CN117559399A - Micro-grid power generation strategy determination method and device, electronic equipment and storage medium - Google Patents
Micro-grid power generation strategy determination method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a method, a device, electronic equipment and a storage medium for determining a micro-grid power generation strategy, wherein the method comprises the following steps: acquiring a target output model corresponding to target power generation equipment in a target micro-grid; wherein the power generation plant comprises a photovoltaic plant and a gas turbine plant; determining an output prediction result corresponding to the target power generation equipment based on the target output model, and determining a target revenue model corresponding to the target micro-grid based on the processing prediction result; and determining a target power generation strategy corresponding to the target micro-grid based on the target revenue model. Based on the technical scheme, according to the processing model corresponding to each power generation device in the target micro-grid, the processing prediction result corresponding to the target micro-grid is determined, and then the power generation strategy corresponding to the target micro-grid is determined, so that the technical effect of improving the photovoltaic power generation utilization rate in the micro-grid is achieved.
Description
Technical Field
The invention relates to the field of optical energy storage micro-grids, in particular to a micro-grid power generation strategy determining method, a micro-grid power generation strategy determining device, electronic equipment and a storage medium.
Background
The main distributed power generation units in the micro grid are wind power plants, solar power plants, fuel cell power plants, micro gas (or diesel) power plants, and the like. The micro-grid organically combines a plurality of distributed power generation units and energy storage systems in a small area for cooperative control, so that the local distributed energy can be fully utilized, and the micro-grid has the advantages of economy, environmental protection, flexibility and reliability.
Solar energy is one of the renewable energy sources with the most development potential due to the characteristics of rich resources, wide distribution, cleanness and the like. However, the existing technical scheme cannot accurately predict the output of solar power generation, and cannot fully utilize the energy of solar power generation in the micro-grid, so that the benefit of the micro-grid is low.
Disclosure of Invention
The invention provides a method, a device, electronic equipment and a storage medium for determining a power generation strategy of a micro-grid, which are used for determining a processing prediction result corresponding to a target micro-grid through a processing model corresponding to each power generation equipment in the target micro-grid, further determining the power generation strategy corresponding to the target micro-grid, and achieving the technical effect of improving the photovoltaic power generation utilization rate in the micro-grid.
According to an aspect of the present invention, there is provided a micro-grid power generation strategy determination method, the method including:
acquiring a target output model corresponding to target power generation equipment in a target micro-grid; wherein the power generation plant comprises a photovoltaic plant and a gas turbine plant;
determining an output prediction result corresponding to the target power generation equipment based on the target output model, and determining a target revenue model corresponding to the target micro-grid based on the processing prediction result;
and determining a target power generation strategy corresponding to the target micro-grid based on the target revenue model.
According to another aspect of the present invention, there is provided a micro grid power generation strategy determination apparatus, the apparatus including:
the output model acquisition module is used for acquiring a target output model corresponding to target power generation equipment in the target micro-grid; wherein the power generation plant comprises a photovoltaic plant and a gas turbine plant;
the profit model determining module is used for determining an output prediction result corresponding to the target power generation equipment based on the target output model and determining a target profit model corresponding to the target micro-grid based on the processing prediction result;
And the power generation strategy determining module is used for determining a target power generation strategy corresponding to the target micro-grid based on the target profit model.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of determining a microgrid generation strategy according to any one of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to implement the method for determining a micro grid power generation strategy according to any embodiment of the present invention when executed.
According to the technical scheme, the target output model corresponding to the target power generation equipment in the target micro-grid is obtained, the output prediction result corresponding to the target power generation equipment is determined based on the target output model, the target gain model corresponding to the target micro-grid is determined based on the processing prediction result, and finally the target power generation strategy corresponding to the target micro-grid is determined based on the target gain model. Based on the technical scheme, according to the processing model corresponding to each power generation device in the target micro-grid, the processing prediction result corresponding to the target micro-grid is determined, and then the power generation strategy corresponding to the target micro-grid is determined, so that the technical effect of improving the photovoltaic power generation utilization rate in the micro-grid is achieved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a method for determining a power generation strategy of a micro-grid according to an embodiment of the present invention;
fig. 2 is a flowchart of a method for determining a power generation strategy of a micro-grid according to an embodiment of the present invention;
fig. 3 is a block diagram of a micro-grid power generation strategy determining device according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
Fig. 1 is a schematic flow chart of a method for determining a power generation policy of a micro-grid, which is provided by an embodiment of the present invention, where the method may be applicable to a case of determining a power generation policy matched with a micro-grid according to a power generation model of each power generation device in the micro-grid, where the method may be performed by a micro-grid power generation policy determining device, and the micro-grid power generation policy determining device may be implemented in a form of hardware and/or software, and the micro-grid power generation policy determining device may be configured in an electronic device, where the electronic device may be a terminal device or a server device, etc.
