CN114529056A - Method and device for optimizing operation of integrated energy system and readable storage medium - Google Patents

Method and device for optimizing operation of integrated energy system and readable storage medium Download PDF

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CN114529056A
CN114529056A CN202210072747.2A CN202210072747A CN114529056A CN 114529056 A CN114529056 A CN 114529056A CN 202210072747 A CN202210072747 A CN 202210072747A CN 114529056 A CN114529056 A CN 114529056A
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胡志坚
李天格
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Abstract

The invention provides a method and equipment for optimizing operation of an integrated energy system and a readable storage medium. The method comprises the following steps: carrying out mathematical modeling on an IES coupling device and a stepped carbon cost metering model which comprise adjustable thermoelectric ratio and natural gas-hydrogen mixed combustion characteristics and various integrated energy systems; classifying according to the IES element characteristics, classifying demand response resources, and making an optimization plan under multiple time scales; establishing a coordination optimization model aiming at rolling in day-ahead and day-in-day and real-time three-stage multi-time scale, adopting introduced auxiliary variables and Big-M to carry out model linearization transformation in view of the mixed integer nonlinear property of the model, obtaining a mixed integer linear model, and calling a mathematical solver to solve. The invention realizes the optimization of the operation of the comprehensive energy system.

Description

Method and device for optimizing operation of integrated energy system and readable storage medium
Technical Field
The invention relates to the technical field of energy, in particular to a method and equipment for optimizing operation of a comprehensive energy system and a readable storage medium.
Background
With the revolution of energy forms and the pursuit of low carbon vision, the role of Integrated Energy Systems (IES) is increasingly highlighted. The IES can carry conversion and supply of various energy forms and play a role in coordinating and optimizing operation as a management unit. Along with the development of the energy market and the carbon trading market and the progress of the intelligent control technology, new requirements are put forward on an IES operation method.
The research of the traditional IES operation method is mainly served in the planning research of the IES, the model and the solving algorithm are simple, and the situation of large time span such as the day-ahead stage is mainly considered on the time scale, and the actual operation problem of the IES cannot be specifically guided. In addition, in the face of uncertain factors in IES actual operation, the traditional method mainly uses means such as robust optimization, opportunity constraint, random optimization and the like to carry out modeling solution, cannot better cope with the ever-changing actual situation, and has a certain contrast between theoretical effect and actual effect.
The multi-time scale optimization method is initially applied to a power system, and is gradually popularized to the scheduling problem of IES in recent years. The multi-time scale optimization method is based on the flexibility difference of system components, and working schemes on different time scale levels are formulated, so that the multi-time scale optimization method plays a role in refining guidance and overcomes the defects of practice levels brought by traditional robust optimization and other methods to a certain extent.
In addition, with the advocation of low carbon targets in recent years, the "carbon" index is considered more and more in the research of the operation method of the IES, and the most typical method at present includes carbon emission amount constraint conditioning, carbon emission penalty economic targeting and the like, and for the carbon price making method of the carbon emission economic target, a single carbon price method and a competitive price method are mainly used, while the former has no distinguishing effect on users with different carbon emission levels, and the latter is not suitable for market participants with no scale to participate in competition and is complex in practical application. In addition, the demand response mechanism is used as a regulation and control mode of a demand side, in the current research of various energy forms of the IES, loads and demand responses thereof in traditional forms such as electric heat and cold are mostly considered, and the situation of hydrogen energy with great application potential is rarely considered.
In summary, the existing IES operation optimization methods have various disadvantages, or less simultaneous consideration of multi-time scale adjustment, low-carbon targets and demand response, or no hydrogen energy utilization scenario is involved, or the established low-carbon targets are too single or not suitable for non-scale competitive subjects. Based on this, a new method for optimizing the operation of the integrated energy system is needed.
Disclosure of Invention
In order to solve the technical problems, the invention provides a method and equipment for optimizing the operation of an integrated energy system and a readable storage medium.
In a first aspect, the present invention provides a method for optimizing operation of an integrated energy system, where the method for optimizing operation of an integrated energy system includes:
step 1: carrying out mathematical modeling on an IES coupling device and a stepped carbon cost metering model which comprise adjustable thermoelectric ratio and natural gas-hydrogen mixed combustion characteristics and various integrated energy systems;
step 2: classifying according to the IES element characteristics, classifying demand response resources, and making an optimization plan under multiple time scales;
and step 3: establishing a coordination optimization model aiming at rolling in day-ahead and day-in-day and real-time three-stage multi-time scale, adopting introduced auxiliary variables and Big-M to carry out model linearization transformation in view of the mixed integer nonlinear property of the model, obtaining a mixed integer linear model, and calling a mathematical solver to solve.
In a second aspect, the present invention further provides an integrated energy system operation optimization device, which includes a processor, a memory, and an integrated energy system operation optimization program stored on the memory and executable by the processor, wherein the integrated energy system operation optimization program, when executed by the processor, implements the steps of the integrated energy system operation optimization method described above.
In a third aspect, the present invention further provides a readable storage medium, on which an integrated energy system operation optimization program is stored, wherein when the integrated energy system operation optimization program is executed by a processor, the steps of the integrated energy system operation optimization method are implemented.
In the invention, mathematical modeling is carried out on a model which comprises adjustable thermoelectric ratio, natural gas-hydrogen mixed combustion characteristics, various integrated energy system IES coupling equipment and a stepped carbon cost metering model; classifying according to the IES element characteristics, classifying demand response resources, and making an optimization plan under multiple time scales; establishing a coordination optimization model aiming at rolling in day-ahead and day-in-day and real-time three-stage multi-time scale, adopting introduced auxiliary variables and Big-M to carry out model linearization transformation in view of the mixed integer nonlinear property of the model, obtaining a mixed integer linear model, and calling a mathematical solver to solve. In the invention, a low-carbon target is considered in an economic target of an IES operation model, namely, the actual carbon emission and the virtual carbon emission in the IES operation process are calculated and the carbon emission cost is collected, so that the effect of controlling the carbon emission from the economic aspect is achieved; under the condition of considering the low-carbon target, a step-type carbon emission cost mechanism is adopted, namely, the interval progressive price is charged for the total carbon emission amount of the IES in a unit period, and compared with the traditional price or a single carbon emission price or a competitive equilibrium pricing mode, the method optimizes the characteristic that the single carbon emission price is difficult to distinguish users with different carbon emission levels, and overcomes the defects that the competitive equilibrium pricing mode in practical application is not suitable for market participants without scale to participate in competition and is complex to a certain extent; the invention applies a three-stage multi-time scale coordination optimization method of rolling-real-time optimization in day-ahead-in-day to the preparation of the IES operation strategy, and the method prepares working schemes on different time scale levels based on the flexibility difference of IES components, thereby not only playing a role of refining guidance, but also overcoming the defects of practice level brought by the traditional robust optimization and other methods to a certain extent; the invention introduces a demand response mechanism into the manufacture of an IES operation strategy, and participates in the operation optimization of the IES from the side surface of the demand, thereby not only improving the flexible regulation capability of the system, but also facing the regulation and control prospect of the intelligent terminal of the energy user in the future scene; the invention introduces the utilization situation of emerging hydrogen energy in IES, decouples the traditional P2G flow, expands the application scene of hydrogen energy, and supplements the deficiency of the traditional IES optimization strategy on considering hydrogen energy; the invention optimizes the working characteristics of IES coupling equipment, introduces GT equipment with adjustable thermoelectric ratio and GT and GB equipment with natural gas-hydrogen mixed combustion, expands the new application scene of IES, and demonstrates the superiority of the optimization method of the invention in IES application under the new working characteristics of the IES coupling equipment.
