CN113794242A - Interval optimization scheduling method considering dynamic characteristics of natural gas network - Google Patents

Interval optimization scheduling method considering dynamic characteristics of natural gas network Download PDF

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CN113794242A
CN113794242A CN202111097149.2A CN202111097149A CN113794242A CN 113794242 A CN113794242 A CN 113794242A CN 202111097149 A CN202111097149 A CN 202111097149A CN 113794242 A CN113794242 A CN 113794242A
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于永进
吉兴全
张玉敏
荆如兵
刘健
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Shandong University of Science and Technology
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Abstract

The invention discloses an interval optimization scheduling method considering dynamic characteristics of a natural gas network, which comprises the following steps of: describing the uncertainty of the wind power based on interval estimation, converting the uncertainty of the wind energy into a confidence interval by a mathematical method, and obtaining the randomness degree of the wind power from the magnitude of the confidence interval; then constructing a natural gas dynamic storage and management model; constructing an interval optimization scheduling model considering the dynamic characteristics of the gas network by taking the minimum total operation cost of the electric-gas interconnection system as an optimization target; and finally, solving the interval optimization scheduling model by adopting a linearization method. According to the invention, a relatively perfect electricity-gas coupling system is constructed, wind power uncertainty interval estimation is calculated, the phenomenon of 'wind abandon' can be well avoided, and the utilization rate of energy is improved; compared with the prior art, the interval optimization method considering the dynamic characteristics of the air grid can effectively improve the operation stability of the power grid.

Description

Interval optimization scheduling method considering dynamic characteristics of natural gas network
Technical Field
The invention relates to the technical field of optimization scheduling of comprehensive energy, in particular to an interval optimization scheduling method considering dynamic characteristics of a natural gas network.
Background
With the development of energy internet, the application of energy storage technology and equipment, and the large access of distributed renewable energy to an energy system, more users can meet terminal energy consumption in the forms of cold/heat/electricity and the like in the manner of distributed energy stations in the future. When a user accesses the wide-area comprehensive energy system in the form of a cold and heat energy station, the energy dispatching system presents a hierarchical, subarea and autonomous coordination framework, and the comprehensive energy market is gradually developed. Therefore, a distributed supply and demand coordinated scheduling and market balancing mechanism considering uncertainty becomes a hot spot of comprehensive energy system research.
Under the background of energy crisis, the development and utilization of novel renewable energy sources are beneficial to solving the problem of energy shortage, and the dependence and consumption on traditional energy sources are reduced. However, the new energy source has instability compared to the conventional energy source. The contradiction between the consumption of energy and the demand of electricity is more prominent. Therefore, the method has important significance for adjusting the structure of the existing energy system.
P2G converts wind power into hydrogen or natural gas, and the gas is converted into electric power or directly applied, thus providing a new idea for electricity-gas conversion. However, the case of gas reconversion to electricity would be wasteful of energy, and the best plan for storage of P2G is to utilize waste electricity at a convenient point in time. A multi-objective optimization design (power grid technology) of a comprehensive energy system facing a park microgrid introduces a model of a plurality of energy center systems, optimizes the capacity of a P2G power station, and verifies the effectiveness of the cooperative planning for promoting the energy supply of an integrated system. Robust economic dispatching and standby configuration (reported in electrotechnics) considering wind power uncertainty and power grid operation constraint indicate that a gas turbine unit is different from a thermal power unit, the standby problem of the gas turbine unit needs to be considered when the system operation capacity is increased, and models of a power grid and a gas grid need to be established simultaneously when an electric-gas coupling system is optimized to obtain an optimal dispatching solution. The natural gas is high in price, the carbon emission of the gas turbine is low, when an electric-gas system optimization model is established, environmental protection and economic benefits must be effectively measured, the gas turbine and the P2G can improve the consumption capacity of an electric-gas coupling system to new energy, and the peak clipping and valley filling capacity of the system is also considered in multi-objective optimization operation.
The model of the power system is mainly a steady-state model, and the natural gas network model generally has two types of steady-state and dynamic states. A Static Equipment Model of Natural Gas Network for electric-Gas Co-Optimization (IEEE Transactions on stable Energy) adopts a Static Model of a Natural Gas Network, and the Static Model is Equivalent by constraint and loss so as to reduce the influence of incomplete data exchange between an electric Network and an air Network on the coupling of the electric Network and the air Network.
Under the background of continuous development of new energy power generation, an energy storage technology is also rapidly developed. The common energy storage technologies at present include pumped storage, heat storage tanks, batteries with different performances and the like. Distributed photovoltaic and energy storage location and volume planning of a power distribution network based on cluster division (China Motor engineering institute) does not continue optimization of energy storage cost, and starts with capacity indexes of equipment to establish a photovoltaic energy storage optimization model. A short-term load prediction model (a power system and an automatic report thereof) considering energy storage scheduling factors considers electricity prices, and provides the short-term load prediction model on the basis of considering the technical means and the principle of the model.
