CN111950808A - Comprehensive energy system random robust optimization operation method based on comprehensive demand response - Google Patents
Comprehensive energy system random robust optimization operation method based on comprehensive demand response Download PDFInfo
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Abstract
The comprehensive energy system random robust optimization operation method based on comprehensive demand response comprises the following steps: considering response characteristics of fixed loads, adjustable loads, transfer loads and alternative loads in the comprehensive energy system, and establishing a user-side comprehensive demand response model of the comprehensive energy system; considering external energy input of a park in the comprehensive energy system and multi-type energy production, conversion and storage equipment in the park, establishing a park side comprehensive demand response model of the comprehensive energy system, and forming a comprehensive demand response model of the comprehensive energy system with a user side comprehensive demand response model; and establishing a load random uncertain model and an air-light robust uncertain model, combining the load random uncertain model and the air-light robust uncertain model with a comprehensive demand response model to form a comprehensive energy system random robust optimization operation model based on comprehensive demand response, and solving the model by adopting a mixed integer linear programming method. The invention can exert the complementary and cooperative advantages among various energy sources and realize the economic, flexible and efficient operation of a comprehensive energy system.
Description
Technical Field
The invention relates to an optimized operation method of a comprehensive energy system. In particular to a random robust optimization operation method of a comprehensive energy system based on comprehensive demand response.
Background
With the gradual depletion of fossil energy and the increasing increase of environmental pollution, the world energy pattern faces huge challenges, and further adjustment of energy production and consumption modes is urgently needed to meet the development requirements of a new era. Integrated Energy Systems (IES) can realize cascade effective utilization of various energy sources such as cold, heat, electricity, gas and the like, effectively promote the consumption of renewable energy sources, and become a hotspot of research in the energy field at present.
At present, a great deal of achievements are obtained in domestic and foreign relevant research aiming at the optimized operation of the comprehensive energy system, the traditional power demand response is gradually expanded into the comprehensive demand response along with the gradual enhancement of the coupling depth of cold, heat, electricity and gas under the comprehensive energy network architecture, the comprehensive demand response (IDR) becomes an important component part of the research of the comprehensive energy field, and the regulation and control function of the IDR in the optimized operation of the comprehensive energy system still needs to be further researched and analyzed. Therefore, the comprehensive energy system random robust optimization operation research based on the comprehensive demand response has important significance.
Disclosure of Invention
The invention aims to solve the technical problem of providing a comprehensive energy system random robust optimization operation method based on comprehensive demand response, which can give full play to the complementary and economic advantages among energy sources.
The technical scheme adopted by the invention is as follows: a comprehensive energy system random robust optimization operation method based on comprehensive demand response comprises the following steps:
1) considering response characteristics of fixed loads, adjustable loads, transfer loads and alternative loads in the comprehensive energy system, and establishing a user-side comprehensive demand response model of the comprehensive energy system;
2) considering external energy input of a park in the comprehensive energy system and multi-type energy production, conversion and storage equipment in the park, establishing a park side comprehensive demand response model of the comprehensive energy system, and forming a comprehensive demand response model of the comprehensive energy system together with the user side comprehensive demand response model of the comprehensive energy system established in the step 1);
3) respectively adopting a random optimization method and a robust optimization method to establish a load random uncertain model and an air-light robust uncertain model, combining with the comprehensive demand response model of the comprehensive energy system in the step 2), jointly forming a comprehensive energy system random robust optimization operation model based on comprehensive demand response, and adopting a mixed integer linear programming method to solve the comprehensive energy system random robust optimization operation model based on comprehensive demand response.
The comprehensive energy system random robust optimization operation method based on comprehensive demand response has the following advantages:
1. the invention respectively adopts robust optimization and random optimization methods to represent the uncertainty of the distributed power supply and the load, can effectively reduce the influence of the randomness and the volatility of the source load power on the system, and improves the accuracy of the dispatching plan.
2. According to the invention, by establishing the comprehensive demand response model of the comprehensive energy system, the regulation and control potential of the demand side in the system optimization operation can be fully exerted, and the multi-energy flow supply and demand balance is promoted.
3. The invention can effectively exert the complementary and cooperative advantages among various energy sources, further reduce the comprehensive operation cost of the system and realize the economic, flexible and efficient operation of the comprehensive energy system.
