CN114039366B - Power grid secondary frequency modulation control method and device based on peacock optimization algorithm - Google Patents

Power grid secondary frequency modulation control method and device based on peacock optimization algorithm Download PDF

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CN114039366B
CN114039366B CN202111331357.4A CN202111331357A CN114039366B CN 114039366 B CN114039366 B CN 114039366B CN 202111331357 A CN202111331357 A CN 202111331357A CN 114039366 B CN114039366 B CN 114039366B
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peacock
power
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female
peacocks
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何廷一
杨博
束洪春
和鹏
孟贤
何鑫
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Electric Power Research Institute of Yunnan Power Grid Co Ltd
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Abstract

The application provides a power grid secondary frequency modulation control method and device based on a peacock optimization algorithm, which comprises the following steps of S10, constructing an AGC control system according to the output power of a plurality of different power plants in a control area to be tested, and establishing a multi-source optimal cooperative control model for the AGC control system; the plurality of different power plants includes at least a wind power plant and a photovoltaic power plant; s20, obtaining power distribution schemes under different power disturbance conditions according to the multi-source optimal cooperative control model; and S30, calculating the power distribution scheme under different power disturbance conditions based on a peacock optimization algorithm to obtain an optimal scheme of power distribution. The method solves the problem of dynamic power distribution from the real-time power total instruction of secondary frequency modulation to different types of frequency modulation power supplies by using a peacock optimization algorithm, thereby improving the dynamic response regulation performance of the whole regional power grid.

Description

Power grid secondary frequency modulation control method and device based on peacock optimization algorithm
Technical Field
The application relates to the technical field of power interference balance, in particular to a power grid secondary frequency modulation control method and device based on a peacock optimization algorithm.
Background
In the past decade, problems of resource shortage, environmental pollution, weather change and the like are caused along with the rapid development of industry and economy. In order to cope with the change of climate, the proportion of new energy sources such as wind, light and the like which are accessed into a power grid is rapidly increased. Unlike traditional thermal power, wind and light new energy is greatly affected by climate, and the randomness of power fluctuation is large. Because the construction of the network framework and the spare capacity of the regional power grid is relatively slow, the phenomena of wind abandon, light abandon and the like are easy to cause. Because the traditional thermal power plant peak regulation and frequency modulation response speed is slower, the wind turbine generator and the photovoltaic array have faster response speed and climbing speed, and can balance fast power fluctuation faster.
Therefore, for the regional power grid with high wind and light new energy occupation, the wind power plant and the photovoltaic power station can be controlled in a fixed power point control mode in a running working condition lower than a maximum power point by being limited by the safe running constraint of the system, particularly in a load low valley period, and certain spare capacity is reserved to participate in secondary frequency modulation, namely automatic power generation control (automatic generation control, AGC). The problem of AGC multi-source optimal cooperative control is a complex nonlinear optimization problem, and at present, power is often distributed in a mode of adjustable capacity proportion, climbing speed sequencing and the like in engineering, but the problem cannot meet the optimal control of a system. On the other hand, the traditional mathematical optimization method (such as the interior point method) has high solving speed, but has poor global searching capability and is easy to fall into a local optimal solution. Compared with the method, intelligent optimization algorithms such as genetic algorithm (genetic algorithm, GA), particle swarm optimization (particle swarm optimization, PSO) and the like are higher in application flexibility, higher in global searching capability, and lower in solving speed, and cannot meet AGC online control requirements of a large-scale regional power grid. Therefore, in order to solve the problem of dynamic power distribution from real-time power total instructions of secondary frequency modulation of the regional power grid to different types of frequency modulation power supplies, a high-quality power distribution scheme under the condition of different power disturbance is designed, and accordingly dynamic response regulation performance of the whole regional power grid is improved.
Disclosure of Invention
The application aims at solving the problem of dynamic power distribution from real-time power total instructions of secondary frequency modulation of a regional power grid to different types of frequency modulation power supplies, and proposes to distribute delta P to each AGC unit by using POA. The peacock optimizing algorithm (Peafowl optimization algorithm, POA) is inspired by the puppet, foraging and chasing actions of a peacock group, simulates the hierarchical structure of three peacocks of a male peacock, a female peacock and a young peacock for years and gradually approximates to the optimal solution of the problem by the dynamic group actions of the peacock during foraging, effectively solves the defects of low convergence speed, low precision, poor stability and the like, has stronger global searching capability, and can quickly obtain a high-quality power distribution scheme under different power disturbance, thereby effectively improving the dynamic response capability of the whole regional power grid.
On the one hand, the application provides a power grid secondary frequency modulation control method based on a peacock optimization algorithm, which comprises the following steps:
a power grid secondary frequency modulation control method based on a peacock optimization algorithm, the method comprising:
s10, constructing an AGC control system according to output power of a plurality of different power plants in a control area to be detected, and establishing a multi-source optimal cooperative control model for the AGC control system; the plurality of different power plants includes at least a wind power plant and a photovoltaic power plant;
s20, obtaining power distribution schemes under different power disturbance conditions according to the multi-source optimal cooperative control model;
and S30, calculating the power distribution scheme under different power disturbance conditions based on a peacock optimization algorithm to obtain an optimal scheme of power distribution.