The technical composition of the integrated energy system is very diverse, and many scholars will incorporate as many techniques as possible into the integrated energy system during planning and operation research to adapt to the actual energy system. A Two-Stage storage Programming Model for The Optimal Design of Distributed Energy Systems (Applied Energy) directly gives The area range and Energy, and under The condition of obtaining configuration in advance, a general Energy system Model is provided and solved by a mixed integer linear method. Regional comprehensive energy station optimization design method considering cold, heat and electricity storage (power grid technology) and using CO2The discharge amount and the total net cost are taken as optimization targets, a two-stage optimization method of the electric heating and cooling combined system is provided, and the capacity, the type and the parameters of equipment are optimized in the first stageAnd (4) optimizing output in the second stage by respectively adopting a second-generation non-dominated genetic algorithm and a mixed integer linear programming method.
In existing research, some characteristics of the gas grid are often analogized to the grid, such as power constraints, pressure constraints, etc. However, the energy transmission of the actual grid and gas network differs, with the electrical energy traveling in the line at near the speed of light and the natural gas traveling in the pipeline at a speed much less than the speed of light. This characteristic of natural gas also contributes to its delayed nature in pipelines, and in addition, friction in natural gas pipelines also loses part of the energy.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides the interval optimization scheduling method considering the dynamic characteristics of the natural gas network, which has reasonable design, overcomes the defects of the prior art and has good effect.
In order to achieve the purpose, the invention adopts the following technical scheme:
an interval optimization scheduling method considering dynamic characteristics of a natural gas network comprises the following steps:
s1, estimating and describing the uncertainty of the wind power based on the interval; converting the uncertainty of the wind energy into a confidence interval by a mathematical method, and obtaining the randomness degree of the wind power from the size of the confidence interval;
s2, constructing a natural gas dynamic storage and management model;
s3, constructing an interval optimization scheduling model considering the dynamic characteristics of the gas network by taking the minimum total operation cost of the electric-gas interconnection system as an optimization target;
the total operating cost includes the cost of the natural gas grid and the cost of the thermal power generating unit;
the constraint conditions of the interval optimization scheduling model comprise: IES constraints and unit operation constraints;
and S3, solving the interval optimization scheduling model by adopting a linearization method.
Further, in S1, a wind farm m providing wind energy is assumed, which provides wind power at time t
Figure BDA0003269222170000021
Comprises the following steps:
Figure BDA0003269222170000031
wherein v isciRepresenting cut-in wind speed, vcoRepresenting cut-out wind speed, vrRepresenting rated wind speed; v ism,tRepresenting the wind speed of the wind farm m at the moment t;
Figure BDA0003269222170000032
representing the installed capacity of the wind farm m at the moment t;
suppose a scale factor km,tThe ratio of the power of the wind power access IES to the total installed capacity is expressed, and the mathematical expression is as follows:
Figure BDA0003269222170000033
wherein the content of the first and second substances,
Figure BDA0003269222170000034
representing installed capacity of a wind farm;
if the formula (2) is substituted into the formula (1), the IES receives the wind power provided by the wind power plant
Figure BDA0003269222170000035
Comprises the following steps:
Figure BDA0003269222170000036
assuming that the variation in wind speed is a distribution-compliant variable, given a confidence level β, the wind speed variation should satisfy the following equation:
Figure BDA0003269222170000037
where P (-) is the probability of event delivery, Δ vk,tFor predicted wind speed error, Δv k,t(β)Representing the lower bound of the predicted wind speed error at confidence level beta,
Figure BDA0003269222170000038
represents the upper limit of the predicted wind speed error at the confidence level beta;
formula (4) is a predicted wind speed interval based on interval estimation, and can be substituted for formula (3) to obtain a wind power output interval predicted by using probability theory:
Figure BDA0003269222170000039
wherein the content of the first and second substances,
Figure BDA00032692221700000310
the wind farm provides a lower wind power limit at the confidence level beta,
Figure BDA00032692221700000311
representing the upper limit of wind power provided by the wind farm at a confidence level beta, vm,t(ep)Representing the expectation of wind speed.