Drawings
FIG. 1 is a block diagram of a typical integrated energy park in accordance with an embodiment of the present invention;
FIG. 2 is a graph of wind, photovoltaic and load forecast power in an example of the invention;
FIG. 3a is a result of optimized scheduling of electric energy supply and demand in a park according to an embodiment of the present invention;
FIG. 3b is the result of the optimized scheduling of natural gas supply and demand for a campus in an example of the present invention;
FIG. 3c is the result of the optimized scheduling of heat energy supply and demand for a campus in accordance with an embodiment of the present invention;
FIG. 3d is the result of scheduling optimization of energy flow for cooling energy supply and demand in the campus according to the embodiment of the present invention.
Detailed Description
The invention provides a stochastic robust optimization operation method of an integrated energy system based on integrated demand response, which is described in detail below with reference to embodiments and drawings.
The invention relates to a comprehensive energy system random robust optimization operation method based on comprehensive demand response, which comprises the following steps:
1) considering response characteristics of fixed loads, adjustable loads, transfer loads and alternative loads in the comprehensive energy system, and establishing a user-side comprehensive demand response model of the comprehensive energy system; wherein the content of the first and second substances,
(1) the response characteristics of the fixed load, the adjustable load, the transfer load and the alternative load in the comprehensive energy system are as follows:
(1.1) fixed type load
Fixed-type loads do not normally participate in demand response, but are forced to shed load Δ L when the system is in an accident or emergency situationelsSo as to ensure the safety of the system;
(1.2) Regulation type load
In the formula (I), the compound is shown in the specification,
l is the load before and after response respectively; Δ Ladj、Ladj,maxRespectively is the variable quantity and the upper limit of the variable quantity of the adjustable load;
(1.3) transfer type load
In the formula,. DELTA.Lsft、Lsft,maxRespectively representing the variable quantity and the upper limit of the change of the transfer type load; e is an energy price elastic matrix; delta p is an energy price change rate matrix;
(1.4) alternative Loading
In the formula,. DELTA.Lrpl、Lrpl,maxRespectively representing the variation of the alternative load and the variation upper limit; k is an energy substitution conversion matrix.
(2) The user side comprehensive demand response model of the comprehensive energy system is as follows:
in the formula,. DELTA.LDRResponding to the total variation for the load demand; Δ LelsThe load is a fixed load emergency cutting amount.
2) Considering external energy input of a park in the comprehensive energy system and multi-type energy production, conversion and storage equipment in the park, establishing a park side comprehensive demand response model of the comprehensive energy system, and forming a comprehensive demand response model of the comprehensive energy system together with the user side comprehensive demand response model of the comprehensive energy system established in the step 1); wherein the content of the first and second substances,
(1) the park side comprehensive demand response model of the comprehensive energy system is as follows:
wherein L is the load after response; pin、Pde、Ptr、PsRespectively an energy input matrix outside the park and an energy production, conversion and storage variable matrix inside the park; cin、Cde、Ctr、CsRespectively inputting a coupling coefficient matrix for energy outside the park and producing, converting and storing the coupling coefficient matrix for energy in the park; etat、ηheThe conversion efficiency of the transformer and the heat exchanger respectively;
the gas-to-electricity and gas-to-heat efficiency of the micro-combustion engine are respectively; hngIs natural gas with low heat value; etaec、ηacThe refrigeration coefficients of the electric refrigerator and the absorption refrigerator are respectively; pb、Gb、HbThe electricity purchasing quantity, the gas purchasing quantity and the heat purchasing quantity of the system are respectively; ppv、PwtRespectively photovoltaic power output and wind power output; gmtThe consumption of natural gas of the micro-combustion engine; pecConsuming power for the electric refrigerator; hacIs the heat consumption power of the absorption refrigerator; pes,c、Pes,dRespectively charging and discharging energy of the electric energy storage; pgs,c、Pgs,dRespectively storing the charging power and the discharging power of the gas energy; phs,c、Phs,dRespectively charging and discharging energy power for heat energy storage; pcs,c、Pcs,dRespectively the charging and discharging power of the cold energy storage.
