CN114006414B - MPC-based wind power active power hierarchical control method and device - Google Patents
MPC-based wind power active power hierarchical control method and device Download PDFInfo
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- H—ELECTRICITY
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind energy
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Abstract
The invention discloses a hierarchical control method and device for wind power active power based on MPC, wherein the method comprises the following steps: respectively constructing a field group layer optimal cut air volume function model, a unit layer optimal function model and a sub-field layer optimal function model; forming a rolling optimization module by using a field group layer optimal cut air volume function model, a unit layer optimization function model and a sub-field layer optimization function model; the prediction module, the rolling optimization module and the ultra-short-period wind power predicted value error correction module combine prediction and operation, comprehensively consider the operation characteristics of a wind power plant and a wind turbine unit, and gradually correct the predicted value to realize the improvement of tracking precision. The device comprises: a processor and a memory. The invention directly divides the control of the wind power plant into the plant group layer, the sub-plant layer and the unit layer to carry out layered control, so that the control result is more accurate and the layer-by-layer progressive operation is realized.
Description
Technical Field
The invention relates to the field of high wind power permeability in a power system, in particular to a wind power active power layered hierarchical control method and device based on MPC (model predictive control).
Background
Coal, oil, and natural gas have been the main bodies of energy structures for hundreds of years, and with the rapid development of economy, the production of traditional fossil energy exacerbates environmental deterioration such as air pollution and environmental problems. Nowadays, global warming and climate change problems caused by the greenhouse effect have been focused on the hot spot of the human society, and humans are actively searching for the large-scale development and utilization of renewable clean energy sources such as solar energy, water energy, wind energy, geothermal energy and biomass energy to reduce the emission of greenhouse gases generated by the use of fossil fuels. Therefore, clean energy instead of fossil energy will become an important trend in the development of global energy.
In recent years, china always promotes the development of renewable energy sources with high quality, and effectively solves the problem of clean energy source digestion as key work. By 2020, the installed capacity of the nationwide grid-connected wind power reaches 2.81 hundred million kilowatts, and the same ratio is increased by 34.6 percent, which accounts for 12.79 percent of the total installed capacity. The national photovoltaic grid-connected installation machine is 2.53 hundred million kilowatts, and the same ratio is increased by 23.9 percent, and the photovoltaic grid-connected installation machine accounts for 11.52 percent of the total installation capacity. The cumulative power generation amount of the wind power and the solar power generation is 7270 hundred million kilowatt-hours in 2020, and the same ratio is increased by 15.1 percent. The proportion of the wind power and solar energy accumulated generating capacity to the total generating capacity is 9.5%, the same proportion is increased by 0.9%, the proportion of the wind power and solar energy accumulated generating capacity is steadily increased, and the green substitution effect of new energy is continuously enhanced.
The energy system with the characteristics of cleanness, low carbon, safety and high efficiency is emphasized to be constructed, the total amount of fossil energy is controlled, the utilization efficiency is improved, the renewable energy substitution action is implemented, the reform of the electric power system is deepened, and a novel electric power system taking new energy as a main body is constructed. Under the background, new energy power generation such as wind energy, solar energy and the like still continuously and rapidly grows, and rapid construction of a novel power system mainly comprising new energy is promoted.
However, with the increase of the power generation ratio of the new energy, the fluctuation and uncertainty of the output of the new energy also bring great challenges to the power balance and the standby capacity of the system, and serious wind abandoning phenomenon is caused. Meanwhile, a large-scale new energy power station is connected with the grid, and the situation that the high-proportion new energy electric quantity cannot be reasonably consumed and utilized is also generated. Taking wind power as an example, the wind power resources and the electricity market in China are reversely distributed, on one hand, the local digestion capability is insufficient, the construction of a cross-region power grid is lagged, the response capability of a user demand side is limited, and on the other hand, the randomness and the volatility of wind power output are strong, so that the wind power networking influences the supply and demand balance mechanism of the power grid, and the problem of wind power abandoning and electricity limiting is caused. Therefore, under the contradictory background that the large-scale grid connection of new energy and the new energy consumption of the power grid are difficult, how to coordinate and control the active output of each unit of the new energy to realize the optimal distribution of the output tasks among the units, thereby reducing the phenomena of wind and light abandoning of the power grid and improving the generating efficiency of the units and the economic benefit of the power grid becomes a problem to be solved urgently.
Disclosure of Invention
Aiming at the problem that the actual output power of a wind power plant cannot reach the expected value due to errors of the power predicted value of each wind power plant at present, the invention provides a hierarchical control method and device for wind power active power based on MPC, which are used for directly controlling and subdividing the wind power plant into a plant group layer, a sub-plant layer and a unit layer to conduct hierarchical control, so that the control result is more accurate, the layer-by-layer progressive is realized, and the method and the device are described in detail below:
in a first aspect, a hierarchical control method for wind power active power based on MPC, the method includes:
respectively constructing a field group layer optimal cut air volume function model, a unit layer optimal function model and a sub-field layer optimal function model;
forming a rolling optimization module by using a field group layer optimal cut air volume function model, a unit layer optimization function model and a sub-field layer optimization function model;
the prediction module, the rolling optimization module and the ultra-short-period wind power predicted value error correction module combine prediction and operation, comprehensively consider the operation characteristics of a wind power plant and a wind turbine unit, and gradually correct the predicted value to realize the improvement of tracking precision.
