CN112636373A - Optimal scheduling method for electric and thermal comprehensive energy system - Google Patents

Optimal scheduling method for electric and thermal comprehensive energy system Download PDF

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CN112636373A
CN112636373A CN202011540605.1A CN202011540605A CN112636373A CN 112636373 A CN112636373 A CN 112636373A CN 202011540605 A CN202011540605 A CN 202011540605A CN 112636373 A CN112636373 A CN 112636373A
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毛晓波
薛溟枫
陈心扬
吴寒松
费彬
程恩林
卫志农
孙国强
臧海祥
汪永军
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Wuxi Power Supply Co of State Grid Jiangsu Electric Power Co Ltd
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Abstract

The application discloses an optimal scheduling method for an electric and thermal integrated energy system, which comprises the steps of acquiring operation data of the electric-gas-thermal integrated energy system and parameters of the integrated energy system; establishing a P2G operation cost model; respectively establishing an integrated energy system operation cost objective function and an air power absorption power objective function; setting a power system constraint condition, a natural gas system constraint condition, a thermodynamic system constraint condition and an electric-gas-thermal system coupling constraint; and solving the multi-objective optimization problem of the operation cost and the wind power absorption power of the comprehensive energy system. The method provided by the invention is reasonable and feasible, the characteristic of the cost of converting electricity into gas has obvious influence on the scheduling of the comprehensive energy system, and the optimal compromise solution of the operation cost and the wind-electricity absorption power of the comprehensive energy system can be effectively obtained.

Description

Optimal scheduling method for electric and thermal comprehensive energy system
Technical Field
The invention belongs to the technical field of optimization operation of an integrated energy system, and relates to an optimization scheduling method of an electric and thermal integrated energy system.
Background
Worldwide, serious environmental pollution and climate change are caused by the massive use of fossil energy. Wind power has great potential in effectively solving energy and environmental problems due to its clean, cheap and sustainable characteristics. However, the uncertainty of wind power brings difficulty to the utilization of the wind power, and a large amount of wind abandon is caused to keep the balance of supply and demand of a power system.
To overcome this drawback, researchers have explored many ways to improve wind power utilization. Due to the complementary nature of the various energy sources, the integration of multiple energy systems, such as power, natural gas, and heating systems, is an effective way to increase energy efficiency and system flexibility. In a power-to-gas integrated system (PGHIS), a power-to-gas (P2G) device is typically used to convert electrical energy into natural gas. Therefore, the residual wind power that cannot be consumed by the traditional power system can be converted into natural gas. Natural gas is convenient to store and can generate low-carbon electricity or heat through combustion. Therefore, P2G can effectively improve the operation flexibility of the comprehensive system to adapt to the fluctuation of wind power and enhance the wind power absorption capability of the system.
The P2G operation cost is relatively high, and the wind power utilization rate and the PGHIS scheduling economy are influenced. The high operating cost of P2G limits its throughput compared to cases where the operating cost of P2G is not considered. Therefore, the increased wind utilization by PGHIS utilizing P2G is reduced and the overall operating cost of the system will also increase. Based on this, PGHIS faces a trade-off between wind power utilization and operational economy. How to achieve optimal wind power utilization and operation economy is a key problem of utilizing P2G to absorb more wind power.
Disclosure of Invention
In order to overcome the defects in the prior art, the application provides an optimal scheduling method for an electric and thermal comprehensive energy system.
In order to achieve the above purpose, the invention adopts the following technical scheme:
an optimal scheduling method for an electric and thermal integrated energy system is characterized by comprising the following steps:
step 1: acquiring operation data of the electro-gas-heat comprehensive energy system and parameters of the comprehensive energy system;
step 2: establishing a P2G operation cost model;
and step 3: respectively establishing an integrated energy system operation cost objective function and an wind power absorption power objective function by combining a P2G operation cost model;
and 4, step 4: setting a power system constraint condition, a natural gas system constraint condition, a thermodynamic system constraint condition and an electric-gas-thermal system coupling constraint;
and 5: and solving the multi-objective optimization problem of the operation cost and the wind power absorption power of the comprehensive energy system.
The invention further comprises the following preferred embodiments:
preferably, the electricity-gas-heat integrated energy system comprises an electric power system, a natural gas system and a thermal system;
the power system is connected with a cogeneration unit and a thermal power unit and is connected with a wind power plant and a P2G device, and the P2G device is simultaneously connected with a gas storage tank of a natural gas system;
the natural gas system comprises a gas source point and a gas storage tank;
the heating system is connected with the cogeneration unit and comprises a gas boiler.
Preferably, in step 1, the operation data of the integrated energy system includes electricity consumption, gas consumption, heat supply load power and predicted wind power.