Further, in S2, it is assumed that any one of the natural gas transmission pipelines is a pipeline k1, and k and 1 are a node of a gas inflow pipeline and a node of an outflow pipeline, respectively,
Figure BDA0003269222170000041
representing the inflow of the gas line,
Figure BDA0003269222170000042
representing the outflow of a gas pipeline, the volume of gas stored in the pipeline is:
Figure BDA0003269222170000043
Figure BDA0003269222170000044
wherein, pix,tAnd piy,tRepresenting the pipe pressure at time t, Qkl,tRepresenting the volume of gas stored in the pipeline at time t, pA,klIs a coefficient, the influencing factors include the diameter, length and ambient temperature of the pipe; x and y represent arbitrary points of the pipeline;
the capacity of the gas inflow pipeline and the gas outflow pipeline and the pressure at two ends of the pipeline satisfy the following equation:
Figure BDA0003269222170000045
Figure BDA0003269222170000046
where ρ isB,klIs a coefficient of influence including pipe diameter, length and ambient temperature, θkl,tIndicating the direction of gas flow in the pipeline, judging the gas flow direction by the values of 1 and-1, and when the pressure at the output end is smaller than that at the input end, theta kl,t1, θ when the output pressure is greater than the input pressurekl,t＝-1；
The equations (6) to (9) form a natural gas transmission model considering pipeline storage;
for convenience of discussing the dynamic characteristics of the natural gas pipeline, the compressor in the network is not discussed at all, and the flow balance formula is as follows:
Figure BDA0003269222170000047
wherein kl is oI(kl) k represents the natural gas flow through node k into line k1, kl oT(kl) represents the natural gas flow out of pipeline k1 through node 1, c represents the number of plants producing the natural gas,
Figure BDA0003269222170000048
representing the flow rate of natural gas from the source,
Figure BDA0003269222170000049
a natural gas inflow rate representative of a received gas load;
all the nodes are regarded as a unified whole, and the formula (7) is substituted into the formula (10), so that the equation of the whole natural gas system after the pipeline delay effect is considered is obtained:
Figure BDA00032692221700000410
equations (10) and (12) form a natural gas dynamic inventory model, and as mentioned above, the inventory characteristic of natural gas is affected by the negative feedback regulation, and is not a single constant characteristic, but dynamic, and the characteristic is called the 'buffering characteristic' of natural gas.
Further, in S3, the objective function of the optimization objective is:
minOF＝EC+GC； (13)
wherein OF represents the cost OF an electricity-gas interconnection system, GC represents the cost OF a natural gas pipe network, and EC represents the cost OF a thermal power generating unit;
further, in S3, the natural gas pipe network equation is described by equation (14):
Figure BDA0003269222170000051
wherein, cnThe cost coefficients of the natural gas network are shown, Ck, l the overall coefficients relating to the gas network, see chapter 1 for a detailed calculation.
Further, in S3, the grid equation is described by equation (15):
Figure BDA0003269222170000052
wherein alpha isg、bgAnd cgA cost coefficient of the thermal power generating unit is represented,
Figure BDA0003269222170000053
representing the power loss load of the i node at the time t, cVWPenalty cost, δ, representing power loss loadi,tRepresenting the phase angle of node i.
Further, in S3, the IES constraints include electrical flow constraints and natural gas flow constraints:
electrical current constraint, adopting a power flow model for modeling a power grid:
Figure BDA0003269222170000054
wherein i represents any node of the power grid, j represents the number of branches contained in the power grid, e represents the number of coal-fired units, and Pe,tRepresenting the power consumed by all branches connected to i, d representing the number of gas turbine groups,
Figure BDA0003269222170000055
represents the power at which the gas turbine generates electrical energy,
Figure BDA0003269222170000056
indicating the power consumed by the consumer, Pij,tWhich represents the power on each of the branches,
Figure BDA0003269222170000057
represents the power at which the CHP generates electrical energy;
Figure BDA0003269222170000058
representing the power loss load quantity of the i node at the time t;
and (3) natural gas flow constraint, further considering the CHP unit, the gas turbine and the compressor on the basis of the formula (10), and obtaining a flow balance equation:
Figure BDA0003269222170000061
wherein the content of the first and second substances,
Figure BDA0003269222170000062
which is indicative of the natural gas flow rate to the compressor,
Figure BDA0003269222170000063
the natural gas flow rate at the output of the compressor is indicated,
Figure BDA0003269222170000064
indicating the flow of natural gas consumed by the gas turbine.
Further, in S3, the operation constraints of each unit are:
Figure BDA0003269222170000065
Figure BDA0003269222170000066
Figure BDA0003269222170000067
Figure BDA0003269222170000068
Figure BDA0003269222170000069
Figure BDA00032692221700000610
Figure BDA00032692221700000611
Figure BDA00032692221700000612
Pemin≤Pe,t≤Pemax (26)
Figure BDA00032692221700000613
Figure BDA00032692221700000614
wherein, Pemin、Pemax、
Figure BDA00032692221700000615
Respectively the minimum output, the maximum output, the uplink regulation rate and the downlink regulation rate of the coal-fired unit;
Figure BDA00032692221700000616
respectively the minimum processing, the maximum processing, the uplink regulation rate and the downlink regulation rate of the gas turbine;
Figure BDA00032692221700000617
the minimum output, the maximum output, the uplink regulation rate and the downlink regulation rate of the CHP are respectively;
Figure BDA00032692221700000618
respectively the minimum output and the maximum output of the conventional unit.
The invention has the following beneficial effects:
the invention constructs a relatively perfect electric-gas coupling system and effectively solves the defects in the prior art. Meanwhile, interval estimation of wind power uncertainty is considered, so that compared with the prior art, the phenomenon of wind abandon can be well avoided, and the utilization rate of energy is improved; compared with the prior art, the interval optimization method considering the dynamic characteristics of the air grid can effectively improve the operation stability of the power grid.