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Fig. 1 is a schematic diagram of a hardware structure of an integrated energy system operation optimization device according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of an embodiment of the method for optimizing the operation of the integrated energy system according to the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In a first aspect, an embodiment of the present invention provides an integrated energy system operation optimization apparatus, which may be a Personal Computer (PC), a notebook computer, a server, or other apparatuses having a data processing function.
Referring to fig. 1, fig. 1 is a schematic diagram of a hardware structure of an integrated energy system operation optimization device according to an embodiment of the present invention. In an embodiment of the present invention, the integrated energy system operation optimizing device may include a processor 1001 (e.g., a Central Processing Unit, CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used for realizing connection communication among the components; the user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard); the network interface 1004 may optionally include a standard wired interface, a WIreless interface (e.g., a WI-FI interface, WI-FI interface); the memory 1005 may be a Random Access Memory (RAM) or a non-volatile memory (non-volatile memory), such as a magnetic disk memory, and the memory 1005 may optionally be a storage device independent of the processor 1001. Those skilled in the art will appreciate that the hardware configuration depicted in FIG. 1 is not intended to be limiting of the present invention, and may include more or less components than those shown, or some components in combination, or a different arrangement of components.
With continued reference to fig. 1, the memory 1005 of fig. 1, which is one type of computer storage medium, may include an operating system, a network communication module, a user interface module, and an integrated energy system operation optimization program. The processor 1001 may call the integrated energy system operation optimization program stored in the memory 1005, and execute the integrated energy system operation optimization method according to the embodiment of the present invention.
In a second aspect, an embodiment of the present invention provides a method for optimizing operation of an integrated energy system.
In an embodiment, referring to fig. 2, fig. 2 is a schematic flow chart of an embodiment of the method for optimizing the operation of the integrated energy system according to the present invention. As shown in fig. 2, the method for optimizing the operation of the integrated energy system includes:
step 1: carrying out mathematical modeling on an IES coupling device and a stepped carbon cost metering model which comprise adjustable thermoelectric ratio and natural gas-hydrogen mixed combustion characteristics and various integrated energy systems;
in one embodiment, step 1 comprises:
step 1.1: introducing an adjustable thermoelectric ratio and natural gas-hydrogen mixed combustion characteristic, improving the energy conversion process of a gas turbine GT and a gas boiler GB, and adjusting the heating and generating power ratio in the working process according to the electricity price and the gas price at different time periods;
step 1.2: the hydrogen obtained by water electrolysis and carbon dioxide are directly decoupled through a series of processes of synthesizing natural gas by a methane reactor MR by an electrolytic cell EL in the process of converting electricity into gas P2G, so that the hydrogen energy utilization scene is enlarged;
in the step 1.2, a process of decoupling a traditional electric gas conversion (P2G) link is introduced, and a series of processes that hydrogen obtained by water electrolysis and carbon dioxide are directly synthesized into natural gas through a Methane Reactor (MR) by an electrolytic cell (EL) in the traditional P2G process are decoupled, so that a hydrogen energy utilization scene is enlarged.
Step 1.3: performing mathematical modeling on coupling equipment in IES, wherein the coupling equipment comprises EL, MR, GT, GB and an electric boiler EB;
the mathematical model of the EL is:
Figure BDA0003482867110000051
wherein, Pe,EL(t) inputting the electric energy of the EL at the time t; pH2,EL(t) outputting the hydrogen energy of the EL at the time t; etaELTo EL energy conversion efficiency;
Figure BDA0003482867110000052
the upper and lower limits of the input power of the EL are respectively;
Figure BDA0003482867110000053
the upper limit and the lower limit of the climbing of the EL are respectively;
the mathematical model of the MR is as follows:
Figure BDA0003482867110000054
wherein, PH2,MR(t) inputting hydrogen energy of the MR at the time t; pg,MR(t) natural gas outputting MR at time t; etaMRThe energy conversion efficiency of hydrogen to methane in the MR is obtained;
Figure BDA0003482867110000055
the upper and lower limits of the input power of the MR are respectively;
Figure BDA0003482867110000056
the upper limit and the lower limit of the climbing of the MR are respectively; pe,MR(t) the MR synthesis reaction consumes electric energy at the moment t; etaMR,eIs the power consumption proportion of the MR synthesis reaction;
the mathematical model of the GT is as follows:
Figure BDA0003482867110000061
wherein, Pe,GT(t)、Ph,GT(t) outputting the electric energy and the heat energy of the GT at the time t respectively; pmg,GT(t) inputting the natural gas-hydrogen mixed gas of GT at the time t; etaGTGT energy conversion efficiency;
Figure BDA0003482867110000062
upper and lower limits of the input power of the GT respectively;
Figure BDA0003482867110000063
the upper and lower limits of the climbing of the GT respectively;
Figure BDA0003482867110000064
Figure BDA0003482867110000065
the upper limit and the lower limit of the thermoelectric ratio of the GT are respectively; pH2,GT(t)、Pg,GT(t) the amounts of hydrogen and natural gas in the natural gas-hydrogen mixed gas input into the GT at time t respectively;
Figure BDA0003482867110000066
is the lowest proportion of the natural gas content in the gas input into the GT;
the mathematical model of GB is:
Figure BDA0003482867110000067
wherein, Pmg,GB(t) inputting the amount of GB natural gas-hydrogen mixed gas at the moment t; ph,GB(t) outputting heat energy of GB at the time t; etaGBGB energy conversion efficiency;
Figure BDA0003482867110000068
the upper and lower limits of input power of GB respectively;
Figure BDA0003482867110000069
the upper limit and the lower limit of the climbing slope of GB respectively; pH2,GB(t)、Pg,GB(t) the amounts of hydrogen and natural gas in the GB natural gas-hydrogen mixed gas are respectively input at the moment t;
Figure BDA00034828671100000610
the minimum proportion of the natural gas content in the gas input into GB;
the mathematical model of the EB is as follows:
Figure BDA00034828671100000611
wherein, Pe,EB(t) inputting the electric energy of the EB at the time t; ph,EL(t) outputting heat energy of EB at the time t; etaEBEB energy conversion efficiency;
Figure BDA00034828671100000612
the upper and lower limits of the input power of EB respectively;
Figure BDA00034828671100000613
respectively the upper limit and the lower limit of the climbing of the EB;
step 1.4: introducing a step-type carbon cost model and a carbon fixation patch model, wherein the step-type carbon cost model has the mathematical model as follows:
Figure BDA0003482867110000071
Figure BDA0003482867110000072
Figure BDA0003482867110000073
Figure BDA0003482867110000074
wherein the content of the first and second substances,
Figure BDA0003482867110000075
the carbon emission tax of the upper-level electricity purchasing and gas purchasing and the sum of the two are respectively;Ee,buy,a、Eg,buy,athe carbon-containing emission amount of the power and gas purchase is purchased for the upper level; chi-type food processing machinee、χgCarbon emissions per unit electricity consumption, per unit natural gas consumption, respectively; pe,buy(t)、Pg,buy(t) respectively represents the power purchase and gas purchase of the upper stage at the time t; t is an optimization period; lambda [ alpha ]e、λgThe carbon cost base price of the electric power and the natural gas are respectively; le、lgThe lengths of the carbon intervals for electric power and natural gas step-type tax calculation are respectively; alpha is the price increase amplitude;
the mathematical model of the carbon fixation patch model is as follows:
Figure BDA0003482867110000076
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003482867110000077
carbon sequestration benefit for the MR device; lambdasubThe carbon fixation amount is the unit subsidy fee; chi shapesubThe fixed carbon amount for the unit natural gas production; pg,MR(t) is the MR output power at time t.