As shown in fig. 1, the method includes:
s110, acquiring a target output model corresponding to the target power generation equipment in the target micro-grid.
The target micro-grid can be a micro-grid selected by an operator according to requirements. The power generation plant includes a photovoltaic plant and a gas turbine plant. The target output model may be a power generation process model corresponding to the target power generation device.
Specifically, the target output model corresponding to the target power generation device in the target micro-grid may be obtained, for example, by selecting, by an operator, a micro-grid having the target power generation device from among all micro-grids to be selected in the target area as the target micro-grid, and exemplarily, obtaining all micro-grids in the target area and taking the micro-grids as the micro-grids to be selected, taking the micro-grid including the photovoltaic device as the target micro-grid, or by selecting, by an operator, the micro-grid including the photovoltaic device as the target micro-grid according to the requirement, and obtaining the target output model corresponding to the target power generation device in the target micro-grid.
It should be noted that, the power generation main body of the micro-grid mainly includes photovoltaic power generation, gas turbine power generation, energy storage system, etc., wherein, each power generation main body output has uncertainty, photovoltaic power generation uncertainty: the output curve of a photovoltaic power plant generally follows the Beta distribution, and the specific probability density function is as follows:
Wherein, alpha and Beta are respectively the shape parameters of Beta distribution; θ is the radiation intensity.
Wherein, mu and delta are respectively the mean value and the normal distribution value of solar radiation, and the probability of the solar radiation state can be calculated by the following formula. />Wherein θ c 、θ d The upper limit and the lower limit of the solar radiation degree theta are respectively calculated, solar radiation energy is converted into electric energy, and the photovoltaic output can be expressed by the following formula. P (P) PV t＝η PV ×S PV ×θ t The method comprises the steps of carrying out a first treatment on the surface of the Wherein eta pv Is conversion efficiency; s is S pv Is the total area of the photovoltaic module; θ t Is the solar radiation intensity at t.
Further, the power generation amount of the gas turbine power generation body is also affected to some extent based on the uncertain characteristics of the photovoltaic output. Accordingly, the power generation cost of the gas turbine is related to the output thereof, and the income is finally influenced, and the specific formula can be as follows: c (q (t))=a×q (t) 2 +b×q (t) +c; wherein, C (q (t)) is the gas turbine output cost; q (t) is the output of the gas turbine unit; a, b, c are constant coefficients, respectively. The output constraint of the gas turbine unit, a specific mathematical expression, is as follows: q (Q) min u(t)≤q(t)≤Q max u (t); wherein Q is min u (t) and Q max u (t) is the upper and lower limits of gas turbine output, respectively.
In the operation process of the energy storage equipment, the energy storage equipment is also affected by photovoltaic uncertainty, and is constrained by electric quantity and power, and the specific mathematical expression is as follows:
Wherein T is s The energy storage operation period is; mu (mu) n Taking 1 when charging and 0 when discharging as state variables; p (P) n Charging or discharging power for the energy storage battery; η (eta) c And eta d Respectively charging and discharging efficiency of the energy storage battery; e (E) min And E is max Respectively the upper limit and the lower limit of the electric quantity of the energy storage battery; p (P) B Is the upper limit of the output of the energy storage battery. For the uncertainty described above, the output of the power generation body needs to be predicted to determine its profit, and since the uncertainty of the micro grid containing the photovoltaic device is based on the photovoltaic uncertainty, and thus the output of the photovoltaic power generation is predicted based on the photovoltaic uncertainty.
S120, determining an output prediction result corresponding to the target power generation equipment based on the target output model, and determining a target benefit model corresponding to the target micro-grid based on the processing prediction result.
The output model may be an output mathematical model corresponding to each power generation device. The output prediction result may be understood as the amount of power generation corresponding to the target power generation device. The target revenue model may be a revenue mathematical model corresponding to the target microgrid.
Specifically, determining the output prediction result corresponding to the target power generation device based on the target output model, and determining the target revenue model corresponding to the target micro-grid based on the processing prediction result may be, for example, constructing the target revenue model corresponding to the target micro-grid based on the processing prediction result of the target power generation device.
On the basis of the technical scheme, the determining the output prediction result corresponding to the target power generation equipment based on the target output model comprises the following steps: determining a target eigenmode function corresponding to the target power generation device based on the target output model; and determining the output prediction result based on the target eigenmode function and a preset long-short-term memory network.
The preset long-term memory network can be a network model obtained through pre-training, and the weight coefficient in the preset long-term memory network is inertia weight. The target eigenmode function may be a mode function obtained by performing a variational modal decomposition on the original signal.