(2) The comprehensive demand response model of the comprehensive energy system is as follows:
L＝CP
the concrete expression is as follows:
wherein L is the load after response; p, C are the comprehensive energy matrix and the comprehensive coupling coefficient matrix of the comprehensive energy system respectively; l is the load before response; Δ Ladj、ΔLsft、ΔLrplAnd Δ LelsRespectively is an adjusting load variable quantity, a transferring load variable quantity, a replacing load variable quantity and a fixed load emergency cutting quantity; cin、Cde、Ctr、CsRespectively inputting a coupling coefficient matrix for energy outside the park and producing, converting and storing the coupling coefficient matrix for energy in the park; pin、Pde、Ptr、PsRespectively, an energy input matrix outside the park and an energy production, conversion and storage related variable matrix inside the park.
3) Respectively adopting random optimization and robust optimization methods to establish a load random uncertain model and an air-light robust uncertain model, combining with the comprehensive demand response model of the comprehensive energy system in the step 2), jointly forming a comprehensive energy system random robust optimization operation model based on comprehensive demand response, and adopting a mixed integer linear programming method to solve the comprehensive energy system random robust optimization operation model based on comprehensive demand response; wherein the content of the first and second substances,
(1) the load random uncertainty model is as follows:
in the formula,. DELTA.PLFor load power prediction error, obeying a normal distribution, f (Δ P)L) Is DeltaPLWith a mean of 0 and a standard deviation of σL(ii) a e. Pi is a mathematical constant;
(2) the wind-solar robust uncertain model is as follows:
ΔPDG∈[-λ1ΔPDG,h,λ2ΔPDG,h]
wherein, Δ PDGPredicting an error for wind and light output; lambda [ alpha ]1、λ2Is a robust adjustable coefficient, and is more than or equal to 0 and less than or equal to lambda1,λ2≤1；ΔPDG,hIs DeltaPDGMaximum offset of power.
(3) The comprehensive energy system random robust optimization operation model based on the comprehensive demand response takes the minimum comprehensive operation cost of the comprehensive energy system as an objective function and takes robust constraint, energy balance constraint, energy purchasing constraint, energy equipment constraint, demand response constraint and energy utilization satisfaction constraint as constraint conditions. Wherein the content of the first and second substances,
the expression of the objective function in (3.1) is as follows:
in the formula, CallThe comprehensive operation cost of the comprehensive energy system is obtained; u and w are respectively a system decision variable and a wind-light uncertain variable, and U, W is respectively a set of the system decision variable and the wind-light uncertain variable; s, PrsTypical scenarios and probabilities of the load, respectively; call,sFor the comprehensive operation cost of the system under the scene s, including the energy purchasing cost CbuyAnd operation and maintenance cost ComAnd environmental protection cost CepAnd a demand response cost CdrWherein:
(3.1.1) energy purchase cost Cbuy
In the formula, T is a scheduling period of 24 h;
and
respectively representing the electricity purchasing quantity, the gas purchasing quantity and the heat purchasing quantity in the t period;
and
respectively the electricity, gas and heat purchase prices in the time period t;
(3.1.2) operation and maintenance cost Com
In the formula, N is the total number of the operation and maintenance equipment; p is a radical ofom,nThe unit power operation and maintenance cost of the equipment n;
operating power for device n during time t;
(3.1.3) cost for environmental protection Cep
In the formula, alpha is unit CO2The cost of treatment of; beta is ap、βgAnd betahRespectively obtaining equivalent carbon emission coefficients of electricity purchase, gas purchase and heat purchase in the park;
(3.1.4) demand response cost Cdr
Cdr＝pelsPels+psftPsft+pcapPcap+padjPadj
In the formula, Pels、pelsRespectively representing the load emergency cutting-off amount and unit punishment cost; psft、psftRespectively the total load change amount after the transfer type response and the unit compensation cost; p is a radical ofcap、padjCapacity price and energy price, respectively; pcap、PadjRespectively reserving a responsive load amount and an actual responsive load amount.