In step 1.4, in order to embody the idea of low-carbon operation of the IES and promote energy conservation and emission reduction, a stepped carbon cost model and a carbon sequestration model are introduced in this embodiment. The sources of carbon emissions in IES are mainly reflected in two aspects: the first is the upper electricity and gas purchasing, and the second is the carbon emission in the coupling unit. The use of electric energy does not generate direct carbon emission, but the carbon emission in the consumption behavior of electric energy consumption can be measured by introducing the concept of 'virtual carbon emission', and the reasonable taxation is carried out on the carbon emission. Meanwhile, for the carbon sequestration benefit of the MR, the MR can be subsidized and included in an IES economic cost model as economic benefit. The mechanism of the stepped carbon cost model is a stepped carbon cost mechanism, a plurality of carbon emission intervals are divided, and the carbon emission of consumption behaviors in a certain time is differentiated and priced according to the intervals, wherein the higher the emission is, the heavier the tax is.
Step 2: classifying according to the IES element characteristics, classifying demand response resources, and making an optimization plan under multiple time scales;
in one embodiment, the IES element includes a coupling device, a distributed power source and an energy storage element, the demand response resource includes four types of energy loads including electricity, gas, heat and hydrogen, and step 2 includes:
classifying according to the operating characteristics of the IES element; wherein the operating characteristics are classified as:
(1) the flexibility of the coupling equipment is not as good as that of energy storage equipment, and the method is not used as a quick adjustment mode, but the spare adjustment capacity of the coupling equipment has a certain adjustment function;
(2) the distributed power supply can quickly abandon wind and light within the upper limit range of output, the output has good adjustability, but uncertainty and volatility of the output;
(3) the energy storage element has the advantages of flexible arrangement and quick adjustment, but the energy storage capacity is limited to a certain extent, so that the energy storage element is suitable for quickly adjusting a supply and demand curve which fluctuates randomly in IES;
the demand response resources are classified into a price type and an incentive type, wherein in the embodiment, the demand response resources in the IES include four types of energy loads, i.e., electricity, gas, heat and hydrogen, which can be managed by using demand response characteristics. Four types of demand response loads are divided into a price type (PDR) and an excitation type (IDR) according to the management mode of regional power grids on demand response resources (DR) of electric loads. The price of electric energy and gas energy in the model established by the embodiment adopts a day-ahead pricing mode, so that PDR is not considered in optimization, and only IDR load response is considered. The motivation type is divided into the following according to the length of the response IES optimization instruction time:
class a IDR, plan to be made 1 day ahead;
b type IDR, the response time is 15 min-1 h;
c-type IDR, the response time is 5-15 min;
class D IDR, real-time response;
the method for making the optimization plan under the multi-time scale comprises three stages of rolling in the day-ahead and day-in and real-time optimization, and the specific frame is as follows:
day-ahead optimization: the time step is 1h, and the execution period is 24 h; the stage is used for determining a working plan and a class A IDR load calling plan of the coupling equipment;
rolling optimization in days: the time step is 15min, and the execution period is 4 h; the stage is used for making an output plan of distributed power generation, a standby output plan of coupling equipment and a calling plan of B-type IDR (identification data register) so as to correct the deviation of a day-ahead optimization plan;
and (3) real-time optimization: the execution period is 5 min; the stage is used for formulating the working states of various energy storage devices and the C-type and D-type IDR calling quantities, and finally determining the electricity purchasing quantity and the gas purchasing quantity to the grading power grid and the natural gas grid;
in the optimization plan under the multi-time scale, the control quantity obtained by optimization in the previous stage is taken as a determined quantity to be brought into an optimization model in the subsequent stage for calculation.
And step 3: establishing a coordination optimization model aiming at rolling in day-ahead and day-in-day and real-time three-stage multi-time scale, adopting introduced auxiliary variables and Big-M to carry out model linearization transformation in view of the mixed integer nonlinear property of the model, obtaining a mixed integer linear model, and calling a mathematical solver to solve.
In one embodiment, step 3 comprises:
step 3.1, establishing a day-ahead optimization model;
further, in an embodiment, a multi-scenario stochastic programming method suitable for large uncertainty is adopted for day-ahead optimization, and the IES operation safety is met for errors under different various load and distributed power output prediction scenarios. Step 3.1 comprises:
establishing an objective function of a day-ahead optimization model, wherein the objective function of the day-ahead optimization model converts the wind curtailment quantity and the load loss quantity into penalty cost to be added into the system operation cost on the basis of the minimum total system operation cost, and considers the energy purchasing carbon cost and the carbon fixing income, and the day-ahead optimization model is expressed as follows:
Figure BDA0003482867110000091
Figure BDA0003482867110000101
wherein, f1An objective function for a day-ahead optimization model, representing the operating cost of the IES; f. ofbuy(t)、fsto(t)、fcpl(t)、fcpl(t)、fcpl(t) cost functions of upper-level energy purchasing, energy storage devices, coupling equipment, distributed power generation and user load management at the moment t are respectively; f. ofcpl(t) considering the number of scenes for the day-ahead optimization model; f. ofcpl(t) is the occurrence probability coefficient of the s scene in the optimization process in the day ahead; f. ofcpl(t) power for purchasing power and gas from the upper level at the time of the s-th scene t; f. ofcpl(t) the unit cost of electricity and gas purchase; f. ofcpl(t) is the charge-discharge energy power of electricity/gas/heat/hydrogen stored at s scene t moment; c (P)e/g/h/H2,sto,s(t)) is an energy storage device cost function; w (P)e/g/h/H2,sto,s(t)) is a maintenance cost function of the energy storage device; p isPV/WT,s(t) the output of photovoltaic and fan in distributed power generation at s scene t moment; k is a radical ofc,PV/WT(t) punishing a cost coefficient for abandoning wind and abandoning light;
Figure BDA0003482867110000111
the predicted output of photovoltaic and fan in distributed power generation at s scene t moment; c (P)PV/WT,s(t)) is a cost function of distributed generation at time t of scenario s; n in n epsilon { e, g, H, H2} is a variable for four types of loads, electricity/gas/heat/hydrogen, and is used for simplifying the space of formula description; k is a radical ofn,IDR,A、kn,IDR,BCost coefficients of A, B class IDRs for each class of load; | Δ Pn,IDR,A,s(t)|、|ΔPn,IDR,B,s(t) | is the calling amount of A, B types of IDRs of various types of loads at t moment of s scene respectively; k is a radical ofc,load,nPenalty coefficient for each type of load missing; ploss,n,s(t) is the loss of various loads at the time of s scene t;
establishing constraint conditions of the day-ahead optimization model, wherein the constraint conditions comprise power balance constraint, coupling equipment operation constraint, distributed generation output constraint, energy storage equipment operation constraint, each scene adjustment constraint and various demand response resource constraints;
the power balance constraints comprise an electric power balance constraint, a natural gas balance constraint, a thermal power balance constraint and a hydrogen energy balance constraint;
the electric power balance constraint is established as follows:
Figure BDA0003482867110000112
the natural gas power balance constraint is established as follows:
Figure BDA0003482867110000113
the thermal power balance constraint is established as follows:
Figure BDA0003482867110000114
the hydrogen energy balance constraint is established as follows:
Figure BDA0003482867110000115
wherein the content of the first and second substances,
Figure BDA0003482867110000121
respectively the rated power of external charging and discharging energy of electricity/gas/heat/hydrogen stored at the moment of s scene t;
Figure BDA0003482867110000122
for the expected electrical/gas/heat/hydrogen load in the optimization model at time t;
the distributed generation output force constraint model is established as follows:
Figure BDA0003482867110000123
the distributed generation output constraint represents that the distributed generation output value is smaller than the predicted value;
coupling device operational constraints: models and constraints for the respective coupling devices EL, MR, GT, GB, EB in the IES are established in step 1.3. It should be noted that, in the optimization model in the day ahead, multi-scenario stochastic programming is considered, so the operation of the coupling device should also satisfy the constraint under multi-scenario, and the symbol variable corresponds to the variable under multi-scenario, such as Pe,EL(t) corresponds to Pe,EL,s(t)；
The energy storage equipment operation constraint is as follows:
Figure BDA0003482867110000124
wherein the content of the first and second substances,
Figure BDA0003482867110000125
respectively charging and discharging the electricity storage;
Figure BDA0003482867110000126
capacity for electricity storage; se,s(t)、
Figure BDA0003482867110000127
Figure BDA0003482867110000128
Respectively the energy storage-capacity ratio state and the upper and lower limits of the electricity storage at the time of s scene t; the energy storage-capacity ratio states of the energy storage device are equal at the beginning and the end in an optimization period T; obtaining the operation constraint of the energy storage equipment in other forms based on the consistency between the operation constraint of the energy storage equipment in other forms and the operation constraint of the electric hydrogen storage equipment;
and regulating constraints in each scene, and establishing a model as follows:
|Pmachine,s(t)-Pmachine,bs(t)|≤ψmachine
wherein, Pmachine,bs(t) reference scene output value of various coupling devices；Pmachine,s(t) is a force output value of each type of coupling equipment in the s scene; psimachineAdjusting the margin for various coupling devices;
the model of each type of demand response resource constraint is established as follows:
Figure BDA0003482867110000129
Figure BDA0003482867110000131
wherein the content of the first and second substances,
Figure BDA0003482867110000132
increased load capacity for each type of load, class A and class B IDRs, respectively;
Figure BDA0003482867110000133
the load loss reduction quantity of IDR of various types of loads A and B is respectively.
