CN107919685A - A kind of wind power plant AGC instructs optimized tuning method - Google Patents
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Abstract
The present invention proposes a kind of wind power plant AGC instructions optimized tuning method, and main method is as follows：The thinking that wind power plant generated output is adjusted is " control height is lowerd ", establishes the object function of wind power plant AGC instruction optimum programmings and solves object function method.So that adjusting for generated output reference value follows dispatch curve, the result according to wind power prediction carries out maximum limitation, ensures the generating capacity instructed no more than wind power plant that each controlling cycle is assigned.And for power fluctuation limit, module is adjusted in addition to maximum is limited, the global optimization of setting valve should be also carried out according to the limitation index and power prediction result of fluctuation, reach the maximum optimization with power swing minimum of generated energy to balance, as power limitation condition conflicts with dispatch command, power-generating control system needs preferential trace scheduling instruction.
Description
Technical Field
The invention belongs to the technical field of wind power generation, and relates to an AGC (automatic gain control) instruction optimizing and setting method for a wind power plant. The method is applied to AGC command setting of the wind power plant.
Background
The traditional power generation control instruction setting method does not consider wind power prediction data, and only carries out planning according to output variable quantity limitation converted into a control period by two time scale maximum fluctuation standards specified by a power grid, namely a slope control mode, and has two representative implementation schemes:
uniform limitation of the rate of change over the examination time scale: the power change rates defined in 1 minute and 10 minutes are equally divided into each control period, and the maximum power change rate is determined according to the intersection of the limiting indexes. I.e. 10 control cycles with a 1 minute rate of change <10% translated to 6 seconds <1%, and 100 control cycles with a 10 minute rate of change <33.3% translated to 6 seconds <0.333%, intersecting the power rate of change limit within each cycle of the control <0.333%.
The scheme has the advantages that the limiting condition is relatively strict, the power of the wind power plant is highly suppressed when the wind speed changes upwards, the wind power plant is slowly increased, and if the wind speed is reduced and the power generation power of the wind power plant is forcibly reduced, the probability that the reduction degree exceeds the limiting standard and is checked is greatly reduced, the fluctuation overrun can be effectively prevented from being punished, and the economic loss is reduced. However, the scheme can smooth the fluctuation of the generated power, greatly reduce the following degree of the power and the input wind speed, ensure that the generated power has insufficient response degree to the positive change of the wind speed, abandon part of the generated power in the rising process of the wind speed and cause the loss of the generated energy of the wind power plant.
Maximum saturation limit of the rate of change over the examination time scale: the method comprises the steps of firstly allocating a power change rate limited according to 1 minute to 10 control cycles, determining the maximum power change rate in the cycle to be less than 1%, limiting the remaining space of the power change rate according to 10 minutes in the remaining time, and further limiting the remaining space of the power change rate, wherein one theoretical extreme case is that the wind speed continuously and rapidly rises, the power generated by the wind farm is increased according to the maximum limiting condition, the change rate of 33% can be reached in the first 3.3 minutes of 33 control cycles, all up-regulation spaces with the limit change rate of less than 33.3% in 10 minutes are exhausted, the power of the wind farm cannot be increased in the following 6.7 minutes, and the wind farm works in a state with a constant limiting value.
The scheme has the advantages that the limiting conditions are loose, the power of the wind power plant is increased fastest when the wind speed changes upwards, and the wind power plant can work for the maximum power all the time after the power is increased to the limiting value in a short time, so that larger generated energy is generated. However, the scheme enables the generated power of the wind power plant to reach the maximum value quickly, the generated power of the wind power plant is forced to be reduced by a larger amplitude when the wind speed falls back, the reduction degree exceeding the limit standard is punished easily, and more generated energy is lost.