Preferably, in the step S10, the establishing a multi-source optimal cooperative control model for the AGC multi-source optimal cooperative control system is:
establishing a dynamic response model of the unit:
according to the Laplace inverse transformation of the frequency domain transfer function, the actual output of the time domain adjusting power can be obtained through the input power calculation, and the method comprises the following steps:
wherein: i represents the ith AGC unit; k represents a kth discrete control period;input regulating power command representing the ith AGC unit and +.>Representing the actual output of the regulated power of the ith AGC unit; Δt is the control period of AGC.
Preferably, in S20, according to the multi-source optimal cooperative control model, the power allocation scheme under the condition of different power disturbance is obtained as follows:
taking the minimized total power response deviation as an objective function, namely the sum of the deviation absolute values of the regulating power command values and the power response values of all the units, and establishing the objective function as follows:
wherein,an input adjustment power command representing an ith AGC unit; />The actual output regulation power of the ith frequency modulation unit is shown; n is the number of control time periods; n is nThe number of AGC units;
besides the dynamic response transmission process of the unit, the power balance constraint, the unit capacity constraint and the climbing constraint are considered in the power distribution process, so that the following steps are shown:
wherein:a total power adjustment instruction; />Representing the maximum ramp rate of the ith AGC unit.
Preferably, in S30, the optimal power allocation scheme obtained by optimizing and calculating the power allocation scheme under the different power disturbance conditions based on the peacock is as follows:
S-A, initializing se:Sup>A peacock optimization algorithm;
S-B, calculating a fitness function according to a formula (4), and calculating a fitness value;
S-C, calculating the position of the male peacock:
the mechanism of the position update of male peacock during the coupling process can be described as:
wherein x is c,1 And x c,n Positions of the 1 st and n th male peacock, n=2, 3, …,5; sigma and epsilon are operators for determining the position update of male peacocks, and when n=2, 3,4,5, sigma/epsilon is 1.5/0.9,2/0.8,3/0.6,5/0.3 respectively; x is x c,1 And x c,n Is a set of random vectors, as in equation (10); r is R s The radius of rotation of the male peacock about the food source is represented by formula (11).
x r ＝2·rand(1,Dim)-1 (10)
Wherein Dim is the number of decision variables; k is the current iteration number and k max The maximum iteration number; r is R s0 Is an initial radius of rotation vector; c (C) v The male peacock twiddle factor is set to 0.2; x is x ub For the upper sum x of decision variables lb Is the lower bound of the decision variable;
S-D, calculating the position of the female peacock:
the behavior of female peacock self-adaptation approaching male peacock is described as:
x h (t+1)＝x h (t)+3·θ(x c,n -x h (t)) if r 5 ∈A (12)
Wherein x is h Is the position of female peacock; r is (r) 5 Is [0,1]Random numbers within a range; a is an operator for determining the update of the female peacock position, and when n=1, 2,3,4,5, a is [0.6,1 ]],[0.4,0.6],[0.2,0.4],[0.1,0.2],[0,0.1]The method comprises the steps of carrying out a first treatment on the surface of the θ represents an operator that balances the local exploration and global search of female peacocks, calculated as:
θ＝θ 0 +(θ 1 -θ 0 )·(k/k max )(13)
wherein θ 0 Is set to 0.1 and theta 1 Is set to 1.
Under this mechanism, as an initial iteration stage when θ <1/3, female peakers mainly tend to the selected male peakers, representing the local survey process of the female peakers; when theta is more than 1/3, the female peacock is inclined to move to the relative position of the selected male peacock, and represents the global searching process of the female peacock;
S-E, position of young peacocks:
wherein x is cu Is the sum of the young peacock position vectors x pu A male peacock position followed by a young peacock; r is (r) 6 Is [0,1]Random numbers within a range; b is an operator for determining the update of the young peacock position, and when n=1, 2,3,4,5, B is [0.8,1 ]],[0.6,0.8],[0.4,0.6],[0.2,0.4],[0,0.2]. Alpha and B are operators which dynamically change along with the iteration times, and are defined as:
wherein alpha is 0 Is 0.9, alpha 1 Is 0.4, beta 0 Is 0.1 and beta 1 1.
Under the mechanism, when alpha is larger than beta, as an iteration initial stage, the young peacocks mainly perform random search; when beta is larger than alpha, the young peacock gradually converges to 5 male peacocks at the middle and late stages of iteration;
S-F, male peacock interaction positions:
the 1 st male peacock had the best food source, which was considered the leader, and the 2 nd to 4 th male peacocks were guided by the 1 st male peacock and gradually moved toward it. The other 4 male peakers randomly moved toward the 1 st male peaker within 90 ° of the 1 st male peaker. The 2 nd to 4 th male peacock positions are updated as follows:
wherein r 'is' n Is [0,1]Random numbers within a range; x's' r,n Is a random vector, calculated according to equation (10);
S-G, collecting power deviation and frequency deviation of each regional interconnecting line under current power distribution, and converting the power deviation and the frequency deviation into regional control deviation in a PI controller for subsequent iteration;
S-H, iterating for a plurality of times until the peacock optimization algorithm converges to the minimum power response total deviation to be used as the optimal power distribution;
in order to meet constraint conditions (5) - (8), a penalty function method is applied in the target fitness function Fit, and the method is as follows:
wherein M is a penalty factor, often a sufficiently large positive number; z is Z u Is the sum of the u constraint conditionsFor which a limit value is defined.
