CN116388268A - Method and device for reporting daily power calculation of optical-storage station - Google Patents

Method and device for reporting daily power calculation of optical-storage station Download PDF

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CN116388268A
CN116388268A CN202310254716.3A CN202310254716A CN116388268A CN 116388268 A CN116388268 A CN 116388268A CN 202310254716 A CN202310254716 A CN 202310254716A CN 116388268 A CN116388268 A CN 116388268A
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reporting
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photovoltaic power
scene
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刘淑军
王博
王金仕
庄炜焕
姜添元
章超
王世静
古含
赵伟然
卜晓坤
王存
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China Three Gorges Renewables Group Co Ltd
Electric Power Planning and Engineering Institute Co Ltd
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Electric Power Planning and Engineering Institute Co Ltd
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    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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Abstract

The invention discloses a method and a device for reporting daily power calculation of an optical-storage station, wherein the method comprises the following steps: acquiring errors of solar photovoltaic power predicted values and historical predicted data; based on the error, constructing a solar photovoltaic power scene set in a general distribution fitting mode; constructing an intra-day light-stored power reporting model of an intra-day t moment meter and energy storage correction according to an intra-day photovoltaic power scene set; and calculating the daily power of the light-storage station based on the daily light-storage power reporting model. And constructing a content photovoltaic power scene by a general distribution and Latin hypercube sampling method, thereby establishing a power optimization reporting model of the light-storage station, formulating a reporting strategy and providing support for the economy and the robustness of the operation of the light-storage station.

Description

Method and device for reporting daily power calculation of optical-storage station
Technical Field
The invention relates to the technical field of new energy station control, in particular to a method and a device for reporting daily power calculation of an optical-storage station.
Background
Photovoltaic is used as a representative new energy source for power generation, and because of the randomness and volatility characteristics of the photovoltaic, the photovoltaic is connected into a power grid in a large scale and simultaneously brings load to the power and electricity balance of the system. Therefore, how to promote the safe absorption of the photovoltaic and improve the economy of the photovoltaic station become a current hot research topic. On the one hand, the power grid in each place has issued new rules (hereinafter referred to as two rules) for implementing grid-connected operation management and auxiliary service management of the power plant, and the photovoltaic power station is prompted to promote the forecasting accuracy from the perspective of forecasting and reporting. The method comprises the steps of calculating the predicted power reported to the power grid by the photovoltaic power station and calculating the deviation electric quantity, so that assessment is carried out, and the deviation punishment cost is charged for the photovoltaic power station. On the other hand, the photovoltaic power station is provided with a distributed energy storage forming light-storage system, so that the photovoltaic fluctuation can be stabilized, and a margin is provided for the arrangement of reported power. In order to improve the overall operation economy of the photovoltaic power station, the report power of the optimized light-storage station needs to be calculated and reported effectively, so that the serious loss caused by the problems is avoided.
Disclosure of Invention
The embodiment of the invention aims to provide a method and a device for calculating and reporting daily power of an optical-storage station, which are used for constructing a content photovoltaic power scene in a general distribution mode so as to establish an optical-storage station power optimization reporting model and formulate a reporting strategy to provide support for the economical efficiency and the robustness of the operation of the optical-storage station.
To solve the above technical problems, a first aspect of the embodiments of the present invention provides a method for reporting daily power calculation of an optical-storage station, including the following steps:
acquiring errors of solar photovoltaic power predicted values and historical predicted data;
based on the error, constructing a solar photovoltaic power scene set in a general distribution fitting mode;
constructing an intra-day light-stored power reporting model of an intra-day t moment meter and energy storage correction according to the intra-day photovoltaic power scene set;
and calculating and reporting the daily power of the light-storage station based on the daily light-storage power reporting model.
Further, the construction of the intra-day photovoltaic power scene set by the general distribution fitting method comprises the following steps:
acquiring solar photovoltaic power predicted values of a plurality of steps based on a preset time interval;
wherein the prediction error e of the s-th step s Fitting the cumulative probability function of (e) by a general distribution s The calculation formula of (2) is as follows:
Figure BDA0004129154570000021
Figure BDA0004129154570000022
wherein alpha, beta and gamma are generalQ is a given confidence level with the shape parameter of the distribution; f (F) s (.) is a general distribution function of the prediction error of the s-th step,
Figure BDA0004129154570000024
is a corresponding general distribution inverse function.