And selecting the running state quantity of the coupling device and the A-type IDR call quantity as determination parameters from the solving result of the day-ahead optimization model, and substituting the determination parameters into the subsequent day-ahead rolling and real-time optimization model calculation.
Step 3.2, establishing a rolling optimization model in the day;
further, in one embodiment, step 3.2 includes:
and establishing an intra-day rolling optimization model which is basically the same as the optimization model before the day, wherein the objective function of the intra-day rolling optimization model is the minimum total cost of IES operation, the adjustable spare capacity output of the coupled equipment and the call volume cost of IDR type loads, the IDR parameter of the type A is determined, and the total cost of the loads is the sum of IDRs of the type B and the type C. On the basis of an objective function of a day-ahead optimization model, the day-in rolling optimization model is as follows:
Figure BDA0003482867110000134
Figure BDA0003482867110000135
wherein N iss,dayinConsidering the number of scenes for the intra-day rolling optimization model; p is a radical ofs,dayinThe probability coefficient of occurrence of the s scene in the rolling optimization process in the day; k is a radical ofn,IDR,CA cost factor for a class C IDR for a certain class of loads; delta Pn,IDR,C,s(t) | is the calling quantity of a certain load C type IDR at the t moment of s scene;
establishing a constraint condition of the intra-day rolling optimization model, wherein the intra-day rolling optimization model adopts a multi-scene random planning method to cope with the influence caused by uncertainty as the same as the intra-day rolling optimization model, so that the constraint condition is basically consistent with that of the intra-day rolling optimization model, and only on the basis of the constraint condition of the intra-day rolling optimization model, C-class demand response resource constraint and coupling equipment spare capacity output constraint caused by the addition of C-class IDR are additionally added;
the class C demand response resource constraints are established as follows:
Figure BDA0003482867110000136
wherein the content of the first and second substances,
Figure BDA0003482867110000137
respectively increasing and decreasing the load quantity of various load C-type IDRs;
the coupling device reserve capacity contribution constraint is established as follows:
Figure BDA0003482867110000141
wherein, Δ Pmachine,sAnd (t) is a spare output value of each type of coupling equipment, and the formula shows that the spare output values of the various types of coupling equipment meet the upper and lower limits, and the sum of the spare capacity output and the day-ahead output plan meets the capacity limit.
The in-day rolling optimization model feeds system data obtained by actual measurement back to the in-day rolling optimization model, and an optimal control sequence is solved by combining ultra-short-term prediction data of wind and light loads with the time step length of 15min in 4h in the future; and selecting the output plan of distributed power generation, the output plan of coupling equipment spare capacity and the B-type IDR load call quantity as determination parameters on the basis of the operation parameters determined by the day-ahead optimization model according to the solution result of the day-in-time rolling optimization model, and substituting the determination parameters into the subsequent calculation of the real-time optimization model.
Step 3.3, establishing a real-time optimization model;
further, in one embodiment, step 3.3 includes:
and (3) establishing a real-time optimization model, wherein the optimization time step of the real-time optimization model is 5min, the real-time load fluctuation amplitude under the time scale is very small, and the multi-scene random optimization method is not suitable. In the embodiment, an opportunity constraint method is adopted, and a certain constraint condition is set, so that the probability of the establishment of the constraint condition is not less than a certain confidence level; establishing an objective function of a real-time optimization model, which is basically the same as a day-ahead optimization model and a day-inside rolling optimization model, wherein the objective function of the real-time optimization model is the minimum total cost of IES operation, the variable cost is only the call quantity cost of IDR type loads, specifically the sum of the IDR types C and D, and the real-time optimization model is as follows:
Figure BDA0003482867110000142
Figure BDA0003482867110000143
wherein k isn,IDR,DCost factor for class D IDR for a certain class of load; | Δ Pn,IDR,D(t) | is the calling amount of a certain load D type IDR at the time t;
and (4) establishing a constraint condition of the real-time optimization model, and adopting an opportunity constraint method for the real-time optimization model. At the moment, the deviation values of the real-time loads of electricity, gas, heat and hydrogen obtained by short-term prediction are considered to respectively meet a certain truncated normal distribution, the regulation and control requirements on the energy storage device are that the supply rates of various loads under respective set confidence levels are only required to be met, and the balance of the rest weak deviation can be met through real-time upper-level electricity, gas and load purchasing regulation. Therefore, the constraint conditions are basically the same as those of day-ahead and day-inside rolling optimization which does not distinguish various scenes, and are not repeated, but the constraint conditions are changed on the power balance and the partial constraint of distributed generation output; the change of the power balance constraint in the real-time optimization model is specifically represented by an inequality constraint which is changed from an equality constraint of a random constraint to an opportunity constraint and mainly comprises an electric power balance constraint, a natural gas balance constraint, a thermal power balance constraint and a hydrogen energy balance constraint, and the mathematical model of the constraint is established as follows:
the electric power balance constraint is established as follows:
Figure BDA0003482867110000151
the natural gas equilibrium constraints are established as follows:
Figure BDA0003482867110000152
the thermal power balance constraint is established as follows:
Figure BDA0003482867110000153
the hydrogen energy balance constraint is established as follows:
Figure BDA0003482867110000154
wherein Pr {. is a confidence expression;
Figure BDA0003482867110000155
is the electrical/gas/thermal/hydrogen power balance confidence level;
the change of the distributed generation output constraint in the real-time optimization model is reflected in that the output of the distributed generation in the real-time optimization cannot exceed the real-time maximum output upper limit under the constraint of the practical condition on the basis of the rolling optimization value within the continuous day, and the constraint is established as follows:
Figure BDA0003482867110000156
wherein the content of the first and second substances,
Figure BDA0003482867110000161
respectively optimizing the actual upper limits of the photovoltaic output and the actual upper limits of the fans in real time;
Figure BDA0003482867110000162
respectively are the output plans of the photovoltaic and the fan obtained in the rolling optimization in the day.