Wherein, the optimal cut air volume function model of the field group layer is as follows:
wherein:
wherein F is 11 The wind power station is a wind power station wind cutting quantity function; f (F) 12 The wind power plant wind cutting rate function is used for reducing the power;
constraint conditions:
and (3) cutting air quantity and power balance constraint:
real-time power constraint of wind farm:
wind farm regulation capacity constraints:
wind farm climbing constraints:
in the method, in the process of the invention,the predicted power of the wind farm i at the time t is obtained; />The actual output of the wind power plant at the upper period of the power increasing and the power decreasing respectively; />The power is increased, and the cutting air quantity of the power wind power plant i at the time t is increased; />An active scheduling instruction issued to a wind farm group at the time t for a power grid; />The installed capacity of the wind farm i; d, d 1 The control proportion of the power reduction can be achieved for the wind power plant,%; />The maximum upward regulation rate and the maximum downward regulation rate of the output power of the wind farm i are respectively obtained.
Further, the sub-field layer optimization function model is:
objective function:
min F 2 (P i,1,t ,P i,2,t )＝F 21 (P i,1,t ,P i,2,t )+F 22 (P i,1,t )+F 23 (P i,2,t )
wherein:
F 21 (P i,1,t ,P i,2,t )＝(P i,1,t +P i,2,t -P i,t ) 2
wherein P is i,t An active scheduling instruction value at time t in a wind power plant i is issued for a farm group layer; p (P) i,1,t 、P i,2,t The active power output of the power increasing machine group and the power decreasing machine group in the wind power plant i at the moment t respectively;
constraint conditions:
real-time power constraint of wind turbine generator system:
regulating capacity constraint of the wind turbine generator system:
climbing constraint of wind turbine generator system:
wherein d2 is the control proportion of the wind power generation group capable of achieving the reduced output;and respectively adjusting the speed upwards and downwards at which the output power of the j-th set in the wind power plant i is maximum.
The objective function of the unit layer optimization function model is as follows:
wherein P is i,j,t An active instruction value of a j-th fan t of a wind power plant i issued by a sub-field layer; p (P) i,j,k,t Active output at the moment t of a kth fan in a jth set in a wind power plant i is obtained; p (P) i,j,k,t-Δt Actual output of a kth fan in a jth set in the wind power plant i at t-delta t;
constraint conditions:
real-time power constraint of wind turbine generator system:
regulating capacity constraint of the wind turbine generator system:
in the method, in the process of the invention,the method comprises the steps of 1, predicting a wind power value of a kth wind turbine in an ith wind power plant cluster j;
climbing constraint of wind turbine generator system:
wherein d 3 The control proportion,%;and respectively adjusting the speed upwards and downwards at the maximum output power of a kth fan in a jth set in the wind power plant i.
Further, the ultra-short-term wind power predicted value error correction model is as follows:
wherein H is an error correction matrix; e is the error matrix for the past 15 min.
In a second aspect, a device for hierarchical control of wind power active power based on MPC, the device comprising:
a processor and a memory having stored therein program instructions that invoke the program instructions stored in the memory to cause an apparatus to perform the method steps of any of the first aspects.
In a third aspect, a computer readable storage medium stores a computer program comprising program instructions that, when executed by a processor, cause the processor to perform the method steps of any of the first aspects.
The technical scheme provided by the invention has the beneficial effects that:
1. the existing control mode mainly controls each wind farm directly, but after large-scale wind power grid connection, the control mode has the problems of large calculated amount and long time, and is difficult to realize online application, the method provides a layered control idea, the control layer is divided into a farm group layer, a sub-farm layer and a unit layer, the control period of each layer is different, the calculated time can be effectively reduced on the premise of ensuring the accuracy, and the system optimization efficiency is improved;
2. aiming at the characteristics of the prior wind power plant, fan grouping diversification and undefined physical characteristics of grouping, the invention provides a wind power plant and wind power cluster dynamic grouping method based on ultra-short-term predicted values and current actual output, and the physical characteristics are defined, thus laying a foundation for subsequent unified control;
3. compared with the variable proportion distribution method and the fixed proportion distribution method which are widely adopted at present, the method can effectively reduce the fluctuation quantity of the wind power clusters, is beneficial to reducing the adjustment difficulty of wind power stations and wind turbines, can reduce the abrasion of fans, and is beneficial to prolonging the service life of the wind power stations.
Drawings
FIG. 1 is a flow chart of the overall computation of an MPC;
FIG. 2 is a schematic diagram of a data interaction process between different control layers;
FIG. 3 is a schematic diagram of the wind capacity (period 1) of each wind farm under different wind shedding strategies;
FIG. 4 is a schematic diagram of the adjustment amount (period 1) of each wind farm under different wind shedding strategies;
FIG. 5 is a schematic diagram of the variation of the wind rate of the power-up cluster under different wind-cutting strategies;
FIG. 6 is a schematic structural diagram of a hierarchical control device for wind power active power based on MPC.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in further detail below.
Example 1
According to the embodiment of the invention, a hierarchical control model of the wind power active power based on MPC is established, the calculation solving speed is improved, the adjustment difficulty of a wind power plant is reduced, a scheme is provided for online operation, and referring to fig. 1 and 2, the hierarchical control method of the wind power active power comprises the following steps:
101: respectively constructing a field group layer optimal cut air volume function model, a unit layer optimal function model and a sub-field layer optimal function model;
102: forming a rolling optimization module by using a field group layer optimal cut air volume function model, a unit layer optimization function model and a sub-field layer optimization function model;
103: the prediction module, the rolling optimization module and the ultra-short-period wind power predicted value error correction module combine prediction and operation, comprehensively consider the operation characteristics of a wind power plant and a wind turbine unit, and gradually correct the predicted value to realize the improvement of tracking precision.