Further, the shape parameters of the general distribution are obtained by calculating error accumulation probability functions under each prediction time step based on a nonlinear least square method.
Further, the construction of the intra-day photovoltaic power scene set by the general distribution fitting method further comprises:
generating an ith prediction error scene deltap in step s by Latin hypercube sampling s,i ，Δp s,i The calculation formula of (2) is as follows:
Figure BDA0004129154570000023
wherein: n (N) I Is the total scene number;
obtaining a prediction error scene set delta p of the s-th step in the historical prediction data s,i And then, superposing the solar photovoltaic power prediction value with the solar photovoltaic power prediction value of the s-th step at the time t in the day to obtain the solar photovoltaic power scene set:
Figure BDA0004129154570000031
wherein: p (P) t,s,i The photovoltaic power of the ith scene in the s-th step at the moment t.
Further, the t time is N in total s The objective function of the intra-day light-stored power reporting model for =16 steps is as follows:
Figure BDA0004129154570000032
wherein: p (P) total Punishment is carried out on the total check electric quantity in the day;
Figure BDA00041291545700000318
and (5) checking the electric quantity in the day under the ith scene.
Further, the energy storage constraint condition in the objective function includes:
Figure BDA0004129154570000033
Figure BDA0004129154570000034
Figure BDA0004129154570000035
Figure BDA0004129154570000036
Figure BDA0004129154570000037
wherein:
Figure BDA0004129154570000038
and->
Figure BDA0004129154570000039
Respectively storing energy discharging and charging state binary variables in the s step; />
Figure BDA00041291545700000310
And->
Figure BDA00041291545700000311
Discharging and charging power for the s-th energy storage; />
Figure BDA00041291545700000312
For storing energy in step s, E stomax Is the total energy stored; η (eta) c 、η d Mu for charging and discharging efficiency up 、μ down The upper and lower limit coefficients of the stored energy; mu (mu) deep Is the depth of discharge coefficient.
Further, the calculation formula of the assessment electric quantity and the reporting accuracy in each scene is as follows:
Figure BDA00041291545700000313
Figure BDA00041291545700000314
Figure BDA00041291545700000315
wherein:
Figure BDA00041291545700000316
the photovoltaic power scene value after energy storage correction in the ith scene of the s step is obtained; accu (Accu) i Reporting accuracy rate for the ith scene; />
Figure BDA00041291545700000317
Reporting power for the 16 th point of the s step; cap is the installed capacity of the photovoltaic power station.
Further, the decision variable set of the daily light-storage power reporting model is shown as follows;
Figure BDA0004129154570000041
the method comprises the step of reporting the power value in the step s and the energy storage related variable.
Further, the daily light-storage power reporting model is solved by adopting a particle swarm algorithm, wherein the speed and the position of particles are updated as follows:
Figure BDA0004129154570000042
wherein:
Figure BDA0004129154570000043
and->
Figure BDA0004129154570000044
The speeds of the particles n at the kth and next iteration, respectively; />
Figure BDA0004129154570000045
And->
Figure BDA0004129154570000046
The position of the particle n in the kth and next iteration, respectively; ρ is an inertial weight; l1 and l 2 Is [0,1]The random numbers which are uniformly distributed are taken in the range; c 1 And c2 is a learning constant; o (o) n The position corresponding to the individual extremum of the particle i; g is the position corresponding to the global extremum.
Accordingly, a second aspect of the embodiment of the present invention provides an apparatus for reporting intra-day power calculation of an optical-storage station, including:
the error calculation module is used for acquiring errors of the solar photovoltaic power predicted value and the historical predicted data;
the scene construction module is used for constructing a solar photovoltaic power scene set in a general distribution fitting mode based on the error;
the model construction module is used for constructing an intra-day light-stored power reporting model of the intra-day time t moment meter and the energy storage correction according to the intra-day photovoltaic power scene set;
and the power calculation module is used for calculating and reporting the daily power of the light-storage station based on the daily light-storage power reporting model.
