CN114881328A - Comprehensive energy system economic dispatching method considering gas network hydrogen mixing and low-carbon reward - Google Patents

Comprehensive energy system economic dispatching method considering gas network hydrogen mixing and low-carbon reward Download PDF

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CN114881328A
CN114881328A CN202210496629.4A CN202210496629A CN114881328A CN 114881328 A CN114881328 A CN 114881328A CN 202210496629 A CN202210496629 A CN 202210496629A CN 114881328 A CN114881328 A CN 114881328A
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gas
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CN114881328B (en
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周步祥
陈阳
臧天磊
张远洪
闵昕玮
赵雯雯
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Sichuan University
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Abstract

The invention discloses an economic dispatching method for a comprehensive energy system considering gas network hydrogen mixing and low-carbon reward. Aiming at the gas-electric coupling model, the starting constraint and the climbing constraint of the electric hydrogen production process are considered in combination with the characteristics of P2H equipment, so that the scheduling result is more consistent with the actual running state. In the NGECS low-carbon optimization scheduling, a reward mechanism under the condition of setting the carbon quota surplus is considered, a reward ladder type carbon transaction mechanism is established, and stricter constraint is formed on carbon emission. And meanwhile, a reward benchmark reference price is provided, the optimal carbon reduction effect of unit investment is ensured, and the carbon reduction effect can be ensured while the capital budget is controlled. Finally, the gas-electricity comprehensive energy system obviously improves the consumption level of renewable energy sources, and effectively reduces the carbon emission level of the system while ensuring certain economical efficiency.

Description

Comprehensive energy system economic dispatching method considering gas network hydrogen mixing and low-carbon reward
Technical Field
The invention relates to a comprehensive energy system economic dispatching method considering gas network hydrogen mixing and low-carbon reward.
Background
In order to protect the ecological environment, China proposes a '3060' double-carbon target [1 ]. The key path of realizing 'double carbon' is to develop and utilize renewable energy resources vigorously, and to adhere to the direction of market reformation, and to accelerate the perfection of the carbon trading market [2-3 ]. The wind power and photovoltaic total installed ratio in China is estimated to be up to 50% [4] in 2050, but the randomness and the intermittence of a large amount of renewable energy sources during grid connection lead to the problems of wind abandonment, light abandonment and the like. Meanwhile, hydrogen production from renewable energy is an important technical means for realizing the aim of 'double carbon', and the electricity-to-gas and air grid hydrogen doping technology has the functions of flexibly consuming renewable energy and reducing carbon emission and provides an optimization idea for low-carbon optimization scheduling of a system [5 ].
Gas-electric Coupling Systems (NGECS) and the like are reliable carriers for coordinating comprehensive energy output and reducing carbon emission [6-8 ]. Power-To-Hydrogen (P2H) and Power-To-Methane (P2M) are electricity-To-gas technologies, and the generated gas is injected into a gas network, so that renewable energy sources can be consumed and carbon emission can be reduced [9-12 ]. The two electricity-to-gas technologies and the gas turbine are arranged in the system together, so that the gas-electricity coupling system forms a closed-loop energy system, and the flexibility of the comprehensive energy system is enhanced.
In the area of energy system carbon reduction research, there are two common forms of carbon trading. First, a unified carbon transaction mechanism, wherein the carbon transaction price is fixed; second, a ladder-type carbon transaction mechanism, the carbon transaction price is set to ladder-type. The two methods are designed with punishment measures of different degrees aiming at the scene of insufficient carbon quota of the system.
The existing research considering the gas network hydrogen adding technology in a comprehensive energy system considers the gas network hydrogen adding as the process of evenly distributing the hydrogen adding in the whole gas network pipeline, does not consider the difference of the hydrogen adding quantity among different pipelines due to the influence of actual topology, does not consider the change of the heat value of mixed gas after hydrogen mixing in gas network nodes, and further ignores the gas load change caused by the change of the heat value of the nodes, so that a series of influences of the actual hydrogen adding on system scheduling are ignored.
At present, model construction aiming at the gas-electric coupling link generally considers the electric gas production link as a simple efficiency model, neglects the characteristics of actual electric hydrogen production equipment and has difference with the actual operation state.
At present, a common carbon trading mechanism generally utilizes a punishment mechanism, namely, punishment is set for carbon emission exceeding carbon quota in a system, but with the improvement of energy structure, the new energy occupation ratio is improved year by year, the scenes of carbon quota surplus are gradually common, and the punishment of a traditional mechanism in the scenes of carbon quota surplus is zero, so that carbon can not be further reduced and controlled.
Disclosure of Invention
The invention aims to provide an economic dispatching method of a comprehensive energy system considering gas network hydrogen mixing and low-carbon reward.
In order to achieve the purpose, the invention is implemented according to the following technical scheme:
the invention comprises the following steps:
s1: establishing an NGECS model containing gas network hydrogen loading: in the NGECS low-carbon optimized dispatching, the influence of hydrogen after being mixed on the operation of a gas network is considered, the heat value change of mixed gas, the gas load change and the hydrogen mixing proportion limitation are considered, the dispatching result accords with the actual condition, and the starting constraint and the climbing constraint of the electrical hydrogen production process are considered by aiming at the NGECS model and combining the characteristics of P2H equipment, so that the dispatching result accords with the actual operation state;
s2: establishing an NGECS low-carbon optimized scheduling model based on reward ladder type carbon transaction: in the NGECS low-carbon optimization scheduling, a reward mechanism under the condition of setting the carbon quota surplus is considered, a reward ladder type carbon transaction mechanism is established, and the carbon emission is restrained; and meanwhile, a reward benchmark reference price is provided, the optimal carbon reduction effect of unit investment is ensured, and the carbon reduction effect can be ensured while the capital budget is controlled.
The establishing of the NGECS model containing the gas network hydrogen comprises gas-electricity coupling refined modeling, electric power system modeling and natural gas system modeling, and the establishing of the NGECS low-carbon optimized dispatching model based on the reward-penalty ladder-type carbon trading comprises a reward-penalty ladder-type carbon trading model, an NGECS low-carbon optimized dispatching objective function and model solving.
The invention has the beneficial effects that:
compared with the prior art, the invention considers the influence of hydrogen doping on the operation of the gas network in the NGECS low-carbon optimized dispatching process, and considers the heat value change of mixed gas, the gas load change and the hydrogen doping proportion limitation, so that the dispatching result is more in line with the actual situation. Aiming at the gas-electric coupling model, the starting constraint and the climbing constraint of the electric hydrogen production process are considered in combination with the characteristics of P2H equipment, so that the scheduling result is more consistent with the actual running state. In the NGECS low-carbon optimization scheduling, a reward mechanism under the condition of setting the carbon quota surplus is considered, a reward ladder type carbon transaction mechanism is established, and stricter constraint is formed on carbon emission. And meanwhile, a reward benchmark reference price is provided, the optimal carbon reduction effect of unit investment is ensured, and the carbon reduction effect can be ensured while the capital budget is controlled. Finally, the gas-electricity comprehensive energy system obviously improves the consumption level of renewable energy sources, and effectively reduces the carbon emission level of the system while ensuring certain economical efficiency.