Preferably, in step 2, the P2G operation cost model is established as follows:
Figure BDA0002854450310000021
Figure BDA0002854450310000022
in the formula:
Figure BDA0002854450310000023
respectively representing the consumed electric power and the generated natural gas flow of the P < 2 > 2G th device t; etaegRepresents the conversion efficiency of P2G; gHHVRepresenting the heating value of natural gas;
Figure BDA0002854450310000024
represents the running cost of P2G in the whole scheduling period T; cE、CMRespectively representing electric power and CO2The price of (c); alpha represents CO per gas production2The consumption coefficient.
Preferably, in step 3, the established integrated energy system operation cost objective function is:
Figure BDA0002854450310000025
in the formula: f1Represents the operation cost of the comprehensive energy system,
Figure BDA0002854450310000026
representing the output power of the thermal power generating unit m at the time t;
Figure BDA0002854450310000027
representing the output power of the wind farm n at the time t;
Figure BDA0002854450310000028
the natural gas supply flow rate of a gas source point w at the time t is shown; a ism、bm、cmRepresenting the cost coefficient of the thermal power generating unit m;
Figure BDA0002854450310000031
representing a wind power cost coefficient;
Figure BDA0002854450310000032
representing the natural gas price at source point w; alpha represents CO per gas production2A consumption coefficient; cMRespectively represent CO2The price of (c); etaegRepresents the conversion efficiency of P2G;
Figure BDA0002854450310000033
represents the electric power consumed by the P < th > P2G device at time t; omegatu、Ωw、Ωwell、Ωp2gRespectively representing a thermal power generating unit, a wind power plant, an air source point and a P2G device set;
the established objective function of the wind power absorption power is as follows:
Figure BDA0002854450310000034
F2and representing the wind power absorption power, and T is a scheduling period.
Preferably, in step 4, the power system constraint conditions are set:
Figure BDA0002854450310000035
Figure BDA0002854450310000036
Figure BDA0002854450310000037
Figure BDA0002854450310000038
Figure BDA0002854450310000039
in the formula:
Figure BDA00028544503100000310
representing the output power of the thermal power generating unit m at the time t;
Figure BDA00028544503100000311
representing the output power of the wind farm n at the time t;
Figure BDA00028544503100000312
representing the generated power of a Combined Heat and Power (CHP) unit at the time t; pij,tRepresents the power flow of line ij;
Figure BDA00028544503100000313
represents the load of the node i at the time t;
Figure BDA00028544503100000314
represents the electric power consumed by the P < th > P2G device at time t; pe,tRepresenting the power generation power of the thermal power generating unit and the CHP;
Figure BDA00028544503100000315
Figure BDA00028544503100000316
respectively representing maximum of thermal power generating unit and CHPMinimum generated power; e represents a thermal power generating unit or CHP; omegatuRepresenting a thermal power generating unit set; omegachpRepresents a CHP set;
Figure BDA00028544503100000317
respectively representing the upward and downward climbing rates of the thermal power generating unit and the CHP; Δ t represents one scheduling period;
Figure BDA00028544503100000318
representing the predicted power of the wind farm at the time instant;
Figure BDA00028544503100000319
representing the maximum transmission capacity of the line.
Preferably, in step 4, natural gas system constraints are set:
Figure BDA00028544503100000320
Figure BDA00028544503100000321
Figure BDA00028544503100000322
ωl,t≤βcom·ωk,t (13)
Figure BDA0002854450310000041
in the formula:
Figure BDA0002854450310000042
the natural gas supply flow rate of a gas source point w at the time t is shown;
Figure BDA0002854450310000043
indicating the natural gas flow rate generated at the time t of the P < th > P2G device;
Figure BDA0002854450310000044
the natural gas injection flow and the natural gas output flow of the gas storage tank s at the moment t are shown; q. q.skl,tRepresenting the natural gas flow through the pipeline kl at the moment t;
Figure BDA0002854450310000045
representing the natural gas load of a node k at the time t;
Figure BDA0002854450310000046
respectively representing the natural gas flow consumed by the gas boiler g and the cogeneration unit c at the moment t;
Figure BDA0002854450310000047
respectively representing the natural gas supply flow at the source point w
Figure BDA0002854450310000048
Upper and lower limits of (d); omegak,tThe natural gas pressure value of a node k at the moment t is represented;
Figure BDA0002854450310000049
respectively represent the upper limit and the lower limit of the k pressure value of the node. Omegal,tThe natural gas pressure value of a node l at the moment t is represented; beta is acomRepresenting a compression factor of the compressor; cklRepresenting constants related to the kl temperature, length, internal diameter, compression factor of the pipe.