Drawings
FIG. 1 is a flow chart of a method for interval-optimized scheduling in the present invention;
FIG. 2 is a model of an electrical network in one embodiment of the invention;
FIG. 3 is a graph illustrating a load curve and a wind power forecast according to an embodiment of the present invention;
FIG. 4 is a graph illustrating the relationship between the output of the gas source and the change in the storage capacity of the tube in accordance with one embodiment of the present invention;
FIG. 5 is a graph of gas turbine output variation for one embodiment of the present invention;
Detailed Description
The following description of the embodiments of the present invention will be made with reference to the accompanying drawings:
as shown in fig. 1, an interval optimization scheduling method considering the dynamic characteristics of a natural gas network includes the following steps:
s1, estimating and describing the uncertainty of the wind power based on the interval; converting the uncertainty of the wind energy into a confidence interval by a mathematical method, and obtaining the randomness degree of the wind power from the size of the confidence interval;
specifically, the IES receives various energy sources from different devices, wherein the amount of received wind power is mainly determined by two factors, the first factor is the capacity of the wind turbine generator set connected to the IES, and the second factor is the influence of wind speed. Suppose a wind farm m providing wind energy, which provides wind power at time t
Figure BDA0003269222170000071
Comprises the following steps:
Figure BDA0003269222170000072
wherein v isciRepresenting cut-in wind speed, vcoRepresenting cut-out wind speed, vrRepresenting rated wind speed, vm,tRepresenting the wind speed of the wind farm at time t;
Figure BDA0003269222170000073
representing the installed capacity of the wind farm m at the moment t;
Figure BDA0003269222170000074
not a constant value but depends on the scheduling of the system; the formula (1) can also reflect the influence of wind speed on the wind power provided by the wind field, one part of electricity generated by wind power is directly supplied to an electric load, and the other part of electricity is supplied to IES so as to avoid wind abandonment.
In order to control the power of the wind power input IES, a scale factor kappa is assumedm,tThe ratio of the power of the wind power access IES to the total installed capacity is expressed, and the mathematical expression is as follows:
Figure BDA0003269222170000075
wherein the content of the first and second substances,
Figure BDA0003269222170000076
representing installed capacity of a wind farm; kappam,tIs not constant and varies with scheduling results.
If the formula (2) is substituted into the formula (1), the IES receives the wind power provided by the wind power plant
Figure BDA0003269222170000077
Comprises the following steps:
Figure BDA0003269222170000081
the wind farm power supply is uncertain because the wind speed is random, which, as known from the above equation, affects the wind power. In preparation for describing the variation of the wind power output, assumptions are made on the wind speed and thus on the wind power using the concept of probability theory.
Assuming that the variation in wind speed is a distribution-compliant variable, given a confidence level β, the wind speed variation should satisfy the following equation:
Figure BDA0003269222170000082
where P (-) is the probability of event delivery, Δ vk,tFor predicted wind speed error, Δv k,t(β)Representing the lower bound of the predicted wind speed error at confidence level beta,
Figure BDA0003269222170000083
represents the upper limit of the predicted wind speed error at the confidence level beta;
formula (4) is a predicted wind speed interval based on interval estimation, and is substituted into formula (3), namely the wind power output interval predicted by probability theory is obtained:
Figure BDA0003269222170000084
wherein the content of the first and second substances,
Figure BDA0003269222170000085
the wind farm provides a lower wind power limit at the confidence level beta,
Figure BDA0003269222170000086
representing the upper limit of wind power provided by the wind farm at a confidence level beta, vm,t(ep)Representing the expectation of wind speed.
S2, constructing a natural gas dynamic storage and management model;
the transmission speed of natural gas in a pipeline is far lower than that of electric energy, the difference is more obvious in a multi-energy-source coupled IES system, the delay of the natural gas in the pipeline is expressed by the concept of a 'storage-in-pipe model', the natural gas is easy to store, and the model can be similar to a model of system energy storage.