In another aspect, the present application provides a device for controlling a secondary frequency modulation of a power grid based on a peacock optimization algorithm, the device comprising:
a control system module is established, an AGC control system is established according to the output power of a plurality of different power plants in a control area to be detected, and a multi-source optimal cooperative control model is established for the AGC control system; the plurality of different power plants includes at least a wind power plant and a photovoltaic power plant;
the power distribution scheme obtaining module is used for obtaining power distribution schemes under different power disturbance conditions according to the multi-source optimal cooperative control model;
and the optimization scheme module is used for calculating the power distribution scheme under different power disturbance conditions based on a peacock optimization algorithm to obtain an optimal power distribution scheme.
Preferably, the control system establishment module is:
establishing a dynamic response model of the unit:
according to the Laplace inverse transformation of the frequency domain transfer function, the actual output of the time domain adjusting power can be obtained through the input power calculation, and the method comprises the following steps:
wherein: i represents the ith AGC unit; k represents a kth discrete control period;input regulating power command representing the ith AGC unit and +.>Representing the actual output of the regulated power of the ith AGC unit; Δt is the control period of AGC.
Preferably, the power allocation scheme obtaining module is as follows:
taking the minimized total power response deviation as an objective function, namely the sum of the deviation absolute values of the regulating power command values and the power response values of all the units, and establishing the objective function as follows:
wherein,an input adjustment power command representing an ith AGC unit; />The actual output regulation power of the ith frequency modulation unit is shown; n is the number of control time periods; n is the number of AGC units;
besides the dynamic response transmission process of the unit, the power balance constraint, the unit capacity constraint and the climbing constraint are considered in the power distribution process, so that the following steps are shown:
wherein:a total power adjustment instruction; />Representing the maximum ramp rate of the ith AGC unit.
Preferably, the optimization scheme module is as follows:
S-A, initializing se:Sup>A peacock optimization algorithm;
S-B, calculating a fitness function according to a formula (4), and calculating a fitness value;
S-C, calculating the position of the male peacock:
the mechanism of the position update of male peacock during the coupling process can be described as:
wherein x is c,1 And x c,n Positions of the 1 st and n th male peacock, n=2, 3, …,5; σ and ε are operators that determine the position update of the male peacock, and when n=2, 3,4,5,sigma/epsilon is 1.5/0.9,2/0.8,3/0.6,5/0.3, respectively; x is x c,1 And x c,n Is a set of random vectors, as in equation (10); r is R s The radius of rotation of the male peacock about the food source is represented by formula (11).
x r ＝2·rand(1,Dim)-1 (10)
Wherein Dim is the number of decision variables; k is the current iteration number and k max The maximum iteration number; r is R s0 Is an initial radius of rotation vector; c (C) v The male peacock twiddle factor is set to 0.2; x is x ub For the upper sum x of decision variables lb Is the lower bound of the decision variable;
S-D, calculating the position of the female peacock:
the behavior of female peacock self-adaptation approaching male peacock is described as:
x h (t+1)＝x h (t)+3·θ(x c,n -x h (t)) if r 5 ∈A (12)
Wherein x is h Is the position of female peacock; r is (r) 5 Is [0,1]Random numbers within a range; a is an operator for determining the update of the female peacock position, and when n=1, 2,3,4,5, a is [0.6,1 ]],[0.4,0.6],[0.2,0.4],[0.1,0.2],[0,0.1]The method comprises the steps of carrying out a first treatment on the surface of the θ represents an operator that balances the local exploration and global search of female peacocks, calculated as:
θ＝θ 0 +(θ 1 -θ 0 )·(k/k max ) (13)
wherein θ 0 Is set to 0.1 and theta 1 Is set to 1.
Under this mechanism, as an initial iteration stage when θ <1/3, female peakers mainly tend to the selected male peakers, representing the local survey process of the female peakers; when theta is more than 1/3, the female peacock is inclined to move to the relative position of the selected male peacock, and represents the global searching process of the female peacock;
S-E, position of young peacocks:
wherein x is cu Is the sum of the young peacock position vectors x pu A male peacock position followed by a young peacock; r is (r) 6 Is [0,1]Random numbers within a range; b is an operator for determining the update of the young peacock position, and when n=1, 2,3,4,5, B is [0.8,1 ]],[0.6,0.8],[0.4,0.6],[0.2,0.4],[0,0.2]. Alpha and B are operators which dynamically change along with the iteration times, and are defined as:
wherein alpha is 0 Is 0.9, alpha 1 Is 0.4, beta 0 Is 0.1 and beta 1 1.