Drawings
FIG. 1 is a schematic diagram of the NGECS model;
FIG. 2 is a scheduling result for scenario 2;
FIG. 3 is a scheduling result for scenario 3;
FIG. 4 is a scheduling result for scenario 4;
FIG. 5 is an illustration of the effect of reward base price on reward cost and carbon emissions;
FIG. 6 is a model solution flow diagram;
FIG. 7 is a diagram of a NGECS test model architecture;
FIG. 8 is a NGECS wind forecast, electrical load, and air load.
Detailed Description
The invention will be further described with reference to the drawings and specific embodiments, which are illustrative of the invention and are not to be construed as limiting the invention.
The NGECS model of the invention is shown in figure 1, wherein the electric gas conversion device enables the energy between the original electric networks to flow in two directions, the produced gas can be stored and transferred in large capacity by means of the existing natural gas network, the investment is saved, and the consumption of the energy in different places is realized. The P2H technical principle is that hydrogen and oxygen are generated by the reaction of electrolytic water, and the conversion efficiency is high. The hydrogen energy has the advantages of environmental friendliness, high conversion efficiency and the like, and is one of the most promising clean energy sources. Among the types of electric gas conversion, P2H is currently the most energy-efficient and simple solution. Aiming at the problem of pipeline hydrogen brittleness possibly caused by excessive hydrogen doping in natural gas, the limit of hydrogen injection proportion is not unified internationally, and the upper limit of the hydrogen doping volume ratio is set to be 3% in order to strictly ensure the hydrogen doping safety.
The P2M technology is a sabatier catalyzed reaction of hydrogen and carbon dioxide to produce methane and water. Carbon dioxide can be collected from power plant exhaust, plant exhaust and air by a carbon capture device; methane can be injected into a natural gas network without limitation, the process reaction is rapid, the output fluctuation of renewable energy sources can be alleviated, and the renewable energy sources can be indirectly stored.
The transformation rationale is as follows:
1) electrolytic water reaction:
Figure 270528DEST_PATH_IMAGE001
2) and (3) catalytic reaction:
Figure 301938DEST_PATH_IMAGE002
1.1: gas-electric coupling refined modeling
At present, the P2H technology mainly comprises 3 mainstream hydrogen production modes, namely alkaline electrolysis, proton exchange membrane electrolysis and solid oxide electrolysis. There is a large difference between the start-up and climbing states of the three devices in operation. In the starting aspect, because the solid oxide electrolysis is influenced by the temperature rise of the electric pile, the time for temperature rise in the prior art is about two hours, the time for alkaline electrolysis and proton exchange membrane electrolysis is short, and the output result of equipment can be obviously influenced by different starting characteristics; in the aspect of climbing, the climbing capacities of alkaline electrolysis and solid oxide electrolysis are respectively 50% and 70% smaller than that of proton exchange membrane electrolysis, and a large difference exists, so that the invention discloses the principle differences of three main flow devices by considering setting the starting constraint and climbing constraint of hydrogen production equipment, selects a proton exchange membrane technology, and has both economy and quick response capacity.
1) P2M force constraints
Figure 456976DEST_PATH_IMAGE003
（1）
In the formula:
Figure 8174DEST_PATH_IMAGE004
、
Figure 387203DEST_PATH_IMAGE005
and
Figure 781275DEST_PATH_IMAGE006
respectively, P2M devices
Figure 599058DEST_PATH_IMAGE007
In that
Figure 129397DEST_PATH_IMAGE008
The power consumption and the upper and lower limits thereof.
2) P2M coupling constraints
Figure 993841DEST_PATH_IMAGE009
（2）
In the formula:
Figure 875209DEST_PATH_IMAGE010
indicating P2M devices
Figure 496683DEST_PATH_IMAGE007
In that
Figure 943845DEST_PATH_IMAGE008
Preparing gas flow of methane in time intervals;
Figure 867939DEST_PATH_IMAGE011
represents a high heating value of methane;
Figure 580811DEST_PATH_IMAGE012
indicating P2M devices
Figure 615763DEST_PATH_IMAGE007
The conversion efficiency of (a).
3) P2H force constraints
Figure 510907DEST_PATH_IMAGE013
（3）
In the formula:
Figure 402640DEST_PATH_IMAGE014
、
Figure 993021DEST_PATH_IMAGE015
and
Figure 205565DEST_PATH_IMAGE016
respectively, P2H devices
Figure 565002DEST_PATH_IMAGE017
In that
Figure 955532DEST_PATH_IMAGE008
The power consumption of the time interval and the upper limit and the lower limit of the time interval.
4) P2H coupling constraint
Figure 33210DEST_PATH_IMAGE018
（4）
In the formula:
Figure 206702DEST_PATH_IMAGE019
indicating P2H devices
Figure 561591DEST_PATH_IMAGE017
In that
Figure 998389DEST_PATH_IMAGE008
Preparing the gas flow of the hydrogen in time intervals;
Figure 422417DEST_PATH_IMAGE020
represents a high heating value of hydrogen;
Figure 602863DEST_PATH_IMAGE021
device for indicating electric hydrogen production
Figure 313723DEST_PATH_IMAGE017
The conversion efficiency of (a).
5) P2H Start State constraint
Figure 718160DEST_PATH_IMAGE022
（5）
In the formula:
Figure 770429DEST_PATH_IMAGE023
indicating P2H devices
Figure 879200DEST_PATH_IMAGE017
The accumulated opening time of (c);
Figure 802156DEST_PATH_IMAGE024
is a device
Figure 377494DEST_PATH_IMAGE017
Minimum on time;
Figure 792426DEST_PATH_IMAGE025
indicating P2H devices
Figure 314674DEST_PATH_IMAGE017
The on-power of (a) is,
Figure 747930DEST_PATH_IMAGE026
indicating P2H devices
Figure 431852DEST_PATH_IMAGE017
Minimum boot power.
6) P2H hill climbing restraint
Figure 832615DEST_PATH_IMAGE027
（6）
In the formula:
Figure 892975DEST_PATH_IMAGE028
、
Figure 852841DEST_PATH_IMAGE029
is a device
Figure 363456DEST_PATH_IMAGE017
The landslide, the climbing rate limit.
7) Mixed gas heat value calculation mode of hydrogen-doped gas network
The calorific value in a conventional natural gas grid is a constant; the conversion of P2M technology to produce methane for injection into the gas grid can still be assumed without changing the natural gas heating value. After the gas network is doped with hydrogen, because the difference between the heat value of the hydrogen and the heat value of the natural gas is larger, after the hydrogen is injected into the natural gas network, the mixed heat value of the gas network after doping is recalculated for each gas network node, and if the gases with different heat values are converged into the node, the gases with uniform mixed heat value are uniformly mixed and flow out from the node, and the mixed heat value of the node is updated as follows.
Figure 612035DEST_PATH_IMAGE030
（7）
In the formula:
Figure 85873DEST_PATH_IMAGE031
representing the mixed heat value at the y node.