Disclosure of Invention
Aiming at the existing problems, the invention provides an AGC instruction optimizing and setting method for a wind power plant, which is characterized by comprising the following steps:
s1, designing a target function by taking generated energy and power fluctuation as optimization targets; when the power generation control is not carried out, the discrete sampling point of the power in the time T isThe power generation limit for this period can be calculated as:
wherein Pi is the sampling point of output of the wind power plant in the power generation control mode, P i a Maximum capacity for corresponding sampling time
S2, considering the communication network delay of the wind power plant monitoring system and the time required by algorithm execution, and selecting 6 seconds as an instruction cycle; setting the optimal programming sequence in the T period asConstructing an objective function, wherein the maximum output which can be reached by the control period j under the action of the power generation control of the wind power plant isThe generated energy of the wind power plant lost due to the power generation control action in the T time period can be calculated as follows:
wherein, [ k ] 0 ,k 1 ,k 2 ,…,k T/t ]The weighting coefficient is used for controlling the power generation to be ineffective and have no loss of electric quantity when the power which can be generated by the wind power plant is smaller than the limit power, the weighting coefficient is used for shielding the accumulation of the loss of electric quantity, and the value rule is as follows:
according to the formula, the value of the setting sequence of the wind power plant generation control command determines the lost generating capacity, and the value is largerCan make k i The frequency of =0 increases and also decreases in the formula (3)Finally reducing the loss power generationImprovement ofWill increase the maximum power P in the investigation period T max Aggravating power fluctuation, converting the fluctuation exceeding the standard into penalty power generation amount, and recording as:
wherein,to control the fluctuation limiting condition of the period j, [ l 0 ,l 1 ,l 2 ,…,l T /t]When the power fluctuation of the wind power plant is in a limit range, the fluctuation electric quantity is not counted, the accumulation of the fluctuation electric quantity can be shielded, and the value rule is as follows:
in the above formula, the fluctuation amount is a function of the standard amountAs penalty weight; as shown in the formula (5) and the formula (6), the value of the setting sequence also influences the punished generating capacity of power fluctuation overrun, and the reduction of the power fluctuation overrunThe frequency of li =0 can be increased and reducedNumerical value and penalty weight ofCorrespondingly decreases;
the effective grid-connected electric quantity calculation formula of the wind power plant is as follows:
the aim of the setting of the wind power plant power generation command sequence is to enableMaximum, and therefore the objective function is established as:
according to the formula, the solving process of the objective function is the setting process of the power generation instruction sequence of the wind power plant and is essentially to obtain the optimal
Collecting to minimize the sum of the output loss and the fluctuation punishment of the wind power plant;
s3, setting single particle position as set of generated power regulating quantity of each unitSet of flight speed as regulation change speedDimension d of the particle is the number of members of the sequence, and d =100 is taken as before; taking the number of particles as 50, and randomly generating an initialization position p of the particle population i,j (t) and velocity v i,j (t), wherein i represents the serial number of each particle in the particle population, and j represents the component serial number of a single particle, namely the serial number of a control cycle limiting instruction;
calculating the result obtained after each particle is substituted into the objective function, taking the result as the fitness index for evaluating the optimal particle, storing the position and the fitness index of the current particle into a Pbest set, obtaining the optimal fitness particle in the Pbest, and storing the position and the fitness index into the Gbest set;
performing evolutionary iteration on the particles, wherein the iteration equation is as follows:
v i,j (t+1)＝v i,j (t)+c 1 r 1 [p i,j -2x i,j (t)+x i,j (t-1)]+c 2 r 2 [p g,j -2x i,j (t)+x i,j (t-1)]
x i,j (t+1)＝x i,j (t)+v i,j (t+1),j＝1,2,L,d (9)
in the formula, c1 and c2 are learning factors, the learning factors represent the self-learning capacity of the particles, larger values can generally achieve optimized values by a small number of iteration steps, but are easy to fall into the oscillation process, c1= c2=0.6 is taken, r1 and r2 are random numbers which are uniformly distributed from 0 to 1, each iteration step can be changed to enable the particles to move to cover more possible directions, t represents each iteration step and is increased progressively along with the iteration until the maximum iteration times set by the algorithm are completed;
comparing the current fitness of each particle with the historical optimal fitness of each particle during iteration, covering the positions and the fitness of the historical storage particles if the current fitness is better, and storing the positions and the fitness of the historical storage particles into P best Gathering;
comparing P of all current particles bes t set value and G best Value, updating G on the optimal coverage principle best Gathering;
judging whether the iteration reaches the set maximum times, if not, returning to the step 3) to continue executing the algorithm, if so, ending the algorithm process, and outputting the position result of the optimal particleAs the optimal power generation control command sequence of all control cycles in the subsequent time period T;
corresponding instructions in the sequence can be distributed to each unit to be executed in each control period through a subsequent instruction distribution module, and the generated power of the wind field can reach an expected set target through the coordination control of the units.