Under the mechanism, when alpha is larger than beta, as an iteration initial stage, the young peacocks mainly perform random search; when beta is larger than alpha, the young peacock gradually converges to 5 male peacocks at the middle and late stages of iteration;
S-F, male peacock interaction positions:
the 1 st male peacock had the best food source, which was considered the leader, and the 2 nd to 4 th male peacocks were guided by the 1 st male peacock and gradually moved toward it. The other 4 male peakers randomly moved toward the 1 st male peaker within 90 ° of the 1 st male peaker. The 2 nd to 4 th male peacock positions are updated as follows:
wherein r 'is' n Is [0,1]Random numbers within a range; x's' r,n Is a random vector, calculated according to equation (10);
S-G, collecting power deviation and frequency deviation of each regional interconnecting line under current power distribution, and converting the power deviation and the frequency deviation into regional control deviation in a PI controller for subsequent iteration;
S-H, iterating for a plurality of times until the POA converges to the minimum power response total deviation to be used as the optimal power distribution;
in order to meet constraint conditions (5) - (8), a penalty function method is applied in the target fitness function Fit, and the method is as follows:
wherein M is a penalty factor, often a sufficiently large positive number; z is Z u Is the sum of the u constraint conditionsFor which a limit value is defined.
According to the application, under the background of taking the wind-solar new energy into consideration for secondary frequency modulation, a multi-source optimal cooperative control method for improving the dynamic response performance of the system is built, an AGC regulated reference model is provided for the power grid containing high-proportion wind-solar new energy, the overall control effect of the system is obviously improved, the POA is adopted to distribute the total regulated power delta P to each generator set in real time, each frequency modulation set can not only meet the real-time on-line regulation and control requirement of AGC, but also can rapidly obtain a high-quality regulation and control scheme with high convergence stability, and the dynamic response performance of the whole regional power grid is greatly improved.
Drawings
FIG. 1 is a flowchart of a power grid secondary frequency modulation control method based on a peacock optimization algorithm in an embodiment;
FIG. 2 is a schematic diagram of a two-zone frame down-mix multi-source optimal cooperative control in an embodiment;
FIG. 3 is a flowchart illustrating the operation of a POA algorithm according to an embodiment;
fig. 4 is a pareto front diagram obtained by POA according to an embodiment;
FIG. 5 is a diagram of unit input power distribution under POA algorithm in an embodiment;
fig. 6 is a block diagram of an optimization device based on atomic search in an embodiment.
Detailed Description
The application aims at solving the problem of dynamic power distribution from real-time power total instructions of secondary frequency modulation of a regional power grid to different types of frequency modulation power supplies, and proposes to distribute delta P to each AGC unit by using POA. The POA algorithm is inspired by the puppet, foraging and chasing actions of the peacock group, simulates the hierarchical structure of the three peacocks of the adult male peacock, the adult female peacock and the young peacock and the dynamic group actions of the peacock to gradually approach the optimal solution of the problem, effectively solves the defects of low convergence speed, low precision, poor stability and the like, has stronger global searching capability, and can quickly obtain a high-quality power distribution scheme under different power disturbance, thereby effectively improving the dynamic response capability of the whole regional power grid.
Fig. 1 is a flowchart of a power grid secondary frequency modulation control method based on a peacock optimization algorithm.
S10, constructing an AGC control system according to output power of a plurality of different power plants in a control area to be detected, and establishing a multi-source optimal cooperative control model for the AGC control system; the plurality of different power plants includes at least a wind power plant and a photovoltaic power plant.
Referring to fig. 2, under the background of taking the wind-solar new energy into consideration for secondary frequency modulation, a multi-source optimal cooperative control method for improving the dynamic response performance of the system is built, an AGC regulated reference model is provided for a power grid containing high-proportion wind-solar new energy, and the overall control effect of the system is remarkably improved.
The multi-source optimal cooperative control model is established, and is mainly used for describing the power dynamic response process of the unit after receiving the power adjustment instruction more accurately. For different types of units, the dynamic response model comprises common links such as capacity upper and lower limits, climbing rate, frequency modulation time delay and the like, and also comprises a transmission link with self energy conversion characteristics. According to the Laplace inverse transformation of the frequency domain transfer function, the actual output of the time domain adjusting power can be obtained through the input power calculation, and the method comprises the following steps:
wherein: i represents the ith AGC unit; k represents a kth discrete control period;input regulating power command representing the ith AGC unit and +.>Representing the actual output of the regulated power of the ith AGC unit; Δt is the control period of AGC, typically 1 to 16 seconds.
S20, obtaining power distribution schemes under different power disturbance conditions according to the multi-source optimal cooperative control model.
In order to improve the economic and comprehensive benefits of the power grid, the wind and the photoelectric stations are not used for passively discarding wind and light, but exert self-regulating potential and actively participate in the system power AGC regulating task. The optimization target is to stand on the power grid side, and the dynamic response regulation performance of the whole control area power grid is mainly pursued. To achieve this objective, the minimum power response total deviation is taken as an objective function, namely the sum of the deviation absolute values of the regulated power command values and the power response values of all the units, and the established objective function is:
wherein,an input adjustment power command representing an ith AGC unit; />The actual output regulation power of the ith frequency modulation unit is shown; n is the number of control time periods; n is the number of AGC units.
Besides the dynamic response transmission process of the unit, the power balance constraint, the unit capacity constraint and the climbing constraint are considered in the power distribution process, so that the following steps are shown:
wherein:a total power adjustment instruction; />Representing the maximum ramp rate of the ith AGC unit.
And S30, calculating the power distribution scheme under different power disturbance conditions based on a peacock optimization algorithm to obtain an optimal scheme of power distribution.
