CN109995076B - Energy storage-based photovoltaic collection system power stable output cooperative control method - Google Patents
Energy storage-based photovoltaic collection system power stable output cooperative control method Download PDFInfo
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Abstract
The application provides a photovoltaic collection system power stable output cooperative control method based on energy storage, which comprises the following steps: identifying a photovoltaic power change mode of a current control period according to the photovoltaic output ultra-short-term prediction data, adaptively adjusting and optimizing objective functions according to different modes, acquiring an optimal filter coefficient through the objective function and a particle swarm algorithm, controlling energy storage output according to the optimal filter coefficient, smoothing fluctuation of photovoltaic power, and reducing fluctuation of light wave power; on the basis, the charge state of the energy storage battery is used as feedback to carry out secondary correction on the charge and discharge of the energy storage battery, so that the final light-storage combined output can be located in a predicted output interval, the photovoltaic predicted output is tracked, the accuracy of the photovoltaic prediction capability is improved, the problem that the photovoltaic output is stabilized to fluctuate in compensating the photovoltaic output prediction error is combined, and the two purposes of using the small-scale energy storage battery are achieved.
Description
Technical Field
The application relates to the technical field of photovoltaic systems, in particular to a power stable output cooperative control method of a photovoltaic collection system based on energy storage.
Background
In recent years, renewable energy sources represented by photovoltaics are rapidly developed, however, the utilization rate of photovoltaics in China is relatively low, and the main reasons are as follows: firstly, the fluctuation of the output of a photovoltaic system, and the large fluctuation of the output of the photovoltaic system can cause the problems of voltage flickering, frequency fluctuation, excessive harmonic waves and the like of grid-connected points; the photovoltaic output fluctuation can be divided into high-frequency fluctuation and low-frequency fluctuation, wherein the power grid has enough reaction time for responding to the low-frequency fluctuation, so that the influence of the low-frequency fluctuation on the power grid is negligible, and the reduction of the photovoltaic output fluctuation mainly refers to the reduction of the high-frequency fluctuation of the photovoltaic output; and secondly, the accuracy of photovoltaic output prediction is low, and the power grid predicts the output plan and system reserve of the photovoltaic power station according to the photovoltaic output, so that the prediction accuracy directly influences the photovoltaic internet space.
The energy storage-based photovoltaic power generation system is characterized in that an energy storage device is added on the basis of the photovoltaic power generation system, and the high-frequency fluctuation of the output force of the photovoltaic power generation system is stabilized through the function of rapidly releasing and absorbing electric energy by the energy storage system, so that the purpose of smoothly controlling the output power of the system can be achieved; however, in the prior art, while the photovoltaic power generation system based on energy storage suppresses photovoltaic fluctuation by smoothing photovoltaic power, the photovoltaic output prediction can be performed by well utilizing the historical data of the photovoltaic output, but the influence of future photovoltaic output on the energy storage charging and discharging behaviors at the current moment is not considered; or the prediction data is directly used for replacing the actual output to be input into the simulation model, so that the prediction accuracy of the photovoltaic treatment is reduced.
Therefore, there is a need for a power stable output system control method that aims at smoothing the photovoltaic power and tracking the photovoltaic predicted power.
Disclosure of Invention
The application provides a photovoltaic collection system power stable output cooperative control method based on energy storage, which aims to smooth photovoltaic power stable photovoltaic power output and track predicted output to improve accuracy of photovoltaic predicted output.