In summary, in the embodiment of the present invention, the control of the wind farm is subdivided into the farm group layer, the sub-farm layer and the unit layer through the steps 101 to 103, so that the control result is more accurate, and the layer-by-layer progressive is realized.
Example 2
The scheme of example 1 is further described below in conjunction with specific formulas and examples, as described below:
201: an ultra-short-term wind power prediction model;
since wind power is a time series with non-stationary, front-to-back data exhibiting strong correlation, an autoregressive integral moving average model (Autoregressive Integrated Moving Average Model, ARIMA) may be employed for wind power prediction.
The ARIMA model modeling mainly comprises the following steps: stability detection, model order determination and white noise detection.
The ARIMA (p, d, q) model can be expressed as:
wherein, p is the order of the autoregressive model; q is the order of the moving average model; d is the differential order; b is a delay operator; epsilon t White noise sequence of 0 mean; epsilon s Zero mean and variance ofThe white noise of (2) takes value at the time s; Θ (B) is a moving average coefficient polynomial, x s For the time series X at s time, t is time, E is expected, var is variance, then X t Can be obtained by the following formula:
X t ＝β 0 +β 1 X t-1 +β 2 X t-2 +...+β p X t-p +ε t +α 1 ε t-1 +α 2 ε t-2 +...+α q ε t-q (2)
wherein beta is p Is the autoregressive coefficient of the order p; x is X t-p The value of time series x at time t-p; alpha q The q-order moving average coefficient; epsilon t Zero mean, variance ofThe white noise of (2) takes on the value at time t.
202: dynamic grouping of wind farms;
in order to analyze an active control strategy of a wind power plant group under the constraint of limited output, the embodiment of the invention is divided into two main types of power-up wind power plants and power-down wind power plants according to the direction of power variation of the wind power plants along with time. The power-up wind power plant is a wind power plant with an output power predicted value at the moment t being larger than an actual output power value at the moment before, and the power-up wind power plant set is recorded as W1; the power-down wind power plant refers to a wind power plant with the predicted value of the output power at the moment t being smaller than the actual output power value at the moment before the predicted value, and the power-down wind power plant set is recorded as W2. And dynamically grouping all wind power plants in the cluster every 30min according to the actual active power output of the wind power plant and the ultra-short-term power predicted value of the wind power plant.
As described above, the up-power wind farm and the down-power wind farm may be represented in the form of formula (3).
In the method, in the process of the invention,the active power predicted value of the wind power plant i at the moment t; p (P) i,t-Δt The actual output value of the wind power plant i at the moment t-delta t. Under the condition of wind farm group limited output power, the adjustment quantity of the output power of the power-increasing wind farm can be expressed as a form of a formula (4):
wherein:the power adjustment quantity of the wind farm i at the time t is obtained; />The actual output force of the wind power plant at the t-delta t moment is the actual output force of the wind power plant at the power rise; />The air cut quantity of the power-increasing wind power plant i at the time t is obtained.
For a reduced power wind farm, the amount of adjustment of its output power can be expressed in the form of equation (5).
Wherein:the power adjustment quantity of the wind farm i at the time t is obtained; />The actual output force of the power-reducing wind power plant at the time t-delta t is obtained; />The air cut quantity of the power-reducing wind power plant i at the time t is obtained.
203: constructing an optimal air cutting quantity function of a field group layer;
in the wind-limited state, the basic idea of optimizing and distributing the active power of the wind power clusters is how to reasonably distribute the active power values of each wind power plant under the conditions of real-time power constraint, climbing rate constraint, wind power plant regulating capacity constraint and the like of each wind power plant in the clusters by utilizing the ultra-short-term power prediction result of each wind power plant on the premise of given power grid dispatching instructions, so that the regulating quantity of the active power values of each wind power plant is minimum.
The following objective function may be established:
wherein:
wherein F is 11 The wind power station is a wind power station wind cutting quantity function; f (F) 12 The wind power plant wind cutting rate function is used for reducing the wind power plant wind cutting rate.
Constraint conditions:
a) And (3) cutting air quantity and power balance constraint:
b) Real-time power constraint of wind farm:
c) Wind farm regulation capacity constraints:
d) Wind farm climbing constraints:
in the method, in the process of the invention,the predicted power of the wind farm i at the time t is obtained; />The actual output of the wind power plant at the upper period of the power increasing and the power decreasing respectively; />The power is increased, and the cutting air quantity of the power wind power plant i at the time t is increased; />An active scheduling instruction issued to a wind farm group at the time t for a power grid; />The installed capacity of the wind farm i; d, d 1 The control proportion of the power reduction can be achieved for the wind power plant,%; />The maximum upward regulation rate and the maximum downward regulation rate of the output power of the wind farm i are respectively obtained.
The constraint condition is converted into the form of the upper and lower limit of the cut air volume, and thus the above models (9) - (13) are changed into the following forms:
wherein:
and converting the abandoned wind power of each wind power plant into an active power output value at the moment t of each wind power plant according to the following formula.