In particular, suppose that any one of the natural gas transmission pipelines is pipeline k1, and k and 1 are the node of the gas inflow pipeline and the node of the gas outflow pipeline respectively,
Figure BDA0003269222170000087
representing the inflow of the gas line,
Figure BDA0003269222170000088
representing the outflow of a gas pipeline, the volume of gas stored in the pipeline is:
Figure BDA0003269222170000089
Figure BDA00032692221700000810
wherein, pix,tAnd piy,tRepresenting the pipe pressure at time t, Qkl,tRepresenting the volume of gas stored in the pipeline at time t, pA,klIs a coefficient, the influencing factors include the diameter, length and ambient temperature of the pipe; x and y represent arbitrary points of the pipeline;
the capacity of the gas inflow pipeline and the gas outflow pipeline and the pressure at two ends of the pipeline satisfy the following equation:
Figure BDA0003269222170000091
Figure BDA0003269222170000092
where ρ isB,klIs a coefficient of influence including pipe diameter, length and ambient temperature, θkl,tIndicating the direction of gas flow in the pipeline, judging the gas flow direction by the values of 1 and-1, and when the pressure at the output end is smaller than that at the input end, theta kl,t1, θ when the output pressure is greater than the input pressurekl,t＝-1；
Equations (6) to (9) represent the relationship between the pipeline gas volume and the flow rate and the relationship between the pipeline pressure and the gas flow rate, respectively, and constitute a natural gas transmission model taking the pipeline storage into account, wherein the natural gas transmission model is derived from a differential equation describing the dynamic characteristics of natural gas. The formula (6) shows that the volume capacity of the stored gas in the pipeline is related to the pressure at the two ends of the pipeline, and the relationship is positive correlation;
the gas network often contains a compressor, so that the compressor in the network is not discussed for the moment in order to conveniently discuss the dynamic characteristics of the natural gas pipeline, the nodes of the natural gas network meet the energy conservation, and the flow balance formula is as follows:
Figure BDA0003269222170000093
wherein kl is oI(kl) k represents the natural gas flow through node k into line k1, kl oT(kl) represents the natural gas flow out of pipeline k1 through node 1, c represents the number of plants producing the natural gas,
Figure BDA0003269222170000094
representing the flow rate of natural gas from the source,
Figure BDA0003269222170000095
a natural gas inflow rate representative of a received gas load;
when the air network dynamic characteristics are not considered, the flow balance of the nodes can be expressed as follows:
Figure BDA0003269222170000096
all the nodes are regarded as a unified whole, and the formula (7) is substituted into the formula (10), so that the equation of the whole natural gas system after the pipeline delay effect is considered is obtained:
Figure BDA0003269222170000097
analyzing a relation between the storage volume and the flow of the natural gas pipeline and a relation between the flow balance: regardless of the gas grid dynamics, if the demand for natural gas increases at a certain time, the source of the produced natural gas must be increased by the same increment to ensure equilibrium. However, the increase in the gas supply does not completely compensate for the load variation, which is obtained according to equation (7), and is related to the volume of the pipeline storing the natural gas at the same time. From equation (7), if the demand for natural gas increases, the gas pressure in the gas network will decrease, and the capacity of the pipeline for storing natural gas will also decrease, so the variable value on the right side of equation (7) will decrease, resulting in unbalanced gas flow. Thus, rather than ignoring the air net dynamics, the incremental increase in air supply as the load increases will decrease. Similarly, if the demand for natural gas decreases, the gas network pressure will increase, and the decrease in the source gas at reduced load will decrease without ignoring the dynamics of the gas network.
Equations (10) and (12) form a natural gas dynamic inventory model, and as mentioned above, the inventory characteristic of natural gas is affected by the negative feedback regulation, and is not a single constant characteristic, but dynamic, and the characteristic is called the 'buffering characteristic' of natural gas.
S3, constructing an interval optimization scheduling model considering the dynamic characteristics of the gas network by taking the minimum total operation cost of the electric-gas interconnection system as an optimization target;
the objective function of the optimization objective is:
minOF＝EC+GC (13)
wherein OF represents the cost OF an electricity-gas interconnection system, GC represents the cost OF a natural gas pipe network, and EC represents the cost OF a thermal power generating unit;
specifically, the natural gas pipe network equation is described by equation (14):
Figure BDA0003269222170000101
wherein, cnThe cost coefficients of the natural gas network are shown, Ck, l the overall coefficients relating to the gas network, see chapter 1 for a detailed calculation.
The grid equation is described by equation (15):
Figure BDA0003269222170000102
wherein alpha isg、bgAnd cgA cost coefficient of the thermal power generating unit is represented,
Figure BDA0003269222170000103
representing the power loss load of the i node at the time t, cVWPenalty cost, δ, representing power loss loadi,tRepresenting the phase angle of node i.
The constraint conditions of the interval optimization scheduling model comprise: IES constraints and crew operating constraints.
IES constraints:
specifically, the energy flow in the IES includes power flow and natural gas flow, so the IES constraints include electrical flow constraints and natural gas flow constraints:
electrical current constraint, adopting a power flow model for modeling a power grid:
Figure BDA0003269222170000104
and (3) natural gas flow constraint, further considering the CHP unit, the gas turbine and the compressor on the basis of the formula (10), and obtaining a flow balance equation:
Figure BDA0003269222170000111
wherein i represents any node of the power grid, j represents the number of branches contained in the power grid, e represents the number of coal-fired units, and Pe,tRepresenting the power consumed by all branches connected to i, d representing the number of gas turbine groups,
Figure BDA0003269222170000112
represents the power at which the gas turbine generates electrical energy,
Figure BDA0003269222170000113
indicating the power consumed by the consumer, Pij,tRepresenting power per branch, c representing the number of plants producing natural gas
Figure BDA0003269222170000114
Represents the power at which the CHP generates electrical energy;
Figure BDA0003269222170000115
representing the power loss load quantity of the i node at the time t;
Figure BDA0003269222170000116
which is indicative of the natural gas flow rate to the compressor,
Figure BDA0003269222170000117
the natural gas flow rate at the output of the compressor is indicated,
Figure BDA0003269222170000118
indicating the flow of natural gas consumed by the gas turbine.