Accordingly, a third aspect of the embodiment of the present invention provides an electronic device, including: at least one processor; and a memory coupled to the at least one processor; the memory stores instructions executable by the one processor, and the instructions are executed by the one processor, so that the at least one processor executes the method for reporting the daily power calculation of the optical-storage station.
Accordingly, a fourth aspect of the embodiments of the present invention provides a computer-readable storage medium having stored thereon computer instructions which, when executed by a processor, implement the above-described method for reporting intra-day power calculations for an optical-storage station.
The technical scheme provided by the embodiment of the invention has the following beneficial technical effects:
and constructing a content photovoltaic power scene by a general distribution and Latin hypercube sampling method, thereby establishing a power optimization reporting model of the light-storage station, formulating a reporting strategy and providing support for the economy and the robustness of the operation of the light-storage station.
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FIG. 1 is a flowchart of a method for reporting daily power calculation of an optical-storage station according to an embodiment of the present invention;
FIG. 2 is a logic step diagram of reporting daily power calculation of an optical-storage station according to an embodiment of the present invention;
fig. 3 is a block diagram of an apparatus for reporting daily power calculation in an optical-storage station according to an embodiment of the present invention.
Reference numerals:
1. the system comprises an error calculation module, a scene construction module, a model construction module, a power calculation module and a model construction module.
Detailed Description
The objects, technical solutions and advantages of the present invention will become more apparent by the following detailed description of the present invention with reference to the accompanying drawings. It should be understood that the description is only illustrative and is not intended to limit the scope of the invention. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to unnecessarily obscure the present invention.
Referring to fig. 1, a first aspect of the embodiment of the present invention provides a method for reporting daily power calculation of an optical-storage station, including the following steps:
and step S100, acquiring errors of the solar photovoltaic power predicted value and the historical predicted data.
And step S200, constructing a solar photovoltaic power scene set in a general distribution fitting mode based on the error.
And step S300, constructing an intra-day light-stored power reporting model of the intra-day time t moment and the energy storage correction according to the intra-day photovoltaic power scene set.
Step S400, calculating and reporting the daily power of the light-storage station based on the daily light-storage power reporting model.
Specifically, in step S100, the solar ultra-short term photovoltaic power prediction is to predict the photovoltaic power of 16 steps (taking 15min as the time resolution) for four hours, which is obtained by the model prediction shown in the following formula:
Figure BDA0004129154570000061
wherein: n (N) s The number of predicted steps is 16 steps; n (N) Tdown And N Tup In order to predict/report the starting and ending time, the invention takes N to consider that the photovoltaic is only output in daytime Tdown At 8, N Tup 20 times; h (-) represents a prediction model, and beta is a trained prediction model parameter; z is Z t As an input vector for intra-day t-time prediction,
Figure BDA0004129154570000064
and the predicted value of the photovoltaic power is the step s at the moment t. And counting the historical 16-step predicted power and the corresponding actual power, and obtaining a predicted error of the historical photovoltaic power by the following formula:
Figure BDA0004129154570000062
wherein: n (N) d Lumped days for training; z is Z d,t The input vector is the t time of the history day d; p is p d,t,s 、e d,t,s Real corresponding to the s-th step prediction time of the t-th time of the history day d respectivelyAnd the actual photovoltaic power and the prediction error.
Specifically, in step S200, a solar photovoltaic power scene set is constructed by a general distribution fitting method, which includes:
acquiring solar photovoltaic power predicted values of a plurality of steps based on a preset time interval;
wherein the prediction error e of the s-th step s Fitting the cumulative probability function of (e) by a general distribution s The calculation formula of (2) is as follows:
Figure BDA0004129154570000063
Figure BDA0004129154570000071
where α, β and γ are shape parameters of a general distribution, q is a given confidence level (probability value); f (F) s (.) is a general distribution function of the prediction error of the s-th step,
Figure BDA0004129154570000077
is a corresponding general distribution inverse function.
Further, the shape parameter of the general distribution in step S200 is obtained by calculating the cumulative probability function of the error under each prediction time step based on the nonlinear least square method.