Wherein F is 1 The air volume cutting function is used for the field group layer; f (F) 11 The wind cutting rate function is used for a power-up wind field; f (F) 12 The wind power plant wind cutting rate function is used for reducing the power;the minimum wind cutting quantity of the wind farm i is set; />The maximum wind cutting quantity of the wind farm i is set; />The ascending slope climbing speed of the wind farm i; d is the maximum drop-out force ratio that can be achieved by the wind farm.
204: dynamic grouping of units;
the large wind power plant has large occupied area, a large number of internal wind turbines and inconsistent models. Because wake effects exist, the running states of all fans are different, and in order to improve the accuracy and effectiveness of a sub-field layer wind power active regulation strategy, the embodiment of the invention divides wind power machines into two main types, namely a power-up fan and a power-down fan according to the predicted value of active power of the wind power machines in a wind power field at the next moment and the actual output value of the wind power machines at the current moment by means of a grouping regulation thought, wherein the power-up machine refers to the wind power machine with the predicted value of output power at the moment being larger than the actual output value at the previous moment, and the power-down machine refers to the wind power machine with the predicted value of output power at the moment being smaller than the actual output value at the previous moment. For convenience of description, the active prediction value of the power-up cluster at time t is set to beThe active power actually emitted at the time t-delta t is set as P i,1,t-Δt The method comprises the steps of carrying out a first treatment on the surface of the The predicted value of the power output of the power-down cluster at the time t is set as +.>The power actually emitted by the corresponding set of the set at the time t-delta t is recorded as P i,2,t-Δt 。
205: constructing a sub-field layer optimization function;
the basic idea of active regulation and control of the wind power active power of the sub-field layer is how to reasonably distribute active power output values of various fans according to ultra-short-term power prediction results of various fans on the premise of tracking wind power field scheduling instructions, comprehensively considering conditions such as real-time power constraint, climbing rate constraint, regulation capacity constraint and the like of the clusters.
Therefore, the project is based on the definition of the power-up fans and the power-down fans, all fans in the wind farm i are divided into two main types of the power-up fans and the power-down fans according to the difference of ultra-short-term predicted power, and the regulation and control are carried out according to the classification of different types. The active regulation model of the wind power plant is as follows:
objective function:
min F 2 (P i,1,t ,P i,2,t )＝F 21 (P i,1,t ,P i,2,t )+F 22 (P i,1,t )+F 23 (P i,2,t ) (21)
wherein:
F 21 (P i,1,t ,P i,2,t )＝(P i,1,t +P i,2,t -P i,t ) 2 (22)
wherein P is i,t An active scheduling instruction value at time t in a wind power plant i is issued for a farm group layer; p (P) i,1,t 、P i,2,t Active power output of power-up machine group and power-down machine group in wind power plant i at time t respectively, F 21 Tracking upper layer dispatch instruction functions for clusters, F 22 As a function of the output power of the power-up cluster, F 23 Is a function of the output power of the power-down cluster.
Constraint conditions:
a) Real-time power constraint of wind turbine group:
b) Wind turbine group adjustment capacity constraint:
c) Wind turbine group climbing constraint
Wherein d 2 The control proportion,%;and respectively adjusting the speed upwards and downwards at which the output power of the j-th set in the wind power plant i is maximum.
206: constructing a unit layer optimization function;
the basic idea of active regulation and control of the wind power active power of the sub-field layer is how to reasonably distribute active power output values of various fans according to ultra-short-term power prediction results of various fans on the premise of tracking wind power field scheduling instructions, comprehensively considering conditions such as real-time power constraint, climbing rate constraint, regulation capacity constraint and the like of the clusters.
Therefore, the embodiment of the invention is based on the definition of the power-up fans and the power-down fans, all fans in the wind farm i are divided into two main types of power-up fans and power-down fans according to the difference of ultra-short-term predicted power, and the regulation and control are carried out according to the classification of different types. The active regulation model of the wind power plant is as follows:
objective function:
wherein P is i,j,t An active instruction value of a j-th fan t of a wind power plant i issued by a sub-field layer; p (P) i,j,k,t Active output at the moment t of a kth fan in a jth set in a wind power plant i is obtained; p (P) i,j,k,t-Δt The actual output of a kth fan in a jth group in the wind power plant i at the t-delta t moment.
Constraint conditions:
a) Real-time power constraint of wind turbine generator system:
b) Regulating capacity constraint of the wind turbine generator system:
in the method, in the process of the invention,and (3) predicting the wind power of the kth wind turbine in the ith wind farm cluster j.
c) Climbing constraint of wind turbine generator system:
wherein d 3 The control proportion,%;and respectively adjusting the speed upwards and downwards at the maximum output power of a kth fan in a jth set in the wind power plant i.
207: constructing an error correction model of the ultra-short-term wind power predicted value;
because the wind power prediction method has systematic errors, systematic control precision and active power changes caused by wind energy fluctuation and randomness, the method can cause errors of different degrees for the scheduling control of the active load of the wind power plant, and the embodiment of the invention uses the following method for error analysis and feedback correction.
And aiming at errors caused by the power prediction method and the system control precision, carrying out feedback correction on the prediction model through actual errors summarized by the wind power plant. The corrected prediction model is:
wherein H is an error correction matrix; e is the error matrix for the past 15 min.