In order to solve the technical problems, the embodiment of the application discloses the following technical scheme:
the application provides a power stable output cooperative control method of a photovoltaic collection system based on energy storage, which comprises the following steps:
ultra-short-term prediction of photovoltaic output power to obtain photovoltaic predicted output power P f ；
Predicting output power P from the photovoltaic f Determining a photovoltaic output trend mode, wherein the photovoltaic output trend mode comprises an ascending mode, a descending mode and a fluctuation mode;
obtaining lambda according to the photovoltaic output trend mode, wherein lambda is a weight coefficient representing a battery charge and discharge intensity item;
establishing an objective function J, wherein the objective function J is
Wherein P is O For optical storage combined output power, P b Outputting power for the energy storage battery;
acquiring an optimal filter coefficient alpha by adopting a particle swarm algorithm according to the objective function J opt ；
According to the optimal filter coefficient alpha opt Smoothing the photovoltaic output power based on a low-pass filtering algorithm, and simultaneously smoothing the optimal filtering coefficient alpha opt Transmitting to a cooperative control module to obtain primary light-storage combined output power P o,temp Battery output value P b,pri ；
The preliminary light-storage combined output power P is subjected to SOC according to the charge state of the energy storage battery o,temp Compensating the prediction error to obtain energy storage correction power P b,rec ；
According to the battery output value P b,pri And the stored energy correction power P b,rec Calculating the output power P of the energy storage battery b 。
Preferably, said predicting output power P from said photovoltaic f The determining of the photovoltaic output trend pattern includes:
definition function p= [ P ] o (t-1),P PV (t),P f (t+1),P f (t+2),P f (t+3)]Function DeltaP m ＝P f (t+3)-P o (t-1) wherein P PV (t) is the photovoltaic output power at the current moment, P o (t-1) is the optical storage combined output power at the previous moment, P f (t+1)、P f (t+2) and P f (t+3) is the photovoltaic predicted output power at the future time t+1, time t+2 and time t+3 respectively;
judging monotonicity of the function P, wherein the photovoltaic output trend mode is an ascending mode when the function P monotonically increases, and is a descending mode when the function P monotonically decreases;
when the function P has a single pole and is maximum, if ΔP m More than or equal to 0, the photovoltaic output trend mode is an ascending mode, if delta P m The photovoltaic output trend mode is a descending mode if epsilon is less than or equal to delta P m The photovoltaic output trend mode is a fluctuation mode;
when the function P has a single pole and is a minimum, if ΔP m If not less than epsilon, the photovoltaic output trend mode is an ascending mode, and if delta P m If the voltage is less than 0, the photovoltaic output trend mode is a descending mode, and if the voltage is less than or equal to 0 and less than or equal to delta P m The photovoltaic output trend mode is a fluctuation mode;
when the function P has two poles, if delta P m The photovoltaic output trend mode is a rising mode if [ epsilon ], if [ delta ] P m The photovoltaic output trend mode is a descending mode if epsilon is less than or equal to delta P m ＜ε0≤ΔP m And < epsilon, the photovoltaic output trend mode is a fluctuation mode.
Preferably, the obtaining λ according to the photovoltaic output trend mode includes:
when the photovoltaic output trend mode is a rising mode, λ=soc (t);
when the photovoltaic output trend mode is a descending mode, lambda=100% -SOC (t);
when the photovoltaic output trend mode is a fluctuation mode, lambda=2|50% -SOC (t) |;
and the SOC (t) is the state of charge value of the energy storage battery at the current moment.
Preferably, said optimizing said filter coefficient α opt Transmitting to a cooperative control module to obtain the light-storage combined output power P o,temp Battery output value P b,pri Comprising the following steps:
according to P o,temp ＝α opt PPV(t)+(1-α opt ) Po (t-1) acquisition of P o,temp ；
According to P b,pri ＝P PV (t)-P o,temp (t) acquisition of P b,pri 。
Preferably, the battery output value P is based on b,pri And the stored energy correction power P b,rec Calculating the output power P of the energy storage battery b Comprising the following steps:
according to P b ＝P b,pri +P b,rec Calculating the output power of the energy storage batteryRate P b 。
Compared with the prior art, the beneficial effects of this application are:
(1) According to the photovoltaic output ultra-short-term prediction data, the photovoltaic power change mode of the current control period is identified, the objective function is adaptively adjusted and optimized according to different modes, the optimal filter coefficient is obtained through the combination of the objective function and the particle swarm algorithm, the energy storage output capacity is controlled according to the optimal filter coefficient, fluctuation of photovoltaic power is smoothed, stability of light wave output is stabilized, and the photovoltaic utilization rate is improved.
(2) The application describes the optimal filter coefficient alpha o pt Transmitting the power to a cooperative control module to obtain primary light-storage combined output power Po, temp battery output value P b,pri The method comprises the steps of carrying out a first treatment on the surface of the The preliminary combined light and storage output power Po is outputted according to the charge state SOC of the energy storage battery, temp compensating the prediction error to obtain energy storage correction power P b,rec The method comprises the steps of carrying out a first treatment on the surface of the According to the battery output value P b,pri And the stored energy correction power P b,rec Calculating the output power P of the energy storage battery b The method realizes cooperative control, completes secondary correction of charge and discharge of the energy storage battery, enables final light storage combined output to be located in a predicted output interval, tracks photovoltaic predicted output, improves accuracy of photovoltaic prediction capacity, reduces deviation of photovoltaic output and dispatching output, and improves adjustment depth and space of combined output of a light storage system.
