CN114548844A - Offshore oil and gas field power grid wind power bearing capacity evaluation method considering electrical coupling constraint - Google Patents
Offshore oil and gas field power grid wind power bearing capacity evaluation method considering electrical coupling constraint Download PDFInfo
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Abstract
The invention discloses a method for evaluating wind power bearing capacity of an offshore oil and gas field power grid considering electrical coupling constraint. The method provided by the invention can realize the offshore oil and gas field power grid risk bearing capacity evaluation which effectively deals with wind power uncertainty and volatility.
Description
Technical Field
The invention relates to the technical field of new energy, in particular to an evaluation method for wind power bearing capacity of an offshore oil and gas field power grid considering electrical coupling constraint.
Background
In the offshore oil and gas production platform, in order to avoid operators to lay long-distance transmission lines to supply power through shore power, the offshore oil and gas production platform is integrated with offshore wind power generation to form an island offshore oil and gas field power supply system, and fossil fuel cost is saved due to integration of wind power generation. The offshore oil and gas field is supplied with power through a gas generator and a wind driven generator and independently operates as an island micro-grid.
In the process of integrating wind power generation, wind power bearing capacity of an island offshore oil and gas field micro-grid after integrating wind power generation needs to be evaluated firstly, so that a construction scheme of a wind power facility is determined or an existing distribution scheme of wind power resources among various oil and gas field platforms is determined, different from a traditional power distribution network or a micro-grid, the offshore oil and gas platform has an induction motor with extremely high power and a very high reactive power requirement, the offshore platform power grid is closely interacted with a natural gas pipe network through a gas generator and a compressor, wind power has uncertainty and volatility, when the wind power is networked with the offshore oil and gas field power grid, the other function of the gas generator is unbalance of smooth power supply and demand, therefore, intermittent behavior of the wind power is transferred to a gas system, and the gas pressure fluctuates along with the change of wind power output, and therefore, the evaluation of the maximum wind power bearing capacity of the island offshore oil and gas field platform is a method for solving the randomness and the fluctuation of the wind power The problem of fussy optimization is that in the prior art, a wind power maximum bearing capacity evaluation method of an island offshore oil and gas field platform, which can effectively cope with randomness and volatility of wind power, does not exist.
Thus, there is a need for improvements and enhancements in the art.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a wind power bearing capacity evaluation method for an offshore oil and gas field power grid considering electrical coupling constraint, and aims to solve the problem that no wind power bearing capacity evaluation method for an island offshore oil and gas field platform capable of effectively coping with randomness and volatility of wind power exists in the prior art.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
in a first aspect of the invention, a method for evaluating wind power bearing capacity of an offshore oil and gas field power grid considering electrical coupling constraint is provided, and the method comprises the following steps:
determining the maximum upward climbing flexibility requirement caused by wind power in a target power grid system according to a preset scheduling period, and establishing a gas power generation and wind power cooperative climbing flexibility constraint according to the upward climbing capacity of a gas generator connected with each bus in the target power grid system and the maximum upward climbing flexibility requirement;
establishing gas power generation capacity and wind capacity cooperative flexibility constraint according to the rated capacity of the gas generator connected with each bus in the target power grid system;
establishing operation characteristic constraints of the gas generator set according to output power factors of the gas generator set connected with each bus in the target power grid system, and establishing operation characteristic constraints of the gas compressor according to operation parameters of each gas compressor in the target power grid system;
establishing a grid-connected operation characteristic constraint of the wind driven generator according to the power factor of the wind driven generator connected with each bus in the target power grid system;
constructing a target linear programming model, wherein constraints of the target linear programming model comprise a gas power generation and wind power cooperative climbing flexibility constraint, a gas power generation capacity and wind power capacity cooperative flexibility constraint, a gas generator set operation characteristic constraint, a gas compressor operation characteristic constraint and a wind power generator grid-connected operation characteristic constraint, and an objective function of the target linear programming model is as follows:
wherein
for the power of the wind turbine connected to the bus i in the target grid system,
a bus set which can be connected with a wind driven generator in the target power grid system;
and solving the target linear programming model to obtain the wind power bearing capacity of the target power grid system.
In a second aspect of the present invention, there is provided an offshore oil and gas field power grid wind power bearing capacity evaluation system considering electrical coupling constraints, the system comprising:
the first constraint module is used for determining the maximum upward climbing flexibility requirement caused by wind power in a target power grid system according to a preset scheduling period, and establishing gas power generation and wind power cooperative climbing flexibility constraint according to the upward climbing capacity of a gas generator connected with each bus in the target power grid system and the maximum upward climbing flexibility requirement;
the second constraint module is used for establishing gas power generation capacity and wind capacity cooperative flexibility constraint according to the rated capacity of the gas generator connected with each bus in the target power grid system;
the third constraint module is used for establishing operation characteristic constraints of the gas generator set according to output power factors of the gas generator set connected with each bus in the target power grid system and establishing operation characteristic constraints of the gas compressor according to operation parameters of each gas compressor in the target power grid system;
the fourth constraint module is used for establishing wind driven generator grid-connected operation characteristic constraints according to the power factors of the wind driven generators connected with the buses in the target power grid system;
a model building module, configured to build a target linear programming model, where constraints of the target linear programming model include a gas power generation and wind power cooperative climbing flexibility constraint, a gas power generation capacity and wind power capacity cooperative flexibility constraint, a gas generator set operation characteristic constraint, a gas compressor operation characteristic constraint, and a wind power generator grid-connected operation characteristic constraint, and an objective function of the target linear programming model is:
wherein, in the process,
for the power of the wind turbine connected to the bus i in the target grid system,
a bus set which can be connected with a wind driven generator in the target power grid system;
and the model solving module is used for solving the target linear programming model to obtain the wind power bearing capacity of the target power grid system.
In a third aspect of the present invention, a terminal is provided, where the terminal includes a processor, and a storage medium communicatively connected to the processor, where the storage medium is adapted to store a plurality of instructions, and the processor is adapted to call the instructions in the storage medium to execute the steps of implementing any one of the methods for evaluating wind power bearing capacity of an offshore oil and gas field power grid in consideration of electrical coupling constraints described above.
In a fourth aspect of the present invention, a computer readable storage medium is provided, which stores one or more programs, which are executable by one or more processors, to implement the steps of the offshore oil and gas field power grid wind power bearing capacity assessment method taking into account electrical coupling constraints of any of the above.