Figure 369087DEST_PATH_IMAGE032
、
Figure 457128DEST_PATH_IMAGE019
、
Figure 317637DEST_PATH_IMAGE010
、
Figure 985379DEST_PATH_IMAGE033
、
Figure 222632DEST_PATH_IMAGE034
、
Figure 684837DEST_PATH_IMAGE035
And
Figure 32642DEST_PATH_IMAGE036
to represent
Figure 35233DEST_PATH_IMAGE008
Time interval natural gas source, P2H, P2M and pipeline
Figure 27460DEST_PATH_IMAGE037
Inflow gas flow rate, pipe
Figure 535933DEST_PATH_IMAGE037
The outflow gas flow, the gas consumption flow of the gas turbine unit and
Figure 246400DEST_PATH_IMAGE038
a load value at the gas node;
Figure 114999DEST_PATH_IMAGE039
、
Figure 24049DEST_PATH_IMAGE040
are respectively shown in
Figure 828057DEST_PATH_IMAGE038
A pipeline set as an input node and
Figure 399721DEST_PATH_IMAGE038
is a collection of pipes at the output node.
After the heat value of each node of the gas network is changed, the gas load of the corresponding connection node is correspondingly changed, and the calculation method comprises the following steps:
Figure 681798DEST_PATH_IMAGE041
（8）
in the formula:
Figure 773251DEST_PATH_IMAGE042
representing the initial energy of the gas load at each node prior to non-loading.
8) Hydrogen loading ratio constraint
The actual popularization and application condition of the gas network hydrogen loading is combined, the volume ratio of hydrogen loading is generally lower than 5%, and the hydrogen loading proportion is set to be 3% on the basis of strictly considering the actual hydrogen loading safety. The loading ratio constraint is expressed as:
Figure 13739DEST_PATH_IMAGE043
（9）
in the formula:
Figure 308586DEST_PATH_IMAGE044
represents the upper limit of the hydrogen loading ratio.
1.2 Power System modeling
1) Power system DC power flow constraint
Figure 659933DEST_PATH_IMAGE045
（10）
In the formula:
Figure 277996DEST_PATH_IMAGE046
representing the branch power;
Figure 814019DEST_PATH_IMAGE047
and
Figure 720795DEST_PATH_IMAGE048
respectively representing a branch admittance coefficient matrix and a branch admittance coefficient diagonal matrix;
Figure 987085DEST_PATH_IMAGE049
representing a branch node incidence matrix;
Figure 928496DEST_PATH_IMAGE050
、
Figure 635421DEST_PATH_IMAGE051
、
Figure 763914DEST_PATH_IMAGE052
、
Figure 253801DEST_PATH_IMAGE053
、
Figure 659506DEST_PATH_IMAGE054
and
Figure 412698DEST_PATH_IMAGE055
respectively represents the node loads of each thermal power generator set, each wind power generator set, each gas generator set, each electric hydrogen production device, each electric methane production device and each electric power system in the vector form
Figure 418701DEST_PATH_IMAGE056
The power of the time period.
2) Node active power balance constraint
Figure 649962DEST_PATH_IMAGE057
（11）
In the formula:
Figure 674287DEST_PATH_IMAGE058
、
Figure 332802DEST_PATH_IMAGE059
、
Figure 498204DEST_PATH_IMAGE060
、
Figure 657790DEST_PATH_IMAGE061
、
Figure 428300DEST_PATH_IMAGE062
、
Figure 867502DEST_PATH_IMAGE063
respectively representing the number of thermal generator sets, wind generator sets, gas generator sets, electric hydrogen production devices, electric methane production devices and load nodes.
In addition, the phase angle constraint and the line transmission capacity constraint of the direct current power flow, the output constraint, the start-stop constraint, the climbing constraint of the thermal generator set and the output constraint of the wind turbine set are considered at the same time, and the details are shown in the attached formulas (A1) - (A6). The wind curtailment electricity quantity modeling is detailed in the appendix (A7).
1.3: natural gas system modeling
Elements of natural gas system modeling include gas sources, pipelines, compressors, and gas loads. The invention integrates network structure, operation mechanism and safety constraint for modeling.
1) Gas network flow constraint
The gas grid flow is modeled using the Weymouth equation.
Figure 723463DEST_PATH_IMAGE064
（12）
Figure 686740DEST_PATH_IMAGE065
（13）
Figure 46177DEST_PATH_IMAGE066
（14）
In the formula:
Figure 108811DEST_PATH_IMAGE067
indicating a pipe
Figure 563319DEST_PATH_IMAGE037
In that
Figure 205653DEST_PATH_IMAGE056
Average airflow over a period of time;
Figure 544231DEST_PATH_IMAGE068
to and the pipeline
Figure 981028DEST_PATH_IMAGE037
The Weymouth constant with respect to cross-sectional area and length, etc.;
Figure 421368DEST_PATH_IMAGE069
、
Figure 398551DEST_PATH_IMAGE070
representing nodes
Figure 732581DEST_PATH_IMAGE038
And
Figure 464913DEST_PATH_IMAGE071
the air pressure of (a).
2) Airflow balance constraints at gas network nodes
Figure 517183DEST_PATH_IMAGE072
（15）
In the formula:
Figure 344062DEST_PATH_IMAGE073
to represent
Figure 798177DEST_PATH_IMAGE056
The air consumption flow of the time-interval compressor,
Figure 373515DEST_PATH_IMAGE033
And
Figure 37715DEST_PATH_IMAGE034
respectively representing natural gas lines
Figure 559963DEST_PATH_IMAGE037
The natural gas flow rate during the initial to the end period is scheduled. The gas network flow is nonlinear constraint, and the method is used for processing by using a second-order cone relaxation method.
3) Inventory constraint
By stored gas is meant that the natural gas pipeline is affected by the buffering characteristics of the gas flow in the pipeline, and the pipeline is capable of storing a certain volume of natural gas. Since the inventory is not equivalent to the source or load, the inventory before and after a scheduling period is set equal.
Figure 743951DEST_PATH_IMAGE074
（16）
In the formula:
Figure 427873DEST_PATH_IMAGE075
is a pipe
Figure 313789DEST_PATH_IMAGE037
In that
Figure 639729DEST_PATH_IMAGE056
Managing and storing time intervals;
Figure 599594DEST_PATH_IMAGE076
is a natural gas pipeline
Figure 96828DEST_PATH_IMAGE037
The characteristic coefficient of (a);
Figure 610986DEST_PATH_IMAGE077
、
Figure 334091DEST_PATH_IMAGE078
are respectively pipelines
Figure 148464DEST_PATH_IMAGE037
Buffering at initial and final time periods.