(3.2) the constraint is specifically expressed as:
(3.2.1) robust constraint
∑w≤
In the formula (I), the compound is shown in the specification,wrobust parameters which are uncertain variables w; an adjustable upper limit for robustness;
(3.2.2) energy balance constraints
L＝CP
Wherein L is the load after response; p, C are the comprehensive energy matrix and the comprehensive coupling coefficient matrix of the comprehensive energy system respectively;
(3.2.3) energy purchase constraint
In the formula (I), the compound is shown in the specification,
respectively the electricity, gas and heat of the system at the time t; pb,max、Gb,max、Hb,maxRespectively the upper limit of electricity purchase, gas purchase and heat purchase of the system;
(3.2.4) energy conversion device constraints
In the formula (I), the compound is shown in the specification,
respectively representing the output power of photovoltaic and wind power at the time interval t;
the output powers of the transformer, the heat exchanger and the micro-combustion engine in the time period t are respectively;
the consumed power of the electric refrigerator is t time period;
the heat consumption power of the absorption refrigerator is t time period; ppv,max、Pwt,max、Pt,max、Hhe,max、Pmt,max、Pec,maxAnd Hac,maxThe power upper limits of the photovoltaic, wind power, the transformer, the heat exchanger, the micro-combustion engine, the electric refrigerator and the absorption refrigerator are respectively set; subscript x represents the energy storage type;
energy charging and discharging power and energy storage states for storing energy at the time t respectively;
respectively in the initial energy storage state and the final energy storage state of the energy storage equipment; px,c,max、Px,d,maxRespectively the maximum charging and discharging energy power of the energy storage equipment; ex,min、Ex,maxMinimum and maximum states of the energy storage device, respectively; z is a radical ofx,c、zx,dRespectively a charge state and a discharge state are 0-1 variables;
(3.2.5) demand response constraints
In the formula,. DELTA.Ladj、Ladj,maxRespectively is the variable quantity and the upper limit of the variable quantity of the adjustable load; Δ Lsft、Lsft,maxRespectively representing the variable quantity and the upper limit of the change of the transfer type load; Δ Lrpl、Lrpl,maxRespectively representing the variation of the alternative load and the variation upper limit; Δ Lels、Lepl,maxRespectively fixed load emergency cutting-off amount and cutting-off upper limit;
(3.2.6) constraint with satisfaction
1≥ICSI≥ICSImin
In the formula, ICSI and ICSIminRespectively the comprehensive energy satisfaction and the minimum value of the user; t is a scheduling period of 24 h;
e, h, c, g are the load of the energy type i in t periods before and after response respectivelyRepresenting electricity, heat, cold, gas.
(4) The hybrid integer linear programming method is used for solving the comprehensive energy system random robust optimization operation model based on the comprehensive demand response, aiming at the established comprehensive energy system random robust optimization operation model based on the comprehensive demand response, and the Yalmip tool box is used for calling a Gurobi solver to carry out model solution based on the hybrid integer linear programming method to obtain the optimization operation scheme of the comprehensive energy system.
Specific examples are given below.
The simulation analysis is carried out on the basis of the typical comprehensive energy park example, and the specific structure is shown in figure 1. Wind power and photovoltaic adopt an MPPT mode, energy load and wind-solar output prediction curves are shown in fig. 2, energy prices are shown in table 1, a wind-solar output fluctuation interval is +/-20%, a robust parameter is set to be 1.75, load power prediction errors obey normal distribution, the mean value is 0, and a standard deviation is 0.05 times of a predicted value.
TABLE 1 energy purchase price
To verify the effectiveness of the comprehensive demand response strategy proposed by the present invention, the effects of different demand response strategies are explored in the following 4 scenarios, as shown in table 2. The daily integrated operating costs of the parks in different scenarios are shown in table 3.
TABLE 2 scene Classification
TABLE 3 comprehensive daily operating costs of parks in different scenarios
By analyzing and comparing the operation costs in different scenes in the table 3, compared with the scenario 1 in which only the DR on the campus side is considered, the daily comprehensive operation cost of the campus can be further reduced by introducing the DR on the user side in the scenes 2, 3 and 4, and the operation cost in the scenario 4 in which the multi-type comprehensive demand response strategy is considered is the lowest, namely 10879 yuan, and 646 yuan is reduced compared with the scenario 1, so that the effectiveness of the method is proved. The user side can give full play to the optimal regulation and control potential of the user side to the system by carrying out interactive response on the given price/excitation signal, so that the energy supply pressure is relieved to a certain extent, and the operation cost is reduced.