Compared with the prior art, the invention provides a wind power bearing capacity evaluation method of a power grid of an offshore oil and gas field considering electrical coupling constraint, the wind power bearing capacity evaluation method of the power grid of the offshore oil and gas field considering the electrical coupling constraint is used for constructing a target linear programming model aiming at the uncertainty and the volatility of wind power, the target function result of the model is the maximum wind power bearing capacity of the power grid system, the constraint of the model comprises the constraint of the gas power generation and wind power cooperative climbing flexibility, the constraint of the gas power generation capacity and the wind power capacity cooperative flexibility, the constraint of the gas power generation unit operation characteristic, the constraint of the gas compressor operation characteristic and the constraint of the wind power generator grid-connected operation characteristic, wherein the constraint of the gas power generation and wind power cooperative climbing is constructed according to the wind power climbing requirement in a scheduling period and the climbing capacity of the wind power and the gas power generator, and thus the finally calculated wind power bearing capacity can be ensured to meet the requirement of solving the wind power climbing to electricity in the scheduling period The method for evaluating the wind power bearing capacity of the offshore oil and gas field power grid can effectively deal with wind power uncertainty and volatility, and provide reference data for wind power facility construction and wind power resource distribution in an offshore oil and gas field power grid.
Drawings
FIG. 1 is a flow chart of an embodiment of a method for evaluating wind power bearing capacity of an offshore oil and gas field power grid in consideration of electrical coupling constraints, provided by the invention;
FIG. 2 is a schematic diagram of an offshore oil and gas field platform power grid structure;
FIG. 3 is a schematic diagram of the electrical-gas interconnection of the offshore oil and gas platform power grid;
FIG. 4 is a schematic diagram of a scheduling cycle in an embodiment of a wind power bearing capacity evaluation method for an offshore oil and gas field power grid in consideration of electrical coupling constraints, provided by the invention;
FIG. 5 is a schematic view of a stable operating region of the compressor;
FIG. 6 is a schematic diagram of a stabilized operation area of a linearized compressor in an embodiment of a method for evaluating wind power bearing capacity of an offshore oil and gas field power grid in consideration of electrical coupling constraints, provided by the invention;
FIG. 7 is a structural schematic diagram of an effective application example I of the method for evaluating the wind power bearing capacity of the offshore oil and gas field power grid in consideration of the electrical coupling constraint, provided by the invention;
FIG. 8 is a schematic structural diagram of a second effective application example of the method for evaluating the wind power bearing capacity of the offshore oil and gas field power grid in consideration of the electrical coupling constraint, provided by the invention;
FIG. 9 is a schematic diagram of the effect of the single variable piecewise linearization function on different segment numbers in the validity verification of the offshore oil and gas field power grid wind power bearing capacity evaluation method considering the electrical coupling constraint provided by the invention;
FIG. 10 is a schematic diagram of a wind power bearing capacity evaluation result of an example of an effective application of the wind power bearing capacity evaluation method for the offshore oil and gas field power grid considering electrical coupling constraints, provided by the invention;
fig. 11 is a schematic view of a stable operation state of a compressor corresponding to a wind power bearing capacity evaluation result of an effective application example of the wind power bearing capacity evaluation method for an offshore oil and gas field power grid based on electrical coupling constraint provided by the invention;
FIG. 12 is a schematic diagram of a gas generator set combination and power output corresponding to a wind power bearing capacity evaluation result of an effective application example of the wind power bearing capacity evaluation method for the offshore oil and gas field power grid in consideration of electrical coupling constraints, provided by the invention;
FIG. 13 is a schematic diagram of a wind power bearing capacity evaluation system of an offshore oil and gas field power grid considering electrical coupling constraints, provided by the invention;
fig. 14 is a schematic diagram of an embodiment of a terminal provided in the present invention.
Detailed Description
In order to make the objects, technical solutions and effects of the present invention clearer and clearer, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example one
The method for evaluating the wind power bearing capacity of the offshore oil and gas field power grid considering the electrical coupling constraint provided by the invention can be applied to a terminal, and the terminal can evaluate the wind power bearing capacity of the offshore oil and gas field through the method for evaluating the wind power bearing capacity of the offshore oil and gas field power grid considering the electrical coupling constraint provided by the invention, thereby providing reference data for a wind power facility construction scheme or a wind power resource distribution scheme before wind power is introduced into the offshore oil and gas field power grid.
As shown in fig. 1, in an embodiment of the method for evaluating wind power bearing capacity of an offshore oil and gas field power grid considering electrical coupling constraints, the method includes the steps of:
s100, determining a maximum upward climbing flexibility requirement caused by wind power in a target power grid system according to a preset scheduling period, and establishing a gas power generation and wind power cooperative climbing flexibility constraint according to the upward climbing capacity of a gas generator connected with each bus in the target power grid system and the maximum upward climbing flexibility requirement;
s200, establishing gas power generation capacity and wind capacity cooperative flexibility constraint according to the rated capacity of the gas generator connected with each bus in the target power grid system;
s300, establishing operation characteristic constraints of the gas generator set according to output power factors of the gas generator set connected with each bus in the target power grid system, and establishing operation characteristic constraints of the gas compressor according to operation parameters of each gas compressor in the target power grid system;
s400, establishing a grid-connected operation characteristic constraint of the wind driven generator according to the power factor of the wind driven generator connected with each bus in the target power grid system;
s500, constructing a target linear programming model, wherein constraints of the target linear programming model comprise a gas power generation and wind power cooperative climbing flexibility constraint, a gas power generation capacity and wind power capacity cooperative flexibility constraint, a gas generator set operation characteristic constraint, a gas compressor operation characteristic constraint and a wind power generator grid-connected operation characteristic constraint, and an objective function of the target linear programming model is as follows:
wherein
for the power of the wind turbine connected to the bus i in the target grid system,
a bus set which can be connected with a wind driven generator in the target power grid system;
s600, solving the target linear programming model to obtain the wind power bearing capacity of the target power grid system.