The POA algorithm is inspired by the puppet, foraging and chase behaviors of the peacock group, simulates the hierarchical structure of three peacocks, namely, an adult female peacock and a young peacock, and gradually approximates to the optimal solution of the problem by the dynamic group behaviors of the peacock group in the foraging process. Firstly, all peacocks perform role allocation according to the ordering of initial fitness values, namely: the first 5 with the highest fitness value are adult male peacocks; the first 30% of peacock is adult female peacock; the balance of young peacocks. The adult female peacock and the young peacock are guided by the adult male peacock to update the positions, and at the same time, the position is randomly searched in the space to find a higher quality food source, and the roles are redistributed according to the updated peacock fitness value after each iteration. The algorithm has strong global searching capability and high flexibility, and is not limited to a local optimal solution.
See fig. 3 for specific calculation steps.
S-A, setting parameters and initializing population.
S-B, calculating a fitness value:
the fitness function is calculated according to equation (4), and fitness function F (x) is the objective function constructed above.
S-C, calculating the position of the male peacock:
after the male peacock finds the food source, it may be rotated in place or around the food source to draw attention of the female peacock. The mechanism of the position update of male peacock during the coupling process can be described as:
wherein x is c,1 And x c,n Positions of the 1 st and n th male peacock, n=2, 3, …,5; sigma and epsilon are operators for determining the position update of male peacocks, and when n=2, 3,4,5, sigma/epsilon is 1.5/0.9,2/0.8,3/0.6,5/0.3 respectively; x is x c,1 And x c,n Is a set of random vectors, as in equation (10); r is R s The radius of rotation of the male peacock about the food source is represented by formula (11).
x r ＝2·rand(1,Dim)-1 (10)
Wherein Dim is the number of decision variables; k is the current iteration number and k max The maximum iteration number; r is R s0 Is an initial radius of rotation vector; c (C) v The male peacock twiddle factor is set to 0.2; x is x ub For the upper sum x of decision variables lb Is the lower limit of the decision variable.
Under the mechanism, the higher the fitness value is, the greater the probability that the male peacock rotates around the food source is, and the smaller the radius of the winding is; conversely, the male peacock tends to spin more in situ and the larger the radius of spin.
S-D, calculating the position of the female peacock:
female peacock is influenced by male peacock's puppet action, gradually approaches male peacock, observes all around simultaneously, the dynamic adjustment own position. Here, the probability that a male peacock attracts a female peacock is greater as the fitness value is higher. The behavior of female peacock self-adaptation approaching male peacock is described as:
x h (t+1)＝x h (t)+3·θ(x c,n -x h (t)) if r 5 ∈A (12)
Wherein x is h Is the position of female peacock; r is (r) 5 Is [0,1]Random numbers within a range; a is an operator for determining the update of the female peacock position, and when n=1, 2,3,4,5, a is [0.6,1 ]],[0.4,0.6],[0.2,0.4],[0.1,0.2],[0,0.1]The method comprises the steps of carrying out a first treatment on the surface of the θ represents an operator that balances the local exploration and global search of female peacocks, calculated as:
θ＝θ 0 +(θ 1 -θ 0 )·(k/k max ) (13)
wherein θ 0 Is set to 0.1 and theta 1 Is set to 1.
Under this mechanism, as an initial iteration stage when θ <1/3, female peakers mainly tend to the selected male peakers, representing the local survey process of the female peakers; when theta is more than 1/3, the female peacock is inclined to move to the relative position of the selected male peacock, and represents the global searching process of the female peacock.
S-E, position of young peacocks:
the young peacock adaptively searches for a food source, and on one hand, the young peacock randomly selects and moves to one male peacock; on the other hand, young peacocks conduct random searches in the search space by means of the Levy flight mechanism. The location update of young peacocks is described as:
wherein x is cu Is the sum of the young peacock position vectors x pu A male peacock position followed by a young peacock; r is (r) 6 Is [0,1]Random numbers within a range; b is an operator for determining the update of the young peacock position, and when n=1, 2,3,4,5, B is [0.8,1 ]],[0.6,0.8],[0.4,0.6],[0.2,0.4],[0,0.2]. Alpha and beta are operators which dynamically change along with the iteration times, and are defined as:
wherein alpha is 0 Is 0.9, alpha 1 Is 0.4, beta 0 Is 0.1 and beta 1 1.
Under the mechanism, when alpha is larger than beta, as an iteration initial stage, the young peacocks mainly perform random search; when beta is larger than alpha, the young peacock gradually converges to 5 male peacocks at the middle and late stages of iteration.
S-F, male peacock interaction positions:
male Peacock interaction, since the 1 st Male Peacock has the best food source, is considered the leader, and the 2 nd to 4 th Male Peacocks are guided by the 1 st Male Peacock and gradually move towards the same. In particular, the other 4 male peakers do not move directly to the 1 st male peaker, but move randomly to the 1 st male peaker within 90 ° of the 1 st male peaker. The 2 nd to 4 th male peacock positions are updated as follows:
wherein r 'is' n Is [0,1]Random numbers within a range; x's' r,n Is a random vector, calculated according to equation (10).
S-G, collecting power deviation and frequency deviation of each regional interconnecting line under current power distribution, and converting the power deviation and the frequency deviation into regional control deviation in a PI controller for subsequent iteration.