Specific analysis is carried out for the scene 4, and the optimal scheduling result of the electricity, gas, heat and cold energy flows in the park is shown in fig. 3. Energy input is shown above the horizontal axis and energy output is shown below the horizontal axis.
As can be seen from fig. 3a, in the time period of low electricity price, the park preferentially selects the power grid to purchase electricity to meet the electric energy demand, and the insufficient part is supplied by the wind power and the micro-combustion engine; in a time period with higher electricity price, the output of the micro-combustion engine is increased while the electricity purchasing of the power grid is reduced, and the shortage is partially met by photovoltaic power, wind power and stored electricity. As can be seen from fig. 3b, in the time period of low electricity price, the output of the micro-combustion engine is low, and the gas purchase from the gas network in the garden mainly meets the gas load of the user; and in a period of higher electricity price, the gas consumption of the micro-combustion engine is increased, and the gas purchasing quantity of the gas network is increased along with the increase of the gas consumption. As can be seen from fig. 3c, the thermal demand of the whole day park is mainly supplied by the heat supply network, the heat consumption of the absorption refrigerator is increased in the time period of higher electricity price, and the thermal output of the micro-combustion engine is increased to meet the thermal demand as the electrical output of the micro-combustion engine is increased; as can be seen from fig. 3d, during the time period when the electricity prices are low, the cooling load is mainly satisfied by the electric refrigerator, and the deficiency is supplied by the absorption refrigerator, and as the electricity prices rise, the absorption refrigerator increases in output to satisfy most of the demand of the cooling load, and the deficiency is supplied by the electric refrigerator and the cold storage. In conclusion, the park can reasonably make a scheduling plan according to the changes of electricity, gas, heat and cold loads and energy purchase prices, reasonably considers the economy, reliability and user energy consumption satisfaction of the system, further reduces the comprehensive operation cost of the park, promotes energy supply and demand balance, and proves the correctness and effectiveness of the comprehensive demand response-based random robust optimization operation method of the comprehensive energy system.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are also within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A comprehensive energy system random robust optimization operation method based on comprehensive demand response is characterized by comprising the following steps:
1) considering response characteristics of fixed loads, adjustable loads, transfer loads and alternative loads in the comprehensive energy system, and establishing a user-side comprehensive demand response model of the comprehensive energy system;
2) considering external energy input of a park in the comprehensive energy system and multi-type energy production, conversion and storage equipment in the park, establishing a park side comprehensive demand response model of the comprehensive energy system, and forming a comprehensive demand response model of the comprehensive energy system together with the user side comprehensive demand response model of the comprehensive energy system established in the step 1);
3) respectively adopting a random optimization method and a robust optimization method to establish a load random uncertain model and an air-light robust uncertain model, combining with the comprehensive demand response model of the comprehensive energy system in the step 2), jointly forming a comprehensive energy system random robust optimization operation model based on comprehensive demand response, and adopting a mixed integer linear programming method to solve the comprehensive energy system random robust optimization operation model based on comprehensive demand response.
2. The integrated energy system stochastic robust optimization operation method based on integrated demand response according to claim 1, wherein the response characteristics of the fixed load, the adjustable load, the shifting load and the alternative load in the integrated energy system of step 1) are as follows:
(1) fixed type load
Fixed-type loads do not normally participate in demand response, but are forced to shed load Δ L when the system is in an accident or emergency situationelsSo as to ensure the safety of the system;
(2) regulated load
In the formula (I), the compound is shown in the specification,
l is the load before and after response respectively; Δ Ladj、Ladj,maxRespectively is the variable quantity and the upper limit of the variable quantity of the adjustable load;
(3) transfer type load
In the formula,. DELTA.Lsft、Lsft,maxRespectively representing the variable quantity and the upper limit of the change of the transfer type load; e is an energy price elastic matrix; delta p is an energy price change rate matrix;
(4) replacement type load
In the formula,. DELTA.Lrpl、Lrpl,maxRespectively representing the variation of the alternative load and the variation upper limit; k is an energy substitution conversion matrix.
3. The integrated energy system stochastic robust optimization operation method based on integrated demand response according to claim 1, wherein the user-side integrated demand response model of the integrated energy system in step 1) is as follows:
in the formula,. DELTA.LDRResponding to the total variation for the load demand; Δ LelsThe load is a fixed load emergency cutting amount.