A typical structure of an island offshore oil and gas field power grid is shown in fig. 2, wherein a plurality of oil and gas platforms are interconnected with a seabed oil and gas pipeline through submarine cables, and a traditional island micro-grid uses a natural gas driven gas generator to meet the power demand of the production thereof, and comprises an electric submersible pump, processing equipment (such as a delivery pump, an electric pipeline heater, a water injection pump and a compressor), a crane, a lamp and the like. The power and gas supply and demand relationship of the micro-grid of the offshore oil and gas field island is shown in figure 3. The main products of oil and gas fields are natural gas, crude oil and associated gas. Crude oil and natural gas are mainly delivered to a processing site, associated gas is preferentially used as fuel of a gas generator, and redundant associated gas is injected into a gas lift well or is combusted and discharged to the atmosphere. The power supply of each platform of the island micro-grid can be supplied by only a gas generator, and after the offshore oil and gas field power grid with the wind power grid connected is adopted, the power supply can be also carried out through the wind power.
Regarding an island offshore oil and gas field power grid with wind power bearing capacity to be evaluated as the target power grid system, evaluating the wind power bearing capacity of the target power grid system through steps S100-S600, specifically, evaluating the maximum wind power grid-connected capacity of the target power grid system under the condition of not violating the operation constraint of the electricity-gas interconnection system when evaluating the wind power bearing capacity of the target power grid system, in this embodiment, a target linear programming model with constraint is constructed, and the target function of the target linear programming model is
Wherein, in the process,
for the power of the wind turbine connected to the bus i in the target grid system,
for the set of busbars which can be connected with wind power generators in the target power grid system, that is, the solution goal of the target linear programming model is to find the busbar which is connected into the target power gridAnd the power of the wind driven generator is the maximum, so that the solving result of the target linear programming model is the wind power bearing capacity of the target power grid system.
As can be seen from the introduction of the island oil and gas field power grid, the island micro power grid and the natural gas system are closely coupled together through the gas generator and the gas compressor, and the combined action of the island micro power grid and the natural gas system influences the offshore wind power bearing capacity of the island micro power grid. And wind power is used as a new energy source and has randomness and volatility. In this embodiment, in order to reduce the conservatism of the wind power bearing capacity of the target power grid system obtained through evaluation and ensure the realizability, and effectively combat the randomness and volatility of wind power, in this embodiment, a plurality of constraints are constructed in the target linear programming model, and the constraints in the target linear programming model are explained in detail below.
In order to ensure that the wind power bearing capacity of the target power grid system obtained through evaluation can cope with the randomness and the volatility of wind power, namely, to ensure that the gas engine can make up the generation capacity shortage in the target power grid system when the wind power fluctuates randomly, in the embodiment, a part of constraints in the target linear programming model are determined by combining a climbing requirement and a capacity adjusting requirement, so that the result obtained through solution can cope with the randomness and the volatility of the wind power.
On the one hand, based on the offshore wind power ultra-short term power generation prediction technology and the real-time safe economic dispatch, in this embodiment, a dispatch cycle is set, as shown in fig. 4, the real-time safe economic dispatch includes two time windows:
and
one of the main roles of real-time safe economic dispatch is to
Resolving next within a time windowA period of time
And the influence of wind power climbing in the time window on the flexible and safe operation of the target power grid system. That is, it needs to be guaranteed that the scheduling period is preset
In addition, power fluctuation caused by fluctuation of wind power can be compensated by the gas generator. In actual operation, only need pay close attention to wind-powered electricity generation when climbing downwards to the climbing ability demand of making progress of gas generator, because, when wind-powered electricity generation power was climbing upwards, can carry out the wind-powered electricity generation power climbing restriction of initiative through energy management system, however when wind-powered electricity generation power because wind speed reduces when climbing downwards, gas generator must have the ascending climbing ability of reorganization and compensate island microgrid's power shortage, because the reactive power regulation ability of generator mainly leans on excitation system, corresponding speed is very fast, only need consider active power's climbing rate here. In order to ensure normal operation, the worst scene is considered, namely the wind power is in a preset scheduling period
In, the power is directly reduced to the condition when 0 by the maximum access power, and the maximum upward climbing flexibility requirement of the wind power is as follows:
scheduling period
The method can be selected according to the actual application scene, for example, 5min, 6min and the like.
The climbing ability that makes progress of gas generator among the target power grid system needs to satisfy the biggest climbing flexibility demand that makes progress of wind-powered electricity generation, and gas generator's climbing ability that makes progress still need to satisfy simultaneously the climbing demand that makes progress of all loads among the target power grid system, and before integrating wind-powered electricity generation in marine oil gas field electric wire netting, all that need consider among the electric wire netting system open and stop the state, so there is the restraint:
wherein,
an uphill demand for the gas generator for a load of the target power grid system,
is a collection of busbars of the target grid system,
a set of gas generators connected for a bus i in the target grid system,
for the upward climbing capability of the gas generator at the bus i in the target power grid system,
indicating the start/stop state of a gas generator,
。
on the other hand, in the target power grid system, the active power demand of wind power is as follows:
and in marine island microgrid, reactive power is also very precious, also need to evaluate to wind power's reactive power demand, and wind power's reactive power demand is:
in order to ensure that the target power grid system can flexibly and safely operate in the worst scene, the gas generator needs to balance the power fluctuation of wind power, has a certain adjustment adequacy, and needs to consider all possible start-stop states of the gas generator, so for the power and wind capacity of the gas generator, the following constraints are provided:
wherein,
the total capacity of wind power connected to the target power grid system;
a hot spinning reserve capacity fraction required for the target grid system;
the reactive power is the rated output reactive power of the wind driven generator connected with the target power grid system bus i;
to correspond to
The total reactive power of the wind power;
and
active power and reactive power of the gas generator are respectively;
and
are respectively
And
the upper limit of (d), typically the rated capacity of the gas generator,
indicating the start/stop state of a gas generator,
，
is a collection of busbars of the target grid system,
a set of gas generators connected for a bus i in the target grid system.
As explained before, the objective function of the objective linear programming model for evaluating the wind power bearing capacity of the objective grid system is:
and when assessing offshore oil and gas field electric wire netting wind-powered electricity generation bearing capacity, the feasibility needs to be considered, the wind-powered electricity generation bearing capacity that the assessment result corresponds promptly can not surpass the gas generating set in the electric wire netting, the gas compressor and the operating characteristic that aerogenerator is incorporated into the power networks, consequently, in the linear programming model of target, except gas electricity generation and wind-powered electricity generation cooperation climbing flexibility constraint, gas power generation capacity and wind electric capacity cooperation flexibility constraint, still included gas generating set operating characteristic constraint, gas compressor operating characteristic constraint and aerogenerator are incorporated into the power networks operating characteristic constraint.