S-H, iterating for a plurality of times until the POA converges to the minimum power response total deviation, namely the optimal power distribution.
In order to meet constraint condition formulas (5) - (8), a penalty function method is applied in the target fitness function Fit, and the penalty function method is applied as follows:
wherein M is a penalty factor, often a sufficiently large positive number; z is Z u Is the sum of the u constraint conditionsFor which a limit value is defined.
In a specific embodiment, static simulation is performed on the two-region model, where n=15, k of POA is set max =100, load disturbance Δp L = -80MW, fig. 4 gives the pareto front acquired by POA in this case. From the figure, the POA can be converged to the pareto front of a solution set equipartition distribution, and the solution set can comprehensively consider mileage expenditure and total power deviation. Then, dynamic simulation is carried out, and the N=50 and k of the POA are set max =100, load disturbance Δp L = -80MW. Fig. 5 shows the allocation of the crew inputs acquired by the POA in this case. As can be derived from fig. 5, the POA can obtain a real-time scheduling scheme for coordinating each unit.
In another aspect, referring to fig. 6, the present application provides a secondary frequency modulation device for a power grid based on a peacock optimization algorithm, including:
a control system module is established, an AGC control system is established according to the output power of a plurality of different power plants in a control area to be detected, and a multi-source optimal cooperative control model is established for the AGC control system; the plurality of different power plants includes at least a wind power plant and a photovoltaic power plant;
the power distribution scheme obtaining module is used for obtaining power distribution schemes under different power disturbance conditions according to the multi-source optimal cooperative control model;
and the optimization scheme module is used for calculating the power distribution scheme under different power disturbance conditions based on a peacock optimization algorithm to obtain an optimal power distribution scheme.
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.

Claims (4)

1. The utility model provides a power grid secondary frequency modulation control method based on a peacock optimization algorithm, which is characterized by comprising the following steps:
s10, constructing an AGC control system according to the output power of a plurality of different power plants in a control area to be detected, and establishing a multi-source optimal cooperative control model for the AGC control system, wherein the method comprises the following steps:
establishing a dynamic response model of the unit:
according to the Laplace inverse transformation of the frequency domain transfer function, the actual output of the time domain adjusting power can be obtained through the input power calculation, and the method comprises the following steps:
wherein: i represents the ith AGC unit; k represents the kthA discrete control period;input regulated power command and ΔP representing the ith AGC unit i out Representing the actual output of the regulated power of the ith AGC unit; delta T is the control period of AGC; the plurality of different power plants includes at least a wind power plant and a photovoltaic power plant;
s20, obtaining a power distribution scheme under different power disturbance conditions according to the multi-source optimal cooperative control model, wherein the power distribution scheme comprises the following steps:
taking the minimized total power response deviation as an objective function, namely the sum of the deviation absolute values of the regulating power command values and the power response values of all the units, and establishing the objective function as follows:
wherein,an input adjustment power command representing an ith AGC unit; />The actual output regulation power of the ith frequency modulation unit is shown; n is the number of control time periods; n is the number of AGC units;
besides the dynamic response transmission process of the unit, the power balance constraint, the unit capacity constraint and the climbing constraint are considered in the power distribution process, so that the following steps are shown:
wherein:a total power adjustment instruction; />Representing the maximum ramp rate of the ith AGC unit;
and S30, calculating the power distribution scheme under different power disturbance conditions based on a peacock optimization algorithm to obtain an optimal scheme of power distribution.
2. The method for controlling secondary frequency modulation of a power grid based on a peacock optimization algorithm according to claim 1, wherein the step S30 is characterized in that the optimal power distribution scheme obtained by calculating the power distribution scheme under different power disturbance based on peacock optimization is as follows:
S-A, initializing se:Sup>A peacock optimization algorithm;
S-B, calculating a fitness function according to a formula (4), and calculating a fitness value;
S-C, calculating the position of the male peacock:
the mechanism of the position update of male peacock during the coupling process can be described as:
wherein x is c，1 Position and x of the 1 st male peacock c，n Position of the nth male peacock, n=2, 3, 5; sigma and epsilon are the determining male holesOperator for sparrow position update, when n=2, 3,4,5, sigma/epsilon is 1.5/0.9,2/0.8,3/0.6,5/0.3 respectively; x is x C，1 And x c，n Is a set of random vectors, as in equation (10); r is R s A radius representing the rotation of the male peacock about the food source, as in formula (11);
x r ＝2·rand(1，Dim)-1 (10)