(3) The problem of stabilizing photovoltaic output fluctuation in compensating photovoltaic output prediction error is combined, and secondary correction is carried out on charging and discharging of an energy storage system, so that the two aims of using a small-scale energy storage battery to complete are achieved.
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In order to more clearly illustrate the technical solutions of the present application, the drawings that are needed in the embodiments will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
Fig. 1 is a schematic structural diagram of an energy storage-based photovoltaic collection system in the present application;
fig. 2 is a schematic flow chart of a method for controlling power stable output cooperation of a photovoltaic collection system based on energy storage;
FIG. 3 is a schematic flow chart of the optimal filter coefficient solving in the present application;
fig. 4 is a schematic diagram of a photovoltaic output variation mode in an embodiment of the present invention.
Detailed Description
In order to better understand the technical solutions in the present application, the following description will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present application.
The application provides a photovoltaic collection system power stable output cooperative control method based on energy storage, which aims to smooth photovoltaic power, reduce photovoltaic fluctuation, track predicted output and improve accuracy of the predicted output of the photovoltaic, and improve photovoltaic utilization rate.
The structure of the light and storage combined system is shown in fig. 1, fig. 1 is a schematic structural diagram of a photovoltaic collection system based on energy storage in the application, and as can be seen from fig. 1, the system comprises a large photovoltaic electric field, an energy storage battery, a DC/DC converter, a high transformation ratio DC/DC converter and a grid-connected DC/AC converter. The centralized energy storage battery is arranged at the photovoltaic outlet to coordinate the output of renewable energy sources, and the controller provides a reference value of the charge and discharge power of the energy storage battery. Neglecting energy loss in transmission and conversion processes, there are:
P o ＝P PV -P b
wherein P is O For optical storage combined output power, P PV For the original output power of the photovoltaic power station, P b Outputting power for the energy storage battery; p (P) b And > 0 represents battery charge and vice versa.
In order to achieve the dual purposes of stabilizing optical power fluctuation and compensating prediction errors, the adaptive optimization filter coefficient module is adopted to optimize the filter coefficient for smoothing fluctuation, and then the energy storage actual output is further set through the cooperative control module, so that the aim of tracking the prediction errors is fulfilled. The power stable output cooperative control strategy of the optical storage system mainly comprises three modules, namely ultra-short-term prediction of photovoltaic output by a BPNN method, optimization of a self-adaptive filter coefficient and cooperative control.
Conventional low pass filtering outputs a photovoltaic field power sequence P PV A first-order inertia link with the input time constant of T is adopted to obtain smoother output P o The transfer function is:
discretizing the above to obtain:
wherein Δt is the time interval, defining the filter coefficient
α∈[0,1]。
And
Indicating that the degree of post-filter output power smoothing is related to the filter coefficients: when α=0, P o (t)＝P o And (t-1), the output power of the optical storage combined system is stable, but the fluctuation of the photovoltaic power is completely compensated by the energy storage battery, and the battery has larger single charge and discharge power and is easy to saturate or empty rapidly. If the energy storage battery is required to continuously participate in photovoltaic output regulation, larger battery capacity is required to be configured, and the investment cost is high; p when α=1 o (t)＝P PV And (t) blocking the battery, namely not participating in the fluctuation stabilization of the photovoltaic power station.
Specifically, the present application provides a power stable output cooperative control method of a photovoltaic collection system based on energy storage, and specifically referring to fig. 2, fig. 2 is a flow chart diagram of the power stable output cooperative control method of the photovoltaic collection system based on energy storage, where the method includes:
s01: ultra-short-term prediction of photovoltaic output power to obtain photovoltaic predicted output power P f 。
Ultra-short-term prediction of photovoltaic output power to obtain photovoltaic predicted output power P f Specifically, the original output data sampling time interval T of the photovoltaic power station s ，P f Predicting output power of the photovoltaic electric field, and predicting ultra-short-term output of the photovoltaic electric field by using BP neural network, wherein the prediction duration is T l The predicted data time interval is T f And T is f ＞T s . The purpose of ultra-short term prediction of photovoltaic output is at T f And on a time scale, the prediction data is utilized to complete the optimization of the filter coefficient. The photovoltaic output ultra-short-term prediction in the patent adopts a traditional BPNN method. The BPNN method is a common technical means in the art, and thus is not described herein.