Further, in step S200, the intra-day photovoltaic power scene set is constructed by means of general distribution fitting, and further includes:
generating an ith prediction error scene deltap in step s by Latin hypercube sampling s,i ，Δp s,i The calculation formula of (2) is as follows:
Figure BDA0004129154570000072
wherein: n (N) I Is the total scene number;
obtaining a prediction error scene set of the s-th step in the historical prediction dataΔp s,i And then, superposing the solar photovoltaic power prediction value with the solar photovoltaic power prediction value of the s-th step at the time t in the day to obtain a solar photovoltaic power scene set:
Figure BDA0004129154570000073
wherein: p (P) t,s,i The photovoltaic power of the ith scene in the s-th step at the moment t.
Further, the t time is N in total s The objective function of the intra-day light-stored power reporting model for =16 steps is as follows:
Figure BDA0004129154570000074
wherein: p (P) total Punishment is carried out on the total check electric quantity in the day;
Figure BDA0004129154570000075
and (5) checking the electric quantity in the day under the ith scene.
Furthermore, the energy storage constraints in the objective function include:
Figure BDA0004129154570000076
Figure BDA0004129154570000081
Figure BDA0004129154570000082
Figure BDA0004129154570000083
Figure BDA0004129154570000084
wherein:
Figure BDA0004129154570000085
and->
Figure BDA0004129154570000086
Respectively storing energy discharging and charging state binary variables in the s step; p (P) s disch And P s ch Discharging and charging power for the s-th energy storage; />
Figure BDA0004129154570000087
For storing energy in step s, E stomax Is the total energy stored; η (eta) c 、η d Mu for charging and discharging efficiency up 、μ down The upper and lower limit coefficients of the stored energy; mu (mu) deep Is the depth of discharge coefficient.
Further, the calculation formula of the assessment electric quantity and the reporting accuracy in each scene is as follows:
Figure BDA0004129154570000088
Figure BDA0004129154570000089
Figure BDA00041291545700000810
wherein:
Figure BDA00041291545700000811
the photovoltaic power scene value after energy storage correction in the ith scene of the s step is obtained; accu (Accu) i Reporting accuracy rate for the ith scene; p (P) s repo Reporting power for the 16 th point of the s step; cap is the installed capacity of the photovoltaic power station.
Specifically, the decision variable set of the solar light-storage power reporting model is shown below;
Figure BDA00041291545700000817
the method comprises the step of reporting the power value in the step s and the energy storage related variable.
Further, the solar light-storage power reporting model is solved by adopting a particle swarm algorithm, wherein the speed and the position of particles are updated as follows:
Figure BDA00041291545700000812
wherein:
Figure BDA00041291545700000813
and->
Figure BDA00041291545700000814
The speeds of the particles n at the kth and next iteration, respectively; />
Figure BDA00041291545700000815
And->
Figure BDA00041291545700000816
The position of the particle n in the kth and next iteration, respectively; ρ is an inertial weight; l (L) 1 And l 2 Is [0,1]The random numbers which are uniformly distributed are taken in the range; c 1 And c 2 Is a learning constant; o (o) n The position corresponding to the individual extremum of the particle i; g is the position corresponding to the global extremum.
Referring to fig. 2, in a specific embodiment of the present invention, the overall solution flow of the above-mentioned calculation reporting method is as follows:
step 1: and (5) statistics of solar photovoltaic power predicted values and historical prediction errors. The photovoltaic power of 16 steps (with 15min as time resolution) for four hours is predicted as a solar ultra-short term photovoltaic power prediction, which is obtained by predicting a trained model as shown in the formula (1), and is regarded as a known quantity in the invention:
Figure BDA0004129154570000091
wherein: n (N) s The number of predicted steps is 16 steps; n (N) Tdown And N Tup In order to predict/report the starting and ending time, the invention takes N to consider that the photovoltaic is only output in daytime Tdown At 8, N Tup 20 times; h (-) represents a prediction model, and beta is a trained prediction model parameter; z is Z t As an input vector for intra-day t-time prediction,
Figure BDA0004129154570000092
and the predicted value of the photovoltaic power is the step s at the moment t. And (3) counting the historical 16-step predicted power and the corresponding actual power, wherein the predicted error of the historical photovoltaic power can be obtained by the formula (2):
e d,t,s ＝p d,t,s -H(Z d,t ,β),d＝1,2,...,N d (2)
wherein: n (N) d Lumped days for training; z is Z d,t The input vector is the t time of the history day d; p is p d,t,s 、e d,t,s The actual photovoltaic power and the prediction error corresponding to the s-th prediction time of the t-th time of the historical day d are respectively obtained.