Specifically, considering the start-stop states of all the gas generators, the operating characteristic constraints of the gas generator set in the target power grid system are as follows:
wherein,
and
respectively the active power and the reactive power of the gas generator,
and
are respectively
And
the upper limit of (1) is the rated capacity of the gas generator,
is composed of
The lower limit of (d);
is composed of
The lower limit of (d);
and
the upper and lower limits of the output power factor of the gas generator connected with the target power grid system bus i are respectively set,
indicating the start/stop state of a gas generator,
，
a set of gas generators connected for a bus i in the target grid system.
Equation (8) is the active output constraint of the gas generator, equation (9) represents the reactive output constraint of the gas generator, equation (10) represents the relationship between the active and reactive output powers of the gas generator, and the power factor of the output is limited to ensure the safe and stable operation of the gas generator.
The wind driven generator grid-connected operation characteristic constraints are as follows:
wherein,
and
respectively the lower limit and the upper limit of the active power of the wind driven generator connected with the target power grid system bus i,
and
the upper limit and the lower limit of the reactive power of the wind driven generator connected with the target power grid system bus i,
and
normally set to 0, the range of the power factor of the wind power connected with the microgrid bus i is
(capacitive) ~
(perceptual).
The formula (11) and the formula (12) respectively represent the active output constraint and the reactive output constraint of the wind driven generator, and the large wind driven generator has certain reactive power regulation capacity, and the regulation range can be represented by a power factor
(capacitive) —
(inductive), equations (13) and (14) represent the reactive power regulation performance of the wind turbine.
For the operation characteristic constraints of the gas compressor, the operation characteristic constraints comprise a prime mover power constraint and a stable operation region constraint, wherein the prime mover power constraint of the gas compressor can be expressed as:
equation (15) represents the active power consumption of the gas compressor prime mover when delivering natural gas, and equation (16) represents the gas compressor limited by the prime mover's rated power.
In practice, the stable operation area of the gas compressor is shown in fig. 5, which is a polygonal area surrounded by four non-linear curves (an inrush current limiting curve, a prime mover maximum/minimum rotation speed limiting curve, and a maximum gas flow rate curve), and each operation point in the stable operation area is a stable operation point, and exceeds the stable operation area, so that the gas compressor is prone to unstable phenomena, such as surge phenomena and the like. The nonlinear model has many disadvantages, such as difficulty in finding an optimal solution and low solving speed, so in this embodiment, as shown in fig. 6, a stable operation area of the gas compressor is simplified in a linearization manner, and an inscribed quadrangle of the nonlinear area is used to replace the original nonlinear area, so that the nonlinear constraint of the stable operation area of the gas compressor can be expressed as a linear constraint and has sufficient calculation accuracy. In the method provided by this embodiment, the gas compressor steady operation region constraint may be expressed as:
equation (17) represents the four boundaries of the linearized steady operation region, and equation (18) represents the compression ratio of the gas compressor.
Rewriting equations (15) and (18) yields:
and (3) obtaining the operating characteristic constraint of the gas compressor in the target power grid system by integrating the formulas (15) to (21) as follows:
（22）
wherein,
is a collection of gas compressors, and is,
for gas compressor
Is a linear function of the k-th boundary of (c),
、
and
in order to linearize the parameters of the process,
for gas compressor
The compression ratio of (a) is made,
and
respectively the upper limit and the lower limit of the material,
and
is the pressure of the inlet and the outlet of the gas compressor,
，
，
is the intermediate variable(s) of the variable,
、
、
and
for gas compressor
Active demand, flow, polytropic factor and mechanical efficiency,
is the rated power of the prime motor of the gas compressor.
In order to further consider an electrical coupling relationship in an offshore island microgrid system and improve accuracy of a wind power bearing capacity evaluation result, in this embodiment, constraints of the target linear programming model further include power flow operation constraints of a power network and operation constraints of a gas network, and the method further includes:
constructing power flow operation constraints of the power network according to power parameters of all buses in the target power grid system;
and constructing the operation constraint of the gas network according to the operation parameters of each natural gas pipeline in the target power grid system.
Specifically, a linear power flow model suitable for micro-grid of offshore island oil and gas fieldThe characteristics of high charging power and large reactive interaction of a submarine cable in a micro-grid are fully met, and a power branch between a bus i and a bus j is used
For example, the power flow operation constraint of the power network is as follows:
wherein,
a set of power branches is represented as,
，
and
representing the real and reactive power flowing to the busbars i to j in branch i,
and
representing the active and reactive power flowing to the busbars j to i in branch l,
and
representing the resistance and the inductive reactance of the branch,
and
representing the square of the voltage magnitude of the bus bars i and j,
representing a busbar iThe magnitude of the voltage is such that,
and
are respectively as
The maximum value and the minimum value of (c),
is a collection of busbars of the target grid system,
representing the square of the branch current magnitude,
the equivalent charging capacitance of the branch is represented,
and
respectively representing the equivalent charging reactive power of the bus i and the bus j connection,
is the rated capacity of the power branch,
a set of gas generators connected for a bus i in the target grid system,
the set of loads of the bus bar i is represented,
representing the set of branches connected to the bus bar i,
and
respectively the active power and the reactive power of the gas generator,
the reactive power of the wind driven generator connected with the target power grid system bus i under rated output is obtained,
representing the real power of the load to which the bus i is connected,
representing the reactive power of the load connected to bus i. Equation (37) represents the active power flow constraint of the power network, and equation (38) represents the reactive power flow constraint of the power network.
In this embodiment, a natural gas pipeline model is established by using the Weymouth equation, and the gas grid constraint is established accordingly, specifically:
in the formula,
、
、
and
as an auxiliary variable, the number of variables,
for the coefficients of the Weymouth equation for natural gas pipelines,
is the intermediate variable(s) of the variable,
a collection of natural gas pipelines is shown,
is the air flow rate of the pipeline and,
and
to represent
The maximum and minimum values of (a) and (b),
and
respectively, the air pressure of the pipe nodes m and n, and
and
，
and
to represent
The maximum and minimum values of (a) and (b),
and
to represent
The maximum and minimum values of (a) and (b),
in the form of a collection of pipe nodes,
is a gas generator set connected at a pipeline node m,
is a collection of pipes connected at pipe node m,
is the collection of air loads connected at the pipe node m,
is a gas source assembly connected at a pipeline node m,
the air flow rate consumed by the gas generator;
an air flow rate that is an air load demand;
the rate of air flow supplied to the air supply.