Setting gas storage constraint conditions:
Figure BDA00028544503100000410
Figure BDA00028544503100000411
Figure BDA00028544503100000412
Figure BDA00028544503100000413
Figure BDA00028544503100000414
in the formula: ss,tIndicating the gas storage amount of the gas storage tank s at the time t;
Figure BDA00028544503100000415
the natural gas injection flow and the natural gas output flow of the gas storage tank s at the moment t are shown;
Figure BDA00028544503100000416
represents the maximum and minimum capacity of the air storage tank s;
Figure BDA00028544503100000417
Figure BDA00028544503100000418
representing the upper limits of the natural gas input flow and output flow of the storage tank s.
Preferably, in step 4, thermodynamic system constraints are set:
Figure BDA00028544503100000419
in the formula (I), the compound is shown in the specification,
Figure BDA00028544503100000420
respectively representing the heat output of the cogeneration unit c and the gas boiler g at the moment t;
Figure BDA00028544503100000421
represents the thermal load of node h; omegachpRepresents a CHP set；ΩgbRepresenting a gas boiler set; omegahloadRepresenting a set of heat load nodes.
Preferably, in step 4, the electrical-gas-thermal system coupling constraints are set:
Figure BDA00028544503100000422
Figure BDA00028544503100000423
Figure BDA00028544503100000424
Figure BDA0002854450310000051
Figure BDA0002854450310000052
Figure BDA0002854450310000053
in the formula:
Figure BDA0002854450310000054
representing the power generation power of the cogeneration unit c at the moment t;
Figure BDA0002854450310000055
represents the electro-thermal coefficient of CHP;
Figure BDA0002854450310000056
respectively representing the heat output of the cogeneration unit c and the gas boiler g at the moment t;
Figure BDA0002854450310000057
Figure BDA0002854450310000058
respectively representing the natural gas flow consumed by the gas boiler g and the cogeneration unit c at the moment t; etachpRepresents the energy conversion coefficient of the CHP; etagbRepresenting an energy conversion coefficient of the gas boiler;
Figure BDA0002854450310000059
indicating the natural gas flow rate generated at the time t of the P < th > P2G device; gHHVRepresenting the heating value of natural gas;
Figure BDA00028544503100000510
represents the maximum flow of natural gas generated by the P < th > P2G device;
Figure BDA00028544503100000511
representing the maximum value of the generated power of the CHP unit c;
Figure BDA00028544503100000512
which represents the maximum value of the output heat of the gas boiler g.
Preferably, the step 5 of solving the multi-objective optimization problem of the operation cost and the wind power consumption power of the integrated energy system includes:
step 5.1: the wind power is absorbed by the power F2And processing the parameters into epsilon inequality constraint to obtain an optimized scheduling model:
minF1
Figure BDA00028544503100000513
in the formula: f1Represents the operation cost of the comprehensive energy system; f2Constrained by a parameter epsilon;
step 5.2: the parameter epsilon value is gradually changed from 0 to the maximum wind power, an optimal scheduling model is solved corresponding to each parameter epsilon value, a single-target problem optimal solution is obtained, and a Pareto optimal solution of the original multi-target problem is formed;
step 5.3: and according to a fuzzy set theory, expressing the satisfaction degree of each Pareto optimal solution corresponding to each objective function value by using a fuzzy membership function:
Figure BDA00028544503100000514
Figure BDA00028544503100000515
Figure BDA0002854450310000061
Figure BDA0002854450310000062
in the formula: 1,2, …, Np；j＝1,2,…,Nobj；Np、NobjRespectively representing the number of Pareto optimal solutions and target functions; si,jThe values of 0 and 1 respectively represent that the jth objective function value is completely unsatisfactory or completely satisfactory; fi,jRepresenting that the ith Pareto optimal solution corresponds to the jth objective function value;
Figure BDA0002854450310000063
respectively representing the maximum value and the minimum value of the jth objective function; hjRepresenting the entropy of the jth objective function; omegajIs the weight of the jth entropy; siRepresenting the satisfaction degree of the Pareto optimal solution;
step 5.4: and selecting the Pareto optimal solution with the maximum satisfaction as a compromise optimal solution.
The beneficial effect that this application reached:
the method provided by the invention is reasonable and feasible, the characteristic of the cost of converting electricity into gas has obvious influence on the scheduling of the comprehensive energy system, and the optimal compromise solution of the operation cost and the wind-electricity absorption power of the comprehensive energy system can be effectively obtained.
Drawings
FIG. 1 is a flow chart of an optimal scheduling method for an electric and thermal integrated energy system according to the present invention;
FIG. 2 is a schematic diagram of an electric-gas-thermal integrated energy system including P2G;
FIG. 3 is a graph of electrical, gas and thermal load curves and a predicted wind power curve in example 1 of the present invention;
FIG. 4 is a graph of the effect of P2G feedstock cost on the objective function in case 1.