4) Compressor restraint
The invention assumes that a gas compressor with fixed transformation ratio is configured in a natural gas pipeline model to solve the problem of air pressure drop generated in operation. With a flow rate consumption of
Figure 705347DEST_PATH_IMAGE079
（17）
In the formula:
Figure 316588DEST_PATH_IMAGE080
indicating compressor
Figure 718750DEST_PATH_IMAGE081
Energy conversion coefficient of (2);
Figure 715525DEST_PATH_IMAGE082
indicating compressor
Figure 443310DEST_PATH_IMAGE081
The working efficiency of (2);
Figure 40382DEST_PATH_IMAGE083
、
Figure 42973DEST_PATH_IMAGE084
indicating compressor
Figure 35200DEST_PATH_IMAGE081
Output node pressure and input node pressure;
Figure 792940DEST_PATH_IMAGE085
representing the compressor's coefficient of variation.
NGECS low-carbon optimization scheduling model based on reward and penalty ladder type carbon transaction
2.1 ladder-type carbon trading model considering reward penalty
The policy of China for issuing carbon emission quotas is based on the actual power generation capacity of enterprises, and the carbon emission quotas are proportionally distributed in a gratuitous manner. At present, the carbon quota allocation mode is based on the existing energy structure (the thermal power occupation ratio is large), and the thermal power generation needs to be properly emphasized. However, the model is set to be a scene in which the clean energy ratio is increased and the thermal power unit ratio is reduced. Therefore, a unified carbon emission quota baseline, a carbon emission quota modeling reference, is set for thermal power generation and gas power generation in the present model [32 ]. The main carbon emission sources in the system are a thermal generator set and a gas generator set, so that the carbon emission quota is as follows:
Figure 237828DEST_PATH_IMAGE086
（18）
in the formula:
Figure 122739DEST_PATH_IMAGE087
represents the total carbon credit in one period in the system;
Figure 235051DEST_PATH_IMAGE088
representing the carbon emission credit corresponding to each unit of output;
Figure 163693DEST_PATH_IMAGE089
and
Figure 361456DEST_PATH_IMAGE060
respectively representing the total number of the thermal generator set and the gas generator set;
Figure 705850DEST_PATH_IMAGE090
and
Figure 61219DEST_PATH_IMAGE091
respectively shows a thermal generator set and a gas generator set
Figure 36128DEST_PATH_IMAGE056
The electricity generation output of the period.
On the other hand, the reference [26] for actual carbon emissions of thermal power and gas power generation is expressed as:
Figure 845821DEST_PATH_IMAGE092
（19）
in the formula:
Figure 197168DEST_PATH_IMAGE093
represents the actual carbon emissions;
Figure 893860DEST_PATH_IMAGE094
and the carbon emission correlation coefficient of the thermal generator set is shown.
Figure 305249DEST_PATH_IMAGE095
Representing the carbon emission correlation coefficient of the gas generator set.
In the reaction process of the electric methane production, carbon dioxide is taken as a raw material, and the carbon dioxide absorbed in the P2M process is considered to be included in a penalty carbon trading model as a trading item, so that the carbon emission cost can be further reduced. The carbon emission model for P2M is:
Figure 336659DEST_PATH_IMAGE096
（20）
in the formula:
Figure 226118DEST_PATH_IMAGE097
represents the carbon emission of P2M;
Figure 541430DEST_PATH_IMAGE098
represents the mass of carbon dioxide absorbed per unit of electricity consumed by the P2M plant;
Figure 592563DEST_PATH_IMAGE099
representing a scheduling period;
Figure 111269DEST_PATH_IMAGE062
indicating the number of P2M.
In order to strictly control the carbon emission, the invention adopts a reward-penalty step-type carbon emission model. A mode of setting rewards is adopted in the transaction, and when the carbon quota surplus exists in the system, a certain incentive is given to the whole system; and meanwhile, a penalty factor is set, and when the carbon quota of the system is insufficient, a certain pressure is applied to the whole system. The reward ladder type carbon transaction model is as follows:
Figure 397074DEST_PATH_IMAGE101
（21）
in the formula:
Figure 25633DEST_PATH_IMAGE102
representing a system carbon transaction cost;
Figure 641422DEST_PATH_IMAGE103
the price is a reward reference price, namely a reward price corresponding to the first unit residual carbon quota amount;
Figure 669420DEST_PATH_IMAGE104
expressing the increment of a reward factor, namely the reward multiplying power added by the carbon emission per increment of the residual carbon quota per unit interval;
Figure 444478DEST_PATH_IMAGE105
an interval representing carbon emission;
Figure 368572DEST_PATH_IMAGE106
the penalty factor is that the carbon emission is increased by the penalty multiplying power increased by the unit interval when the carbon quota is insufficient.
2.2 NGECS Low carbon optimized scheduling objective function
The invention provides an NGECS low-carbon optimization scheduling function comprehensively considering carbon transaction cost, unit operation cost, start-stop cost of a thermal unit, start-stop cost of a gas turbine, natural gas purchase cost and carbon raw material cost by combining with a reward step type carbon transaction mechanism, as shown in a formula (22), and in addition, the electricity consumption cost of electric hydrogen production and electric methane production is counted in the output cost of the unit.
Figure 848488DEST_PATH_IMAGE107
（22）
In the formula:
Figure 883440DEST_PATH_IMAGE108
to the total operating cost;
Figure 513005DEST_PATH_IMAGE109
、
Figure 404738DEST_PATH_IMAGE110
、
Figure 260698DEST_PATH_IMAGE111
representing a cost coefficient of the thermal generator set;
Figure 443549DEST_PATH_IMAGE112
、
Figure 68565DEST_PATH_IMAGE113
representing the starting and stopping costs of the thermal generator set;
Figure 459095DEST_PATH_IMAGE114
、
Figure 536773DEST_PATH_IMAGE115
representing the starting and stopping cost of the gas generator set;
Figure 553008DEST_PATH_IMAGE116
、
Figure 32531DEST_PATH_IMAGE117
representing the on and off state variables of the thermal generator set;
Figure 266066DEST_PATH_IMAGE118
、
Figure 955674DEST_PATH_IMAGE119
representing the on and off state variables of the gas generator set;
Figure 870540DEST_PATH_IMAGE120
represents the unit gas purchase cost,
Figure 79935DEST_PATH_IMAGE121
Representing the unit cost of purchasing carbon.
2.3 model solution
The detailed solving flow chart of the energy model is shown in the attached figure 6, and the solving steps are as follows:
step 1: inputting initial data, including setting an initial value of a gas network node;
step 2: judging whether to add hydrogen, selecting a carbon transaction mechanism and selecting a target function;
step 3: solving by using CPLEX, and performing first iteration;
step 4: substituting the obtained gas network trend result into a formula (7) and a formula (8) to obtain the updated heat value of each node of the gas network and the node gas load value;
step 5: and judging the heat value precision of each node of the air network and the air load flow precision of the nodes before and after iteration, stopping calculation and outputting a result if the precision is met, and returning to Step3 for next iteration if the precision is not met.
3 example analysis
The invention adopts a Belgium 20-node gas network and an IEEE 39-node power system as analysis and verification examples, and the detailed structure is shown in figure 7. The power system comprises 8 thermal generator sets, 2 wind generator sets and 2 gas turbines; the natural gas system comprises 4 groups of natural gas sources, 2P 2M equipment and 2P 2H equipment. The wind power predicted output, electrical load and air load data are shown in the appendix 8.