S02: predicting output power P from the photovoltaic f And determining a photovoltaic output trend mode, wherein the photovoltaic output trend mode comprises an ascending mode, a descending mode and a fluctuation mode.
According to the photovoltaic output ultra-short-term prediction data, the photovoltaic power change mode of the current control period is identified, so that three photovoltaic output change modes are defined: rising, falling and smooth wave pattern. The power used for pattern recognition is the light-storage combined output power at the previous moment, the photovoltaic actual output power at the current moment and the photovoltaic predicted output power in a future period of time respectively. And considering that the accuracy of the photovoltaic prediction is reduced along with the prediction time, selecting a predicted value in 30 minutes in the future to participate in the mode judgment.
Specifically, the output power P is predicted from the photovoltaic f The determining of the photovoltaic output trend pattern includes:
definition function p= [ P ] o (t-1),P PV (t),P f (t+1),P f (t+2),P f (t+3)]Function DeltaP m ＝P f (t+3)-P o (t-1) wherein P PV (t) is whenPhotovoltaic output power at previous moment, P o (t-1) is the optical storage combined output power at the previous moment, P f (t+1)、P f (t+2) and P f (t+3) is the photovoltaic predicted output power at the future time t+ 1, time t+2 and time t+3 respectively;
judging monotonicity of the function P, wherein the photovoltaic output trend mode is an ascending mode when the function P monotonically increases, and is a descending mode when the function P monotonically decreases;
when the function P has a single pole and is maximum, if ΔP m More than or equal to 0, the photovoltaic output trend mode is an ascending mode, if delta P m The photovoltaic output trend mode is a descending mode if epsilon is less than or equal to delta P m The photovoltaic output trend mode is a fluctuation mode;
when the function P has a single pole and is a minimum, if ΔP m If not less than epsilon, the photovoltaic output trend mode is an ascending mode, and if delta P m If the voltage is less than 0, the photovoltaic output trend mode is a descending mode, and if the voltage is less than or equal to 0 and less than or equal to delta P m The photovoltaic output trend mode is a fluctuation mode;
when the function P has two poles, if delta P m The photovoltaic output trend mode is a rising mode if [ epsilon ], if [ delta ] P m The photovoltaic output trend mode is a descending mode if epsilon is less than or equal to delta P m ＜ε0≤ΔP m And < epsilon, the photovoltaic output trend mode is a fluctuation mode.
The above is expressed as table 1:
TABLE 1 photovoltaic output trend model decision criteria
S03: and obtaining lambda according to the photovoltaic output trend mode, wherein lambda is a weight coefficient representing a battery charge and discharge intensity item. When the photovoltaic output trend mode is a rising mode, λ=soc (t);
when the photovoltaic output trend mode is a descending mode, lambda=100% -SOC (t);
when the photovoltaic output trend mode is a fluctuation mode, lambda=2|50% -SOC (t) |;
and the SOC (t) is the state of charge value of the energy storage battery at the current moment.
Referring to fig. 4, fig. 4 is a schematic diagram illustrating a photovoltaic output variation mode according to an embodiment of the present invention.
In different modes of the photovoltaic output, in order to meet the requirement of reducing power fluctuation, the battery has different charging and discharging modes, and the limitation of the charge state on the output of the battery is different. In the ascending mode, the original output of the photovoltaic is continuously increased, and the unidirectional charging of the energy storage battery is needed for reducing the light storage combined output increasing rate in the control period. The working capacity of the energy storage battery is inversely related to the SOC at this time, i.e., the energy storage working space decreases as the SOC increases. In order to reduce the output intensity of the battery at this time, the output power item weight of the energy storage battery needs to be properly increased, so that λ is set as the energy storage SOC value at the current time in the mode, i.e., λ=soc (t); in contrast, for the descent mode, the energy storage battery needs to be discharged to slow down the photovoltaic power descent, and the working capacity of the energy storage battery is positively correlated with the SOC, so that λ=100% -SOC (t) is set in the mode to increase P when the SOC is low in order to reduce the working strength of the energy storage battery when the SOC is low b A term weight; in the fluctuation mode, the energy storage battery is charged and discharged simultaneously in a control period, so that the ideal state of the SOC at 50% is maintained, and lambda=2|50% -SOC (t) | is set. The coefficient '2' is mainly unified with the ascending and descending modes, and the lambda range is ensured to be 0,1]Between them.