wherein Dim is the number of decision variables; k is the current iteration number and k max The maximum iteration number; r is R s0 Is an initial radius of rotation vector; c (C) v The male peacock twiddle factor is set to 0.2; x is x ub For the upper sum x of decision variables 1b Is the lower bound of the decision variable;
S-D, calculating the position of the female peacock:
the behavior of female peacock self-adaptation approaching male peacock is described as:
x h (t+1)＝x h (t)+3·θ·(x c，n -x h (t)) if r 5 ∈A (12)
Wherein x is h Is the position of female peacock; r is (r) 5 Is [0,1]Random numbers within a range; a is an operator for determining the update of the female peacock position, and when n=1, 2,3,4,5, a is [0.6,1 ]]，[0.4，0.6]，[0.2，0.4]，[0.1，0.2]，[0，0.1]The method comprises the steps of carrying out a first treatment on the surface of the θ represents an operator that balances the local exploration and global search of female peacocks, calculated as:
θ＝θ 0 +(θ 1 -θ 0 )·(k/k max ) (13)
wherein θ 0 Is set to 0.1 and theta 1 Is set to 1;
under this mechanism, as an initial iteration stage when θ <1/3, female peakers mainly tend to the selected male peakers, representing the local survey process of the female peakers; when theta is more than 1/3, the female peacock is inclined to move to the relative position of the selected male peacock, and represents the global searching process of the female peacock;
S-E, position of young peacocks:
wherein x is cu Is the sum of the young peacock position vectors x pu A male peacock position followed by a young peacock; r is (r) 6 Is [0,1]Random numbers within a range; b is an operator for determining the update of the young peacock position, and when n=1, 2,3,4,5, B is [0.8,1 ]]，[0.6，0.8]，
[0.4,0.6], [0.2,0.4], [0,0.2]; alpha and B are operators which dynamically change along with the iteration times, and are defined as:
wherein alpha is 0 Is 0.9, alpha 1 Is 0.4, beta 0 Is 0.1 and beta 1 1 is shown in the specification;
under the mechanism, when alpha is larger than beta, as an iteration initial stage, the young peacocks mainly perform random search; when beta is larger than alpha, the young peacock gradually converges to 5 male peacocks at the middle and late stages of iteration;
S-F, male peacock interaction positions:
the 1 st male peacock has the best food source, and is regarded as the leader, and the 2 nd to 4 th male peacocks are guided by the 1 st male peacock and gradually move towards the 1 st male peacock, and other 4 male peacocks randomly move towards the 1 st male peacock within the range of 90 degrees between the other 4 male peacocks and the 1 st male peacock, and the 2 nd to 4 th male peacocks are updated as follows:
wherein r 'is' n Is [0,1]Random numbers within a range; x's' r，n Is a random vector, calculated according to equation (10);
S-G, collecting power deviation and frequency deviation of each regional interconnecting line under current power distribution, and converting the power deviation and the frequency deviation into regional control deviation in a PI controller for subsequent iteration;
S-H, iterating for a plurality of times until the peacock optimization algorithm converges to the minimum power response total deviation to be used as the optimal power distribution;
in order to meet constraint conditions (5) - (8), a penalty function method is applied in the target fitness function Fit, and the method is as follows:
wherein M is a penalty factor, often a sufficiently large positive number; z is Z u Is the sum of the u constraint conditionsFor which a limit value is defined.
3. A power grid secondary frequency modulation control device based on a peacock optimization algorithm, which is characterized by comprising:
a control system module is established, an AGC control system is established according to the output power of a plurality of different power plants in a control area to be detected, and a multi-source optimal cooperative control model is established for the AGC control system; the plurality of different power plants includes at least a wind power plant and a photovoltaic power plant;
the power distribution scheme obtaining module is used for obtaining power distribution schemes under different power disturbance conditions according to the multi-source optimal cooperative control model;
the optimization scheme module is used for calculating the power distribution scheme under different power disturbance conditions based on a peacock optimization algorithm to obtain an optimal power distribution scheme;
the control system building module is as follows:
establishing a dynamic response model of the unit:
according to the Laplace inverse transformation of the frequency domain transfer function, the actual output of the time domain adjusting power can be obtained through the input power calculation, and the method comprises the following steps:
wherein: i represents the ith AGC unit; k represents a kth discrete control period;input regulated power command and ΔP representing the ith AGC unit i out The actual output of the regulated power of the ith AGC unit; delta T is the control period of AGC;
the obtained power distribution scheme module is as follows:
taking the minimized total power response deviation as an objective function, namely the sum of the deviation absolute values of the regulating power command values and the power response values of all the units, and establishing the objective function as follows:
wherein,an input adjustment power command representing an ith AGC unit; />The actual output regulation power of the ith frequency modulation unit is shown; n is the number of control time periods; n is the number of AGC units;
besides the dynamic response transmission process of the unit, the power balance constraint, the unit capacity constraint and the climbing constraint are considered in the power distribution process, so that the following steps are shown:
wherein:a total power adjustment instruction; />Representing the maximum ramp rate of the ith AGC unit.