3.1 different scheduling model comparison analysis
In order to verify the effects of the air network hydrogen-loading technology and the reward ladder type carbon transaction mechanism considered by the invention on the consumption of the abandoned wind and the reduction of carbon emission, 4 scenes are set and compared according to an example. Scene 1: the air network hydrogen loading technology and the carbon transaction mechanism are not considered; scene 2: consider the air net loading technique but not the carbon trading mechanism; scene 3: considering the air network hydrogen loading technology and the ladder type carbon transaction mechanism; scene 4: consider the air net loading technique and the reward ladder type carbon trading mechanism.
1) Comparative analysis of results from scene 1 and scene 2
Table 1 shows the scheduling results in 4 scenarios, and it can be seen from table 1 that scenario 2 reduces the air loss by 74.1% compared to scenario 1, and the total cost is reduced by 6.88 ten thousand yuan, but the carbon emission is increased by 1.3%. The P2H technology is newly added in scene 2, compared with the P2M technology, the unit gas production has lower electric energy loss and lower cost, the abandoned wind is preferentially converted into hydrogen through the P2H technology and stored in a gas network, the wind power is efficiently consumed, the equivalent abandoned wind is converted into more gas, the gas source investment is reduced, and the operation cost is saved. However, compared with the P2M technology, the P2H has no carbon absorption effect, and the net carbon emission of the system is increased. The method proves that the hydrogen production by electricity has obvious effect on the absorption of wind electricity, and meanwhile, the operation cost is effectively reduced, and the system economy is improved. Fig. 2, 3 and 4 show the scheduling results of scene 2, scene 3 and scene 4, respectively.
Table 1: scheduling results of 4 scenarios
Figure 687634DEST_PATH_IMAGE123
2) Comparative analysis of results from scene 2 and scene 3
As can be seen from table 1, the carbon emission in scenario 3 is reduced by 3.3% compared to scenario 2, but the total cost is increased by only 0.03%. Because the carbon emission level of the single thermal power in the system is far higher than that of the single fuel gas, namely the newly added carbon transaction cost part of the system is equal to the improvement of the output cost of the thermal power unit, the thermal power output is strictly controlled. Also, as the carbon emission level is higher, the penalty is higher, the carbon transaction cost increases, and the total cost increases. As can be seen from the comparison between fig. 2 and fig. 3, the thermal power output is reduced in the system in the period from 7:00 to 22:00, the gas output is improved, the carbon emission remaining condition in the scene 2 is 1000t in excess, the overall carbon emission level is controlled to be 221.79t in excess by using the carbon transaction step penalty in the scene 3, the carbon transaction cost is reduced, and although the operation cost is improved by 0.03% by adding the gas and carbon transaction penalty cost, the carbon emission level is reduced by 649 t. Therefore, the introduction of the stepped carbon trading mechanism forms stricter constraints on carbon emission, and the effectiveness of the introduction of the carbon trading mechanism on carbon reduction is verified.
3) Scene 3 in contrast to scene 4
As can be seen from table 1, the carbon emission is reduced by 5.0%, the amount of waste air in scenario 4 is reduced by 12.3% compared to scenario 3, and the total cost is reduced by 1.41 ten thousand yuan. Because the reward ladder type carbon transaction mechanism in the scenario 4 gives a ladder type reward for the scenario where the carbon quota remains, it means that the more the carbon quota remains, the higher the reward unit price is, and the greater the carbon emission profit weight is, the further the gas power generation and the control of the thermal power generation are encouraged. After the system has the carbon quota surplus, the scene 3 participates in the transaction with the uniform carbon price, and the carbon profit weight is fixed, so the scene 4 can form more strict control on carbon emission, and is beneficial to acquiring the carbon profit and reducing the total cost. As can be seen from comparison of FIGS. 3 and 4, in the scenario 4, thermal power generation is reduced in the time periods of 1: 00-3: 00 and 9: 00-21: 00, gas power generation is increased in the time periods of 11: 00-21: 00, meanwhile, the output of P2M is increased at 3:00 and 24:00, although P2M has lower conversion efficiency compared with P2H, carbon benefits can be obtained with the carbon absorption effect, and the carbon benefits are equivalent to the reduction of the comprehensive output cost of P2M, and the three changes jointly result in the reduction of the overall carbon emission. Meanwhile, the wind power output is increased in the period from 1:00 to 2:00, and the abandoned wind rate is reduced. The reward type carbon transaction is verified to effectively reduce carbon emission, reduce waste wind and control the total cost.
4) Scene 1 in contrast to scene 4
Scenario 4 compared to scenario 1, scenario 4 considers both the air grid hydrogen loading and reward ladder carbon trading mechanism. Combining the analysis in 2) and 3), because the operation cost is reduced by the hydrogen doping and carbon income of the air network, and the carbon trading model is rewarded to provide a stricter carbon emission constraint, the air abandonment amount of the scene 4 is reduced by 77.3%, the carbon emission is reduced by 7.0%, and the total cost is reduced by 4.49 ten thousand yuan compared with the operation cost as shown in table 1.
The above comparison fully shows that the consideration of the air grid hydrogen loading and penalty ladder type carbon transaction mechanism in the NGECS has remarkable effects on accommodating the abandoned wind, reducing the carbon emission and economy.
3.2 reference pricing analysis of reward prices
FIG. 5 reflects the effect of reward costs and carbon emissions when the reward base price is increased. When the reward benchmark price is 1 yuan/t, the reward cost is 600 yuan, and compared with the reward of 0 yuan/t, carbon reduction of 964t is realized, namely, the carbon emission is reduced by 5.1%; when the reward price is 68 yuan/t, the reward cost is 8.16 ten thousand yuan, and carbon 1306t can be reduced, namely the carbon emission is reduced by 6.9%. The reason for the existence of the price inflection point is that the carbon transaction reward mechanism presents a step characteristic, and further causes the system to increase the residual amount of carbon quota in a step way. At the inflection point, the system tries to adjust the output while ensuring the economy, so that the carbon quota residual interval is the critical value of the maximum reward unit price interval, the relatively maximum carbon profit can be obtained at the moment, after the reward reference price is increased, the weight of the carbon profit is larger than the economic operation weight again until the carbon quota residual amount of the system meets the inflection point of the next interval, and the system readjusts the output to enable the carbon quota residual amount of the system to reach the second reward interval. As shown in FIG. 5, 1-and 68-bins/t are the inflection points of the interval. When the reward benchmark price is 68 yuan/t as the reference, the unit carbon reduction cost is 62.48 yuan/t, and the aim of carbon reduction by 6.9 percent is achieved. If the price is changed to 80 yuan/t, as shown in fig. 5, the system achieves the same carbon reduction effect, but the reward cost reaches 95999 yuan, and the new cost is 14399 yuan. It follows that reward pricing is preferably combined with a carbon reduction target and a budget reference inflection price.
According to the analysis, the model has guiding significance on reward unit price, and the optimal reference price can be obtained after budget and a carbon reduction target are combined; meanwhile, when the reward amount is larger than a certain value, only the reward burden is increased, and the effect of lower unit carbon reduction cost cannot be further obtained.