S04: establishing an objective function J, wherein the objective function J is
Wherein P is O For optical storage combined output power, P b And outputting power for the energy storage battery.
In order to meet the smooth effect of the photovoltaic output and simultaneously give consideration to the charge and discharge intensity of the battery, a multi-objective optimization problem model is constructed, an optimal filter coefficient is solved, and an objective function is as follows:
the two items in the method respectively represent the fluctuation constraint of the photo-storage combined output power and the charge and discharge power constraint of the energy storage battery, the two are in a mutual constraint relationship, N is the number of data points, and lambda is the weight coefficient representing the charge and discharge strength item of the battery: lambda=0 then the optimization objective considers only the smoothing effect, lambda increases then P b The influence of the term on the optimization target J is enhanced, and the stabilizing fluctuation effect is weakened.
When the existing method is used for constructing a multi-objective optimization model, a mode of directly assigning weights is adopted, the weight coefficient configuration standard is lacked, the objective function change is easily caused to mainly depend on a certain item under certain conditions, and the objective of multi-objective optimization cannot be met. Aimed at
In the context of multi-objective optimization, an adaptive weight coefficient determination scheme is provided, wherein the weight coefficient is determined by photovoltaic power generation trend and state of charge (SOC) of an energy storage battery in a current period and a future period.
S05: acquiring an optimal filter coefficient alpha by adopting a particle swarm algorithm according to the objective function J opt 。
The energy storage capacity is controlled according to the optimal filter coefficient, fluctuation of photovoltaic power is smoothed, and light wave output is stabilized, so that the optimal filter coefficient alpha opt Is the key of the application, the application adopts a particle swarm algorithm to obtain the optimal filter coefficient alpha according to the objective function J opt The method comprises the steps of carrying out a first treatment on the surface of the Specific solving method referring to fig. 3, fig. 3 is a schematic flow chart of solving an optimal filter coefficient in the present application; from fig. 3, it can be seen that the solution flow is as follows:
setting control parameters of a particle swarm algorithm: total number of particle swarms Γ, inertia constant interval [ W ] min ,W max ]Learning factor c 1 ,c 2 Iteration times L;
initializing the position and speed of a particle swarm and the iteration number k=0;
calculating particle fitness based on the objective function, and updating individual optimal and global optimal;
updating the iteration number k=k+1, and judging whether the maximum iteration number is reached or not: if yes, stopping iteration, and recording a current global optimal solution; otherwise, updating the position and the speed of the particles, and continuously executing the step of 'obtaining the fitness of the particles based on the objective function and updating the individual optimum and the global optimum'.
S06: according to the optimal filter coefficient alpha opt Smoothing the photovoltaic output power based on a low-pass filtering algorithm, and simultaneously smoothing the optimal filtering coefficient alpha opt Transmitting to a cooperative control module to obtain primary light-storage combined output power P o,temp Battery output value P b,pri 。
The adaptive filter coefficient in the application is optimized to obtain the optimal filter coefficient alpha in the current control period opt And transferred to the cooperative control module. By combining alpha opt The method comprises the steps of pre-smoothing the original output of a photovoltaic electric field, identifying a photovoltaic power change mode of a current control period according to ultra-short-term prediction data of the photovoltaic output, adaptively adjusting and optimizing objective functions according to different modes, acquiring an optimal filter coefficient through the objective function in combination with a particle swarm algorithm, controlling energy storage output according to the optimal filter coefficient, smoothing fluctuation of the photovoltaic power, stabilizing light wave output and improving the photovoltaic utilization rate.
Specifically, the pre-smoothing method is based on a low-pass filtering algorithm, and the low-pass rate algorithm is a common technical means in the field, so that the description is omitted. Obtain the primary optical storage combined output power P o,temp Battery output value P b,pri P compared with the original output of the photovoltaic power station o,temp The fluctuation rate decreases. Thereafter, at P o,temp On the basis of the above, the battery charging and discharging actions are further regulated, the prediction error is compensated, and the light-storage combined output is corrected. The SOC of the energy storage battery is taken as a closed-loop feedback quantity to actively participate in the adjustment of the charge and discharge power of the battery, and the energy storage correction power P is calculated b,rec The reference output of the energy storage battery is represented by P b,pri And P b,rec And jointly determining, and realizing cooperative control.