Detailed Description
The present application is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present application is not limited thereby.
As shown in fig. 1, the optimal scheduling method for the electric-thermal integrated energy system according to the present invention analyzes various operation costs of electricity to gas to obtain cost characteristics of electricity to gas; establishing a multi-target comprehensive energy system day-ahead scheduling model to coordinate the operation of the comprehensive energy system, so as to achieve the optimal compromise between the operation cost and the wind power utilization rate; solving the model, specifically comprising the following steps of 1-5:
step 1: acquiring operation data of the electro-gas-heat comprehensive energy system and parameters of the comprehensive energy system;
in specific implementation, electricity, gas and heat supply load power of the electricity-gas-heat comprehensive energy system is collected for 24 hours, wind power is predicted, the time interval is 1 hour, and parameters of the comprehensive energy system are obtained.
The operation data of the comprehensive energy system comprises electricity, gas, heat supply load power and predicted wind power.
Step 2: establishing a P2G operation cost model;
the power and raw material cost of P2G is about 2/3 of the operating cost of P2G, wherein the raw material cost is mainly the CO2 cost.
The P2G running cost model is:
Figure BDA0002854450310000071
Figure BDA0002854450310000072
in the formula:
Figure BDA0002854450310000073
respectively representing the consumed electric power and the generated natural gas flow of the P < 2 > 2G th device t; etaegRepresents the conversion efficiency of P2G; gHHVRepresenting the heat value of natural gas, and taking 39MJ/m3；
Figure BDA0002854450310000074
Represents the running cost of P2G in the whole scheduling period T; cE、CMRespectively representing electric power and CO2The price of (c); alpha represents CO per gas production2The consumption coefficient.
And step 3: respectively establishing an integrated energy system operation cost objective function and an wind power absorption power objective function by combining a P2G operation cost model;
to ensure comprehensive benefits, the operating cost F of the comprehensive energy system1Including thermal power and wind power costs, and production and raw material costs of P2G, where the electricity charge of P2G is included in the thermal power or wind power costs, which is the amount PGHIS pays to the wind farm.
The established comprehensive energy system operation cost objective function is as follows:
Figure BDA0002854450310000075
in the formula:
Figure BDA0002854450310000076
representing the output power of the thermal power generating unit m at the time t;
Figure BDA0002854450310000077
representing the output power of the wind farm n at the time t;
Figure BDA0002854450310000078
the natural gas supply flow rate of a gas source point w at the time t is shown; a ism、bm、cmRepresenting the cost coefficient of the thermal power generating unit m;
Figure BDA0002854450310000079
representing a wind power cost coefficient;
Figure BDA00028544503100000710
representing the natural gas price at source point w; omegatu、Ωw、Ωwell、Ωp2gRespectively representing a thermal power generating unit, a wind power plant, an air source point and a P2G device set;
to emphasize the impact of P2G on wind utilization, the maximum wind power consumed by PGHIS is selected as another objective F2Establishing a target function of wind power absorption power:
Figure BDA0002854450310000081
and 4, step 4: setting a power system constraint condition, a natural gas system constraint condition, a thermodynamic system constraint condition and an electric-gas-thermal system coupling constraint;
setting power system constraint conditions:
Figure BDA0002854450310000082
Figure BDA0002854450310000083
Figure BDA0002854450310000084
Figure BDA0002854450310000085
Figure BDA0002854450310000086
in the formula:
Figure BDA0002854450310000087
representing the power generation power of the cogeneration unit c at the moment t; pij,tRepresents the power flow of line ij;
Figure BDA0002854450310000088
represents the load of the node i at the time t; pe,tRepresenting the power generation power of the thermal power generating unit and the CHP;
Figure BDA0002854450310000089
respectively representing the maximum power generation power and the minimum power generation power of the thermal power generating unit and the CHP; e represents a thermal power generating unit or CHP; omegachpRepresents a CHP set;
Figure BDA00028544503100000810
respectively representing the upward and downward climbing rates of the thermal power generating unit and the CHP; Δ t represents one scheduling period;
Figure BDA00028544503100000811
representing the predicted power of the wind farm at the time instant;
Figure BDA00028544503100000812
representing the maximum transmission capacity of the line.
