Energy storage system collaborative optimization method based on photovoltaic power prediction

Abstract

The invention relates to the technical field of photovoltaic energy storage, in particular to an energy storage system collaborative optimization method based on photovoltaic power prediction. Comprising the steps of generating a fluctuation evaluation value of a current adjustment period according to historical operation parameters; setting the energy storage capacity of the energy storage system in the current adjustment period according to the fluctuation evaluation value; and setting a current adjustment period according to the fluctuation evaluation value to establish a plurality of time intervals, and setting a control strategy in each time interval according to the photovoltaic expected power in each time interval. The safe energy storage capacity of the energy storage system is dynamically adjusted according to the periodic fluctuation evaluation value, multiple time intervals are established in a single adjustment period, the working state of the energy storage system is dynamically adjusted, output or storage of electric energy is controlled, the efficiency of stabilizing photovoltaic output fluctuation is improved, and the energy storage efficiency is improved. Excessive charging and discharging of the energy storage system are avoided, and the service life of the energy storage system is prolonged.

Description

Technical Field

[0001] The present application relates to the field of photovoltaic energy storage technology, and in particular to a method for collaborative optimization of an energy storage system based on photovoltaic power prediction. Background Art

[0002] In the photovoltaic storage power generation system, the energy storage part can store excess photovoltaic power generation, smooth the fluctuation of solar power generation output power, and improve the quality of power. The control strategy of the photovoltaic storage power generation system can dynamically adjust the flow direction of energy between photovoltaic, energy storage system and power grid, more effectively reduce the fluctuation of photovoltaic output power, improve the ability of the power grid to accept photovoltaic power generation on a large scale, and reduce the phenomenon of abandoned light.

[0003] However, the current energy storage system has limitations in battery capacity and the load on the power grid varies at different times. Sometimes excessive grid fluctuations can cause traditional battery energy storage systems to be unable to stabilize the grid in a timely manner. At the same time, it can also cause excessive charging and discharging of the energy storage system, exacerbating the loss of energy storage equipment. Summary of the invention

[0004] The purpose of this application is: to solve the above technical problems, this application provides a collaborative optimization method for energy storage systems based on photovoltaic power prediction, aiming to improve the synergy between photovoltaic and energy storage systems, avoid excessive charging and discharging of energy storage systems, and ensure stable operation of the system.

[0005] In some embodiments of the present application, the energy storage system is dynamically adjusted to set the safe energy storage capacity of the energy storage system according to the periodic fluctuation evaluation value, and multiple time intervals are established within a single adjustment cycle to dynamically adjust the working state of the energy storage system to avoid excessive charging and discharging of the energy storage system and increase the service life of the energy storage system.

[0006] In some embodiments of the present application, the expected photovoltaic power in each time interval is generated according to a preset power prediction model, and the working state of the energy storage system in each time interval is adjusted in combination with the actual photovoltaic power parameters to control the output or storage of electric energy, improve the efficiency of smoothing photovoltaic output fluctuations, and ensure the stable operation of the system.

[0007] In some embodiments of the present application, a method for collaborative optimization of an energy storage system based on photovoltaic power prediction is provided, comprising:

[0008] Generate the fluctuation evaluation value of the current adjustment cycle based on the historical operating parameters;

[0009] The energy storage capacity of the energy storage system in the current regulation cycle is set according to the fluctuation evaluation value;

[0010] The current regulation cycle is set according to the fluctuation evaluation value to establish multiple time intervals, and the control strategy in each time interval is set according to the expected photovoltaic power in each time interval.

[0011] In some embodiments of the present application, when generating a fluctuation evaluation value according to historical operating parameters, it includes:

[0012] Obtain the photovoltaic operation parameters and environmental parameters in the previous regulation cycle of the current regulation time node;

[0013] Generate a fluctuation evaluation value f of the current adjustment time node according to a preset evaluation model;

[0014]

[0015] Among them, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; n1 is the number of time intervals in the previous adjustment cycle; ci is the actual photovoltaic power in the i-th time interval in the previous adjustment cycle; c′i is the expected photovoltaic power in the i-th time interval in the previous adjustment cycle; Y(i) is the selection coefficient, if ci-c′i>0, then Y(i)=0; if ci-c′i<0, then Y(i)=-1; θ is the number of environmental evaluation indicators; βi is the influencing factor of the i-th environmental indicator; ki is the reference value of the i-th environmental evaluation indicator in the previous adjustment cycle.