Drawings
FIG. 1 Power fluctuation for two Generation modes of a wind farm
Detailed Description
Idea for optimal planning of AGC (automatic gain control) instructions of wind power plant
The ultra-short-term power prediction can obtain the power generation capacity of each sampling point in the time scale T of the wind power plant, namely the power sequence of the maximum power tracking power generation modeDue to the limitation of wind resources, the wind power plant can only complete the steady-state control target of reducing power, and the limiting conditions are shown in the formula (1):
wherein Pi is the sampling point of output of the wind power plant in the power generation control mode, P i a The maximum output capacity at the corresponding sampling moment. The output fluctuation comparison between the wind farm maximum power tracking mode and the power generation control mode in the T time period can be obtained, as shown in the figure:
in fig. 1, the upper end light black curve is the power variation trend of the wind farm in the maximum power tracking mode, the lower end dark black curve is the power variation trend of the wind farm in the power generation control mode, and the black curve is always lower than or coincident with the light black curve, which is also consistent with the rule reflected by the formula (1). The original value of power fluctuation Delta PTorg when the wind farm is in free power generation within the time T and the power fluctuation value Delta PTcon when the wind farm is in power generation control can be respectively obtained. When the wind power plant is in a high power state, the maximum power in an investigation period can be reduced by switching to a power generation control mode, a fluctuation interval is reduced, and more hot standby is provided for the possible wind power plant power-up power grid requirement; when the wind power plant is at low power, the minimum power sampling value in the fluctuation investigation index can be improved by the maximum power tracking power generation mode, so that the difference between the minimum power sampling value and the maximum power sampling value is reduced, the fluctuation is reduced, the total power generation amount of the wind power plant is increased, and the economic effect is improved. However, if the wind power plant enters the power generation control process when the power is low, the output minimum power is reduced, but the fluctuation range is expanded, the power generation control system of the wind power plant needs to automatically switch the mode according to the output power, and when the output power is low, the power generation control needs to be quitted and converted into the maximum power generation tracking.
Therefore, the idea of setting the generated power of the wind power plant can be summarized as 'controlling high and reducing low', and a target function is designed by taking the generated energy and power fluctuation as optimization targets. When the power generation control is not carried out, the discrete sampling point of the power in the time T isThe power generation limit for this period can be calculated as:
target function for optimal planning of AGC (automatic gain control) instructions of wind power plant
And 6 seconds are selected as an instruction period by considering the communication network delay of the wind power plant monitoring system and the time required by algorithm execution. Setting the optimal programming sequence in the T period asConstructing an objective function, wherein the maximum output which can be reached by the control period j under the action of the power generation control of the wind power plant isThe data of each sampling point i can be classified into a control period j = [ (it/6)]If the output capacity P of the wind power plant at the sampling point i i a Less than the corresponding control period AGC limit valueThe wind power plant works in a maximum power tracking mode, otherwise the power of the wind power plant is limited toTherefore, the power generation amount of the wind power plant lost due to the power generation control action in the T period can be calculated as follows:
wherein, [ k ] 0 ,k 1 ,k 2 ,…,k T/t ]When the power generated by the wind power plant is smaller than the limit power, the power generation control does not work and no electric quantity is lost, and the weighting coefficient is used for shielding the lost electric quantityThe value rule of the accumulation of (1) is as follows:
according to the formula, the value of the setting sequence of the wind power plant generation control command determines the lost generating capacity, and the value is largerCan make k i The frequency of =0 increases and also decreases in the formula (3)Finally reducing the loss power generationHowever, it is improvedWill increase the maximum power P in the investigation period T max Aggravating power fluctuation, converting the fluctuation exceeding the standard into penalty power generation amount, and recording as:
wherein,in order to control the fluctuation limiting condition of the period j, the fluctuation limiting condition can be determined according to the fluctuation intersection of two time scales of national grid standards, the installed capacity of the wind power plant and the minimum output capacity in the period, and can also be set according to the power and the fluctuation bearing capacity of the PCC point of the wind power plant by self, [ l [ ] 0 ,l 1 ,l 2 ,…,l T /t]When the power fluctuation of the wind power plant is in a limit range, the fluctuation electric quantity is not counted, the accumulation of the fluctuation electric quantity can be shielded, and the value rule is as follows:
in the above formula, the fluctuation amount is a function of the standard amountAs a penalty weight. As shown in the formula (5) and the formula (6), the value of the setting sequence also influences the punished generating capacity of power fluctuation overrun, and the reduction of the power fluctuation overrunThe frequency of li =0 can be increased and reducedNumerical value and penalty weight ofAnd is reduced accordingly.