Specifically, the objective eigenmode function corresponding to the objective power generation equipment is determined based on the objective output model, and the output prediction result is determined based on the objective eigenmode function and a preset long-short-term memory network, for example, the output of photovoltaic power generation can be taken as an original signal x (t), the original signal x (t) is decomposed into a series of eigenmode functions, the decomposition precision of photovoltaic power generation can be improved, the information loss of the photovoltaic power generation power is avoided,wherein K represents the number of modes, A i (t) represents the magnitude of each eigenmode function, Φ i (t) represents the corresponding initial phase. Each eigenmode function can be regarded as a mode function signal u k (t) the process is as follows: 1) For the modal function signal u k (t) performing Hilbert transform to extract a single-side spectrum. />Where δ (t) represents the pulse function and j represents the imaginary part. And carrying out frequency estimation according to the modal function signal, and converting the unilateral frequency spectrum into a corresponding baseband. />The bandwidth of the eigenmode function is estimated by calculating the square of the mode function signal eigenmode function gradient modulus value.
Where k represents the number of eigenmode functions of the decomposition, u k Represents the kth eigenmode function, ω k Representing the corresponding center frequency. A penalty factor α and a lagrangian multiplier θ (t) are introduced. The extended lagrangian is expressed as:
finally, obtaining a modal function u through iterative updating k (t) and the corresponding center frequency ω k 。
It should be noted that if the original photovoltaic power generation power signal contains K characteristic frequencies, the number of eigenmode functions decomposed by the algorithm should be K. However, the number of eigenmode functions is typically set empirically. If the number of eigen-mode functions resolved by the algorithm is greater than the number of theoretical eigen-mode functions, it is shown that the different eigen-mode functions resolved will contain the same frequency components; if the number of eigenmode functions decomposed by the algorithm is smaller than the number of theoretical eigenmode functions, it is indicated that different frequency components exist in the same eigenmode function, and this situation results in that the photovoltaic power generation power signal cannot be effectively decomposed, so that the conventional decomposition mode needs to be optimized.
On the basis of the technical scheme, the determining the target eigenmode function corresponding to the target power generation equipment based on the target output model comprises the following steps: determining a target modal number corresponding to the target output model; acquiring a preset penalty factor interval and a preset value step length, and determining a target penalty factor based on the preset penalty factor interval, the preset value step length and the target eigen-mode function; the target eigenmode function is determined based on the target number of modes and the target penalty factor.
The target mode number may be a decomposition number corresponding to the output model, and may be a decomposition number obtained by processing the output model based on a preset algorithm. The preset penalty factor interval may be understood as a preset penalty factor value range. The preset value step size may be a step size between two adjacent penalty factors that are preset.
Specifically, determining the number of target modes corresponding to the target output model, acquiring a preset penalty factor interval and a preset value step length, determining a target penalty factor based on the preset penalty factor interval, the preset value step length and the target eigenmode function, and determining the target eigenmode function based on the number of target modes and the target penalty factor.
On the basis of the above technical solution, the determining the target modal number corresponding to the target output model includes: acquiring the frequency bandwidth corresponding to the target output model, and determining the characteristic frequency corresponding to the target output model; the target number of modes is determined based on the characteristic frequency and the frequency bandwidth.
Wherein the frequency bandwidth may be bandwidth information of an occupied frequency band of the signal. The characteristic frequency is understood to be a frequency characteristic, which may be, for example, a frequency spectrum density, an energy spectrum density, and a power spectrum density.
Specifically, the frequency bandwidth corresponding to the target output model is obtained, the characteristic frequency corresponding to the target output model is determined, and then the target mode number is determined based on the characteristic frequency and the frequency bandwidth.
On the basis of the above technical solution, the determining the target penalty factor based on the preset penalty factor interval, the preset valued step length and the target eigen-mode function includes: determining at least one penalty factor to be applied based on a preset penalty factor interval and a preset value step length; decomposing the target eigen functions based on the quantity of penalty factors to be applied to determine eigen mode functions to be processed; determining the target penalty factor based on the eigenmode function to be processed, the target output model, and the penalty factor to be applied.
Wherein the penalty factor to be applied may be a penalty factor determined from within a preset penalty factor interval. The number of penalty factors to be applied may be understood as the number of penalty factors to be applied.
Specifically, at least one penalty factor to be applied is determined based on a preset penalty factor interval and a preset value step length, the objective eigen function is decomposed based on the number of penalty factors to be applied to determine an eigen mode function to be processed, and the objective penalty factor is determined based on the eigen mode function to be processed, the objective output model and the penalty factors to be applied. It should be noted that, the penalty factor is another key parameter to be determined, which is related to the algorithm decomposition performance and the reconstruction performance, so that a determination method for optimizing the target penalty factor is required.