4. The integrated energy system stochastic robust optimization operation method based on integrated demand response according to claim 1, wherein the park-side integrated demand response model of the integrated energy system in the step 2) is as follows:
wherein L is the load after response; pin、Pde、Ptr、PsRespectively an energy input matrix outside the park and an energy production, conversion and storage variable matrix inside the park; cin、Cde、Ctr、CsRespectively inputting a coupling coefficient matrix for energy outside the park and producing, converting and storing the coupling coefficient matrix for energy in the park; etat、ηheThe conversion efficiency of the transformer and the heat exchanger respectively;
the gas-to-electricity and gas-to-heat efficiency of the micro-combustion engine are respectively; hngIs natural gas with low heatA value; etaec、ηacThe refrigeration coefficients of the electric refrigerator and the absorption refrigerator are respectively; pb、Gb、HbThe electricity purchasing quantity, the gas purchasing quantity and the heat purchasing quantity of the system are respectively; ppv、PwtRespectively photovoltaic power output and wind power output; gmtThe consumption of natural gas of the micro-combustion engine; pecConsuming power for the electric refrigerator; hacIs the heat consumption power of the absorption refrigerator; pes,c、Pes,dRespectively charging and discharging energy of the electric energy storage; pgs,c、Pgs,dRespectively storing the charging power and the discharging power of the gas energy; phs,c、Phs,dRespectively charging and discharging energy power for heat energy storage; pcs,c、Pcs,dRespectively the charging and discharging power of the cold energy storage.
5. The integrated energy system stochastic robust optimization operation method based on integrated demand response of claim 1, wherein the integrated demand response model of the integrated energy system of step 2) is as follows:
L＝CP
the concrete expression is as follows:
wherein L is the load after response; p, C are the comprehensive energy matrix and the comprehensive coupling coefficient matrix of the comprehensive energy system respectively; l is the load before response; Δ Ladj、ΔLsft、ΔLrplAnd Δ LelsRespectively is an adjusting load variable quantity, a transferring load variable quantity, a replacing load variable quantity and a fixed load emergency cutting quantity; cin、Cde、Ctr、CsRespectively inputting a coupling coefficient matrix for energy outside the park and producing, converting and storing the coupling coefficient matrix for energy in the park; pin、Pde、Ptr、PsRespectively, an energy input matrix outside the park and an energy production, conversion and storage related variable matrix inside the park.
6. The integrated energy system stochastic robust optimization operation method based on integrated demand response of claim 1, wherein the load stochastic uncertainty model and the wind-solar robust uncertainty model of step 3) are as follows:
(1) random uncertainty model of load
In the formula,. DELTA.PLFor load power prediction error, obeying a normal distribution, f (Δ P)L) Is DeltaPLWith a mean of 0 and a standard deviation of σL(ii) a e. Pi is a mathematical constant;
(2) wind-solar robust uncertain model
ΔPDG∈[-λ1ΔPDG,h,λ2ΔPDG,h]
Wherein, Δ PDGPredicting an error for wind and light output; lambda [ alpha ]1、λ2Is a robust adjustable coefficient, and is more than or equal to 0 and less than or equal to lambda1,λ2≤1；ΔPDG,hIs DeltaPDGMaximum offset of power.
7. The integrated energy system random robust optimization operation method based on integrated demand response according to claim 1, wherein the integrated energy system random robust optimization operation model based on integrated demand response in step 3) takes the minimum integrated operation cost of the integrated energy system as an objective function, and takes robust constraint, energy balance constraint, energy purchase constraint, energy equipment constraint, demand response constraint and energy consumption satisfaction constraint as constraint conditions.