In summary, the calculation formula of the effective grid-connected electricity quantity of the wind power plant is as follows:
the aim of the setting of the wind power plant power generation command sequence is to enableMaximum, so the objective function is established as:
according to the formula, the solving process of the objective function is the setting process of the power generation instruction sequence of the wind power plant and is essentially to obtain the optimalAnd (4) integrating to minimize the sum of the output loss and the fluctuation punishment of the wind power plant.
PSO algorithm-based objective function solving
With the current control cycle as a starting point, the planning time length is set to 10 minutes, namely a sequence set of 100 data points. The method comprises the following steps of solving an objective function by combining a PSO algorithm with an ultra-short-term power prediction result of a wind power plant in the future of 10 minutes, wherein the method comprises the following specific steps:
setting single particle position as set of generating power regulating quantity of each unitSet of flight speed as regulation change speedDimension d of the particle is the number of members of the sequence, taking d =100 as before. Taking the number of particles as 50, and randomly generating an initialization position p of the particle population i,j (t) and velocity v i,j (t), wherein i represents the serial number of each particle in the particle population, and j represents the component serial number of a single particle, namely the serial number of a control cycle limiting instruction;
calculating the result obtained after each particle is substituted into the objective function, taking the result as the fitness index for evaluating the optimal particle, storing the position and the fitness index of the current particle into a Pbest set, obtaining the optimal fitness particle in the Pbest, and storing the position and the fitness index into the Gbest set;
performing evolutionary iteration on the particles, wherein the iteration equation is as follows:
v i,j (t+1)＝v i,j (t)+c 1 r 1 [p i,j -2x i,j (t)+x i,j (t-1)]+c 2 r 2 [p g,j -2x i,j (t)+x i,j (t-1)]
x i,j (t+1)＝x i,j (t)+v i,j (t+1),j＝1,2,L,d (9)
in the above formula, c1 and c2 are learning factors, the learning factors represent the self-learning capability of the particles, the values are large, the optimization values can be achieved by a small number of iteration steps, but the particles are easy to fall into the oscillation process, c1= c2=0.6, r1 and r2 are random numbers which are uniformly distributed from 0 to 1, each iteration step can be changed to enable the particle motion to cover more possible directions, t represents each iteration step and is increased progressively along with the iteration until the maximum iteration number set by the algorithm is completed.
Comparing the current fitness of each particle with the historical optimal fitness of each particle during iteration, covering the positions and the fitness of the historical storage particles if the current fitness is better, and storing the positions and the fitness of the historical storage particles into P best And (5) aggregating.
Comparing P of all current particles bes t set value and G best Value, updating G on the optimal coverage principle best And (5) aggregating.
Judging whether the iteration reaches the set maximum times, if not, returning to the step 3) to continue executing the algorithm, if so, ending the algorithm process, and outputting the position result of the optimal particleAs the optimal power generation control command sequence for all control cycles in the subsequent period T.
Corresponding instructions in the sequence can be distributed to each set to be executed in each control period through a subsequent instruction distribution module, and the generated power of the wind field can reach an expected set target through the coordination control of the sets.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, improvement and the like made within the content and principle of the present invention shall be included in the protection scope of the present invention.