On the basis of the above technical solution, the determining the target penalty factor based on the eigen-mode function to be processed, the target output model and the penalty factor to be applied includes: determining a reconstruction performance value and a decomposition performance value corresponding to each penalty factor to be applied based on the eigen-mode function to be processed and the target output model; and determining a target penalty factor from the penalty factors to be applied based on the reconstruction performance value and the decomposition performance value.
Wherein the reconstruction performance value may be a parameter for quantifying the reconstruction performance of the penalty factor currently to be applied. Accordingly, the decomposition performance value may be a parameter for quantifying the decomposition performance at which the penalty is currently to be applied.
Specifically, a reconstruction performance value and a decomposition performance value corresponding to each penalty factor to be applied are determined based on the eigen-model to be processed and the target output model, and then a target penalty factor is determined from each penalty factor to be applied based on the reconstruction performance value and the decomposition performance value. Illustratively, if the decomposed eigenmode function contains main information of the original data of the photovoltaic power generation power, the eigenmode function is more similar to the original data of the photovoltaic power generation power, and the similarity can be reflected by mutual information. Thus, the algorithmic decomposition performance can be evaluated by mutual information. Setting the range alpha epsilon alpha of penalty factor by step size start ,α end ]Step length of K step The method comprises the steps of carrying out a first treatment on the surface of the Decomposing the original signal into K modes; obtaining mutual information MI between each eigenmode function and an original signal of the photovoltaic power generation power;find the local maximum of each penalty factor +.>Namely, the maximum mutual information value between each eigenmode function corresponding to the penalty factor to be applied at present and the original signal is used as the reconstruction performance value of the penalty factor to be applied at present. Further, assume that the original signal x (t) is decomposed into a set of eigenmode functions IMF i (i=1, 2, …, K). Where K represents the number of eigenmode functions, and the signal reconstruction is expressed as: x' (t) =imf 1 +，…，+IMF K The method comprises the steps of carrying out a first treatment on the surface of the The mutual information beta between the reconstructed signal x' (t) and the original signal x (t) reflects the reconstruction performance of the original signal. β=mi (x' (t), x (t)); within the range of penalty factors α, one can obtain: />β α The larger the reconstruction effect of the original signal is, the better. In summary, the optimal penalty factor is determined by a combination of decomposition performance and reconstruction performance.γ α Represents the overall evaluation factor. Finally, the optimal punishment factors are obtained as follows: alpha opt ＝argmax(γ α ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein alpha is opt The optimal penalty factor is represented, and the optimal penalty factor may be determined based on a reconstruction performance value and a decomposition performance value corresponding to each penalty factor to be applied, for example, a product of the reconstruction performance value and the decomposition performance value of each penalty factor to be applied may be determined, and the penalty factor to be applied with the largest product is taken as the target penalty factor.
S130, determining a target power generation strategy corresponding to the target micro-grid based on the target profit model.
The target power generation strategy may be a power generation amount corresponding to each power generation device corresponding to the micro grid.
Specifically, the target generating strategy corresponding to the target micro-grid is determined based on the target profit model, for example, the generating strategy corresponding to each generating device in the target micro-grid may be determined by solving based on the target profit model.
On the basis of the technical scheme, the determining the target power generation strategy corresponding to the target micro-grid based on the target benefit model comprises the following steps: determining an objective function based on the objective gain model, and acquiring a preset constraint condition corresponding to the objective micro-grid; and solving the objective function based on the preset constraint condition, and determining the objective power generation strategy.
The preset constraint conditions comprise at least two of unit output constraint, light rejection constraint, energy storage constraint, balance constraint and expected cost constraint. The objective function may be a function for determining a power generation strategy. The preset constraint condition may be a constraint condition corresponding to the micro grid.
Specifically, an objective function is determined based on the objective gain model, a preset constraint condition corresponding to the objective micro-grid is obtained, and then the objective function is solved based on the preset constraint condition, so that the objective power generation strategy is determined.
Illustratively, the profit model of the photo-electricity storage main body is set as follows according to the above obtained prediction result of the photovoltaic output:
wherein: the number of generated scenes is N, and the probability of the scene s is phi s The scheduling period interval is deltat, the total duration is T,at t time respectivelyPhotovoltaic reporting force; />Respectively the actual photovoltaic output of the micro-grid s at the scene t moment; p is p PV The electricity price of the pole is photovoltaic; ΔP PV，t，s Respectively the deviation of the planned output and the actual output of the micro-grid s under the scene t; p is p + And p - Reserve prices for up and down adjustments, respectively; f (x) is a corresponding piecewise function, as follows: />
The microgrid revenue model is as follows: under the s scene, each power generation main body takes the respective scheduling electric quantity as a strategy, namely: x is X PV，s And X MT，s° . The practical decision is constrained by the unit characteristics, the scene and the like, and the decision value is limited in a certain range, and is specifically as follows: Wherein: omega shape PV，s And omega Mr，s The strategy space of the light storage power generation main body and the gas turbine power generation main body under each constraint condition is respectively provided. And setting an objective function by taking the maximum gain module of the whole micro-grid as the objective. The best behavior strategy is sought in the respective behavior decision space. The revenue model for the entire microgrid is maximized.