As described above, since the nonlinear model has many disadvantages such as difficulty in finding the optimal solution and slow solving speed, in the embodiment, the linearization process is performed on the nonlinear terms in all constraints, that is, the constructing of the target linear programming model includes:
and carrying out linearization processing on the nonlinear terms in each constraint by adopting a univariate piecewise linearization function to obtain the target linear programming model.
In particular, assume that one definition is in the interval
Is expressed as a piecewise linearization function of
Dividing the interval into
A segment, the corresponding division point satisfies
Then the corresponding function is expressed as
，
. Is specifically shown as follows
In the formula,
and
are auxiliary variables.
Thus, the target linear programming model may be represented as:
the target linear programming model is a hybrid shaping linear programming model and can be solved by a general commercial linear programming solver, for example, Matlab R2020a and CPLEX12.9.0 can be used for solving. The solved objective function value is the wind power bearing capacity of the target power grid system, namely the maximum capacity of wind power which can be accessed by the target power grid system, based on the solving result, reference data can be provided for the wind power facility construction scheme before the target power grid system is accessed to the wind power, excessive investment of construction fund is avoided, or reference data can be provided for resource allocation of the same wind power platform among a plurality of power grid systems, unnecessary wind power resources are prevented from being provided for a certain platform, and reasonable allocation of the wind power resources is achieved.
As can be seen, the method provided by the embodiment provides an island microgrid flexibility index and quantification method based on cooperation of gas generator climbing and wind power climbing and cooperation of gas generator capacity and wind power capacity aiming at wind power uncertainty and volatility, and based on an offshore wind power ultra-short term power generation prediction technology and real-time safe economic scheduling, so that flexible and safe operation of the island microgrid during wind power integration can be realized; on the basis, based on the deep coupling relation between the island micro-grid and the natural gas grid, a marine oil-gas field wind power bearing capacity hybrid integral linear programming model considering the grid-connected operation characteristic of the wind turbine generator, the operation characteristic of the gas generator and the operation characteristic of the gas compressor is established by combining flexibility indexes, and the evaluation result of the marine wind power bearing capacity of the island micro-grid is obtained by solving. The method provided by the embodiment can solve the following problems:
the method provided by the embodiment can be used for scientifically evaluating the offshore wind power bearing capacity of the island micro-grid of the offshore oil and gas field and ensuring the flexible and safe operation of the island micro-grid;
the method provided by the embodiment can be used for identifying and analyzing key factors influencing the offshore wind power bearing capacity of the offshore oil and gas field island micro-grid aiming at specific application examples;
the method provided by the embodiment can help an operator determine a proper investment scheme on the basis of the existing infrastructure, and further improves the offshore wind power bearing capacity.
In order to verify the effectiveness of the method provided by the present embodiment, two application examples are provided, the system configuration of example 1 is shown in fig. 7, and the system configuration of example 2 is shown in fig. 8, and the significant difference between the two is the difference in fuel gas supply modes of the gas generators on the offshore oil and gas platforms 1 and 2. In example 1, the fuel gas supply of the gas generators on the offshore oil and gas platforms 1 and 2 is in pipeline dedicated supply mode; in example 2, fuel gas for the gas generators on the offshore oil and gas platforms 1 and 2 is taken from a node of the natural gas transportation pipeline network. The two example arrangements primarily take into account two practical situations that are common to current and future offshore fields. As the offshore oil and gas field rolling development progresses, a part of offshore oil and gas platforms have a situation of shortage of associated gas, and at this time, associated gas or natural gas needs to be transported from other platforms through pipelines as fuel gas of a gas generator, corresponding to example 1. Alternatively, the offshore oil and gas platform is used as a node for natural gas transportation, and the fuel gas of the gas generator on the platform can be naturally and conveniently obtained from the natural gas transportation pipe network, corresponding to example 2.
The specific settings and verification results of examples 1 and 2 are explained below.
(1) Example 1
In fig. 7, the islanding microgrid system comprises four 4.5MW gas generators (G1-G4), and the fuel gas can be natural gas, associated gas or a mixture of the natural gas and the associated gas. The total load of the system is 5.82MW, the reactive power requirement is about 3.5MVar, and two gas compressors are not included. Because the gas compressor is a coupling element of the microgrid and the gas grid, the power demand varies with the fluctuations in the gas flow caused by the variations in the output of the offshore wind power. In order to perform reactive compensation on the system, a Static Var Generator (SVG) is installed on the platform 4. Fuel gas for the gas generators on the platforms 1 and 2 is taken from the nodes 1 and 7 of the gas network.
The gas network system comprises two gas compressors (C1-C2) and two gas sources (S1-S2). The gas compressor is connected to the buses 2 and 3 of the microgrid respectively. S1 is a natural gas source and mainly meets the requirements of natural gas plants; s2 is associated gas source, which mainly satisfies fuel gas supply of gas generator. When associated gas is insufficient, the gas generator consumes natural gas.
(2) Example 2
The main difference between example 2 and example 1 is the type of air supply. In example 2, all sources are natural gas, collected on platforms 1 and 2, and co-delivered via compressor C1. The supply rate of natural gas is constant at S2, and S1 is responsible for the supply of natural gas shortages and is used to regulate gas load fluctuations.
1) Model accuracy verification
The target linear programming model is applied to a univariate piecewise linearization function, and theoretically, the more the number of segments is, the more accurate the model is. Therefore, to verify the accuracy of the target linear programming model, the relevant errors of the grid and gas gateway key variables were statistically analyzed in example 1 in conjunction with scenarios 1 and 2, and the results are shown in fig. 9. As can be seen from the figure, when the number of segments is greater than or equal to 6, the correlation error is less than 1%; when the number of segments is 10 or more, the correlation error is less than 0.4%. Therefore, the piecewise linearization method adopted herein meets the requirements of calculation accuracy and speed, and the number of segments in the following calculation is selected 10.