4. The power grid secondary frequency modulation control device based on the peacock optimization algorithm according to claim 3, wherein the optimization scheme module is as follows:
S-A, initializing se:Sup>A peacock optimization algorithm;
S-B, calculating a fitness function according to a formula (4), and calculating a fitness value;
S-C, calculating the position of the male peacock:
the mechanism of the position update of male peacock during the coupling process can be described as:
wherein x is c，1 Position and x of the 1 st male peacock c，n Position of the nth male peacock, n=2, 3, 5; sigma and epsilon are operators for determining the position update of male peacocks, and when n=2, 3,4,5, sigma/epsilon is 1.5/0.9,2/0.8,3/0.6,5/0.3 respectively; x is x c，1 And x c，n Is a set of random vectors, as in equation (10); r is R s A radius representing the rotation of the male peacock about the food source, as in formula (11);
x r ＝2·rand(1，Dim)-1 (10)
wherein Dim is the number of decision variables; k is the current iteration number and k max The maximum iteration number; r is R s0 Is an initial radius of rotation vector; c (C) v The male peacock twiddle factor is set to 0.2; x is x ub For the upper sum x of decision variables lb Is the lower bound of the decision variable;
S-D, calculating the position of the female peacock:
the behavior of female peacock self-adaptation approaching male peacock is described as:
x h (t+1)＝x h (t)+3·θ·(x c，n -x h (t)) if r 5 ∈A (12)
Wherein x is h Is the position of female peacock; r is (r) 5 Is [0,1]Random numbers within a range; a is an operator for determining the update of the female peacock position, and when n=1, 2,3,4,5, a is [0.6,1 ]]，[0.4，0.6]，[0.2，0.4]，[0.1，0.2]，[0，0.1]The method comprises the steps of carrying out a first treatment on the surface of the θ represents an operator that balances the local exploration and global search of female peacocks, calculated as:
θ＝θ 0 +(θ 1 -θ 0 )·(k/k max ) (13)
wherein θ 0 Is set to 0.1 and theta 1 Is set to 1;
under this mechanism, as an initial iteration stage when θ <1/3, female peakers mainly tend to the selected male peakers, representing the local survey process of the female peakers; when theta is more than 1/3, the female peacock is inclined to move to the relative position of the selected male peacock, and represents the global searching process of the female peacock;
S-E, position of young peacocks:
wherein x is cu Is the sum of the young peacock position vectors x pu A male peacock position followed by a young peacock; r is (r) 6 Is [0,1]Random numbers within a range; b is an operator for determining the update of the young peacock position, and when n=1, 2,3,4,5, B is [0.8,1 ]]，[0.6，0.8]，[0.4，0.6]，[0.2，0.4]，[0，0.2]Alpha and B are operators which dynamically change along with the iteration number, and are defined as:
wherein alpha is 0 Is 0.9, alpha 1 Is 0.4, beta 0 Is 0.1 and beta 1 1 is shown in the specification;
under the mechanism, when alpha is larger than beta, as an iteration initial stage, the young peacocks mainly perform random search; when beta is larger than alpha, the young peacock gradually converges to 5 male peacocks at the middle and late stages of iteration;
S-F, male peacock interaction positions:
the 1 st male peacock has the best food source, and is regarded as the leader, and the 2 nd to 4 th male peacocks are guided by the 1 st male peacock and gradually move towards the 1 st male peacock, and other 4 male peacocks randomly move towards the 1 st male peacock within the range of 90 degrees between the other 4 male peacocks and the 1 st male peacock, and the 2 nd to 4 th male peacocks are updated as follows:
wherein r 'is' n Is [0,1]Within the range ofRandom numbers of (a); x's' r，n Is a random vector, calculated according to equation (10);
S-G, collecting power deviation and frequency deviation of each regional interconnecting line under current power distribution, and converting the power deviation and the frequency deviation into regional control deviation in a PI controller for subsequent iteration;
S-H, iterating for a plurality of times until the POA converges to the minimum power response total deviation to be used as the optimal power distribution;
in order to meet constraint conditions (5) - (8), a penalty function method is applied in the target fitness function Fit, and the method is as follows:
wherein M is a penalty factor, often a sufficiently large positive number; z is Z u Is the sum of the u constraint conditionsFor which a limit value is defined.
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2002018648A2 (en) * 2000-08-25 2002-03-07 President And Fellows Of Harvard College Analysis of binding interactions
WO2014201849A1 (en) * 2013-06-18 2014-12-24 国网辽宁省电力有限公司电力科学研究院 Method for actively optimizing, adjusting and controlling distributed wind power plant provided with energy-storage power station
CN108092323A (en) * 2017-11-16 2018-05-29 云南电网有限责任公司电力科学研究院 A kind of electric system AGC optimal control methods containing DFIG
CN111509785A (en) * 2020-04-26 2020-08-07 云南电网有限责任公司电力科学研究院 Method, system and storage medium for multi-source optimal cooperative control of power grid
CN113241778A (en) * 2021-05-25 2021-08-10 云南电网有限责任公司电力科学研究院 AGC control method based on multi-region interconnected power grid

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2002018648A2 (en) * 2000-08-25 2002-03-07 President And Fellows Of Harvard College Analysis of binding interactions
WO2014201849A1 (en) * 2013-06-18 2014-12-24 国网辽宁省电力有限公司电力科学研究院 Method for actively optimizing, adjusting and controlling distributed wind power plant provided with energy-storage power station
CN108092323A (en) * 2017-11-16 2018-05-29 云南电网有限责任公司电力科学研究院 A kind of electric system AGC optimal control methods containing DFIG
CN111509785A (en) * 2020-04-26 2020-08-07 云南电网有限责任公司电力科学研究院 Method, system and storage medium for multi-source optimal cooperative control of power grid
CN113241778A (en) * 2021-05-25 2021-08-10 云南电网有限责任公司电力科学研究院 AGC control method based on multi-region interconnected power grid

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
考虑风光新能源参与二次调频的多源最优协同控制;杨蕾;李胜男;黄伟;张丹;马红升;许守东;杨博;张孝顺;;电力系统保护与控制(第19期);全文 *

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