H＝[h t+1 ,…,h t+15 ] (33)
e＝[e t+1 ,e t+ 2,…,e t+15 ] T (35)
Wherein e t+m Is the error of time t+m, P i,t+m For the active power of the wind farm i at the time t+m, h t+m Is an element in the error correction matrix.
208: the scheduling instructions can be refined from the wind power clusters to each wind power generator set step by constructing a rolling optimization module formed by models of a generator set layer, a sub-field layer and a field group layer, so that the aim of layering progressive and step-by-step refining is fulfilled;
that is, the rolling optimization is performed at three levels of the unit layer, the sub-field layer, and the field group layer, for example: the initial time is 1min, the output is optimized for 2min after completion, and the time is continuously shifted forward.
209: the prediction module, the rolling optimization module and the ultra-short-term wind power predicted value error correction module combine prediction and operation, comprehensively consider the operation characteristics of a wind power plant and a wind turbine generator, and gradually correct the predicted value to achieve the aim of improving tracking precision.
That is, models of different levels all need predicted values, the predicted values are obtained through a prediction module, each time rolling is optimized for 1 period, the predicted values are corrected through a feedback module and the prediction module, and then the optimization of the next period is performed
In summary, the embodiment of the invention realizes the layered control of the wind farm through the steps 201 to 209, so that the control result is more accurate, and various requirements in practical application are satisfied.
Example 3
The schemes in examples 1 and 2 were validated in conjunction with specific examples, as described in detail below:
301: a field group layer test system;
the embodiment of the invention adopts wind power cluster typical day data containing 4 wind fields to verify the correctness and the effectiveness of the new energy active layering cooperative control method based on the MPC. The 4 wind power stations in the cluster are all provided with wind power prediction systems, wherein the installed capacity of WF 1-WF 4 is 40MW, the initial state of the wind power station and the ultra-short term prediction data of wind power are shown in table 1, and the initial state and the ultra-short term prediction data of wind power are shown in the table 1, wherein 00: the output of each wind power plant at the moment 00 is an initial value.
TABLE 1 wind farm basis data
When the active power scheduling curve of the wind power plant obviously climbs or descends, the active power of the wind power plant tends to easily generate fluctuation errors, and at the moment, the wind power plant needs to reschedule and distribute the active power to meet the active power requirement, so that the following two schemes are selected for comparison:
scheme 1: the field group layer distribution layer optimization method provided by the embodiment of the invention
Scheme 2: the method of tracking the output force proposed in document [4]
In order to verify that the method provided by the embodiment of the invention can effectively reduce the adjustment quantity of the wind power plant, the embodiment of the invention uses 15min as the optimization period of the layer scheduling model, the system of the above example is tested, table 2 shows the calculation results of the active power output and the air discarding quantity of each wind power plant under two schemes, fig. 3 shows the air cutting quantity of each wind power plant under different air cutting strategies (time period 1), and fig. 4 describes the adjustment quantity of each wind power plant under different air cutting strategies (time period 1).
TABLE 2 active output and wind curtailment Power for wind farms under different scenarios
As can be seen from table 2 and fig. 3, compared with the second scheme, the first scheme has different wind power plant cut volumes at the same time. At 00: for example at 15 time, the cutting air quantity of four wind fields is respectively 1.47MW, 0.59MW, 1.02MW and 1.91MW; the air discarding quantity of the four wind fields under the scheme II is 1.25MW. The rapid solution of the embodiment of the invention can keep the power adjustment quantity of each wind power plant in a stable state as much as possible when a plurality of scheduling instructions exist, and distributes more wind cutting tasks to wind power plants (WF 4) with stronger adjustment capability according to the actual running condition of the wind power plant and the ultra-short-term power prediction value of the wind power plant, so that the fluctuation quantity of clusters can be reduced from the whole angle, and the wind cutting tasks are distributed to each wind power plant fairly; when the regulation and control are carried out on the field group layers, the force tracking method distributes the wind cutting tasks into WF 1-WF 4 evenly, so that the wind cutting tasks of all wind fields are consistent.
Further, from fig. 4, it can be seen that 00: the adjustment amounts of the wind field 1 and the wind field 3 in the scheme I at 15 time are respectively-5.62 MW and-2.73 MW which are respectively lower than the adjustment amounts of the wind field 1 and the wind field 3 in the scheme II, namely-6.28 MW and-2.96 MW. This is because the first solution is to solve the problem with the amount of fluctuation of the wind farm suppressed as an objective function, and therefore the adjustment amount of the first solution is made lower than the adjustment amount of the second solution.
302: a sub-field layer test system;
the test is carried out by taking the upper wind farm WF1 as a test object, wherein the wind farm comprises 16 fans, each fan has a capacity of 2.5MW, and the capacity of a total assembly machine is 40MW. And distributing the active power output of the power-up machine group and the power-down machine group in WF1 by taking 5min as a control period according to the active power output command of the wind power plant in the time domain of 00:00-00:15 issued by the field group layer. The grouping results of the power up and power down clusters at each time are shown in table 3, and the predicted values of the active outputs of the power up and power down clusters are shown in table 4.
TABLE 3 WT1-WT 16 dynamic grouping results
Table 4 active output predicted values for power up and power down clusters
In order to verify the correctness and effectiveness of the subfield layer rolling optimization control model provided by the embodiment of the invention, the following three schemes are designed:
scheme 1: the sub-field layer rolling optimization control model provided by the embodiment of the invention;
scheme 2: based on the machine group installed capacity proportion distribution;
scheme 3: and (5) distributing based on the active power output ratio of the cluster.