Setting natural gas system constraint conditions:
Figure BDA00028544503100000813
Figure BDA00028544503100000814
Figure BDA00028544503100000815
ωl,t≤βcom·ωk,t (13)
Figure BDA00028544503100000816
in the formula:
Figure BDA00028544503100000817
the natural gas injection flow and the natural gas output flow of the gas storage tank s at the moment t are shown; q. q.skl,tRepresenting the natural gas flow through the pipeline kl at the moment t;
Figure BDA00028544503100000818
representing the natural gas load of a node k at the time t;
Figure BDA00028544503100000819
Figure BDA00028544503100000820
respectively representing the natural gas flow consumed by the gas boiler g and the cogeneration unit c at the moment t;
Figure BDA00028544503100000821
respectively representing the natural gas supply flow at the source point w
Figure BDA00028544503100000822
Upper and lower limits of (d); omegak,tThe natural gas pressure value of a node k at the moment t is represented;
Figure BDA00028544503100000823
respectively represent the upper limit and the lower limit of the k pressure value of the node. Omegal,tRepresenting node l at time tA natural gas pressure value; beta is acomRepresenting a compression factor of the compressor; cklRepresenting constants associated with the temperature, length, internal diameter, compression factor, etc. of the pipe kl.
Setting gas storage constraint conditions:
Figure BDA0002854450310000091
Figure BDA0002854450310000092
Figure BDA0002854450310000093
Figure BDA0002854450310000094
Figure BDA0002854450310000095
in the formula: ss,tIndicating the gas storage amount of the gas storage tank s at the time t;
Figure BDA0002854450310000096
represents the maximum and minimum capacity of the air storage tank s;
Figure BDA0002854450310000097
representing the upper limits of the natural gas input flow and output flow of the storage tank s.
Setting constraint conditions of a thermodynamic system:
Figure BDA0002854450310000098
in the formula (I), the compound is shown in the specification,
Figure BDA0002854450310000099
respectively representing the heat output of the cogeneration unit c and the gas boiler g at the moment t;
Figure BDA00028544503100000910
represents the thermal load of node h; omegagbRepresenting a gas boiler set; omegahloadRepresenting a set of heat load nodes.
Setting the coupling constraint conditions of the electric-gas-thermal system:
Figure BDA00028544503100000911
Figure BDA00028544503100000912
Figure BDA00028544503100000913
Figure BDA00028544503100000914
Figure BDA00028544503100000915
Figure BDA00028544503100000916
in the formula:
Figure BDA00028544503100000917
represents the electro-thermal coefficient of CHP; etachpRepresents the energy conversion coefficient of the CHP; etagbRepresenting an energy conversion coefficient of the gas boiler;
Figure BDA00028544503100000918
represents the maximum flow of natural gas generated by the P < th > P2G device;
Figure BDA00028544503100000919
representing the maximum value of the generated power of the CHP unit c;
Figure BDA00028544503100000920
which represents the maximum value of the output heat of the gas boiler g.
And 5: the method for solving the multi-objective optimization problem of the operation cost and the wind power absorption power of the comprehensive energy system comprises the following steps:
step 5.1: the wind power is absorbed by the power F2The process is an epsilon inequality constraint, and the obtained optimized scheduling model can be expressed as:
min F1
Figure BDA0002854450310000101
in the formula: f2Constrained by a parameter epsilon;
step 5.2: the parameter epsilon value is gradually changed from 0 to the maximum wind power, an optimal scheduling model is solved corresponding to each parameter epsilon value, a single-target problem optimal solution is obtained, and a Pareto optimal solution of the original multi-target problem is formed; pareto refers to a series of rows, in the invention, in the process of epsilon change, each single target problem obtains a solution, and the solutions are combined into a Pareto optimal solution of the original multi-target problem.
Step 5.3: and according to a fuzzy set theory, expressing the satisfaction degree of each Pareto optimal solution corresponding to each objective function by using a fuzzy membership function:
Figure BDA0002854450310000102
Figure BDA0002854450310000103
Figure BDA0002854450310000104
Figure BDA0002854450310000105
in the formula: 1,2, …, Np；j＝1,2,…,Nobj；Np、NobjRespectively representing the number of Pareto optimal solutions and target functions; si,jThe values of 0 and 1 respectively represent that the jth objective function value is completely unsatisfactory or completely satisfactory; fi,jRepresenting that the ith Pareto optimal solution corresponds to the jth objective function value;
Figure BDA0002854450310000106
respectively representing the maximum value and the minimum value of the jth objective function; hjRepresenting the entropy of the jth objective function; omegajIs the weight of the jth entropy; siRepresenting the satisfaction degree of the Pareto optimal solution;
the target function means F1、F2；
Step 5.4: and selecting the Pareto optimal solution with the maximum satisfaction as a compromise optimal solution.
Example 1:
in the embodiment of the invention, an IEEE-24 node electric power system, a Belgian 20-node natural gas system and 4 8-node heat supply systems are combined to establish an electricity-gas-heat comprehensive energy simulation system. In the power system, nodes 18, 21, 22 and 23 are connected with a cogeneration unit, and nodes 2, 7, 15 and 16 are connected with a thermal power generating unit. The natural gas system has two source points and a gas storage tank. The heating system is connected with 4 cogeneration units and comprises 4 gas boilers.