Appendix A:
1) phase angle constraint
Figure 864538DEST_PATH_IMAGE124
(A1)
In the formula:
Figure 379833DEST_PATH_IMAGE125
representing nodes of an electrical power systembThe phase angle of (d);
Figure 833948DEST_PATH_IMAGE126
representing nodesbThe maximum value of the phase angle at.
2) Line transmission capacity constraints
Figure 989379DEST_PATH_IMAGE127
（A2）
In the formula:
Figure 263366DEST_PATH_IMAGE128
the maximum transmission capacity of the power system line.
3) Output constraint of thermal generator set
Figure 910248DEST_PATH_IMAGE129
（A3）
In the formula:
Figure 15607DEST_PATH_IMAGE130
representing the running state variable of the thermal power unit, taking 1 to represent that the unit is started, and taking 0 to represent that the unit is stopped;
Figure 965108DEST_PATH_IMAGE131
and
Figure 601757DEST_PATH_IMAGE132
respectively indicating thermal power units
Figure 662117DEST_PATH_IMAGE133
Upper and lower limits of the output.
4) Fan output restriction
Figure 949879DEST_PATH_IMAGE134
（A4）
In the formula:
Figure 70282DEST_PATH_IMAGE135
and
Figure 381177DEST_PATH_IMAGE136
respectively representing wind turbine generators
Figure 619129DEST_PATH_IMAGE137
Upper and lower limits of the output.
5) Thermal power unit start-stop constraint
Figure 371185DEST_PATH_IMAGE138
（A5）
In the formula:
Figure 52702DEST_PATH_IMAGE139
、
Figure 788577DEST_PATH_IMAGE140
respectively indicating thermal power units
Figure 987477DEST_PATH_IMAGE133
Tot-a cumulative on-time and a cumulative off-time for a period of 1;
Figure 563DEST_PATH_IMAGE141
、
Figure 462769DEST_PATH_IMAGE142
is a thermal power unit
Figure 544994DEST_PATH_IMAGE133
Minimum on time and minimum off time.
6) Climbing restraint of thermal power unit
Figure 16427DEST_PATH_IMAGE143
(A6)
In the formula:
Figure 805391DEST_PATH_IMAGE144
、
Figure 815329DEST_PATH_IMAGE145
is a thermal power unit
Figure 260217DEST_PATH_IMAGE133
The landslide, the climbing rate limit.
7) Abandoned wind electric quantity model
Figure 394395DEST_PATH_IMAGE146
(A7)
In the formula:
Figure 37866DEST_PATH_IMAGE147
the wind power is the abandoned power in a scheduling period;
Figure 107453DEST_PATH_IMAGE148
and predicting the wind power of the wind generating set.
The technical solution of the present invention is not limited to the limitations of the above specific embodiments, and all technical modifications made according to the technical solution of the present invention fall within the protection scope of the present invention.

Claims (8)

1. A comprehensive energy system economic dispatching method considering gas network hydrogen mixing and low-carbon reward is characterized by comprising the following steps:
s1: establishing an NGECS model containing gas network hydrogen loading: in the NGECS low-carbon optimized dispatching, the influence of hydrogen after being mixed on the operation of a gas network is considered, the heat value change of mixed gas, the gas load change and the hydrogen mixing proportion limitation are considered, the dispatching result accords with the actual condition, and the starting constraint and the climbing constraint of the electrical hydrogen production process are considered by aiming at the NGECS model and combining the characteristics of P2H equipment, so that the dispatching result accords with the actual operation state;
s2: establishing an NGECS low-carbon optimized scheduling model based on reward ladder type carbon transaction: in the NGECS low-carbon optimization scheduling, a reward mechanism under the condition of setting the carbon quota surplus is considered, a reward ladder type carbon transaction mechanism is established, and the carbon emission is restrained; meanwhile, a reward benchmark reference price is provided, the optimal carbon reduction effect of unit investment is ensured, and the carbon reduction effect can be ensured while the capital budget is controlled.
2. The method for economically scheduling the integrated energy system considering the hydrogen mixing of the air grid and the low-carbon reward according to claim 1, wherein the method comprises the following steps: the establishing of the NGECS model containing the gas network hydrogen comprises gas-electricity coupling refined modeling, electric power system modeling and natural gas system modeling, and the establishing of the NGECS low-carbon optimized dispatching model based on the reward-penalty ladder-type carbon trading comprises a reward-penalty ladder-type carbon trading model, an NGECS low-carbon optimized dispatching objective function and model solving.
3. The economic dispatching method of the integrated energy system considering the gas network hydrogen mixing and low-carbon reward according to claim 2, characterized by comprising the following steps: the gas-electric coupling refined modeling comprises the following steps:
1) P2M force constraints:
Figure 263233DEST_PATH_IMAGE001
in the formula:
Figure 571855DEST_PATH_IMAGE002
、
Figure 521356DEST_PATH_IMAGE003
and
Figure 813797DEST_PATH_IMAGE004
respectively, P2M devices
Figure 123425DEST_PATH_IMAGE005
In that
Figure 552132DEST_PATH_IMAGE006
The power consumption and the upper limit and the lower limit of the time;
2) P2M coupling constraints:
Figure 938114DEST_PATH_IMAGE007
in the formula:
Figure 186693DEST_PATH_IMAGE008
indicating P2M devices
Figure 300011DEST_PATH_IMAGE005
In that
Figure 583225DEST_PATH_IMAGE006
Preparing gas flow of methane in time intervals;
Figure 874529DEST_PATH_IMAGE009
represents a high heating value of methane;
Figure 875983DEST_PATH_IMAGE010
indicating P2M devices
Figure 543725DEST_PATH_IMAGE005
The conversion efficiency of (a);
3) P2H force constraints:
Figure 196292DEST_PATH_IMAGE011
in the formula:
Figure 658497DEST_PATH_IMAGE012
、
Figure 147247DEST_PATH_IMAGE013
and
Figure 884259DEST_PATH_IMAGE014
respectively, P2H devices
Figure 876486DEST_PATH_IMAGE015
In that
Figure 775172DEST_PATH_IMAGE006
The power consumption and the upper limit and the lower limit of the time interval;
4) P2H coupling constraints:
Figure 737836DEST_PATH_IMAGE016
in the formula:
Figure 12959DEST_PATH_IMAGE017
indicating P2H devices
Figure 859693DEST_PATH_IMAGE015
In that
Figure 194859DEST_PATH_IMAGE006
Preparing the gas flow of the hydrogen in time intervals;
Figure 127043DEST_PATH_IMAGE018
represents a high heating value of hydrogen;
Figure 455125DEST_PATH_IMAGE019
device for indicating electric hydrogen production
Figure 156365DEST_PATH_IMAGE015
The conversion efficiency of (2);
5) P2H initiates the state constraint:
Figure 396853DEST_PATH_IMAGE020
in the formula:
Figure 81913DEST_PATH_IMAGE021
indicating P2H devices
Figure 698839DEST_PATH_IMAGE015
The accumulated on-time of (d);
Figure 769432DEST_PATH_IMAGE022
is a device
Figure 446401DEST_PATH_IMAGE015
Minimum time to open;
Figure 353177DEST_PATH_IMAGE023
indicating P2H devices
Figure 508215DEST_PATH_IMAGE015
The on-power of (a) is,
Figure 449626DEST_PATH_IMAGE024
indicating P2H devices
Figure 281185DEST_PATH_IMAGE015
Minimum boot power;
6) P2H hill climbing constraint:
Figure 940836DEST_PATH_IMAGE025
in the formula:
Figure 633986DEST_PATH_IMAGE026
、
Figure 429903DEST_PATH_IMAGE027
is a device
Figure 183096DEST_PATH_IMAGE015
The landslide, climbing rate limit;