The active power output optimization results of the power raising machine group and the power lowering machine group corresponding to the three schemes are shown in table 5, and the corresponding cut air volume results are shown in table 6.
TABLE 5 calculation of group output under different strategies for the subfield layers (WF 1)
TABLE 6 calculation of the air volume of a cluster under different policies of the sub-field layer (WF 1)
As can be seen from tables 5 and 6, the difference between the predicted value and the scheduling command value of the power up cluster at each time point in the first scheme is much smaller than that in the second and third schemes. At time 00:05 for example, the difference between the predicted value of the scheme one power-up cluster and the scheduling command is 0.768MW, scheme two is 2.901MW, and scheme three is 0.844MW. In the second scheme, the distribution is carried out only according to the capacity of the machine group, so that a large amount of wind abandoning situations can occur in a certain machine group; the cut air quantity of the three-liter power machine group is increased by 9.95% compared with that of the first scheme, and the difference between the predicted value and the scheduling command value is minimized in the objective function because the scheduling command issued by the tracking station layer is added at one time of the scheme.
In addition, it can be seen that 00 in Table 6: the air cut volume of the scheme one at the moment 05 is 0. This is because the predicted value of the cluster is 15.39MW less than the commanded value issued in WF1, so that all units are in maximum power tracking mode (Maximum Power Point Tracking, MPPT) the wind farm does not produce a cut wind.
Fig. 5 shows the cut air volume of a booster wind farm at three sub-periods under different cut air strategies. It can be seen that the air cutting quantity of the power-up machine group in the second scheme, the third scheme and the first scheme is reduced in sequence. The fixed proportion distribution method is used for distributing output according to the installed capacity and the scheduling index only because future power prediction data of the cluster are not utilized, and therefore the wind farm cannot track the scheduling instruction. According to the sub-field layer active control method provided by the embodiment of the invention, the adjustment capability of each cluster in the wind field is comprehensively considered, so that the wind cutting amplitude of the power-up clusters is reduced by 23.31% compared with that of a variable proportion distribution method, the difficulty in adjusting and controlling fans in a unit layer is reduced, and the abrasion of the fans is reduced.
Table 7 amount of power adjustment for power clusters at each time period
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Table 7 shows that the power boost cluster is at 00:00-00: power adjustment amount at 15 time period. It can be seen that the power adjustment amount of the method according to the embodiment of the present invention is smaller than that of the fixed ratio distribution method and the variable ratio distribution method at each time. At 00: for example, the power adjustment amounts under the second scheme and the third scheme are respectively 0.415MW and 0.7684MW, and the power adjustment amounts are respectively increased by 13.98% and 53.54% compared with the first scheme. Tables 6 and 7 collectively embody the power up cluster control strategy in scheme one, targeting tracking cluster predictions and reducing fluctuation amounts.
303: unit layer test system
This test was performed at WF1 at 00:00-00: 05, which contains WT1, WT2, WT6, WT7, WT8, WT9, WT13, each capacity of 2.5MW, and a power boost cluster capacity of 17.5MW. And distributing the active power output of each unit in the power-up machine group by taking 1min as a control period aiming at the active power output command of the wind power plant in the time domain of 00:00-00:05 issued by the sub-field layer.
In order to verify the correctness and effectiveness of the subfield layer rolling optimization control model provided by the project, the following three schemes are designed:
scheme 1: the embodiment of the invention provides a unit layer rolling optimization control model;
scheme 2: based on the unit capacity proportion distribution;
scheme 3: and distributing based on the active output ratio of the unit.
The active output optimization results of each unit in the power-up machine group corresponding to the three schemes are shown in table 8, the corresponding power variation results are shown in table 9, and the corresponding cut air volume results are shown in table 10.
Table 8 results of the layer assignments for the units (Power-Up clusters)
Table 9 calculation results of unit power variation amount (power-up cluster) under different strategies of unit layer
Table 10 calculation results of unit power variation amount (power-up cluster) under different strategies of unit layers
/>
As can be seen from table 8, scheme one 00:01-00: the sum of the active power output of each unit in the 05 period is 8.817MW, 8.822MW, 8.582MW, 8.487MW and 8.421MW which are gradually close to the power-up cluster scheduling instruction 8.8746MW calculated under the scheme, because the objective function in the scheme I aims at the minimum adjustment quantity of the active power of the cluster, and the deviation between the active power output of each fan and the control target issued by the subfield layer is minimized, so that the sum of the active power output of the unit in each period is gradually close to the active scheduling instruction.
Secondly, it can be seen that the values of the active output of each unit are equal in the same period of time in the second scheme. Such as 00: at the moment 01, the output of each unit under the scheme II is 1.2678MW. The second scheme is based on the unit capacity to carry out equal proportion distribution, and the unit capacity of each unit is equal to 2.5MW, so that the active output of each unit in the same period of time of the second scheme is equal.
As can be seen from table 9, both the fixed ratio distribution method and the variable ratio distribution method require a stronger regulation capability of the blower, and in the 2 nd rolling period, the fixed ratio distribution method requires a power adjustment amount of WT1 that is 82.2% more than the method proposed herein; in the 3 rd scroll cycle, the variable ratio allocation method requires that the WT1 power adjustment be 113.1% more. The maximum WT2 adjustment in the variable ratio dispensing method was 1.58 times greater than the method presented herein over 5 minutes, again demonstrating the superiority of the method presented herein for reducing fan fluctuation.