And (3) selecting the working state and output of various energy storage devices, the C-class and D-class IDR calling quantity, the upper-level electricity purchasing quantity and the upper-level gas purchasing quantity as the determined parameters on the basis of the operation parameters determined in the first two stages according to the solving result of the real-time optimization model, and finally obtaining various operation parameters in the IES multi-time scale operation mode.
Step 3.4, carrying out linearization treatment on nonlinear items existing in the coordination optimization model under multiple time scales, which is composed of a day-ahead optimization model, a day-in rolling optimization model and a real-time optimization model;
further, in one embodiment, step 3.4 includes:
carrying out linearization processing on the absolute value item and the energy storage state constraint in the objective function respectively;
carrying out linearization processing on the absolute value item in the objective function, wherein a user load management cost function f in the objective function in the day-ahead optimization modelload(t) is represented as follows:
Figure BDA0003482867110000163
carrying out equivalent linearization on absolute value terms in the formula, and introducing a real auxiliary variable Un,A,s(t)、Un,B,s(t) and a binary auxiliary variable δn,A,s(t)、δn,B,s(t)、εn,A,s(t)、εn,B,s(t), which can be equivalently expressed as:
Figure BDA0003482867110000164
Figure BDA0003482867110000165
Figure BDA0003482867110000166
wherein: m is a preset constant, the numerical value is larger, and by analogy, the absolute value items in other objective functions are subjected to linear conversion;
and carrying out linearization processing on the operation constraint of the energy storage element, wherein the original constraint conditions of the electric energy storage operation constraint in the day-ahead optimization model are as follows:
Figure BDA0003482867110000171
introducing a binary variable
Figure BDA0003482867110000172
The charge and discharge energy state of the electric storage is represented, a value of 1 and a value of 0 respectively represent that the electric storage is in/not in a certain state, and the electric storage is subjected to linearization equivalent processing to obtain:
Figure BDA0003482867110000173
and in the same way, the other energy storage state operation constraints are subjected to linear conversion.
The coordination optimization model under multiple time scales, which is composed of a day-ahead optimization model, a day-in rolling optimization model and a real-time optimization model, is a mixed integer nonlinear model, so that a global optimal solution is difficult to directly obtain by using a common artificial intelligence algorithm and a mathematical optimization method, and nonlinear items existing in an original model need to be subjected to linearization treatment in order to ensure the solving efficiency and the reliability of a solving result. Specifically, linearization processing is respectively performed on an absolute value term and energy storage state constraint in an objective function causing model nonlinearity.
And 3.5, obtaining a mixed integer linear model after linearization treatment, and solving by adopting a mathematical optimization tool to obtain a result which is a global optimal solution.
In the step, a mixed integer linear model is obtained after linearization treatment, and a mathematical solver such as Cplex and Gurobi is used for solving the mixed integer linear model, so that the obtained result is a global optimal solution.
Based on the above embodiments, the data benchmark values of one embodiment are set as 1000kW, 600kW, 400kW, and 200kW for the peak values of the electric, gas, heat, and hydrogen loads, and the rated capacities of the wind power and the photovoltaic are both 400 kW. The model optimization period is 24 hours a day, and the load and the prediction errors of the distributed power generation in the day-ahead, day-in and real-time are respectively set to be 3%, 1%, 0.5% and 5%, 3% and 1%. The time-of-use electricity price is shown in table 1, and the natural gas price is 0.35 yuan/kWh; the operation parameters, energy storage parameters, economic parameters of the four types of IDRs and other parameters of various devices are shown in tables 2-5 respectively. In the real-time optimization model, the error rates of various loads meet the truncated normal distribution of N (0,1/1200), and the confidence coefficient is 0.9.
TABLE 1 time-of-use electricity price table
Figure BDA0003482867110000181
TABLE 2 operating parameters of various types of coupling devices
Figure BDA0003482867110000182
TABLE 3 energy storage operating parameters
Figure BDA0003482867110000183
TABLE 4 load demand response economic parameters
Figure BDA0003482867110000184
TABLE 5 other parameters
Figure BDA0003482867110000185
By calling Gurobi to solve a multi-time scale optimization model, under the calculation power of an Intel Core i7@2.90GHz host, 2s of calculation time is used in the day ahead, 20s of calculation time is used in single rolling in the day, 80 times of rolling is used, and 15s of calculation time is used in real-time optimization, so that the calculation speed requirement in actual work is met.
In order to verify the effectiveness of the method in multi-time scale low-carbon optimization, IES optimization results under three scenes are selected for comparative analysis:
scene 1: economic targets for carbon emissions are not considered;
scene 2: considering the single carbon emission price and the carbon fixation subsidy;
scene 3: considering the step-type carbon emission price and carbon fixation subsidy.
The three scenes are subjected to multi-time scale calculation and compared and analyzed for IES optimization results in the day-ahead, in-day and real-time stages, and partial day-ahead optimization results of the three scenes are shown in Table 6.
TABLE 6 optimization results of each scene day ahead
Figure BDA0003482867110000191
As can be seen from table 1, in the optimization at the previous stage, the total carbon emissions of scene 2 and scene 3, which consider the economic objective of carbon emission, are reduced by 1.38% and 1.57% respectively, compared to scene 1, indicating that considering the economic objective of low carbon contributes to reducing the consumed carbon emissions of the IES. And the carbon emission cost of scene 2 and scene 3 is compared, the latter carbon emission cost is reduced by 28% and a larger carbon emission reduction benefit is obtained, which shows that the consideration of the stepped carbon cost mechanism has a better carbon emission reduction benefit and an economic benefit.
A partial parameter list for the three types of scenes after real-time optimization is shown in table 7.
TABLE 7 real-time optimization results for each scene
Figure BDA0003482867110000192
Figure BDA0003482867110000201
Looking at table 7, from the IDR and dump load costs, the class B load is almost 0 in all three scenarios, while the class a IDR cost for scenario 2 and scenario 3 is much greater than the 0 cost for scenario 1, which is a result of the interaction of the carbon emission mechanism with the load demand response, i.e. by reducing the carbon emission cost appropriately, while the class B IDR still functions in scenario 3 because the higher order carbon emission cost from the stepped carbon emission mechanism is still acting on the load demand response to reduce the total economic cost. The no obvious difference in IDR cost of class C and class D under the three scenes is due to the fact that the IDR cost is constrained by the real-time load fluctuation balance and the energy storage initial and final states with the same prediction error.
In order to verify the model benefits and the working effectiveness of the method under different clean energy power generation installed capacities in the IES, a PV/WT installed capacity interval of 200-1000kW and a step size of 200kW in the model are selected for simulation, and carbon emission parameters and electricity abandonment conditions are shown in Table 8.
TABLE 8 relevant parameters for different clean energy installed power generation capacities
Figure BDA0003482867110000202
As can be seen from table 8, as the installed capacity of clean distributed power generation increases, the total operating cost of the system tends to decrease greatly, which is mainly due to the decrease of the total energy purchase amount and the reduction of carbon emission cost. The cost of the electric carbon emission can be ensured to be 0 when the clean energy installed machine exceeds 600kW, but the cost of the gas carbon emission is slowly reduced after that, which is caused by the limited capacity of the related device for converting electricity into gas. In order to better absorb the higher proportion of clean energy access, the installed capacity of the coupling device needs to be improved to a certain extent. Meanwhile, the wind and light abandoning power and punishment cost in the model are far less than the reduction of the total cost, which shows that the model has good clean energy consumption capability.