The objective function is the maximum benefit of the micro-grid, and the specific formula is as follows:
further, according to a preset constraint condition, the stability of the operation of the micro-grid is ensured, and the constraint condition may include:
unit output constraint:wherein: />The maximum photovoltaic output force is obtained at the moment of a micro-grid s scene t; p (P) G,t,s The power transmitted to the micro-grid by the main grid at the t moment in the s scene of the micro-grid is obtained;is the highest output limit value of the main network.
Discarding the light constraint:
wherein mu APV Is the upper limit value of the light rejection rate.
Balance constraint:wherein: p (P) G,t,s And the power is transmitted to the micro-grid by the main grid at the t moment in the scene of the micro-grid s. In the above formula, the micro-grid needs to pay further attention to the power balance constraint of the micro-grid so as to meet the self-load demand. In order to ensure the safe and continuous operation of the whole micro-grid system, electricity is purchased from a main network, and corresponding standby service cost is paid.
Energy storage constraint, i.e. when the micro-grid energy storage is scheduled, a reasonable initial residual energy E is given 0 And after the charge and discharge operation in a scheduling period T, the residual energy is still E 0 . Therefore, the optimization solution of the model is facilitated, and the calculation result is effectively ensured to have time continuity, as follows:
P ess (t，s)＝E 0 ，t＝1，24
P ess (t，s)＝E 0 +(u ch P ch，t，s -(1/u dis )P dis，ts )Δt-P loss (t，s)+P ess (t-1，s)，2≤t≤23
P lass (t，s)＝μ loss P ess (t，s)
0≤I ch (t，s)+I dis (t，s)≤1
wherein: p (P) ch，t，s And P dis，t，s Respectively charging and discharging power at t moment under the energy storage s scene; p (P) ess (t, s) and P loss (t, s) are respectively the residual electric quantity and the lost electric quantity of the energy storage system at the moment t under the energy storage s scene; μ lass 、u ch and u dis The lower limit of the climbing rate, the upper limit of the climbing rate, the energy loss rate, the charging efficiency and the discharging efficiency of the energy storage system are respectively set; />And->Rated, minimum and maximum capacity of the energy storage system, respectively; i ch (t, s) and I dis And (t, s) is a 0-1 variable, and represents the charge and discharge states of the energy storage system in a t period under the s scene. Wherein, when I ch When (t, s) is 1, the energy storage system is in a charged state, when I ch (t, s) when 0, the energy storage system is in an uncharged state; i dis When (t, s) is 1, the energy storage system is in a discharge state, I dis And (t, s) is 0, and the energy storage system is in a non-discharge state.
And cost constraints:
and solving a gain model of the micro-grid by adopting a firefly algorithm based on the constraint conditions. The firefly algorithm characterizes the objective function with the firefly algorithm to complete the optimizing process. In the process, each feasible point in the search space is characterized by a firefly individual; characterizing the search and optimizing iteration process by using the moving behavior of fireflies and the attraction characteristics between fireflies; quantifying an objective function of a problem to be solved according to the difference degree of the positions of the cocoon firefly individuals; the principle of the winner and the worse of the cocoon fireinsects is used as one iteration of the optimizing process. Some unnecessary behavior characteristics are abandoned while the common luminescence and the chase process of the cocoon fireworm are simulated.
The firefly algorithm is based on the following rules: the cocoon fireworm individuals are monoscopic, and there is no strong correlation between the mutual attraction characteristics and the sex. The more bright the cocoon firefly individual, the greater the appeal to other individuals. The high brightness cocoon firefly attracts surrounding individuals to move toward themselves. When the brightness of two cocoon fireworm individuals is equal, random movement is carried out respectively. The attractive force is inversely proportional to the distance. The brightness of the cocoon firefly depends on the objective function of the given problem. Brightness and attractiveness are two main parameters in the FA, wherein the brightness characterizes the merits of the objective function value, and the fireflies with higher brightness can attract peripheral fireflies to adjust the action direction, as follows:
wherein: i 0 Is the brightest firefly (i.e., itself) brightness, where r=0; gamma is the light absorption coefficient used for representing light characteristics of fireflies, and is used as a constant variable in the problem, and the brightness of the fireflies gradually decreases when the distance is long; r is (r) ij Is the Cartesian distance between fireflies i and j, as follows: fireflies with higher brightness have higher attractions, where the attractions are expressed as follows: />Wherein: beta 0 Is the maximum (i.e., self) degree of attraction. In the iterative process, the position update rule of each individual is as follows: x is x i ＝x i +β×(x j -x i ) +α× (rand-1/2); wherein: alpha and rand are disturbance random parameters used for ensuring that the algorithm has good global searching capability. The brightness represents the quality of the current feasible solution, and the objective function value represented by the brightest firefly individual is the optimal solution in the current iteration. I.e. the optimal behavior strategy. During each iteration, if an individual is found to be brighter than itself around the perimeter, that individual will move in a brighter direction. Meanwhile, the cocoon worm algorithm defines one feasible solution of the objective function as a cocoon worm, the function can take any value in a feasible solution interval, then the control variable value is the position of the cocoon worm, the fitness function value of the feasible solution is defined as the brightness of a corresponding firefly, the process of solving the optimal solution is defined as the pursuit behavior of the cocoon worm, and meanwhile, the brightness of the cocoon worm can be correspondingly changed in the searching and pursuit processes.