Further, as can be seen from table 10, scheme one, scheme three, 00:04-00: the air cutting quantity of each unit in 05 time period is 0. This is due to 00:04-00: the dispatching command of the power-up machine group in the 05 time period exceeds the sum of the maximum output of the power-up machine group, and the dispatching command of the power-up machine group becomes the maximum output of each machine unit, so that the cut air quantity of each machine unit in the time period is 0.
Example 4
An MPC-based wind power active power hierarchical control device, see fig. 6, comprises: a processor and a memory, the memory having stored therein program instructions, the processor invoking the program instructions stored in the memory to cause the apparatus to perform the following method steps in embodiment 1:
respectively constructing a field group layer optimal cut air volume function model, a unit layer optimal function model and a sub-field layer optimal function model;
forming a rolling optimization module by using a field group layer optimal cut air volume function model, a unit layer optimization function model and a sub-field layer optimization function model;
the prediction module, the rolling optimization module and the ultra-short-period wind power predicted value error correction module combine prediction and operation, comprehensively consider the operation characteristics of a wind power plant and a wind turbine unit, and gradually correct the predicted value to realize the improvement of tracking precision.
The optimal air volume cutting function model of the field group layer is as follows:
wherein:
wherein F is 11 The wind power station is a wind power station wind cutting quantity function; f (F) 12 The wind power plant wind cutting rate function is used for reducing the power;
constraint conditions:
and (3) cutting air quantity and power balance constraint:
real-time power constraint of wind farm:
wind farm regulation capacity constraints:
wind farm climbing constraints:
in the method, in the process of the invention,the predicted power of the wind farm i at the time t is obtained; />The actual output of the wind power plant at the upper period of the power increasing and the power decreasing respectively; />The power is increased, and the cutting air quantity of the power wind power plant i at the time t is increased; />An active scheduling instruction issued to a wind farm group at the time t for a power grid; />The installed capacity of the wind farm i; d, d 1 The control proportion of the power reduction can be achieved for the wind power plant,%; />The maximum upward regulation rate and the maximum downward regulation rate of the output power of the wind farm i are respectively obtained.
Further, the sub-field layer optimization function model is:
objective function:
min F 2 (P i,1,t ,P i,2,t )＝F 21 (P i,1,t ,P i,2,t )+F 22 (P i,1,t )+F 23 (P i,2,t )
wherein:
F 21 (P i,1,t ,P i,2,t )＝(P i,1,t +P i,2,t -P i,t ) 2
wherein P is i,t An active scheduling instruction value at time t in a wind power plant i is issued for a farm group layer; p (P) i,1,t 、P i,2,t The active power output of the power increasing machine group and the power decreasing machine group in the wind power plant i at the moment t respectively;
constraint conditions:
real-time power constraint of wind turbine generator system:
regulating capacity constraint of the wind turbine generator system:
climbing constraint of wind turbine generator system:
wherein d2 is the control proportion of the wind power generation group capable of achieving the reduced output;and respectively adjusting the speed upwards and downwards at which the output power of the j-th set in the wind power plant i is maximum.
The objective function of the unit layer optimization function model is as follows:
wherein P is i,j,t An active instruction value of a j-th fan t of a wind power plant i issued by a sub-field layer; p (P) i,j,k,t In the j-th group in the wind power plant iActive force of the kth fan at the moment t; p (P) i,j,k,t-Δt Actual output of a kth fan in a jth set in the wind power plant i at t-delta t;
constraint conditions:
real-time power constraint of wind turbine generator system:
regulating capacity constraint of the wind turbine generator system:
in the method, in the process of the invention,the method comprises the steps of 1, predicting a wind power value of a kth wind turbine in an ith wind power plant cluster j;
climbing constraint of wind turbine generator system:
wherein d 3 The control proportion,%;and respectively adjusting the speed upwards and downwards at the maximum output power of a kth fan in a jth set in the wind power plant i.
Further, the error correction model of the ultra-short-term wind power predicted value is as follows:
wherein H is an error correction matrix; e is the error matrix for the past 15 min.
It should be noted that, the device descriptions in the above embodiments correspond to the method descriptions in the embodiments, and the embodiments of the present invention are not described herein in detail.
The execution main bodies of the processor 1 and the memory 2 may be devices with computing functions, such as a computer, a singlechip, a microcontroller, etc., and in particular implementation, the execution main bodies are not limited, and are selected according to the needs in practical application.
Data signals are transmitted between the memory 2 and the processor 1 via the bus 3, which is not described in detail in the embodiment of the present invention.
Example 5
Based on the same inventive concept, the embodiment of the present invention also provides a computer readable storage medium, where the storage medium includes a stored program, and when the program runs, the device where the storage medium is controlled to execute the method steps in the above embodiment.
The computer readable storage medium includes, but is not limited to, flash memory, hard disk, solid state disk, and the like.
It should be noted that the readable storage medium descriptions in the above embodiments correspond to the method descriptions in the embodiments, and the embodiments of the present invention are not described herein.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the invention, in whole or in part.
The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted across a computer-readable storage medium. Computer readable storage media can be any available media that can be accessed by a computer or data storage devices, such as servers, data centers, etc., that contain an integration of one or more available media. The usable medium may be a magnetic medium or a semiconductor medium, or the like.