Step 2: and constructing a solar photovoltaic power scene set. Based on step 1, the prediction error e of the s-th step s Can be fitted by a general distribution shown in equation (3):
Figure BDA0004129154570000093
Figure BDA0004129154570000094
wherein: alpha, beta and gamma are shape parameters of the general distribution; q is a given confidence level(probability value); f (F) s (.) is a general distribution function of the prediction error of the s-th step,
Figure BDA0004129154570000095
is a corresponding general distribution inverse function. Based on equation (3), a nonlinear least square method may be used to determine the general distribution shape parameters corresponding to the error CDF at each predicted time step. Further, latin hypercube sampling is used for the ith prediction error scene delta p in the s-th step s，i Is generated as shown in formula (5):
Figure BDA0004129154570000101
wherein: n (N) I Is the total scene number. Obtaining a prediction error scene set delta p of the historical step s s,i And then, superposing the photovoltaic power with the s-th predicted power at the time t in the day to obtain a photovoltaic power scene set in the day, wherein the photovoltaic power scene set in the day is shown as a formula (6):
Figure BDA0004129154570000102
wherein: p (P) t,s,i The photovoltaic power of the ith scene in the s-th step at the moment t.
Step 3: and constructing an intra-day light-stored power reporting model of the energy storage correction according to the time t in the day. Total N at time t s The objective function of the intra-day light-stored power reporting model for =16 steps is as follows:
Figure BDA0004129154570000103
wherein: p (P) total Punishment is carried out on the total check electric quantity in the day; p (P) i id And (5) checking the electric quantity in the day under the ith scene. The energy storage constraint is as shown in formulas (8) - (12):
Figure BDA0004129154570000104
Figure BDA0004129154570000105
Figure BDA0004129154570000106
Figure BDA0004129154570000107
Figure BDA0004129154570000108
wherein:
Figure BDA0004129154570000109
and->
Figure BDA00041291545700001010
Respectively storing energy discharging and charging state binary variables in the s step; p (P) s disch And P s ch Discharging and charging power for the s-th energy storage; />
Figure BDA00041291545700001011
For storing energy in step s, E stomax Is the total energy stored; η (eta) c 、η d Mu for charging and discharging efficiency up 、μ down The upper and lower limit coefficients of the stored energy; mu (mu) deep Is the depth of discharge coefficient. The calculation of the assessment electric quantity and the reporting accuracy rate in each scene is shown in the formulas (13) - (15):
Figure BDA00041291545700001012
Figure BDA00041291545700001013
Figure BDA0004129154570000111
wherein:
Figure BDA0004129154570000112
the photovoltaic power scene value after energy storage correction in the ith scene of the s step is obtained; accu (Accu) i Reporting accuracy rate for the ith scene; />
Figure BDA0004129154570000117
Reporting power for the 16 th point of the s step; cap is the installed capacity of the photovoltaic power station. The decision variable set of the daily light-storage reporting rolling optimization model is shown as (16), and consists of a step s reporting power value and energy storage related variables.
Figure BDA0004129154570000113
Step 4: the above model is solved using a particle swarm algorithm, taking into account the nonlinear complexity of equations (14) - (15). Wherein the velocity and position update of the particles are shown in formula (17).
Figure BDA0004129154570000114
Wherein:
Figure BDA0004129154570000118
and->
Figure BDA0004129154570000119
The speeds of the particles n at the kth and next iteration, respectively; />
Figure BDA0004129154570000115
And->
Figure BDA0004129154570000116
The position of the particle n in the kth and next iteration, respectively; ρ is an inertial weight; l (L) 1 And l 2 Is [0,1]The random numbers which are uniformly distributed are taken in the range; c 1 And c 2 Is a learning constant; o (o) n The position corresponding to the individual extremum of the particle i; g is the position corresponding to the global extremum.