And (3) unit operation constraint:
Figure BDA0003269222170000119
Figure BDA00032692221700001110
Figure BDA00032692221700001111
Figure BDA00032692221700001112
Figure BDA00032692221700001113
Figure BDA00032692221700001114
Figure BDA00032692221700001115
Figure BDA00032692221700001116
Pemin≤Pe,t≤Pemax (26)
Figure BDA00032692221700001117
Figure BDA00032692221700001118
wherein, Pemin、Pemax、
Figure BDA00032692221700001119
Respectively the minimum output, the maximum output, the uplink regulation rate and the downlink regulation rate of the coal-fired unit;
Figure BDA00032692221700001120
respectively the minimum processing, the maximum processing, the uplink regulation rate and the downlink regulation rate of the gas turbine;
Figure BDA0003269222170000121
the minimum output, the maximum output, the uplink regulation rate and the downlink regulation rate of the CHP are respectively;
Figure BDA0003269222170000122
respectively the minimum output and the maximum output of the conventional unit.
S4, solving an interval optimization scheduling model;
the model established aiming at the energy flow and the unit constraint is nonlinear and is not beneficial to solving, a linearization method is adopted for simple calculation, a nonlinear equation is linearized, and the interval optimization scheduling model is solved.
Example analysis:
the invention models the interaction of a gas network and a power network, the electricity-gas network being a 24-node bus network, as shown in figure 2. Fig. 2 shows the relationship between the air grid nodes and the grid nodes, and the arrangement of the wind turbine sets and their capacities are also shown in the figure. This is a transmission network with a voltage level of 138kV, 230 kV. Line data for a 24-node network is shown in table 1, which specifies the reactance, line susceptance, and rated capacity of the power line. The power generation data for a 24-node network is given in table 2, with bus 13 set as the balance node.
TABLE 1 electric Power System line parameters
Figure BDA0003269222170000123
Figure BDA0003269222170000131
TABLE 2 Power System Generation data
Figure BDA0003269222170000132
Along with the IEEE24 node power system and the 20 node natural gas system built with the fig. 3 electro-pneumatic network model. The average daily power consumption in the comprehensive consideration area is about 2GW, and the parameters of various devices and systems are set as follows:
(1) the source class device: the installed capacity of a wind turbine of a wind power plant is 600MW, and the rated output power of a cogeneration unit is 250 MW.
(2) Economic cost: the natural gas cost is 27$/(MW & h), and the thermal power cost is 23$/(MW & h).
(3) Air network characteristics: the initial air net inventory was 1.36X 107 m.
Fig. 2 shows a topological diagram of the above model, and the scheduling study is performed in 24h of a day. The change curves of various energy loads and predicted wind power values in the day ahead are shown in fig. 3.
In order to more intuitively reflect the improvement of the gas network characteristics on the IES performance, the invention establishes 2 simulation scenes:
case I-no dynamic behavior is taken into account.
Case II, accounting for tracheal network dynamics.
As shown in fig. 4, case I does not consider the trend of the source output change in the gas network dynamics consistent with the trend of the gas load change in fig. 3, because if the buffering effect of the natural gas pipeline is not considered, the natural gas network has no extra energy storage capacity, and the energy input into the system and the energy output from the system keep a certain balance. In addition, although CHP and gas turbines consume natural gas, they do not account for the total load and cause only small fluctuations in the source output. The difference in source output variation between case II and case I is mainly reflected in a 0h peak operation and a sharp drop in 24 h. The gas source of case II is output at the peak value at the beginning, and then gradually approaches the change trend of the load, and the phenomenon appears in consideration of the dynamic simulation of the gas network, because when the load consumes natural gas, the natural gas system needs to improve the storage capacity to accept the output of source equipment, and the peak value operation of the gas well prepares for improving the storage capacity when the test is started. After one period of scheduling is finished, the sharp decline of the output change curve of the air source is to release the pipe deposit and ensure the normal pipe deposit of the system in the next period. The air source output of case II is reduced in comparison with case I in 8-11h, and the burden of the air source in the peak load period is reduced. It can also be seen from figure 4 that the inventory area is opposite to the load trend, releasing the right amount of stored gas at peak load and at valley load, indicating that natural gas inventory characteristics can help reduce demand fluctuations.
In 10h and 11h, the natural gas pipe storage brings the movement of the output peak value of the gas source, the gas supply reliability of the gas network is improved, and the scheduling influence of the natural gas pipe storage on the electric-gas interconnection system is discussed in combination with the output change of the gas turbine.