2) Offshore wind power bearing capacity assessment
The wind power bearing capacity evaluation results of examples 1 and 2 are shown in fig. 10, the corresponding compressor operating conditions are shown in fig. 11, and the gas-turbine generator set combination and power output are shown in fig. 12. In general, the offshore wind power carrying capacity in example 2 is greatly improved compared to example 1, especially when the minimum power factor of the wind turbine is less than 0.92. In example 1, offshore wind power bearing is constrained by gas compressor operation when the minimum power factor of the wind turbine is between 0.8 and 0.96. When the bearing capacity of the offshore wind power is improved, the gas consumption of the gas generator is reduced, and the natural gas delivered by the gas compressor is correspondingly reduced. Since when the gas compressor delivers very little gas, its internal pressure must not be too high or else surging is easily caused. Therefore, the supply amount of natural gas to the platforms 1 and 2 at S2 is limited by the operation restriction of the gas compressor. In example 2, the offshore wind power bearing capacity is limited by the minimum output power of the gas generator when the minimum power factor of the wind turbine is between 0.8 and 0.92. Since the gas generator on the platform is sufficiently supplied with fuel gas, its operation is not limited by the operating constraints of the gas compressor. For other offshore wind power bearing capacity results in the examples, the results are all limited by the reactive deficiency of the offshore oil and gas platform. Because, as the minimum power factor of the wind turbine is increased, the reactive power output by the offshore wind power is also continuously reduced. This also shows that offshore oil and gas platform reactive power is the key factor that influences offshore wind power bearing capacity promotion. However, offshore oil and gas platforms have limited overhead and load bearing capabilities, limiting the installed capacity of reactive power compensation equipment.
When the offshore oil and gas field is not accessed by wind, the two gas generators can meet the power supply requirements of all loads. However, when wind power is connected, the difference is very large. In example 1, when the minimum power factor of the fan is between 0.8 and 0.96, two gas generators can meet the requirement; when the minimum power factor of the wind power is more than 0.96, three gas generators are needed to simultaneously work. In example 2, three gas generators are required to meet the demand, when the wind turbine is installed at the air grid node 5 or 1 and 5. When the fan is installed at the air network node 1, the fan is different. Therefore, the offshore wind power bearing capacity is closely related to the unit combination of the gas generator corresponding to different fan installation positions and different fan power factors.
According to the application example, the wind power bearing capacity of the offshore oil and gas field platform microgrid can be evaluated, and key factors influencing the wind power bearing capacity can be identified by comparing the wind power bearing capacities obtained by changing different factors through model evaluation, so that effective reference is provided for builders and operators.
To sum up, the embodiment provides a method for evaluating wind power bearing capacity of an offshore oil and gas field power grid considering electrical coupling constraints, wherein a target linear programming model is constructed for uncertainty and volatility of wind power, an objective function result of the model is the maximum wind power bearing capacity of the power grid system, constraints of the model include gas power generation and wind power cooperative climbing flexibility constraints, gas power generation capacity and wind power capacity cooperative flexibility constraints, gas generator set operation characteristic constraints, gas compressor operation characteristic constraints and wind power generator grid-connected operation characteristic constraints, wherein the gas power generation and wind power cooperative climbing flexibility constraints are constructed according to wind power climbing requirements in a scheduling period and climbing capacities of wind power and a gas power generator, so that the finally calculated wind power bearing capacity can meet the influence of wind power climbing on flexible and safe operation of the power grid system in the scheduling period, the invention provides a method for evaluating the wind power bearing capacity of a power grid of an offshore oil and gas field, which can effectively apply wind power uncertainty and volatility and can ensure that the wind power bearing capacity obtained by calculation can enable a gas generator in the power grid system to have certain adjustment adequacy, and the wind power bearing capacity obtained by calculation can meet the operation characteristics of a wind turbine generator, a gas generator and a gas compressor by virtue of the operation characteristic constraints of a gas compressor, the operation characteristic constraints of the gas generator and the grid-connected operation characteristic constraints of a wind power generator.
It should be understood that, although the steps in the flowcharts shown in the figures of the present specification are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in the flowchart may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of performing the sub-steps or stages is not necessarily sequential, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, databases, or other media used in embodiments provided herein may include non-volatile and/or volatile memory. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
Example two
Based on the above embodiment, the present invention further provides a system for evaluating wind power bearing capacity of an offshore oil and gas field power grid considering electrical coupling constraints, as shown in fig. 13, the system includes:
the first constraint module is used for determining a maximum upward climbing flexibility requirement caused by wind power in a target power grid system according to a preset scheduling period, and establishing a gas power generation and wind power cooperative climbing flexibility constraint according to the upward climbing capability of a gas generator connected with each bus in the target power grid system and the maximum upward climbing flexibility requirement, which is specifically described in embodiment one;
the second constraint module is used for establishing gas power generation capacity and wind capacity cooperative flexibility constraint according to the rated capacity of the gas generator connected with each bus in the target power grid system, and is specifically described in the first embodiment;
a third constraint module, configured to establish a gas generator set operating characteristic constraint according to an output power factor of a gas generator set connected to each bus in the target power grid system, and establish a gas compressor operating characteristic constraint according to an operating parameter of each gas compressor in the target power grid system, as described in embodiment one;
a fourth constraint module, configured to establish a wind turbine grid-connected operation characteristic constraint according to a power factor of a wind turbine connected to each bus in the target grid system, as described in embodiment one;
a model building module, configured to build a target linear programming model, where constraints of the target linear programming model include a gas power generation and wind power cooperative climbing flexibility constraint, a gas power generation capacity and wind power capacity cooperative flexibility constraint, a gas generator set operation characteristic constraint, a gas compressor operation characteristic constraint, and a wind power generator grid-connected operation characteristic constraint, and an objective function of the target linear programming model is:
wherein
for the power of the wind turbine connected to the bus i in the target grid system,
a bus set which can be connected with a wind driven generator in the target power grid system is specifically described in the first embodiment;
and the model solving module is used for solving the target linear programming model to obtain the maximum wind power bearing capacity of the target power grid system, and is specifically described in the first embodiment.
EXAMPLE III
Based on the above embodiments, the present invention further provides a terminal, as shown in fig. 14, where the terminal includes a processor 10 and a memory 20. Fig. 14 shows only some of the components of the terminal, but it is to be understood that not all of the shown components are required to be implemented, and that more or fewer components may be implemented instead.
The memory 20 may in some embodiments be an internal storage unit of the terminal, such as a hard disk or a memory of the terminal. The memory 20 may also be an external storage device of the terminal in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the terminal. Further, the memory 20 may also include both an internal storage unit and an external storage device of the terminal. The memory 20 is used for storing application software installed in the terminal and various data. The memory 20 may also be used to temporarily store data that has been output or is to be output. In an embodiment, the memory 20 stores a wind power bearing capacity evaluation program 30, and the wind power bearing capacity evaluation program 30 can be executed by the processor 10, so as to implement the wind power bearing capacity evaluation method of the offshore oil and gas field power grid under the constraint of the electric coupling in the present application.