8. The integrated energy system stochastic robust optimization operation method based on integrated demand response of claim 7, wherein the objective function expression is as follows:
in the formula, CallThe comprehensive operation cost of the comprehensive energy system is obtained; u and w are respectively a system decision variable and a wind-light uncertain variable, and U, W is respectively a set of the system decision variable and the wind-light uncertain variable; s, PrsTypical scenarios and probabilities of the load, respectively; call,sFor the comprehensive operation cost of the system under the scene s, including the energy purchasing cost CbuyAnd operation and maintenance cost ComAnd environmental protection cost CepAnd a demand response cost CdrWherein:
(1) energy purchase cost Cbuy
In the formula, T is a scheduling period of 24 h;
and
respectively representing the electricity purchasing quantity, the gas purchasing quantity and the heat purchasing quantity in the t period;
and
respectively the electricity, gas and heat purchase prices in the time period t;
(2) operation and maintenance cost Com
In the formula, N is the total number of the operation and maintenance equipment; p is a radical ofom,nFor unit power operation of the apparatus nMaintenance cost;
operating power for device n during time t;
(3) environmental protection cost Cep
In the formula, alpha is unit CO2The cost of treatment of; beta is ap、βgAnd betahRespectively obtaining equivalent carbon emission coefficients of electricity purchase, gas purchase and heat purchase in the park;
(4) cost of demand response Cdr
Cdr＝pelsPels+psftPsft+pcapPcap+padjPadj
In the formula, Pels、pelsRespectively representing the load emergency cutting-off amount and unit punishment cost; psft、psftRespectively the total load change amount after the transfer type response and the unit compensation cost; p is a radical ofcap、padjCapacity price and energy price, respectively; pcap、PadjRespectively reserving a responsive load amount and an actual responsive load amount.
9. The integrated energy system stochastic robust optimization operation method based on integrated demand response of claim 7, wherein the constraint condition is specifically expressed as:
(1) robust constraints
∑w≤
In the formula (I), the compound is shown in the specification,wrobust parameters which are uncertain variables w; an adjustable upper limit for robustness;
(2) energy balance constraint
L＝CP
Wherein L is the load after response; p, C are the comprehensive energy matrix and the comprehensive coupling coefficient matrix of the comprehensive energy system respectively;
(3) energy purchase restraint
In the formula (I), the compound is shown in the specification,
respectively the electricity, gas and heat of the system at the time t; pb,max、Gb,max、Hb,maxRespectively the upper limit of electricity purchase, gas purchase and heat purchase of the system;
(4) energy conversion equipment restraint
In the formula (I), the compound is shown in the specification,
respectively representing the output power of photovoltaic and wind power at the time interval t; pt t、
The output powers of the transformer, the heat exchanger and the micro-combustion engine in the time period t are respectively;
the consumed power of the electric refrigerator is t time period;
the heat consumption power of the absorption refrigerator is t time period; ppv,max、Pwt,max、Pt,max、Hhe,max、Pmt,max、Pec,maxAnd Hac,maxThe power upper limits of the photovoltaic, wind power, the transformer, the heat exchanger, the micro-combustion engine, the electric refrigerator and the absorption refrigerator are respectively set; subscript x represents the energy storage type;
energy charging and discharging power and energy storage states for storing energy at the time t respectively;
respectively in the initial energy storage state and the final energy storage state of the energy storage equipment; px,c,max、Px,d,maxRespectively the maximum charging and discharging energy power of the energy storage equipment; ex,min、Ex,maxMinimum and maximum states of the energy storage device, respectively; z is a radical ofx,c、zx,dRespectively a charge state and a discharge state are 0-1 variables;
(5) demand response constraints
In the formula,. DELTA.Ladj、Ladj,maxRespectively is the variable quantity and the upper limit of the variable quantity of the adjustable load; Δ Lsft、Lsft,maxRespectively representing the variable quantity and the upper limit of the change of the transfer type load; Δ Lrpl、Lrpl,maxRespectively representing the variation of the alternative load and the variation upper limit; Δ Lels、Lepl,maxRespectively fixed load emergency cutting-off amount and cutting-off upper limit;
(6) constrained by energy satisfaction
1≥ICSI≥ICSImin
In the formula, ICSI and ICSIminRespectively the comprehensive energy satisfaction and the minimum value of the user; t is a scheduling period of 24 h;
the load of the energy type i before and after the response time t respectively, and e, h, c and g respectively represent electricity, heat, cold and gas.
10. The comprehensive energy system random robust optimization operation method based on comprehensive demand response according to claim 1, wherein the step 3) of solving the comprehensive energy system random robust optimization operation model based on comprehensive demand response by using a mixed integer linear programming method is to obtain the optimized operation scheme of the comprehensive energy system by calling a Gurobi solver to perform model solution through a Yalmip toolbox based on the established comprehensive energy system random robust optimization operation model based on comprehensive demand response.
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