The gas turbine as a coupling device can convert natural gas into gas energy and is flexible in operation, but gas source power generation has poorer power generation economic performance than a conventional thermal power generating unit, so the GT cannot be used as a power supply in a large amount and is only used as power generation equipment during peak power utilization. Comparing the GT output of the case I, II in fig. 5 with the load curve in fig. 3, it is found that the GT output level under the gas grid dynamic characteristics during the 10-13h peak load is significantly improved, and the gas turbine can provide a part of the extra electric energy for the power grid, thereby promoting the power grid peak shaving. Therefore, certain margin is brought to equipment such as a gas turbine by the delay and storage capacity of the natural gas pipeline, the power loss load is obviously reduced, and when an emergency or a large electric load occurs to the electric power system, the GT can adjust the instability of the power grid by using the natural gas stored in the pipeline, so that the safety and the stability of the electric power system are improved.
Table 3 IES operating costs considering air network dynamics
Total operating cost ($)
Case I 1.6322×105
Case II 1.62733×105
As can be seen from table 3, in case II, when the storage characteristics of the gas network pipeline are considered, the pipeline is equivalent to an energy storage element, and the total cost is lower.
It is to be understood that the above description is not intended to limit the present invention, and the present invention is not limited to the above examples, and those skilled in the art may make modifications, alterations, additions or substitutions within the spirit and scope of the present invention.

Claims (8)

1. An interval optimization scheduling method considering dynamic characteristics of a natural gas network is characterized by comprising the following steps:
s1, estimating and describing the uncertainty of the wind power based on the interval; converting the uncertainty of the wind energy into a confidence interval by a mathematical method, and obtaining the randomness degree of the wind power from the size of the confidence interval;
s2, constructing a natural gas dynamic storage and management model;
s3, constructing an interval optimization scheduling model considering the dynamic characteristics of the gas network by taking the minimum total operation cost of the electric-gas interconnection system as an optimization target;
the total operation cost comprises the cost of a natural gas pipe network and the cost of a thermal power generating unit;
the constraint conditions of the interval optimization scheduling model comprise: IES constraints and unit operation constraints;
and S4, solving the interval optimization scheduling model by adopting a linearization method.
2. The interval optimization scheduling method considering the dynamic characteristics of the natural gas network as claimed in claim 1, wherein in S1, a wind farm m providing wind energy is assumed, which provides wind power at time t
Figure FDA0003269222160000011
Comprises the following steps:
Figure FDA0003269222160000012
wherein v isciRepresenting cut-in wind speed, vcoRepresenting cut-out wind speed, vrRepresenting rated wind speed; v ism,tRepresenting the wind speed of the wind farm m at the moment t;
Figure FDA0003269222160000013
representing the installed capacity of the wind farm m at the moment t;
suppose a scale factor km,tThe ratio of the power of the wind power access IES to the total installed capacity is expressed, and the mathematical expression is as follows:
Figure FDA0003269222160000014
wherein the content of the first and second substances,
Figure FDA0003269222160000015
representing installed capacity of a wind farm;
if the formula (2) is substituted into the formula (1), the IES receives the wind power provided by the wind power plant
Figure FDA0003269222160000016
Comprises the following steps:
Figure FDA0003269222160000017
assuming that the variation in wind speed is a distribution-compliant variable, given a confidence level β, the wind speed variation should satisfy the following equation:
Figure FDA0003269222160000021
where P (-) is the probability of event delivery, Δ vk,tFor predicted wind speed error, Δv k,t(β)Representing the lower bound of the predicted wind speed error at confidence level beta,
Figure FDA0003269222160000022
represents the upper limit of the predicted wind speed error at the confidence level beta;
formula (4) is a predicted wind speed interval based on interval estimation, and can be substituted for formula (3) to obtain a wind power output interval predicted by using probability theory:
Figure FDA0003269222160000023
wherein the content of the first and second substances,
Figure FDA0003269222160000024
the wind farm provides a lower wind power limit at the confidence level beta,
Figure FDA0003269222160000025
representing the upper limit of wind power provided by the wind farm at a confidence level beta, vm,t(ep)Representing the expectation of wind speed.