In order to verify the adjustable thermoelectric ratio and natural gas-hydrogen mixed utilization benefit introduced by the method, three types of variables of 'natural gas price is high and low', 'whether the thermoelectric ratio can be adjusted' and 'whether the natural gas-hydrogen is mixed' are selected to carry out 16 groups of control variable comparison simulation, wherein the gas price is set to be four grades of low, medium, high and extra high according to unit price of 0.35, 0.6, 0.8 and 1.4 yuan/kWh, and the four types of operation conditions of adjustable mixability, adjustable unmixability, non-adjustable mixability and non-adjustable mixability are divided according to whether the thermoelectric ratio is adjustable or not and whether the natural gas-hydrogen is mixed or not. The simulation results are shown in tables 9 and 10.
Table 916 set of economic cost results for group comparison simulation
Figure BDA0003482867110000211
As can be seen from table 9, the system operating cost of the operating condition 1 is the least at the same gas price, which indicates that the most common economic solution is considered in consideration of the adjustable thermoelectric ratio and in consideration of the natural gas-hydrogen mixture. And case 2 is more economical than case 3 when natural gas prices are lower. And by comparing the thermoelectric ratio and the natural gas-hydrogen operation condition under different gas prices, the lower the gas price is, the more obvious the GT thermoelectric ratio variation degree is, and the time interval range is larger, which shows that the adjustable thermoelectric ratio of the GT under the lower gas price can play the economic role better. On the contrary, under the condition of high gas price, the electricity has price advantage, so that hydrogen generated by EL is more involved in the electricity generation and heat generation process of the substitute natural gas, the consumption of the high natural gas is reduced, and the economy of a natural gas-hydrogen mixing strategy is exerted.
Table 1016 carbon displacement results of group comparison simulation
Figure BDA0003482867110000212
As can be seen from table 10, the GT thermoelectric ratio adjustable performance is good to reduce carbon emissions at medium and low gas prices, while the natural gas-hydrogen mixing strategy has little carbon emission reduction effect due to the low gas prices and insufficient economy. The electric energy at high gas price replaces natural gas more to supply heat energy, so that the total carbon emission of the system is increased, the adjusting effect of the GT thermoelectric ratio is not obvious, and the natural gas-hydrogen mixing strategy is used, so that the electric energy is converted into hydrogen energy to participate in GT and GB work, so that the total economic cost is reduced.
The above results were further analyzed:
1) compared with the traditional single-type carbon emission cost, the IES has better carbon emission reduction effect and more excellent operation economy by adopting the stepped carbon emission cost;
2) the multi-time scale optimization operation mode can give consideration to the operation flexibility and the load prediction deviation of various devices in the IES, and can better realize the functions of multi-energy complementation and risk interaction in the IES by matching with various load demand responses at different levels; in the load demand response, A, B type IDRs mainly play a role in adjusting loads in the day-ahead and reducing carbon emission, and C, D type IDRs mainly play a role in balancing load fluctuation and adjusting the energy storage state in the real-time stage;
3) the access of large-scale clean energy can greatly reduce the operation cost of the system, the wind and light abandoning amount of the model is far less than the total output of PV \ WT, the loss of balancing load fluctuation and ensuring the same state of the energy storage device at the beginning and the end of the optimization period basically belongs to, and the access capability of large-scale clean energy is well absorbed;
4) the adjustable thermoelectric ratio performance of the GT and the natural gas-hydrogen mixing capability of the GT and the GB are considered, the multi-energy complementation can be flexibly adjusted according to the actual natural gas price level and the running condition, the energy is saved, the emission is reduced, the step loss of energy sources can be reduced, and the higher economical efficiency is obtained.
In a third aspect, an embodiment of the present invention further provides a readable storage medium.
The readable storage medium of the invention stores the operation optimization program of the integrated energy system, wherein the operation optimization program of the integrated energy system is executed by the processor to realize the steps of the operation optimization method of the integrated energy system.
The method for implementing the operation optimization program of the integrated energy system when executed may refer to various embodiments of the operation optimization method of the integrated energy system of the present invention, and will not be described herein again.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for causing a terminal device to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.

Claims (10)

1. An operation optimization method of an integrated energy system is characterized by comprising the following steps:
step 1: carrying out mathematical modeling on an IES coupling device and a stepped carbon cost metering model which comprise adjustable thermoelectric ratio and natural gas-hydrogen mixed combustion characteristics and various integrated energy systems;
step 2: classifying according to the IES element characteristics, classifying demand response resources, and making an optimization plan under multiple time scales;
and step 3: establishing a coordination optimization model aiming at rolling in day-ahead and day-in-day and real-time three-stage multi-time scale, adopting introduced auxiliary variables and Big-M to carry out model linearization transformation in view of the mixed integer nonlinear property of the model, obtaining a mixed integer linear model, and calling a mathematical solver to solve.
2. The method for optimizing the operation of an integrated energy system according to claim 1, wherein the step 1 comprises:
step 1.1: introducing an adjustable thermoelectric ratio and natural gas-hydrogen mixed combustion characteristic, improving the energy conversion process of a gas turbine GT and a gas boiler GB, and adjusting the heating and generating power ratio in the working process according to the electricity price and the gas price at different time periods;
step 1.2: the hydrogen energy utilization scene is enlarged by decoupling a series of processes that hydrogen obtained by water electrolysis and carbon dioxide are directly synthesized into natural gas by an electrolytic cell EL in the process of converting electricity into gas P2G through a methane reactor MR;
step 1.3: performing mathematical modeling on coupling equipment in IES, wherein the coupling equipment comprises EL, MR, GT, GB and an electric boiler EB;
the mathematical model of the EL is:
Figure FDA0003482867100000011
wherein, Pe,EL(t) inputting the electric energy of the EL at the time t; pH2,EL(t) outputting the hydrogen energy of the EL at the time t; etaELTo EL energy conversion efficiency;
Figure FDA0003482867100000012
the upper and lower limits of the input power of the EL are respectively;
Figure FDA0003482867100000013
the upper limit and the lower limit of the climbing of the EL are respectively;
the mathematical model of the MR is as follows:
Figure FDA0003482867100000021
wherein, PH2,MR(t) inputting hydrogen energy of the MR at the time t; pg,MR(t) natural gas outputting MR at time t; etaMRThe energy conversion efficiency of hydrogen to methane in the MR is obtained;
Figure FDA0003482867100000022
the upper and lower limits of the input power of the MR are respectively;
Figure FDA0003482867100000023
the upper limit and the lower limit of the climbing of the MR are respectively; pe,MR(t) the MR synthesis reaction consumes electric energy at the time t; etaMR,eIs the power consumption proportion of the MR synthesis reaction;
the mathematical model of the GT is as follows:
Figure FDA0003482867100000024
wherein，Pe,GT(t)、Ph,GT(t) outputting the electric energy and the heat energy of the GT at the time t respectively; pmg,GT(t) inputting the natural gas-hydrogen mixed gas of GT at the time t; etaGTGT energy conversion efficiency;
Figure FDA0003482867100000025
upper and lower limits of the input power of the GT respectively;
Figure FDA0003482867100000026
the upper limit and the lower limit of the climbing of the GT are respectively;
Figure FDA0003482867100000027
Figure FDA0003482867100000028
the upper limit and the lower limit of the thermoelectric ratio of the GT are respectively; pH2,GT(t)、Pg,GT(t) the amounts of hydrogen and natural gas in the natural gas-hydrogen mixed gas input into the GT at time t respectively;
Figure FDA0003482867100000029
is the lowest proportion of the natural gas content in the gas input into the GT;
the mathematical model of GB is:
Figure FDA00034828671000000210
wherein, Pmg,GB(t) inputting the amount of GB natural gas-hydrogen mixed gas at the moment t; ph,GB(t) outputting heat energy of GB at the time t; etaGBGB energy conversion efficiency;
Figure FDA00034828671000000211
the upper and lower limits of input power of GB respectively;
Figure FDA00034828671000000212
upper and lower limits of climbing of GB respectively；PH2,GB(t)、Pg,GB(t) the amounts of hydrogen and natural gas in the GB natural gas-hydrogen mixed gas are input at the moment t respectively;
Figure FDA00034828671000000213
the minimum proportion of the natural gas content in the gas input into GB;
the mathematical model of the EB is as follows:
Figure FDA0003482867100000031
wherein, Pe,EB(t) inputting the electric energy of the EB at the time t; ph,EL(t) outputting heat energy of EB at the time t; etaEBEB energy conversion efficiency;
Figure FDA0003482867100000032
the upper and lower limits of the input power of EB respectively;