Reference to the literature
[1] Lin Li, xie Yongjun, zhu Chen, etc. wind farm limited output active control strategy based on priority method [ J ]. Grid technologies, 2013, 37 (4): 960-966.
[2] Li Xueming, rowing boat Chen Zhenhuan, etc. Large clustered wind power active intelligent control System design [ J ]. Power System Automation, 2010, 34 (17): 59-63.
[3] Wind power cluster optimization scheduling [ J ] based on random predictive control theory and power fluctuation correlation, chinese motor engineering journal, 2018, 38 (11): 3172-3183.
[4] Boat running, chen Yonghua, chen Zhenhuan, etc. the large cluster wind power active intelligent control system controls the coordination control [ J ]. Power system automation among wind power stations, 2011, 35 (20): 20-23, 102.
The embodiment of the invention does not limit the types of other devices except the types of the devices, so long as the devices can complete the functions.
Those skilled in the art will appreciate that the drawings are schematic representations of only one preferred embodiment, and that the above-described embodiment numbers are merely for illustration purposes and do not represent advantages or disadvantages of the embodiments.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.
Claims (6)
1. The method for hierarchical control of wind power active power based on MPC is characterized by comprising the following steps:
respectively constructing a field group layer optimal cut air volume function model, a unit layer optimal function model and a sub-field layer optimal function model;
forming a rolling optimization module by using a field group layer optimal cut air volume function model, a unit layer optimization function model and a sub-field layer optimization function model;
the prediction module, the rolling optimization module and the ultra-short-period wind power predicted value error correction model combine prediction and operation, comprehensively consider the operation characteristics of a wind power plant and a wind turbine unit, and gradually correct the predicted value to realize the improvement of tracking precision;
the objective function of the field group layer optimal cut air volume function model is as follows:
wherein:
wherein F is 11 The wind power station is a wind power station wind cutting quantity function; f (F) 12 The wind power plant wind cutting rate function is used for reducing the power;the predicted power of the wind farm i at the time t is obtained; />The actual output of the wind power plant at the upper period of the power increasing and the power decreasing respectively; /> The power is increased, and the cutting air quantity of the power wind power plant i at the time t is increased; the set of the power-up wind power plant is marked as W 1 The collection of the power-reducing wind power plant is marked as W 2 ；
The objective function of the sub-field layer optimization function model is as follows:
min F 2 (P i,1,t ,P i,2,t )＝F 21 (P i,1,t ,P i,2,t )+F 22 (P i,1,t )+F 23 (P i,2,t )
wherein:
F 21 (P i,1,t ,P i,2,t )＝(P i,1,t +P i,2,t -P i,t ) 2
wherein P is i,t An active scheduling instruction value at time t in a wind power plant i is issued for a farm group layer; p (P) i,1,t 、P i,2,t The active power output of the power increasing machine group and the power decreasing machine group in the wind power plant i at the moment t respectively;
the objective function of the unit layer optimization function model is as follows:
wherein P is i,j,t An active instruction value of a j-th fan t of a wind power plant i issued by a sub-field layer; p (P) i,j,k,t Active output at the moment t of a kth fan in a jth set in a wind power plant i is obtained; p (P) i,j,k,t-△t Actual output of a kth fan in a jth set in the wind power plant i at t-delta t;
the ultra-short-term wind power predicted value error correction model is as follows:
wherein H is an error correction matrix; e is the error matrix for the past 15 min.
2. The MPC-based wind power active power hierarchical control method according to claim 1, wherein constraint conditions of the field group layer optimal cut air volume function model are as follows:
and (3) cutting air quantity and power balance constraint:
real-time power constraint of wind farm:
wind farm regulation capacity constraints:
wind farm climbing constraints:
wherein P is t dem An active scheduling instruction issued to a wind farm group at the time t for a power grid; p (P) i N The installed capacity of the wind farm i; d, d 1 The control proportion of the power reduction can be achieved for the wind power plant,%;the maximum upward regulation rate and the maximum downward regulation rate of the output power of the wind farm i are respectively obtained.
3. The method for hierarchical control of wind power active power based on MPC as set forth in claim 1, wherein constraints of the sub-field layer optimization function model are as follows:
real-time power constraint of wind turbine generator system:
regulating capacity constraint of the wind turbine generator system:
climbing constraint of wind turbine generator system:
wherein d2 is the control proportion of the wind power generation group capable of achieving the reduced output;and respectively adjusting the speed upwards and downwards at which the output power of the j-th set in the wind power plant i is maximum.
4. The method for hierarchical control of wind power active power based on MPC as set forth in claim 1, wherein constraint conditions of the unit layer optimization function model are:
real-time power constraint of wind turbine generator system:
regulating capacity constraint of the wind turbine generator system:
in the method, in the process of the invention,the method comprises the steps of 1, predicting a wind power value of a kth wind turbine in an ith wind power plant cluster j;
climbing constraint of wind turbine generator system:
wherein d 3 The control proportion,%;and respectively adjusting the speed upwards and downwards at the maximum output power of a kth fan in a jth set in the wind power plant i.
5. An MPC-based wind power active power hierarchical control device, characterized in that the device comprises: a processor and a memory, the memory having stored therein program instructions that cause the apparatus to perform the method of claim 1, the processor invoking the program instructions stored in the memory.
6. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program comprising program instructions which, when executed by a processor, cause the processor to perform the method of claim 1.
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