According to the technical scheme, the target output model corresponding to the target power generation equipment in the target micro-grid is obtained, the output prediction result corresponding to the target power generation equipment is determined based on the target output model, the target gain model corresponding to the target micro-grid is determined based on the processing prediction result, and finally the target power generation strategy corresponding to the target micro-grid is determined based on the target gain model. Based on the technical scheme, according to the processing model corresponding to each power generation device in the target micro-grid, the processing prediction result corresponding to the target micro-grid is determined, and then the power generation strategy corresponding to the target micro-grid is determined, so that the technical effect of improving the photovoltaic power generation utilization rate in the micro-grid is achieved.
Example two
Fig. 2 is a flowchart of a method for determining a power generation strategy of a micro-grid according to an embodiment of the present invention, where the method for determining a power generation strategy of a micro-grid is further optimized on the basis of the above embodiment. The specific implementation manner can be seen in the technical scheme of the embodiment. Wherein, the technical terms identical to or corresponding to the above embodiments are not repeated herein.
As shown in fig. 2, the method of the embodiment of the present invention includes:
determining a target output model: the obtaining of the target output model corresponding to the target power generation device in the target micro-grid may be, for example, that an operator selects a micro-grid with the target power generation device from among the micro-grids to be selected in the target area as the target micro-grid, and as an example, all micro-grids in the target area are obtained and taken as the micro-grids to be selected, and a micro-grid containing the photovoltaic device is taken as the target micro-grid, or that an operator selects a micro-grid containing the photovoltaic device as the target micro-grid according to the requirement and obtains the target output model corresponding to the target power generation device in the target micro-grid.
Determining a target profit model and determining a target power generation strategy: specifically, an original signal x (t) is input, the number of modes K is optimized, and then the characteristic frequency is extracted: (f) 1 ,f 2 ,…f K ) =fft (x (t)); dividing the frequency band according to the characteristic frequency; determining the number of frequency bands; acquiring the optimized mode quantity K opt The method comprises the steps of carrying out a first treatment on the surface of the Further, optimizing a penalty factor alpha; setting a punishment factor range: alpha epsilon alpha start ，α end ]The method comprises the steps of carrying out a first treatment on the surface of the Setting the step length to alpha step ；
Forα＝α start ：α step ：α end ；Obtaining comprehensive parameters: />And further determining an optimal penalty factor based on the integrated parameters:α opt ＝argmax(γ α ) Outputting the result IMFS of decomposing the eigenmode function opt (t)＝VMD(x(t)，K opt ，α opt ). And after the eigenmode function is obtained, predicting the photovoltaic power generation power fluctuation component represented by the eigenmode function through a long-term and short-term memory network.