Claims (1)
1. A wind power plant AGC instruction optimizing and setting method is characterized by comprising the following steps:
s1 is optimized by generating capacity and power fluctuationA target design objective function is transformed; since the power generation control is not performed, the discrete sampling point of the power within the time T isThe power generation limit over this time period can be calculated as:
wherein Pi is the output sampling point of the wind power plant in the power generation control mode, and P i a Maximum capacity for corresponding sampling time
S2, considering the communication network delay and the time required by algorithm execution of the wind power plant monitoring system, and selecting 6 seconds as an instruction cycle; setting the optimal programming sequence in the T period asConstructing an objective function, wherein the maximum output which can be reached by the control period j under the action of power generation control of the wind power plant isThe generated energy of the wind power plant lost due to the power generation control action in the T time period can be calculated as follows:
wherein, [ k ] 0 ,k 1 ,k 2 ,…,k T/t ]The weighting coefficient is used for controlling the power generation to be ineffective and have no loss of electric quantity when the power which can be generated by the wind power plant is smaller than the limit power, the weighting coefficient is used for shielding the accumulation of the loss of electric quantity, and the value rule is as follows:
according to the above formula, windThe value of the electric field power generation control instruction setting sequence determines the lost power generation capacity, and the value is largerCan make k i The frequency of =0 increases and also decreases in the formula (3)Finally, the loss power generation amount is reducedImprovement ofWill increase the maximum power P in the investigation period T max Aggravating power fluctuation, converting the fluctuation exceeding the standard into penalty power generation amount, and recording as:
wherein,to control the fluctuation limiting condition of the period j, [ l 0 ,l 1 ,l 2 ,…,l T /t]When the power fluctuation of the wind power plant is in a limit range, the fluctuation electric quantity is not counted, the accumulation of the fluctuation electric quantity can be shielded, and the value rule is as follows:
in the above formula, the fluctuation amount is a function of the standard amountAs penalty weight; as can be seen from equations (5) and (6), the value of the tuning sequence is also setThe punishment generating capacity influencing the power fluctuation overrun is reducedThe frequency of li =0 can be increased and reducedNumerical value and penalty weight ofCorrespondingly decreases;
the effective grid-connected electric quantity calculation formula of the wind power plant is as follows:
the aim of the setting of the power generation command sequence of the wind power plant is to enableMaximum, so the objective function is established as:
according to the formula, the solving process of the objective function is the setting process of the power generation instruction sequence of the wind power plant, and the essence is to obtain the optimalThe sum of the output loss and the fluctuation punishment of the wind power plant is minimized;
s3, setting single particle position as set of generated power regulating quantity of each unitSet of flight speed as regulation change speedDimension d of the particle is the number of members of the sequence, taking d =100 according to the foregoing; taking the number of particles as 50, and randomly generating an initialization position p of the particle population i,j (t) and velocity v i,j (t), wherein i represents the serial number of each particle in the particle population, and j represents the component serial number of a single particle, namely the serial number of a control cycle limiting instruction;
calculating the result obtained after each particle is substituted into the objective function, taking the result as the fitness index for evaluating the optimal particle, storing the position and the fitness index of the current particle into a Pbest set, obtaining the optimal fitness particle in the Pbest, and storing the position and the fitness index into the Gbest set;
performing evolutionary iteration on the particles, wherein the iteration equation is as follows:
v i,j (t+1)＝v i,j (t)+c 1 r 1 [p i,j -2x i,j (t)+x i,j (t-1)]+c 2 r 2 [p g,j -2x i,j (t)+x i,j (t-1)]
x i,j (t+1)＝x i,j (t)+v i,j (t+1),j＝1,2,L,d (9)
in the formula, c1 and c2 are learning factors, the learning factors represent the self-learning capacity of the particles, larger values can generally achieve optimized values by a small number of iteration steps, but are easy to fall into the oscillation process, c1= c2=0.6 is taken, r1 and r2 are random numbers which are uniformly distributed from 0 to 1, each iteration step can be changed to enable the particles to move to cover more possible directions, t represents each iteration step and is increased progressively along with the iteration until the maximum iteration times set by the algorithm are completed;
during iteration, the current fitness of each particle is compared with the historical best fitness of each particle, if the current fitness is better, the positions and the fitness of the particles are stored in a covering mode, and the positions and the fitness of the particles are stored in a P mode best Gathering;
comparing P of all current particles bes t set value and G best Value, updating G on the optimal coverage principle best Gathering;
judging whether the iteration reaches the set maximum numberCounting, if not, returning to the step 3) to continue executing the algorithm, if so, ending the algorithm process, and outputting the position result of the optimal particleAs the optimal power generation control command sequence of all control cycles in the subsequent time period T;
corresponding instructions in the sequence can be distributed to each unit to be executed in each control period through a subsequent instruction distribution module, and the generated power of the wind field can reach an expected set target through the coordination control of the units.
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| CN109543873B (en) * | 2018-09-29 | 2023-08-29 | 中国广核电力股份有限公司 | Power peak regulation analysis method and storage medium for nuclear power unit |
| CN112199847A (en) * | 2020-10-15 | 2021-01-08 | 国电大渡河流域水电开发有限公司 | Water and electricity output fluctuation frequency identification method and system based on time domain and magnitude |
| CN112199847B (en) * | 2020-10-15 | 2023-07-14 | 国能大渡河流域水电开发有限公司 | Water power output fluctuation frequency identification method and system based on time domain and magnitude |
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