In particularSaid optimizing said filter coefficients alpha opt Transmitting to a cooperative control module to obtain the light-storage combined output power P o,temp Battery output value P b,pri Comprising the following steps:
according to P o,temp ＝α opt P PV (t)+(1-α opt )P o (t-1) acquisition of P o,temp ；
According to P b,pri ＝P PV (t)-P o,temp (t) acquisition of P b,pri 。
S07: the preliminary light-storage combined output power P is subjected to SOC according to the charge state of the energy storage battery o,temp Compensating the prediction error to obtain energy storage correction power P b,rec 。
The correction of the charge and discharge power of the energy storage battery is based on different P o,temp State interval of SOC. For this purpose, an upper prediction limit and a lower prediction limit are first defined:
ΔP tol is an error margin; according to P o,temp Defining the relation with the prediction interval: below the lower prediction limit (P o,temp ＜P l ) Within the prediction allowance interval (P l ≤P o,temp ≤P h ) Above the upper prediction limit (P o,temp ＞P h ). Meanwhile, the state of charge of the battery is divided into three sections: [0, SOC low ]，[SOC low ,SOC high ]，[SOC high ,100％]，SOC high 、SOC low The upper and lower boundary values of the ideal working interval of the energy storage battery are respectively. The secondary corrected power output of the energy storage battery for each interval combination is shown in table 2.
TABLE 2 energy storage correction Power P for different SOCs b,rec Value of
By P in the table o,temp ＞P h Behavior exampleAnalysis: to track the prediction, the battery needs to be charged if the battery SOC<SOC low Indicating that the battery has enough charging space, P should be fully utilized in order to raise the charge state of the battery as soon as possible o,temp And P l The power space between the two supplements the charge state of the battery; conversely if the battery SOC>SOC high The residual charging space of the battery is insufficient, so that the battery action at the current moment is not secondarily corrected in order to avoid overcharging and reserve space for subsequent charging; when the battery SOC is in an ideal working interval, the secondary correction power of the energy storage battery is linearly related to the current SOC, and the lower the SOC is, the larger the correction power is. For P l ≤P o,temp ≤P h And P o,temp ＜P l And (3) determining the secondary correction power of the energy storage battery by adopting the same thought.
S08: according to the battery output value P b,pri And the stored energy correction power P b,rec Calculating the output power P of the energy storage battery b The method comprises the steps of carrying out a first treatment on the surface of the In particular according to P b ＝P b,pri +P b,rec Calculating the output power P of the energy storage battery b 。
The final actual output power of the energy storage battery is jointly determined by a part for smoothing fluctuation and a secondary correction part for tracking prediction:
P b ＝P b,pri +P b,rec
because the light-storage combined output is regulated after the prediction interval is tracked, the smoothing effect of the combined output is influenced to a certain extent, and the photovoltaic prediction is required to be relatively accurate and delta P is required to be carried out on the premise that a subsequent stabilizing module is not added tol Should not be too large to avoid |P b,pri |＞＞|P b,rec |。
According to the method and the device, cooperative control is achieved, secondary correction is conducted on charging and discharging of the energy storage battery, final light storage combined output can be located in a predicted output interval, photovoltaic predicted output is tracked, accuracy of photovoltaic prediction capacity is improved, deviation of photovoltaic output and dispatching output is reduced, and adjustment depth and space of combined output of a light storage system are improved.
Simultaneously, this application can carry out real-time regulation to energy storage battery state of charge, realizes the precharge prevention of battery, effectively stabilizes state of charge in reasonable working range to extension battery life.
In order to quantitatively evaluate the effect of energy storage on smoothing photovoltaic power and compensating prediction errors, four evaluation indexes are adopted, wherein M in each index represents the number of data in an evaluation period.
Mean value of fluctuation:
maximum value of fluctuation:
predictive tracking out-of-limit average:
P err (t)＝max(|P o (t)-P f (t)|-ΔP tol ,0)
prediction tracking out-of-limit probability:
in-process predictive tracking over-limit value P err And (t) represents the distance between the output power at time t and the adjacent prediction upper/lower limit when the output power at time t is not within the prediction interval.