It should be noted that the long-term and short-term memory network algorithm is as follows:
f(t)＝σ(W f ·[h t-1 ，x t ]+b f )
i(t)＝σ(W i ·[h t-1 ，x t ]+b i )
O t ＝σ(W O ·[h t-1 ，x t ]+b o )
h(t)＝O t *tanh(C t )
wherein σ is a sigmoid function, and the control information passes through the state. When σ is 0, any information cannot pass; when σ is 1, all information can pass. W (W) f ，W i ，W C And W is o Representing the input weights. Corresponding b f ，b i ，b C And b o Representing the bias. t and t-1 represent the current and previous time states. x and h represent inputs and outputs, and C represents the neuron state. The long-term and short-term memory network algorithm comprises some key parameters: neuron number, hidden layer iteration number, and learning rate. The number of neurons is related to the fitting capacity of the long-term and short-term memory network algorithm, the iteration number is related to the training effect of the algorithm, and the learning rate is related to the convergence performance of the algorithm. However, these parameters are usually set empirically, and this method of setting parameters results in that the long-term memory network prediction model is not easy to obtain a good prediction effect. Therefore, the long-term memory network algorithm is further optimized, and the inertia weight is set to replace the weight coefficient in the long-term memory network. The inertial weight varies with the number of iterations and the position of the particles. The global searching capability and the local searching capability of the algorithm are enhanced, and the optimizing precision and the convergence performance of the algorithm are improved. The inertia weight w is expressed as: Wherein W is max Represents the maximum inertial weight, w min Representing the minimum inertial weight, t max Representing the maximum number of iterations. The method for setting the inertia weight can enable the algorithm to have larger inertia weight to enhance global searching capability in the initial stage of the particle swarm algorithm, enable particles to traverse the whole space in the early stage of searching, enable the algorithm to have smaller inertia weight to enhance local searching performance in the later stage of the particle swarm algorithm, and improve convergence rate. The original photovoltaic power generation power is decomposed into a series of eigenmode functions s (eigenmode functions 1, …, IMF) N ) And predicting the fluctuation component represented by each eigenmode function by applying a long-term and short-term memory network prediction model. Overlapping the prediction results of each fluctuation component to obtain a final prediction result, as follows: PV (photovoltaic) system final ＝ILSTM(IMF 1 )+，…，+ILSTM(IMF N ) The method comprises the steps of carrying out a first treatment on the surface of the In PV (photovoltaic) final Representing the final prediction result of the photovoltaic power generation power, I long-short term memory network (eigenmode function 1 ) Representing the application of a long-short term memory network prediction model to a first eigenmode function IMF 1 Prediction results of prediction output of the expressed fluctuation component, ILSTM (IMF N ) Representing the application of a long-short term memory network prediction model to the last eigenmode function IMF N Prediction of the fluctuation component of a representation And outputting a prediction result.
And determining a target profit model based on the prediction result, setting a target function based on the maximum profit of the whole micro-grid as a target, solving the target function based on preset conditions and a firefly algorithm, and determining a target power generation strategy.
According to the technical scheme, the target output model corresponding to the target power generation equipment in the target micro-grid is obtained, the output prediction result corresponding to the target power generation equipment is determined based on the target output model, the target gain model corresponding to the target micro-grid is determined based on the processing prediction result, and finally the target power generation strategy corresponding to the target micro-grid is determined based on the target gain model. Based on the technical scheme, according to the processing model corresponding to each power generation device in the target micro-grid, the processing prediction result corresponding to the target micro-grid is determined, and then the power generation strategy corresponding to the target micro-grid is determined, so that the technical effect of improving the photovoltaic power generation utilization rate in the micro-grid is achieved.
Example III
Fig. 3 is a block diagram of a device for determining a power generation strategy of a micro-grid according to an embodiment of the present invention. As shown in fig. 3, the apparatus includes: an output model acquisition module 310, a revenue model determination module 320, and a power generation strategy determination module 330.
The output model obtaining module 310 is configured to obtain a target output model corresponding to a target power generation device in a target micro-grid; wherein the power generation plant comprises a photovoltaic plant and a gas turbine plant;
a revenue model determination module 320 configured to determine an output prediction result corresponding to the target power generation device based on the target output model, and determine a target revenue model corresponding to the target micro-grid based on the processing prediction result;
the power generation strategy determining module 330 is configured to determine a target power generation strategy corresponding to the target micro-grid based on the target revenue model.
On the basis of the technical scheme, the benefit model determining module is used for determining a target eigenmode function corresponding to the target power generation equipment based on the target output model; and determining the output prediction result based on the target eigenmode function and a preset long-short-term memory network, wherein a weight coefficient in the preset long-short-term memory network is an inertial weight.
On the basis of the technical scheme, the benefit model determining module is used for determining the target mode quantity corresponding to the target output model; acquiring a preset penalty factor interval and a preset value step length, and determining a target penalty factor based on the preset penalty factor interval, the preset value step length and the target eigen-mode function; the target eigenmode function is determined based on the target number of modes and the target penalty factor.
On the basis of the technical scheme, the benefit model determining module is used for acquiring the frequency bandwidth corresponding to the target output model and determining the characteristic frequency corresponding to the target output model; the target number of modes is determined based on the characteristic frequency and the frequency bandwidth.
On the basis of the technical scheme, the profit model determining module is used for determining at least one penalty factor to be applied based on a preset penalty factor interval and a preset value step length; decomposing the target eigen functions based on the quantity of penalty factors to be applied to determine eigen mode functions to be processed; determining the target penalty factor based on the eigenmode function to be processed, the target output model, and the penalty factor to be applied.
On the basis of the technical scheme, the profit model determining module is used for determining a reconstruction performance value and a decomposition performance value corresponding to each penalty factor to be applied based on the eigen-mode function to be processed and the target output model; and determining a target penalty factor from the penalty factors to be applied based on the reconstruction performance value and the decomposition performance value.