And (3) obtaining an optimized reporting strategy and an energy storage charging and discharging strategy which are updated every 15 minutes in the day through continuous iteration of the particle swarm algorithm. And recording the energy storage surplus energy of the step 1
Figure BDA00041291545700001110
Constraint construction of equation (10) is performed with the model for the next period.
Accordingly, referring to fig. 3, a second aspect of the embodiment of the present invention provides an apparatus for reporting intra-day power calculation of an optical storage station, including:
the error calculation module 1 is used for acquiring errors of the solar photovoltaic power predicted value and the historical predicted data;
the scene construction module 2 is used for constructing a solar photovoltaic power scene set in a general distribution fitting mode based on the error;
the model construction module 3 is used for constructing an intra-day light-stored power reporting model of the intra-day time t moment and the energy storage correction according to the intra-day photovoltaic power scene set;
and the power calculation module 4 is used for calculating and reporting the daily power of the light-storage station based on the daily light-storage power reporting model.
The device for reporting the daily power calculation of the optical-storage station corresponds to the method, and can be thinned into a complete process of executing the method by the sub-level unit, and the detailed description is omitted here.
Accordingly, a third aspect of the embodiment of the present invention provides an electronic device, including: at least one processor; and a memory coupled to the at least one processor; the memory stores instructions executable by a processor, the instructions being executable by the processor, to cause at least one processor to perform the method of reporting intra-day power calculations for an optical-storage station.
Accordingly, a fourth aspect of the embodiments of the present invention provides a computer-readable storage medium having stored thereon computer instructions which, when executed by a processor, implement the above-described method for reporting intra-day power calculations for an optical-storage station.
The embodiment of the invention aims to protect a method and a device for reporting daily power calculation of an optical-storage station, wherein the method comprises the following steps: acquiring errors of solar photovoltaic power predicted values and historical predicted data; based on the error, constructing a solar photovoltaic power scene set in a general distribution fitting mode; constructing an intra-day light-stored power reporting model of an intra-day t moment meter and energy storage correction according to an intra-day photovoltaic power scene set; and calculating the daily power of the light-storage station based on the daily light-storage power reporting model. The method has the following effects:
and constructing a content photovoltaic power scene by a general distribution and Latin hypercube sampling method, thereby establishing a power optimization reporting model of the light-storage station, formulating a reporting strategy and providing support for the economy and the robustness of the operation of the light-storage station.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.

Claims (10)

1. The method for reporting the daily power calculation of the light-storage station is characterized by comprising the following steps of:
acquiring errors of solar photovoltaic power predicted values and historical predicted data;
based on the error, constructing a solar photovoltaic power scene set in a general distribution fitting mode;
constructing an intra-day light-stored power reporting model of an intra-day t moment meter and energy storage correction according to the intra-day photovoltaic power scene set;
and calculating and reporting the daily power of the light-storage station based on the daily light-storage power reporting model.
2. The method for reporting daily power calculation of an optical-storage station according to claim 1, wherein the constructing a daily photovoltaic power scene set by means of a general distribution fitting method comprises:
acquiring solar photovoltaic power predicted values of a plurality of steps based on a preset time interval;
wherein the prediction error e of the s-th step s Fitting the cumulative probability function of (e) by a general distribution s The calculation formula of (2) is as follows:
Figure FDA0004129154560000011
Figure FDA0004129154560000012
wherein α, β and γ are shape parameters of the general distribution, q is a given confidence level; f (F) s (.) is a general distribution function of the prediction error of the s-th step,
Figure FDA0004129154560000013
is a corresponding general distribution inverse function.
3. The method for reporting intra-day power calculation of an optical-storage station according to claim 2, wherein the shape parameter of the general distribution is obtained by calculating an error cumulative probability function under each prediction time step based on a nonlinear least square method.