Figure FDA0003482867100000033
respectively the upper limit and the lower limit of the climbing of the EB;
step 1.4: introducing a step-type carbon cost model and a carbon fixation patch model, wherein the step-type carbon cost model has the mathematical model as follows:
Figure FDA0003482867100000034
Figure FDA0003482867100000035
Figure FDA0003482867100000036
Figure FDA0003482867100000037
wherein the content of the first and second substances,
Figure FDA0003482867100000038
the carbon emission tax of the upper-level electricity purchasing and gas purchasing and the sum of the two are respectively; ee,buy,a、Eg,buy,aThe carbon-containing emission amount of the power and gas purchase is purchased for the upper level; chi shapee、χgCarbon emissions per unit electricity consumption, per unit natural gas consumption, respectively; pe,buy(t)、Pg,buy(t) respectively represents the power purchase and gas purchase of the upper stage at the time t; t is an optimization period; lambda [ alpha ]e、λgThe carbon cost base price of the electric power and the natural gas are respectively; le、lgThe lengths of the carbon intervals for electric power and natural gas step-type tax calculation are respectively; alpha is the price increase amplitude;
the mathematical model of the carbon fixation patch model is as follows:
Figure FDA0003482867100000041
wherein the content of the first and second substances,
Figure FDA0003482867100000042
carbon sequestration benefit for the MR device; lambdasubSubsidy fee for unit solid carbon amount; chi shapesubThe fixed carbon amount for the unit natural gas production; pg,MR(t) is the MR output power at time t.
3. The method of claim 2, wherein the IES elements include coupling devices, distributed power sources, and energy storage elements, the demand response resources include four types of energy loads including electricity, gas, heat, and hydrogen, and step 2 includes:
classifying according to the operating characteristics of the IES element;
the demand response resources are divided into a price type and an incentive type, wherein the incentive type is divided into the following steps according to the length of the response IES optimization instruction time:
class a IDR, plan to be made 1 day ahead;
b type IDR, the response time is 15 min-1 h;
c-type IDR, the response time is 5-15 min;
class D IDR, real-time response;
the method for making the optimization plan under the multi-time scale comprises three stages of rolling in the day-ahead and day-in and real-time optimization, and the specific frame is as follows:
optimizing day ahead: the time step is 1h, and the execution period is 24 h; the stage is used for determining a working plan and a class A IDR load calling plan of the coupling equipment;
rolling optimization in days: the time step is 15min, and the execution period is 4 h; the stage is used for making an output plan of distributed power generation, a standby output plan of coupling equipment and a calling plan of B-type IDR (identification data register) so as to correct the deviation of a day-ahead optimization plan;
and (3) real-time optimization: the execution period is 5 min; the stage is used for formulating the working states of various energy storage devices and the C-type and D-type IDR calling quantities, and finally determining the electricity purchasing quantity and the gas purchasing quantity to the upper-level distribution power grid and the natural gas grid;
in the optimization plan under the multi-time scale, the control quantity obtained by optimization in the previous stage is taken as a determined quantity to be brought into an optimization model in the subsequent stage for calculation.
4. The integrated energy system operation optimization method according to claim 3, wherein the step 3 includes:
step 3.1, establishing a day-ahead optimization model;
step 3.2, establishing a rolling optimization model in the day;
step 3.3, establishing a real-time optimization model;
step 3.4, carrying out linearization treatment on nonlinear items existing in the coordination optimization model under multiple time scales, which is composed of a day-ahead optimization model, a day-in rolling optimization model and a real-time optimization model;
and 3.5, obtaining a mixed integer linear model after linearization treatment, and solving by adopting a mathematical optimization tool to obtain a result which is a global optimal solution.
5. The method for optimizing the operation of an integrated energy system according to claim 4, wherein the step 3.1 comprises:
establishing an objective function of a day-ahead optimization model, wherein the objective function of the day-ahead optimization model converts the wind curtailment quantity and the load loss quantity into penalty cost to be added into the system operation cost on the basis of the minimum total system operation cost, and considers the energy purchasing carbon cost and the carbon fixing income, and the day-ahead optimization model is expressed as follows:
Figure FDA0003482867100000051
Figure FDA0003482867100000061
wherein f is1An objective function for a day-ahead optimization model, representing the operating cost of the IES; f. ofbuy(t)、fsto(t)、fcpl(t)、fcpl(t)、fcpl(t) cost functions of upper-level energy purchasing, energy storage devices, coupling equipment, distributed power generation and user load management at the moment t are respectively; f. ofcpl(t) considering the number of scenes for the day-ahead optimization model; f. ofcpl(t) is the occurrence probability coefficient of the s scene in the optimization process in the day ahead; f. ofcpl(t) power for purchasing power and gas from the upper level at the time of the s-th scene t; f. ofcpl(t) the unit electricity and gas purchase cost; f. ofcpl(t) is the charge-discharge energy power of electricity/gas/heat/hydrogen stored at s scene t moment; c (P)e/g/h/H2,sto,s(t)) is an energy storage device cost function; w (P)e/g/h/H2,sto,s(t)) is a maintenance cost function of the energy storage device; pPV/WT,s(t) the output of photovoltaic and fan in distributed power generation at s scene t moment; k is a radical of formulac,PV/WT(t) punishing a cost coefficient for abandoning wind and abandoning light;
Figure FDA0003482867100000071
for distributed generationThe predicted output of the photovoltaic and the fan at the moment of s scene t; c (P)PV/WT,s(t)) is a cost function of distributed generation at time s scene t; n in n epsilon { e, g, H, H2} is a variable for four types of loads, electricity/gas/heat/hydrogen, and is used for simplifying the space of formula description; k is a radical ofn,IDR,A、kn,IDR,BCost coefficients of A, B class IDRs for each class of load; | Δ Pn,IDR,A,s(t)|、|ΔPn,IDR,B,s(t) | is the calling amount of A, B types of IDRs of various types of loads at t moment of s scene respectively; k is a radical ofc,load,nPenalty coefficient for each type of load missing; ploss,n,s(t) is the loss of various loads at the time of s scene t;
establishing constraint conditions of the day-ahead optimization model, wherein the constraint conditions comprise power balance constraint, coupling equipment operation constraint, distributed generation output constraint, energy storage equipment operation constraint, each scene adjustment constraint and various demand response resource constraints;
the power balance constraints comprise electric power balance constraints, natural gas balance constraints, thermal power balance constraints and hydrogen energy balance constraints;
the electric power balance constraint is established as follows:
Figure FDA0003482867100000072
the natural gas power balance constraint is established as follows:
Figure FDA0003482867100000073
the thermal power balance constraint is established as follows:
Figure FDA0003482867100000074
the hydrogen energy balance constraint is established as follows:
Figure FDA0003482867100000075
wherein the content of the first and second substances,
Figure FDA0003482867100000081
respectively the rated power of external charging and discharging energy of electricity/gas/heat/hydrogen stored at the moment of s scene t;
Figure FDA0003482867100000082
for the expected electrical/gas/heat/hydrogen load in the optimization model at time t;
the distributed generation output force constraint model is established as follows:
Figure FDA0003482867100000083
the distributed generation output constraint represents that the distributed generation output value is smaller than the predicted value;
the energy storage equipment operation constraint is as follows:
Figure FDA0003482867100000084
wherein the content of the first and second substances,
Figure FDA0003482867100000085
respectively charging and discharging the electricity storage;
Figure FDA0003482867100000086
capacity for electricity storage; se,s(t)、
Figure FDA0003482867100000087
Figure FDA0003482867100000088
Respectively representing the energy storage-capacity ratio state of the electricity storage at the time of s scene t and the upper and lower limits thereof;the energy storage-capacity ratio states of the energy storage device are equal at the beginning and the end in an optimization period T; obtaining the operation constraints of the energy storage equipment in other forms based on the consistency of the operation constraints of the energy storage equipment in other forms and the operation constraints of the hydrogen storage equipment;
and regulating constraints in each scene, and establishing a model as follows:
|Pmachine,s(t)-Pmachine,bs(t)|≤ψmachine
wherein, Pmachine,bs(t) outputting force values for reference scenes of various coupling devices; pmachine,s(t) is a force output value of each type of coupling equipment in the s scene; psimachineAdjusting the margin for various coupling devices;
the model of each type of demand response resource constraint is established as follows:
Figure FDA0003482867100000089
Figure FDA00034828671000000810
wherein the content of the first and second substances,
Figure FDA00034828671000000811
increased load capacity for each type of load, class A and class B IDRs, respectively;
Figure FDA0003482867100000091
the load loss reduction quantity of IDR of various types of loads A and B is respectively.