The wind power plant is rated at 1000MW and is connected with an 8-node power system, and a P2G device with the rated power of 400MW is also connected with the 8-node and is connected with a Peronnes node of a natural gas system, and the Peronnes node is also connected with a gas storage tank. The CO2 consumption coefficient per gas production was set to 0.2 t/MWh. The electrical, gas, thermal load curves and the predicted wind power curve are shown in fig. 3. According to the prediction in the day, the total available wind energy is 11372.96 MWh.
The following 4 scenarios were selected for comparative analysis in this example.
Reference case: the goal is to minimize the cost of running the system without P2G in PGHIS.
Case 1: the goal is to minimize the cost of running the system in the case where P2G is included in PGHIS.
Case 2: the method aims to simultaneously consider two targets of lowest system operation cost and maximum wind power utilization rate under the condition that P2G is contained in PGHIS.
Case 3: the aim is to maximize the wind power utilization rate under the condition that P2G is contained in PGHIS.
The structure of the electric-gas-thermal integrated energy system including P2G is shown in fig. 2.
The reference case and case 1 were simulated and compared. As can be seen from FIG. 4, in the reference case, F1Is 2.5455X 106$，F28188.58 MWh; due to the limited flexibility of the system operation, the air abandon rate is 28%. In case 1, different P2G raw material cost coefficients α C were setM(unit $/MWh) to analyze. alpha.CMImpact on PGHIS scheduling objective. When alpha C isMWhen equal to 0, F1＝2.5156×106$，F211155.34 MWh. At the moment, the raw material cost of P2G is low, so that P2G can be effectively utilized, and the operation flexibility of PGHIS is ensured. Therefore, the wind power can be fully utilized, and only a small amount of abandoned wind is obtained, which is about 1.9%. Compared with the reference case, the P2G can obviously improve the wind power utilization rate and reduce the total operation cost. Due to the fact that the operating cost of P2G is high, the wind power utilization rate and the operating economy of PGHIS are in contradiction. Therefore, to ensure the benefits of wind farms and PGHIS, an optimal compromise must be reached between wind power utilization and operational economy of PGHIS.
As can be seen from table 1, the wind power utilization ratio of case 2 was increased by 9.52% compared to case 1. Case 2 reduces the total operating cost by 15600 $thancase 3. Simulation results show that the proposed multi-target model can effectively ensure the economic operation of PGHIS and improve the operation flexibility while improving the wind power utilization rate, thereby verifying the effectiveness of the method. As an optimal compromise, the result of case 2 can be directly used as a system dispatch plan.
Table 1 optimization target values in cases 1,2, and 3
Figure BDA0002854450310000111
Figure BDA0002854450310000121
The present applicant has described and illustrated embodiments of the present invention in detail with reference to the accompanying drawings, but it should be understood by those skilled in the art that the above embodiments are merely preferred embodiments of the present invention, and the detailed description is only for the purpose of helping the reader to better understand the spirit of the present invention, and not for limiting the scope of the present invention, and on the contrary, any improvement or modification made based on the spirit of the present invention should fall within the scope of the present invention.

Claims (10)

1. An optimal scheduling method for an electric and thermal integrated energy system is characterized by comprising the following steps:
step 1: acquiring operation data of the electro-gas-heat comprehensive energy system and parameters of the comprehensive energy system;
step 2: establishing a P2G operation cost model;
and step 3: respectively establishing an integrated energy system operation cost objective function and an wind power absorption power objective function by combining a P2G operation cost model;
and 4, step 4: setting a power system constraint condition, a natural gas system constraint condition, a thermodynamic system constraint condition and an electric-gas-thermal system coupling constraint;
and 5: and solving the multi-objective optimization problem of the operation cost and the wind power absorption power of the comprehensive energy system.
2. The electrical and thermal integrated energy system optimal scheduling method according to claim 1, wherein:
the electricity-gas-heat integrated energy system comprises an electric power system, a natural gas system and a thermodynamic system;
the power system is connected with a cogeneration unit and a thermal power unit and is connected with a wind power plant and a P2G device, and the P2G device is simultaneously connected with a gas storage tank of a natural gas system;
the natural gas system comprises a gas source point and a gas storage tank;
the heating system is connected with the cogeneration unit and comprises a gas boiler.
3. The electrical and thermal integrated energy system optimal scheduling method according to claim 1 or 2, wherein:
in the step 1, the operation data of the comprehensive energy system comprises electricity consumption, gas, heat supply load power and predicted wind power.
