimization model of the day-ahead economic dispatch of the park integrated energy system in the step 5 is as follows:

s.t.Ax+By≤b

in the formula: x represents the starting and stopping states of units related to the CHP and the gas turbine, y and z represent the basic scene and the scheduling output of other units of the system adjusted according to the wind-light output transformation, u is an uncertain variable related to the uncertainty of the wind power output and the photovoltaic output, and c_b、c_gA, B, B, F, h, C, D, E, F, G can be derived from the objective function and constraint conditions in 5.

7. The method for day-ahead economic dispatch of the park integrated energy system taking into account wind and light uncertainty as defined in claim 1, wherein the worst scenario identified maximum and minimum subproblems of step 6 are as follows:

△D＝△D₁+△D₂

in the formula: t is scheduling time;

8. the method for the day-ahead economic dispatch of the campus integrated energy systems considering wind and photovoltaic uncertainty as claimed in claim 1, wherein the step 7 of solving the two-stage adjustable robust model considering the joint thermal power demand response by using the CCG method is as follows:

(1) major problems

Ax+By≤b

(2) Sub-problems

(3) CCG solving step

f^Tv_s≤ε^RO

9. The optimal operation method for the distribution network of the integrated gas-electricity energy system considering the gas-electricity combined demand response of claim 1, wherein the data of the park integrated energy system in step 8 further comprises specific composition of the park integrated energy system, energy price, equipment parameters and values of each energy conversion equipment, demand response proportion, new energy output fluctuation situation under basic scene and worst scene, maximum violation of safety regulation value and predicted value of new energy output and load.