On the basis of the technical scheme, the power generation strategy determining module is used for determining an objective function based on the objective gain model and acquiring a preset constraint condition corresponding to the objective micro-grid; the preset constraint conditions comprise at least two of unit output constraint, light rejection constraint, energy storage constraint, balance constraint and expected cost constraint; and solving the objective function based on the preset constraint condition, and determining the objective power generation strategy.
According to the technical scheme, the target output model corresponding to the target power generation equipment in the target micro-grid is obtained, the output prediction result corresponding to the target power generation equipment is determined based on the target output model, the target gain model corresponding to the target micro-grid is determined based on the processing prediction result, and finally the target power generation strategy corresponding to the target micro-grid is determined based on the target gain model. Based on the technical scheme, according to the processing model corresponding to each power generation device in the target micro-grid, the processing prediction result corresponding to the target micro-grid is determined, and then the power generation strategy corresponding to the target micro-grid is determined, so that the technical effect of improving the photovoltaic power generation utilization rate in the micro-grid is achieved.
The micro-grid power generation strategy determining device provided by the embodiment of the invention can execute the micro-grid power generation strategy determining method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the executing method.
Example IV
Fig. 4 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 4, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as the microgrid generation strategy determination method.
In some embodiments, the microgrid generation policy determination method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the microgrid generation policy determination method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the microgrid generation policy determination method by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. A microgrid generation strategy determination method, characterized by comprising:
acquiring a target output model corresponding to target power generation equipment in a target micro-grid; wherein the power generation plant comprises a photovoltaic plant and a gas turbine plant;
determining an output prediction result corresponding to the target power generation equipment based on the target output model, and determining a target revenue model corresponding to the target micro-grid based on the processing prediction result;
And determining a target power generation strategy corresponding to the target micro-grid based on the target revenue model.
2. The method of claim 1, wherein the determining an output prediction corresponding to the target power generation device based on the target output model comprises:
determining a target eigenmode function corresponding to the target power generation device based on the target output model;
and determining the output prediction result based on the target eigenmode function and a preset long-short-term memory network, wherein a weight coefficient in the preset long-short-term memory network is an inertial weight.
3. The method of claim 2, wherein the determining a target eigenmode function corresponding to the target power generation device based on the target output model comprises:
determining a target modal number corresponding to the target output model;
acquiring a preset penalty factor interval and a preset value step length, and determining a target penalty factor based on the preset penalty factor interval, the preset value step length and the target eigen-mode function;
the target eigenmode function is determined based on the target number of modes and the target penalty factor.
4. A method according to claim 3, wherein said determining a target number of modalities corresponding to the target output model comprises:
acquiring the frequency bandwidth corresponding to the target output model, and determining the characteristic frequency corresponding to the target output model;
the target number of modes is determined based on the characteristic frequency and the frequency bandwidth.
5. A method according to claim 3, wherein said determining a target penalty factor based on said preset penalty factor interval, said preset valued step size and said target eigenmode function comprises:
determining at least one penalty factor to be applied based on a preset penalty factor interval and a preset value step length;
decomposing the target eigen functions based on the quantity of penalty factors to be applied to determine eigen mode functions to be processed;
determining the target penalty factor based on the eigenmode function to be processed, the target output model, and the penalty factor to be applied.
6. The method of claim 5, wherein said determining said target penalty factor based on said eigenmode function to be processed, said target output model, and said penalty factor to be applied comprises:
Determining a reconstruction performance value and a decomposition performance value corresponding to each penalty factor to be applied based on the eigen-mode function to be processed and the target output model;
and determining a target penalty factor from the penalty factors to be applied based on the reconstruction performance value and the decomposition performance value.
7. The method of claim 1, the determining a target power generation strategy corresponding to the target microgrid based on the target revenue model, comprising:
determining an objective function based on the objective gain model, and acquiring a preset constraint condition corresponding to the objective micro-grid; the preset constraint conditions comprise at least two of unit output constraint, light rejection constraint, energy storage constraint, balance constraint and expected cost constraint;
and solving the objective function based on the preset constraint condition, and determining the objective power generation strategy.
8. A microgrid generation strategy determination device, characterized by comprising:
the output model acquisition module is used for acquiring a target output model corresponding to target power generation equipment in the target micro-grid; wherein the power generation plant comprises a photovoltaic plant and a gas turbine plant;
The profit model determining module is used for determining an output prediction result corresponding to the target power generation equipment based on the target output model and determining a target profit model corresponding to the target micro-grid based on the processing prediction result;
and the power generation strategy determining module is used for determining a target power generation strategy corresponding to the target micro-grid based on the target profit model.
9. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the microgrid generation policy determination method of any one of claims 1-7.
10. A computer readable storage medium storing computer instructions for causing a processor to implement the microgrid generation policy determination method of any one of claims 1-7 when executed.
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