4. The electrical and thermal integrated energy system optimal scheduling method according to claim 3, wherein:
in step 2, the established P2G operation cost model is:
Figure FDA0002854450300000011
Figure FDA0002854450300000012
in the formula:
Figure FDA0002854450300000013
respectively representing the consumed electric power and the generated natural gas flow of the P < 2 > 2G th device t; etaegRepresents the conversion efficiency of P2G; gHHVRepresenting the heating value of natural gas;
Figure FDA0002854450300000014
represents the running cost of P2G in the whole scheduling period T; cE、CMRespectively representing electric power and CO2The price of (c); alpha represents CO per gas production2The consumption coefficient.
5. The electrical and thermal integrated energy system optimal scheduling method according to claim 4, wherein:
in step 3, the established comprehensive energy system operation cost objective function is as follows:
Figure FDA0002854450300000021
in the formula: f1Represents the operation cost of the comprehensive energy system,
Figure FDA0002854450300000022
representing the output power of the thermal power generating unit m at the time t;
Figure FDA0002854450300000023
representing the output power of the wind farm n at the time t;
Figure FDA0002854450300000024
the natural gas supply flow rate of a gas source point w at the time t is shown; a ism、bm、cmRepresenting the cost coefficient of the thermal power generating unit m;
Figure FDA0002854450300000025
representing a wind power cost coefficient;
Figure FDA0002854450300000026
representing the natural gas price at source point w; alpha represents CO per gas production2A consumption coefficient; cMRespectively represent CO2Price of；ηegRepresents the conversion efficiency of P2G;
Figure FDA0002854450300000027
represents the electric power consumed by the P < th > P2G device at time t; omegatu、Ωw、Ωwell、Ωp2gRespectively representing a thermal power generating unit, a wind power plant, an air source point and a P2G device set;
the established objective function of the wind power absorption power is as follows:
Figure FDA0002854450300000028
F2and representing the wind power absorption power, and T is a scheduling period.
6. The electrical and thermal integrated energy system optimal scheduling method according to claim 4, wherein:
in step 4, setting power system constraint conditions:
Figure FDA0002854450300000029
Figure FDA00028544503000000210
Figure FDA00028544503000000211
Figure FDA00028544503000000212
Figure FDA00028544503000000213
in the formula:
Figure FDA0002854450300000031
representing the output power of the thermal power generating unit m at the time t;
Figure FDA0002854450300000032
representing the output power of the wind farm n at the time t;
Figure FDA0002854450300000033
representing the power generation power of the cogeneration unit c at the moment t; pij,tRepresents the power flow of line ij;
Figure FDA0002854450300000034
represents the load of the node i at the time t;
Figure FDA0002854450300000035
represents the electric power consumed by the P < th > P2G device at time t; pe,tRepresenting the power generation power of the thermal power generating unit and the CHP;
Figure FDA0002854450300000036
respectively representing the maximum power generation power and the minimum power generation power of the thermal power generating unit and the CHP; e represents a thermal power generating unit or CHP; omegatuRepresenting a thermal power generating unit set; omegachpRepresents a CHP set;
Figure FDA0002854450300000037
respectively representing the upward and downward climbing rates of the thermal power generating unit and the CHP; Δ t represents one scheduling period;
Figure FDA0002854450300000038
representing the predicted power of the wind farm at the time instant;
Figure FDA0002854450300000039
representing the maximum transmission capacity of the line.
7. The electrical and thermal integrated energy system optimal scheduling method according to claim 4, wherein:
in step 4, natural gas system constraint conditions are set:
Figure FDA00028544503000000310
Figure FDA00028544503000000311
Figure FDA00028544503000000312
ωl,t≤βcom·ωk,t (13)
Figure FDA00028544503000000313
in the formula:
Figure FDA00028544503000000314
the natural gas supply flow rate of a gas source point w at the time t is shown;
Figure FDA00028544503000000315
indicating the natural gas flow rate generated at the time t of the P < th > P2G device;
Figure FDA00028544503000000316
the natural gas injection flow and the natural gas output flow of the gas storage tank s at the moment t are shown; q. q.skl,tRepresenting the natural gas flow through the pipeline kl at the moment t;
Figure FDA00028544503000000317
representing the natural gas load of a node k at the time t;
Figure FDA00028544503000000318
respectively representing the natural gas flow consumed by the gas boiler g and the cogeneration unit c at the moment t;
Figure FDA00028544503000000319
respectively representing the natural gas supply flow at the source point w
Figure FDA00028544503000000320
Upper and lower limits of (d); omegak,tThe natural gas pressure value of a node k at the moment t is represented;
Figure FDA00028544503000000321
respectively represent the upper limit and the lower limit of the k pressure value of the node. Omegal,tThe natural gas pressure value of a node l at the moment t is represented; beta is acomRepresenting a compression factor of the compressor; cklRepresenting constants related to the kl temperature, length, internal diameter, compression factor of the pipe.