6. The integrated energy system operation optimization method according to claim 4, wherein the step 3.2 comprises:
establishing a rolling optimization model in days, wherein the rolling optimization model in days is as follows:
Figure FDA0003482867100000092
Figure FDA0003482867100000093
wherein N iss,dayinConsidering the number of scenes for the intra-day rolling optimization model; p is a radical ofs,dayinThe probability coefficient of occurrence of the s scene in the rolling optimization process in the day; k is a radical ofn,IDR,CCost factor for a class C IDR for a class of loads; | Δ Pn,IDR,C,s(t) | is the calling quantity of a certain load C type IDR at the t moment of s scene;
establishing a constraint condition of the intra-day rolling optimization model, and additionally adding C-type demand response resource constraint and coupling equipment spare capacity output constraint brought by the addition of C-type IDR on the basis of the constraint condition of the pre-day rolling optimization model;
the class C demand response resource constraints are established as follows:
Figure FDA0003482867100000094
wherein the content of the first and second substances,
Figure FDA0003482867100000095
respectively increasing and decreasing the load quantity of various load C-type IDRs;
the coupling device reserve capacity contribution constraint is established as follows:
Figure FDA0003482867100000096
wherein, Δ Pmachine,sAnd (t) is a spare output value of each type of coupling equipment, and the formula shows that the spare output values of the various types of coupling equipment meet the upper and lower limits, and the sum of the spare capacity output and the day-ahead output plan meets the capacity limit.
7. The method for optimizing the operation of an integrated energy system according to claim 4, wherein the step 3.3 comprises:
establishing a real-time optimization model, wherein the real-time optimization model comprises the following steps:
Figure FDA0003482867100000101
Figure FDA0003482867100000102
wherein k isn,IDR,DA cost factor for a class D IDR for a class of loads; | Δ Pn,IDR,D(t) | is the calling amount of a certain load D type IDR at the time t;
and establishing constraint conditions of the real-time optimization model, wherein the constraint conditions comprise electric power balance constraint, natural gas balance constraint, thermal power balance constraint and hydrogen energy balance constraint:
the electric power balance constraint is established as follows:
Figure FDA0003482867100000103
the natural gas equilibrium constraints are established as follows:
Figure FDA0003482867100000104
the thermal power balance constraint is established as follows:
Figure FDA0003482867100000105
the hydrogen energy balance constraint is established as follows:
Figure FDA0003482867100000106
wherein Pr {. is a confidence expression;
Figure FDA0003482867100000107
is the electrical/gas/thermal/hydrogen power balance confidence level;
the change of the distributed generation output constraint in the real-time optimization model is reflected in that the output of the distributed generation in the real-time optimization cannot exceed the real-time maximum output upper limit under the restriction of the practical condition on the basis of the rolling optimization value in the extended day, and the constraint is established as follows:
Figure FDA0003482867100000111
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003482867100000112
respectively optimizing the actual upper limits of the photovoltaic output and the actual upper limits of the fans in real time;
Figure FDA0003482867100000113
respectively are the output plans of the photovoltaic and the fan obtained in the rolling optimization in the day.
8. The method for optimizing the operation of an integrated energy system according to claim 4, wherein the step 3.4 comprises:
carrying out linearization processing on the absolute value item and the energy storage state constraint in the objective function respectively;
carrying out linearization processing on the absolute value item in the objective function, wherein a user load management cost function f in the objective function in the day-ahead optimization modelload(t) is represented as follows:
Figure FDA0003482867100000114
equivalence is carried out on the absolute value term in the formulaLinearization, introducing a real auxiliary variable Un,A,s(t)、Un,B,s(t) and a binary auxiliary variable δn,A,s(t)、δn,B,s(t)、εn,A,s(t)、εn,B,s(t), which can be equivalently expressed as:
Figure FDA0003482867100000115
Figure FDA0003482867100000116
Figure FDA0003482867100000117
wherein: m is a preset constant, and by analogy, the absolute value items in other objective functions are subjected to linear conversion;
and carrying out linearization processing on the operation constraint of the energy storage element, wherein the original constraint conditions of the electric energy storage operation constraint in the day-ahead optimization model are as follows:
Figure FDA0003482867100000121
introducing a binary variable
Figure FDA0003482867100000122
The charge and discharge energy state of the electric storage is represented, a value of 1 and a value of 0 respectively represent that the electric storage is in/not in a certain state, and the electric storage is subjected to linearization equivalent processing to obtain:
Figure FDA0003482867100000123
and in the same way, the other energy storage state operation constraints are subjected to linear conversion.
9. An integrated energy system operation optimization device, comprising a processor, a memory, and an integrated energy system operation optimization program stored on the memory and executable by the processor, wherein the integrated energy system operation optimization program when executed by the processor implements the steps of the integrated energy system operation optimization method of any one of claims 1 to 8.
10. A readable storage medium having an integrated energy system operation optimization program stored thereon, wherein the integrated energy system operation optimization program when executed by a processor implements the steps of the integrated energy system operation optimization method according to any one of claims 1 to 8.
CN202210072747.2A 2022-01-21 2022-01-21 Method and device for optimizing operation of integrated energy system and readable storage medium Pending CN114529056A (en)

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US20230088217A1 (en) * 2021-09-22 2023-03-23 Michael D. Mercer Intelligent fuel storage system
US20230086470A1 (en) * 2021-09-22 2023-03-23 Michael D. Mercer Energy utilization system
US11885270B2 (en) * 2021-09-22 2024-01-30 Michael D. Mercer Energy utilization system
US11927144B2 (en) * 2021-09-22 2024-03-12 Michael D. Mercer Intelligent fuel storage system

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