7) the mixed gas heat value calculation mode of the hydrogen-doped gas network is as follows:
after hydrogen is injected into a natural gas network, recalculating the mixed heat value of the gas network after hydrogen is added for each gas network node, setting gas with different heat values to converge into the node, uniformly mixing the gas into gas with uniform mixed heat value, and then flowing out of the node, wherein the mixed heat value of the node is updated as follows:
Figure 316661DEST_PATH_IMAGE028
（7）
in the formula:
Figure 813502DEST_PATH_IMAGE029
representing the mixed heat value at the y node;
Figure 729505DEST_PATH_IMAGE030
、
Figure 653599DEST_PATH_IMAGE017
、
Figure 22263DEST_PATH_IMAGE008
、
Figure 306483DEST_PATH_IMAGE031
、
Figure 342572DEST_PATH_IMAGE032
、
Figure 437567DEST_PATH_IMAGE033
and
Figure 27948DEST_PATH_IMAGE034
to represent
Figure 866591DEST_PATH_IMAGE006
Time interval natural gas source, P2H, P2M and pipeline
Figure 6454DEST_PATH_IMAGE035
Inflow gas flow rate, pipe
Figure 272351DEST_PATH_IMAGE035
The outflow gas flow, the gas consumption flow of the gas turbine unit and
Figure 350028DEST_PATH_IMAGE036
a load value at the gas node;
Figure 992362DEST_PATH_IMAGE037
、
Figure 471885DEST_PATH_IMAGE038
are respectively shown in
Figure 157950DEST_PATH_IMAGE036
A pipeline set as an input node and
Figure 988503DEST_PATH_IMAGE036
a set of pipes that are output nodes;
after the heat value of each node of the gas network is changed, the gas load of the corresponding connection node is correspondingly changed, and the calculation method comprises the following steps:
Figure 168948DEST_PATH_IMAGE039
（8）
in the formula:
Figure 502978DEST_PATH_IMAGE040
representing the initial energy of the gas load of each node before loading hydrogen;
8) hydrogen loading proportion constraint:
the actual popularization and application condition of the gas network hydrogen doping is combined, the volume ratio of hydrogen doping is generally lower than 5%, and the hydrogen doping proportion is set to be 3% on the basis of strictly considering the actual hydrogen doping safety; the loading ratio constraint is expressed as:
Figure 362874DEST_PATH_IMAGE041
in the formula:
Figure 415144DEST_PATH_IMAGE042
represents the upper limit of the hydrogen loading ratio.
4. The method for economically scheduling the integrated energy system considering the hydrogen mixing of the air grid and the low-carbon reward according to claim 2, wherein the method comprises the following steps: the power system modeling comprises the following steps:
1) power system DC power flow constraint
Figure 664859DEST_PATH_IMAGE043
In the formula:
Figure 853395DEST_PATH_IMAGE044
representing the branch power;
Figure 897575DEST_PATH_IMAGE045
and
Figure 686408DEST_PATH_IMAGE046
respectively representing a branch admittance coefficient matrix and a branch admittance coefficient diagonal matrix;
Figure 208656DEST_PATH_IMAGE047
representing a branch node incidence matrix;
Figure 782857DEST_PATH_IMAGE048
、
Figure 732358DEST_PATH_IMAGE049
、
Figure 24799DEST_PATH_IMAGE050
、
Figure 334427DEST_PATH_IMAGE051
、
Figure 497555DEST_PATH_IMAGE052
and
Figure 883537DEST_PATH_IMAGE053
respectively represents the node loads of each thermal power generator set, each wind power generator set, each gas generator set, each electric hydrogen production device, each electric methane production device and each electric power system in the vector form
Figure 132116DEST_PATH_IMAGE054
The power of the time period;
2) node active power balance constraint
Figure 261746DEST_PATH_IMAGE055
In the formula:
Figure 794227DEST_PATH_IMAGE056
、
Figure 85531DEST_PATH_IMAGE057
、
Figure 821406DEST_PATH_IMAGE058
、
Figure 754727DEST_PATH_IMAGE059
、
Figure 867346DEST_PATH_IMAGE060
、
Figure 595131DEST_PATH_IMAGE061
respectively representing the number of a thermal generator set, a wind generator set, a gas generator set, an electric hydrogen production device, an electric methane production device and load nodes;
in addition, the phase angle constraint and the line transmission capacity constraint of the direct current power flow, the output constraint, the start-stop constraint, the climbing constraint of the thermal generator set and the output constraint of the wind turbine set are considered at the same time.
5. The method for economically scheduling the integrated energy system considering the hydrogen mixing of the air grid and the low-carbon reward according to claim 2, wherein the method comprises the following steps: the natural gas system modeling comprises the following steps:
1) and (3) air network flow constraint:
the gas network flow is modeled by using the Weymouth equation:
Figure 818302DEST_PATH_IMAGE062
Figure 24155DEST_PATH_IMAGE063
Figure 265650DEST_PATH_IMAGE064
in the formula:
Figure 429915DEST_PATH_IMAGE065
indicating a pipe
Figure 140382DEST_PATH_IMAGE035
In that
Figure 884347DEST_PATH_IMAGE054
Average airflow over a period of time;
Figure 262239DEST_PATH_IMAGE066
to and the pipeline
Figure 315514DEST_PATH_IMAGE035
The Weymouth constant with respect to cross-sectional area and length, etc.;
Figure 778857DEST_PATH_IMAGE067
、
Figure 326513DEST_PATH_IMAGE068
representing nodes
Figure 293332DEST_PATH_IMAGE069
And
Figure 799399DEST_PATH_IMAGE070
the air pressure of (a);
2) airflow balance constraints at the air network nodes:
Figure 468147DEST_PATH_IMAGE071
in the formula:
Figure 819494DEST_PATH_IMAGE072
to represent
Figure 640819DEST_PATH_IMAGE054
The air consumption flow of the time-interval compressor,
Figure 317788DEST_PATH_IMAGE031
And
Figure 224564DEST_PATH_IMAGE032
respectively representing natural gas pipelines
Figure 366220DEST_PATH_IMAGE035
Scheduling the natural gas flow rate during the initial period to the end period;
the gas network flow is nonlinear constraint, and the method utilizes a second-order cone relaxation method to process;
3) managing and restraining:
the memory before and after a scheduling cycle is set equal:
Figure 573211DEST_PATH_IMAGE073
in the formula:
Figure 155502DEST_PATH_IMAGE074
is a pipe
Figure 815153DEST_PATH_IMAGE035
In that
Figure 773882DEST_PATH_IMAGE054
Managing and storing time intervals;
Figure 553488DEST_PATH_IMAGE075
is a natural gas pipeline
Figure 306680DEST_PATH_IMAGE035
The characteristic coefficient of (a);
Figure 453628DEST_PATH_IMAGE076
、
Figure 950468DEST_PATH_IMAGE077
are respectively pipelines
Figure 600892DEST_PATH_IMAGE035
Caching at initial and final time periods;
4) compressor restraint:
the gas compressor with fixed transformation ratio is arranged in a natural gas pipeline model to solve the problem of pressure drop generated in operation, and the flow consumption is as follows:
Figure 774254DEST_PATH_IMAGE078
in the formula:
Figure 408497DEST_PATH_IMAGE079
indicating compressor
Figure 443449DEST_PATH_IMAGE080
Energy conversion coefficient of (2);
Figure 479539DEST_PATH_IMAGE081
indicating compressor
Figure 308954DEST_PATH_IMAGE080
The working efficiency of (2);
Figure 414182DEST_PATH_IMAGE082
、
Figure 252825DEST_PATH_IMAGE083
indicating compressor
Figure 877842DEST_PATH_IMAGE080
Output node pressure and input node pressure;
Figure 143738DEST_PATH_IMAGE084
representing the compressor's coefficient of variation.