Setting gas storage constraint conditions:
Figure FDA00028544503000000322
Figure FDA00028544503000000323
Figure FDA00028544503000000324
Figure FDA0002854450300000041
Figure FDA0002854450300000042
in the formula: ss,tIndicating the gas storage amount of the gas storage tank s at the time t;
Figure FDA0002854450300000043
the natural gas injection flow and the natural gas output flow of the gas storage tank s at the moment t are shown;
Figure FDA0002854450300000044
represents the maximum and minimum capacity of the air storage tank s;
Figure FDA0002854450300000045
Figure FDA0002854450300000046
representing the upper limits of the natural gas input flow and output flow of the storage tank s.
8. The electrical and thermal integrated energy system optimal scheduling method according to claim 4, wherein:
in step 4, setting constraint conditions of the thermodynamic system:
Figure FDA0002854450300000047
in the formula (I), the compound is shown in the specification,
Figure FDA0002854450300000048
respectively representing the heat output of the cogeneration unit c and the gas boiler g at the moment t;
Figure FDA0002854450300000049
representing a node hA thermal load; omegachpRepresents a CHP set; omegagbRepresenting a gas boiler set; omegahloadRepresenting a set of heat load nodes.
9. The electrical and thermal integrated energy system optimal scheduling method according to claim 4, wherein:
in step 4, setting the coupling constraint conditions of the electric-gas-thermal system:
Figure FDA00028544503000000410
Figure FDA00028544503000000411
Figure FDA00028544503000000412
Figure FDA00028544503000000413
Figure FDA00028544503000000414
Figure FDA00028544503000000415
in the formula:
Figure FDA00028544503000000416
representing the power generation power of the cogeneration unit c at the moment t;
Figure FDA00028544503000000417
represents the electro-thermal coefficient of CHP;
Figure FDA00028544503000000418
respectively representing the heat output of the cogeneration unit c and the gas boiler g at the moment t;
Figure FDA00028544503000000419
Figure FDA00028544503000000420
respectively representing the natural gas flow consumed by the gas boiler g and the cogeneration unit c at the moment t; etachpRepresents the energy conversion coefficient of the CHP; etagbRepresenting an energy conversion coefficient of the gas boiler;
Figure FDA00028544503000000421
indicating the natural gas flow rate generated at the time t of the P < th > P2G device; gHHVRepresenting the heating value of natural gas;
Figure FDA00028544503000000422
represents the maximum flow of natural gas generated by the P < th > P2G device;
Figure FDA0002854450300000051
representing the maximum value of the generated power of the CHP unit c;
Figure FDA0002854450300000052
which represents the maximum value of the output heat of the gas boiler g.
10. The electrical and thermal integrated energy system optimal scheduling method according to claim 1, wherein:
and 5, solving a multi-objective optimization problem on the operation cost and the wind power consumption power of the comprehensive energy system, which comprises the following steps:
step 5.1: the wind power is absorbed by the power F2Is processed into epsilonAnd (3) carrying out equation constraint to obtain an optimized scheduling model:
min F1
Figure FDA0002854450300000053
in the formula: f1Represents the operation cost of the comprehensive energy system; f2Constrained by a parameter epsilon;
step 5.2: the parameter epsilon value is gradually changed from 0 to the maximum wind power, an optimal scheduling model is solved corresponding to each parameter epsilon value, a single-target problem optimal solution is obtained, and a Pareto optimal solution of the original multi-target problem is formed;
step 5.3: and according to a fuzzy set theory, expressing the satisfaction degree of each Pareto optimal solution corresponding to each objective function value by using a fuzzy membership function:
Figure FDA0002854450300000054
Figure FDA0002854450300000055
Figure FDA0002854450300000056
Figure FDA0002854450300000057
in the formula: 1,2, …, Np；j＝1,2,…,Nobj；Np、NobjRespectively representing the number of Pareto optimal solutions and target functions; si,jThe values of 0 and 1 respectively represent that the jth objective function value is completely unsatisfactory or completely satisfactory; fi,jRepresenting that the ith Pareto optimal solution corresponds to the jth objective function value;
Figure FDA0002854450300000058
respectively representing the maximum value and the minimum value of the jth objective function; hjRepresenting the entropy of the jth objective function; omegajIs the weight of the jth entropy; siRepresenting the satisfaction degree of the Pareto optimal solution;
step 5.4: and selecting the Pareto optimal solution with the maximum satisfaction as a compromise optimal solution.
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