4. The method for reporting daily power calculation of an optical-storage station according to claim 2, wherein the method for constructing a daily photovoltaic power scene set by means of universal distribution fitting further comprises:
generating an ith prediction error scene deltap in step s by Latin hypercube sampling s,i ，Δp s,i The calculation formula of (2) is as follows:
Figure FDA0004129154560000021
wherein: n (N) I Is the total scene number;
obtaining a prediction error scene set delta p of the s-th step in the historical prediction data s,i And then, superposing the solar photovoltaic power prediction value with the solar photovoltaic power prediction value of the s-th step at the time t in the day to obtain the solar photovoltaic power scene set:
Figure FDA0004129154560000022
wherein: p (P) t,s,i The photovoltaic power of the ith scene in the s-th step at the moment t.
5. The method for reporting daily power calculation in an optical-storage station as claimed in claim 2, wherein the time of day is N in total s The objective function of the intra-day light-stored power reporting model for =16 steps is as follows:
Figure FDA0004129154560000023
wherein: p (P) total Punishment is carried out on the total check electric quantity in the day; p (P) i id And (5) checking the electric quantity in the day under the ith scene.
6. The method for reporting intra-day power calculation of an optical-storage station according to claim 5, wherein the energy storage constraint condition in the objective function comprises:
Figure FDA0004129154560000024
Figure FDA0004129154560000025
Figure FDA0004129154560000026
Figure FDA0004129154560000027
Figure FDA0004129154560000028
wherein:
Figure FDA0004129154560000029
and->
Figure FDA00041291545600000210
Respectively storing energy discharging and charging state binary variables in the s step; p (P) s disch And P s ch Discharging and charging power for the s-th energy storage; />
Figure FDA00041291545600000211
For storing energy in step s, E stomax Is the total energy stored; η (eta) c 、η d Mu for charging and discharging efficiency up 、μ down The upper and lower limit coefficients of the stored energy; mu (mu) deep Is the depth of discharge coefficient.
7. The method for reporting the daily power calculation of the light-storage station according to claim 6, wherein the calculation formulas of the assessment electric quantity and the reporting accuracy in each scene are as follows:
Figure FDA0004129154560000031
Figure FDA0004129154560000032
Figure FDA0004129154560000033
wherein:
Figure FDA0004129154560000034
the photovoltaic power scene value after energy storage correction in the ith scene of the s step is obtained; accu (Accu) i Reporting accuracy rate for the ith scene; p (P) s repo Reporting power for the 16 th point of the s step; cap is the installed capacity of the photovoltaic power station.
8. The method for reporting the daily power calculation of the optical-storage station as claimed in claim 7, wherein,
the decision variable set of the solar light-storage power reporting model is shown as follows;
Figure FDA0004129154560000035
the method comprises the step of reporting the power value in the step s and the energy storage related variable.
9. The method for reporting the daily power calculation of the optical-storage station as claimed in claim 7, wherein,
the solar light-storage power reporting model is solved by adopting a particle swarm algorithm, wherein the speed and the position of particles are updated as follows:
Figure FDA0004129154560000036
wherein:
Figure FDA0004129154560000037
and->
Figure FDA0004129154560000038
The speeds of the particles n at the kth and next iteration, respectively; />
Figure FDA0004129154560000039
And->
Figure FDA00041291545600000310
The position of the particle n in the kth and next iteration, respectively; ρ is an inertial weight; l (L) 1 And l 2 Is [0,1]The random numbers which are uniformly distributed are taken in the range; c 1 And c 2 Is a learning constant; o (o) n The position corresponding to the individual extremum of the particle i; g is the position corresponding to the global extremum.
10. An optical-storage station daily power calculation reporting device, which is characterized by comprising:
the error calculation module is used for acquiring errors of the solar photovoltaic power predicted value and the historical predicted data;
the scene construction module is used for constructing a solar photovoltaic power scene set in a general distribution fitting mode based on the error;
the model construction module is used for constructing an intra-day light-stored power reporting model of the intra-day time t moment meter and the energy storage correction according to the intra-day photovoltaic power scene set;
and the power calculation module is used for calculating and reporting the daily power of the light-storage station based on the daily light-storage power reporting model.
CN202310254716.3A 2023-03-07 2023-03-07 Method and device for reporting daily power calculation of optical-storage station Pending CN116388268A (en)

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