3. The interval optimization scheduling method considering the dynamic characteristics of the natural gas network as claimed in claim 1, wherein in S2, assuming that any one natural gas transmission pipeline is pipeline k1, k and 1 are the node of the gas inflow pipeline and the node of the gas outflow pipeline respectively,
Figure FDA0003269222160000026
representing the inflow of the gas line,
Figure FDA0003269222160000027
representing the outflow of a gas pipeline, the volume of gas stored in the pipeline is:
Figure FDA0003269222160000028
Figure FDA0003269222160000029
wherein, pix,tAnd piy,tRepresenting the pipe pressure at time t, Qkl,tRepresenting the volume of gas stored in the pipeline at time t, pA,klIs a coefficient, the influencing factors include the diameter, length and ambient temperature of the pipe; x and y represent arbitrary points of the pipeline;
the capacity of the gas inflow pipeline and the gas outflow pipeline and the pressure at two ends of the pipeline satisfy the following equation:
Figure FDA00032692221600000210
Figure FDA00032692221600000211
where ρ isB,klIs a coefficient of influence including pipe diameter, length and ambient temperature, θkl,tIndicating the direction of gas flow in the pipeline, judging the gas flow direction by the values of 1 and-1, and when the pressure at the output end is smaller than that at the input end, thetakl,t1, θ when the output pressure is greater than the input pressurekl,t＝-1；
The equations (6) to (9) form a natural gas transmission model considering pipeline storage;
for convenience of discussing the dynamic characteristics of the natural gas pipeline, the compressor in the network is not discussed at all, and the flow balance formula is as follows:
Figure FDA0003269222160000031
wherein kl is oI(kl) k represents the natural gas flow through node k into line k1, kl oT(kl) represents the natural gas flow out of pipeline k1 through node 1, c represents the number of plants producing the natural gas,
Figure FDA0003269222160000032
representing the flow rate of natural gas from the source,
Figure FDA0003269222160000033
a natural gas inflow rate representative of a received gas load;
all the nodes are regarded as a unified whole, and the formula (7) is substituted into the formula (10), so that the equation of the whole natural gas system after the pipeline delay effect is considered is obtained:
Figure FDA0003269222160000034
equations (10) and (12) form a natural gas dynamic inventory model, and as mentioned above, the inventory characteristic of natural gas is affected by the negative feedback regulation, and is not a single constant characteristic, but dynamic, and the characteristic is called the 'buffering characteristic' of natural gas.
4. The interval optimization scheduling method considering the dynamic characteristics of the natural gas network as claimed in claim 1, wherein in S3, the objective function of the optimization objective is:
min OF＝EC+GC； (13)
wherein OF represents the cost OF an electric-gas interconnection system, GC represents the cost OF a natural gas pipe network, and EC represents the cost OF a thermal power generating unit.
5. The interval optimization scheduling method considering the dynamic characteristics of the natural gas network according to claim 4, wherein in the step S3, the natural gas pipe network equation is described by the following formula (14):
Figure FDA0003269222160000035
wherein, cnRepresenting the cost factor of the natural gas network.
6. The interval optimization scheduling method considering the dynamic characteristics of the natural gas network as claimed in claim 4, wherein in the S3, the network equation is described by equation (15):
Figure FDA0003269222160000036
wherein alpha isg、bgAnd cgA cost coefficient of the thermal power generating unit is represented,
Figure FDA0003269222160000037
representing the power loss load of the i node at the time t, cVWPenalty cost, δ, representing power loss loadi,tRepresenting the phase angle of node i.
7. The interval-optimized scheduling method considering the dynamics of the natural gas grid of claim 1, wherein in the S3, the IES constraints include electrical current constraints and natural gas flow constraints:
electrical current constraint, adopting a power flow model for modeling a power grid:
Figure FDA0003269222160000041
wherein i represents any node of the power grid, j represents the number of branches contained in the power grid, e represents the number of coal-fired units, and Pe,tRepresenting the power consumed by all branches connected to i, d representing the number of gas turbine groups,
Figure FDA0003269222160000042
represents the power at which the gas turbine generates electrical energy,
Figure FDA0003269222160000043
indicating the power consumed by the consumer, Pij,tWhich represents the power on each of the branches,
Figure FDA0003269222160000044
represents the power at which the CHP generates electrical energy;
Figure FDA0003269222160000045
representing the power loss load quantity of the i node at the time t;
and (3) natural gas flow constraint, further considering the CHP unit, the gas turbine and the compressor on the basis of the formula (10), and obtaining a flow balance equation:
Figure FDA0003269222160000046
wherein the content of the first and second substances,
Figure FDA0003269222160000047
which is indicative of the natural gas flow rate to the compressor,
Figure FDA0003269222160000048
the natural gas flow rate at the output of the compressor is indicated,
Figure FDA0003269222160000049
indicating the flow of natural gas consumed by the gas turbine.
8. The interval optimization scheduling method considering the dynamic characteristics of the natural gas network as claimed in claim 1, wherein in S3, the unit operation constraints are:
Figure FDA00032692221600000410
Figure FDA00032692221600000411
Figure FDA00032692221600000412
Figure FDA00032692221600000413
Figure FDA00032692221600000414
Figure FDA00032692221600000415
Figure FDA00032692221600000416
Figure FDA00032692221600000417
Pemin≤Pe,t≤Pemax (26)
Figure FDA0003269222160000051
Figure FDA0003269222160000052
wherein, Pemin、Pemax、
Figure FDA0003269222160000053
Respectively the minimum output, the maximum output, the uplink regulation rate and the downlink regulation rate of the coal-fired unit;
Figure FDA0003269222160000054
respectively the minimum processing, the maximum processing, the uplink regulation rate and the downlink regulation rate of the gas turbine;
Figure FDA0003269222160000055
the minimum output, the maximum output, the uplink regulation rate and the downlink regulation rate of the CHP are respectively;
Figure FDA0003269222160000056
respectively the minimum output and the maximum output of the conventional unit.
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