Since the foregoing embodiments are all described in other modes by reference to the above, the same parts are provided between different embodiments, and the same and similar parts are provided between the embodiments in the present specification. And will not be described in detail herein.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure of the invention herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
The above-described embodiments of the present application are not intended to limit the scope of the present application.
Claims (5)
1. The utility model provides a photovoltaic collection system power stable output cooperative control method based on energy storage, which is characterized in that the method includes:
ultra-short-term prediction of photovoltaic output power to obtain photovoltaic predicted output power P f ；
Predicting output power P from the photovoltaic f Determining a photovoltaic output trend mode, wherein the photovoltaic output trend mode comprises an ascending mode, a descending mode and a fluctuation mode;
obtaining lambda according to the photovoltaic output trend mode, wherein lambda is a weight coefficient representing a battery charge and discharge intensity item;
establishing an objective function J, wherein the objective function J is
Wherein P is O For optical storage combined output power, P b Outputting power for the energy storage battery;
acquiring an optimal filter coefficient alpha by adopting a particle swarm algorithm according to the objective function J opt ；
According to the optimal filter coefficient alpha opt Smoothing the photovoltaic output power based on a low-pass filtering algorithm, and simultaneously smoothing the optimal filtering coefficient alpha opt Transmitting to a cooperative control module to obtain primary light-storage combined output power P o,temp Battery output value P b,pri ；
The preliminary light-storage combined output power P is subjected to SOC according to the charge state of the energy storage battery o,temp Compensating the prediction error to obtain energy storage repairPositive power P b,rec ；
According to the battery output value P b,pri And the stored energy correction power P b,rec Calculating the output power P of the energy storage battery b 。
2. The method according to claim 1, wherein the output power P is predicted from the photovoltaic f The determining of the photovoltaic output trend pattern includes:
definition function p= [ P ] o (t-1),P PV (t),P f (t+1),P f (t+2),P f (t+3)]Function DeltaP m ＝P f (t+3)-P o (t-1) wherein P PV (t) is the photovoltaic output power at the current moment, P o (t-1) is the optical storage combined output power at the previous moment, P f (t+1)、P f (t+2) and P f (t+3) is the photovoltaic predicted output power at the future time t+1, time t+2 and time t+3 respectively;
judging monotonicity of the function P, wherein the photovoltaic output trend mode is an ascending mode when the function P monotonically increases, and is a descending mode when the function P monotonically decreases;
when the function P has a single pole and is maximum, if ΔP m More than or equal to 0, the photovoltaic output trend mode is an ascending mode, if delta P m The photovoltaic output trend mode is a descending mode if epsilon is less than or equal to delta P m The photovoltaic output trend mode is a fluctuation mode;
when the function P has a single pole and is a minimum, if ΔP m If not less than epsilon, the photovoltaic output trend mode is an ascending mode, and if delta P m If the voltage is less than 0, the photovoltaic output trend mode is a descending mode, and if the voltage is less than or equal to 0 and less than or equal to delta P m The photovoltaic output trend mode is a fluctuation mode;
when the function P has two poles, if delta P m The photovoltaic output trend mode is a rising mode if [ epsilon ], if [ delta ] P m The photovoltaic output trend mode is a descending mode if epsilon is less than or equal to delta P m ＜ε0≤ΔP m The photovoltaic output trend mode is a fluctuation mode;
where ε is the fluctuation threshold.
3. The method of claim 1, wherein the obtaining λ from the photovoltaic output trend pattern comprises:
when the photovoltaic output trend mode is a rising mode, λ=soc (t);
when the photovoltaic output trend mode is a descending mode, lambda=100% -SOC (t);
when the photovoltaic output trend mode is a fluctuation mode, lambda=2|50% -SOC (t) |;
and the SOC (t) is the state of charge value of the energy storage battery at the current moment.
4. The method according to claim 1, characterized in that said optimizing the filter coefficients a opt Transmitting to a cooperative control module to obtain the light-storage combined output power P o,temp Battery output value P b,pri Comprising the following steps:
according to P o,temp ＝α opt P PV (t)+(1-α opt )P o (t-1) acquisition of P o,temp ；
According to P b,pri ＝P PV (t)-P o,temp (t) acquisition of P b,pri 。
5. The method according to claim 1, wherein the battery output value P b,pri And the stored energy correction power P b,rec Calculating the output power P of the energy storage battery b Comprising the following steps:
according to P b ＝P b,pri +P b,rec Calculating the output power P of the energy storage battery b 。
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