6. The method for economically scheduling the integrated energy system considering the hydrogen mixing of the air grid and the low-carbon reward according to claim 2, wherein the method comprises the following steps: the penalty-considered ladder-type carbon trading model is as follows:
the carbon emission quota is:
Figure 486995DEST_PATH_IMAGE085
in the formula:
Figure 381526DEST_PATH_IMAGE086
represents the total carbon credit in one period in the system;
Figure 861049DEST_PATH_IMAGE087
expressing the corresponding force per unitCarbon emission credit of (c);
Figure 297846DEST_PATH_IMAGE088
and
Figure 128399DEST_PATH_IMAGE058
respectively representing the total number of the thermal generator set and the gas generator set;
Figure 308845DEST_PATH_IMAGE089
and
Figure 892142DEST_PATH_IMAGE090
respectively shows a thermal generator set and a gas generator set
Figure 765420DEST_PATH_IMAGE054
Generating output power in a time period;
on the other hand, the thermal power and the actual carbon emission amount of the gas power generation are expressed as:
Figure 817689DEST_PATH_IMAGE091
in the formula:
Figure 536247DEST_PATH_IMAGE092
represents the actual carbon emissions;
Figure 255941DEST_PATH_IMAGE093
representing a carbon emission correlation coefficient of the thermal generator set;
Figure 283809DEST_PATH_IMAGE094
representing a carbon emission correlation coefficient of the gas generator set;
in the reaction process of the electricity-generated methane, carbon dioxide is taken as a raw material, and the carbon dioxide absorbed in the process of P2M is also taken as a transaction item to be included in a carbon reward and penalty transaction model, so that the carbon emission cost can be further reduced; the carbon emission model for P2M is:
Figure 88954DEST_PATH_IMAGE095
in the formula:
Figure 611202DEST_PATH_IMAGE096
represents the carbon emission of P2M;
Figure 919824DEST_PATH_IMAGE097
represents the mass of carbon dioxide absorbed per unit of electricity consumed by the P2M plant;
Figure 118593DEST_PATH_IMAGE098
representing a scheduling period;
Figure 145454DEST_PATH_IMAGE060
represents the number of P2M;
in order to strictly control carbon emission, a reward-penalty ladder type carbon emission model is adopted; a mode of setting rewards is adopted in the transaction, and when the carbon quota surplus exists in the system, a certain incentive is given to the whole system; meanwhile, a penalty factor is set, and when the carbon quota of the system is insufficient, a certain pressure is applied to the whole system; the reward ladder type carbon transaction model is as follows:
Figure 900101DEST_PATH_IMAGE100
in the formula:
Figure 20504DEST_PATH_IMAGE101
representing a system carbon transaction cost;
Figure 786859DEST_PATH_IMAGE102
a reward reference price, namely a reward price corresponding to the first unit of residual carbon quota amount;
Figure 650910DEST_PATH_IMAGE103
expressing the increment of a reward factor, namely the reward multiplying power added by the carbon emission per increment of the residual carbon quota per unit interval;
Figure 934123DEST_PATH_IMAGE104
an interval representing carbon emission;
Figure 491007DEST_PATH_IMAGE105
and (4) adding a penalty multiplying power increased by a unit interval to the carbon emission as a penalty factor, namely when the carbon quota is insufficient.
7. The economic dispatching method of the integrated energy system considering the gas network hydrogen mixing and low-carbon reward according to claim 2, characterized by comprising the following steps: the NGECS low-carbon optimized scheduling objective function is as follows:
Figure 226882DEST_PATH_IMAGE106
in the formula:
Figure 409470DEST_PATH_IMAGE107
to the total operating cost;
Figure 281611DEST_PATH_IMAGE108
、
Figure 9396DEST_PATH_IMAGE109
、
Figure 232567DEST_PATH_IMAGE110
representing a cost coefficient of the thermal generator set;
Figure 703999DEST_PATH_IMAGE111
、
Figure 945494DEST_PATH_IMAGE112
representing the starting and stopping costs of the thermal generator set;
Figure 844180DEST_PATH_IMAGE113
、
Figure 554647DEST_PATH_IMAGE114
representing the starting and stopping cost of the gas generator set;
Figure 564191DEST_PATH_IMAGE115
、
Figure 925771DEST_PATH_IMAGE116
representing the on and off state variables of the thermal generator set;
Figure 729779DEST_PATH_IMAGE117
、
Figure 927542DEST_PATH_IMAGE118
representing the on and off state variables of the gas generator set;
Figure 740777DEST_PATH_IMAGE119
represents the unit gas purchase cost,
Figure 707596DEST_PATH_IMAGE120
Representing the unit cost of purchasing carbon.
8. The method for economically scheduling the integrated energy system considering the hydrogen mixing of the air grid and the low-carbon reward according to claim 2, wherein the method comprises the following steps: the model solving steps are as follows:
step 1: inputting initial data, including setting an initial value of a gas network node;
step 2: judging whether to add hydrogen, selecting a carbon transaction mechanism and selecting a target function;
step 3: solving by using CPLEX, and performing first iteration;
step 4: replacing the obtained gas network trend result with a formula (7) and a formula (8) to obtain an updated heat value and a node gas load value of each node of the gas network;
step 5: and judging the heat value precision of each node of the air network and the air load flow precision of the nodes before and after iteration, stopping calculation and outputting a result if the precision is met, and returning to Step3 for next iteration if the precision is not met.
CN202210496629.4A 2022-05-09 2022-05-09 Comprehensive energy system economic dispatching method considering gas network hydrogen mixing and low-carbon rewarding Active CN114881328B (en)

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