The processor 10 may be a Central Processing Unit (CPU), microprocessor or other chip in some embodiments, and is used to run program codes stored in the memory 20 or process data, such as executing the offshore oil and gas field power grid wind power bearing capacity evaluation method considering electrical coupling constraints, and the like.
Example four
The present invention also provides a storage medium having one or more programs stored thereon, the one or more programs being executable by one or more processors to implement the steps of the method for evaluating wind power bearing capacity of an offshore oil and gas field power grid, taking into account electrical coupling constraints, as described above.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A wind power bearing capacity assessment method for an offshore oil and gas field power grid considering electrical coupling constraint is characterized by comprising the following steps:
determining the maximum upward climbing flexibility requirement caused by wind power in a target power grid system according to a preset scheduling period, and establishing a gas power generation and wind power cooperative climbing flexibility constraint according to the upward climbing capacity of a gas generator connected with each bus in the target power grid system and the maximum upward climbing flexibility requirement;
establishing gas power generation capacity and wind capacity cooperative flexibility constraint according to the rated capacity of the gas generator connected with each bus in the target power grid system;
establishing operation characteristic constraints of the gas generator set according to output power factors of the gas generator set connected with each bus in the target power grid system, and establishing operation characteristic constraints of the gas compressor according to operation parameters of each gas compressor in the target power grid system;
establishing a grid-connected operation characteristic constraint of the wind driven generator according to the power factor of the wind driven generator connected with each bus in the target power grid system;
constructing a target linear programming model, wherein constraints of the target linear programming model comprise a gas power generation and wind power cooperative climbing flexibility constraint, a gas power generation capacity and wind power capacity cooperative flexibility constraint, a gas generator set operation characteristic constraint, a gas compressor operation characteristic constraint and a wind power generator grid-connected operation characteristic constraint, and an objective function of the target linear programming model is as follows:
wherein
the power of the wind power generator connected for the bus i in the target power grid system,
a bus set which can be connected with a wind driven generator in the target power grid system;
and solving the target linear programming model to obtain the wind power bearing capacity of the target power grid system.
2. The offshore oil and gas field power grid wind power bearing capacity assessment method considering electrical coupling constraints as claimed in claim 1, wherein the gas power generation and wind power cooperative climbing flexibility constraint is:
wherein,
for the requirement of the maximum upward climbing flexibility of wind power,
for the purpose of the scheduling period,
an uphill demand for the gas generator for a load of the target power grid system,
is a collection of busbars of the target grid system,
a set of gas generators connected for a bus i in the target grid system,
for the upward climbing capability of the gas generator at the bus i in the target power grid system,
indicating the start/stop state of a gas generator,
。
3. the offshore oil and gas field power grid wind power bearing capacity assessment method considering electrical coupling constraints, as claimed in claim 1, wherein said gas power generation capacity and wind power capacity synergy flexibility constraints are:
wherein,
the total capacity of wind power connected to the target power grid system;
a hot spinning reserve capacity fraction required for the target grid system;
the reactive power is the rated output reactive power of the wind driven generator connected with the target power grid system bus i;
to correspond to
The total reactive power of the wind power;
and
active power and reactive power of the gas generator are respectively;
and
are respectively
And
the upper limit of (1) is the rated capacity of the gas generator,
indicating the start/stop state of a gas generator,
，
is a collection of busbars of the target grid system,
a set of gas generators connected for a bus i in the target grid system.
4. The method for evaluating wind power bearing capacity of an offshore oil and gas field power grid considering electrical coupling constraints as claimed in claim 1, wherein the constraints on the operating characteristics of the gas generator set are as follows:
wherein,
and
respectively the active power and the reactive power of the gas generator,
and
are respectively
And
the upper limit of (1) is the rated capacity of the gas generator,
is composed of
The lower limit of (d);
is composed of
The lower limit of (c);
and
the upper and lower limits of the output power factor of the gas generator connected with the target power grid system bus i are respectively set,
indicating the start/stop state of a gas generator,
，
a set of gas generators connected for a bus i in the target grid system;
the operating characteristic constraint of the gas compressor is as follows:
wherein,
is a collection of gas compressors, and is,
for gas compressor
Is a linear function of the k-th boundary of (c),
、
and
in order to linearize the parameters of the process,
for gas compressor
The compression ratio of (a) is made,
and
respectively the upper limit and the lower limit of the material,
and
is the pressure of the inlet and the outlet of the gas compressor,
，
，
is the intermediate variable(s) of the variable,
、
、
and
for gas compressor
Active demand, flow, polytropic factor and mechanical efficiency,
is the rated power of the prime motor of the gas compressor.
5. The offshore oil and gas field power grid wind power bearing capacity assessment method considering electrical coupling constraints as claimed in claim 1, wherein the wind driven generator grid-connected operation characteristic constraints are:
wherein,
and
respectively the lower limit and the upper limit of the active power of the wind driven generator connected with the target power grid system bus i,
the reactive power of the wind driven generator connected with the target power grid system bus i under rated output is obtained,
and
the upper limit and the lower limit of the reactive power of the wind driven generator connected with the target power grid system bus i are set, and the power factor range of the wind power connected with the micro-grid bus i is set as
。
6. The method for evaluating wind power bearing capacity of an offshore oil and gas field power grid considering electrical coupling constraints as claimed in claim 1, wherein the constraints of the target linear programming model further include power network flow operation constraints and gas network operation constraints, and before constructing the target linear programming model, the method further comprises:
constructing power flow operation constraints of the power network according to power parameters of all buses in the target power grid system;
constructing the operation constraint of the gas network according to the operation parameters of each natural gas pipeline in the target power grid system;
the power network tidal current operation constraint is as follows:
wherein,
a set of power branches is represented as,
，
and
representing brancheslThe active and reactive power to which the medium busbars i to j flow,
and
representing brancheslThe active and reactive power to which the medium buses j to i flow,
and
representing the resistance and the inductive reactance of the branch,
and
representing the square of the voltage magnitude of the bus bars i and j,
which represents the voltage amplitude of the bus i,
and
are respectively as
The maximum value and the minimum value of (c),
is a collection of busbars of the target grid system,
representing the square of the branch current magnitude,
the equivalent charging capacitance of the branch is represented,
and
respectively representing the equivalent charging reactive power of the bus i and the bus j connection,
is the rated capacity of the power branch,
a set of gas generators connected for a bus i in the target grid system,
the set of loads of the bus bar i is represented,
representing the set of branches connected to the bus bar i,
and
respectively the active power and the reactive power of the gas generator,
the reactive power of the wind driven generator connected with the target power grid system bus i under rated output is obtained,
representing the real power of the load to which the bus i is connected,
reactive power representing the load connected by bus i;
the operation constraint of the gas network is as follows:
wherein,
、
、
and
as an auxiliary variable, the number of variables,
for the coefficients of the Weymouth equation for natural gas pipelines,
is the intermediate variable(s) of the variable,
a collection of natural gas pipelines is shown,
is the air flow rate of the pipeline and,
and
to represent
The maximum and minimum values of (a) and (b),
and
respectively, the air pressure of the pipe nodes m and n, and
and
，
and
to represent
The maximum and minimum values of (a) and (b),
and
to represent
The maximum and minimum values of (a) and (b),
in the form of a collection of pipe nodes,
is a gas generator set connected at a pipeline node m,
is a collection of pipes connected at pipe node m,
is the collection of air loads connected at the pipe node m,
is a gas source assembly connected at a pipeline node m,
the air flow rate consumed by the gas generator;
an air flow rate that is an air load demand;
the rate of air flow supplied to the air supply.
7. The method for evaluating wind power bearing capacity of an offshore oil and gas field power grid considering electrical coupling constraints as claimed in any one of claims 1 to 6, wherein the constructing of the target linear programming model comprises:
and carrying out linearization processing on the nonlinear terms in each constraint by adopting a univariate piecewise linearization function to obtain the target linear programming model.
8. The utility model provides an offshore oil and gas field electric wire netting wind power bearing capacity evaluation system who takes into account electrical coupling constraint, its characterized in that, offshore oil and gas field electric wire netting wind power bearing capacity evaluation system who takes into account electrical coupling constraint includes:
the first constraint module is used for determining the maximum upward climbing flexibility requirement caused by wind power in a target power grid system according to a preset scheduling period, and establishing gas power generation and wind power cooperative climbing flexibility constraint according to the upward climbing capacity of a gas generator connected with each bus in the target power grid system and the maximum upward climbing flexibility requirement;
the second constraint module is used for establishing gas power generation capacity and wind capacity cooperative flexibility constraint according to the rated capacity of the gas generator connected with each bus in the target power grid system;
the third constraint module is used for establishing operation characteristic constraints of the gas generator set according to output power factors of the gas generator set connected with each bus in the target power grid system and establishing operation characteristic constraints of the gas compressor according to operation parameters of each gas compressor in the target power grid system;
the fourth constraint module is used for establishing wind driven generator grid-connected operation characteristic constraints according to the power factors of the wind driven generators connected with the buses in the target power grid system;
a model building module, configured to build a target linear programming model, where constraints of the target linear programming model include a gas power generation and wind power cooperative climbing flexibility constraint, a gas power generation capacity and wind power capacity cooperative flexibility constraint, a gas generator set operation characteristic constraint, a gas compressor operation characteristic constraint, and a wind power generator grid-connected operation characteristic constraint, and an objective function of the target linear programming model is:
wherein
for the power of the wind turbine connected to the bus i in the target grid system,
a bus set which can be connected with a wind driven generator in the target power grid system;
and the model solving module is used for solving the target linear programming model to obtain the maximum wind power bearing capacity of the target power grid system.
9. A terminal, characterized in that the terminal comprises: a processor, a storage medium communicatively connected to the processor, the storage medium adapted to store a plurality of instructions, the processor being adapted to invoke the instructions in the storage medium to perform the steps of implementing the offshore oil and gas field power grid wind power bearing capacity assessment method taking into account electrical coupling constraints of any of the preceding claims 1-7.
10. A computer readable storage medium, characterized in that the computer readable storage medium stores one or more programs which are executable by one or more processors to implement the steps of the offshore field grid wind power capacity assessment method taking into account electrical coupling constraints according to any of claims 1-7.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100224135A1 (en) * | 2004-06-02 | 2010-09-09 | Aquaculture Engineering Group Ltd. | Offshore aquaculture system |
| CN104727354A (en) * | 2015-02-25 | 2015-06-24 | 中国科学院力学研究所 | Testing system simulating ultimate dynamic bearing capacity of cyclic load lower plate anchor |
| CN107237321A (en) * | 2017-06-15 | 2017-10-10 | 中国能源建设集团江苏省电力设计院有限公司 | Radial direction low height adds wing large diameter steel pipe pile and its horizontal bearing capacity computation method |
| CN110782363A (en) * | 2019-08-15 | 2020-02-11 | 东南大学 | AC/DC power distribution network scheduling method considering wind power uncertainty |
| CN113906465A (en) * | 2019-04-03 | 2022-01-07 | 吉奥奎斯特系统公司 | Local/hybrid blockchain for achieving oil and gas operational integrity |
| CN114218653A (en) * | 2021-12-20 | 2022-03-22 | 中铁大桥局集团第五工程有限公司 | Method for calculating bearing capacity of suction pile foundation of offshore construction platform |
-
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- 2022-04-27 CN CN202210448165.XA patent/CN114548844B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100224135A1 (en) * | 2004-06-02 | 2010-09-09 | Aquaculture Engineering Group Ltd. | Offshore aquaculture system |
| CN104727354A (en) * | 2015-02-25 | 2015-06-24 | 中国科学院力学研究所 | Testing system simulating ultimate dynamic bearing capacity of cyclic load lower plate anchor |
| CN107237321A (en) * | 2017-06-15 | 2017-10-10 | 中国能源建设集团江苏省电力设计院有限公司 | Radial direction low height adds wing large diameter steel pipe pile and its horizontal bearing capacity computation method |
| CN113906465A (en) * | 2019-04-03 | 2022-01-07 | 吉奥奎斯特系统公司 | Local/hybrid blockchain for achieving oil and gas operational integrity |
| CN110782363A (en) * | 2019-08-15 | 2020-02-11 | 东南大学 | AC/DC power distribution network scheduling method considering wind power uncertainty |
| CN114218653A (en) * | 2021-12-20 | 2022-03-22 | 中铁大桥局集团第五工程有限公司 | Method for calculating bearing capacity of suction pile foundation of offshore construction platform |
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