[0016] In some embodiments of the present application, when the current adjustment period is set according to the fluctuation evaluation value to establish multiple time intervals, it includes:

[0017] According to the fluctuation evaluation value f of the current adjustment time node, the number of time intervals n of the current adjustment cycle is set;

[0018] A time interval sequence T is established, T=(t1, t2...ti...tn), wherein ti is the i-th time interval in the current adjustment period; and n is the number of adjustment periods in the current time interval.

[0019] In some embodiments of the present application, setting the energy storage capacity of the energy storage system in the current regulation cycle includes:

[0020] The primary energy storage capacity A1 and the secondary energy storage capacity A2 are set according to the energy storage system parameters, wherein A1<A2;

[0021] Set the correction coefficient r according to the fluctuation evaluation value f of the current adjustment time node;

[0022] The energy storage capacity interval A of the energy storage system in the current regulation cycle is generated according to the correction coefficient r, where A=(r*A1, A2).

[0023] In some embodiments of the present application, when setting the correction coefficient r, it includes:

[0024] Presetting a first fluctuation evaluation value interval (F1, F2), a second fluctuation evaluation value interval (F2, F3) and a third fluctuation evaluation value interval (F3, F4);

[0025] If the fluctuation evaluation value f is within the preset first fluctuation evaluation value interval, the correction coefficient r is set to the preset first correction coefficient r1, that is, r=r1;

[0026] If the fluctuation evaluation value f is within the preset second fluctuation evaluation value interval, the correction coefficient r is set to the preset second correction coefficient r2, that is, r=1;

[0027] If the fluctuation evaluation value f is within the preset third fluctuation evaluation value interval, the correction coefficient r is set to the preset third correction coefficient r3, that is, r=r3, and r1<1<r3.

[0028] In some embodiments of the present application, when setting the control strategy in each time interval, it includes:

[0029] Generate the expected photovoltaic power in each time interval according to the preset power prediction model;

[0030] Establish the expected photovoltaic power P, P = (p1, p2...pi...pn), where pi is the expected photovoltaic power in the i-th time interval;

[0031] According to the time interval sequence T, the i-th time interval is sequentially set as the target time interval;

[0032] Get the actual photovoltaic power P at the start time of the target time interval (ti) ;

[0033] Generate a power fluctuation value f in a target time interval;

[0034] f=Q3*[p (ti) -p] / k;

[0035] Among them, p is the expected photovoltaic power at the target time node; k is the photovoltaic installed capacity; Q3 is the preset third fixed coefficient;

[0036] Preset a first power fluctuation value threshold F1;

[0037] If f<F1, the control strategy of the target time interval is set as the first-level control strategy;

[0038] If f≧F1, the control strategy for the target time interval is set as the secondary control strategy.

[0039] In some embodiments of the present application, the primary control strategy includes:

[0040] Obtain the initial energy value B of the energy storage system at the start time node of the target time interval;

[0041] Generate the storage energy h of the energy storage system within the target time interval;

[0042]

[0043] Wherein, j is the duration of the target time interval; Y(x) is the function of the output power of the energy storage system changing with time in the target time interval;

[0044] The working state of the energy storage system within the target time interval is set according to the storage energy h.

[0045] In some embodiments of the present application, when setting the working state of the energy storage system within the target time interval, it includes:

[0046] According to the energy storage capacity interval A, a first energy storage threshold H1, a second energy storage threshold H2, a third energy storage threshold H3, a fourth energy storage threshold H4 and a fifth energy storage threshold H5 are set;

[0047] Among them, 0

[0048] If 0≤h

[0049] If H1≤h<H2, the working state of the energy storage system in the target time interval is set to the secondary working state;

[0050] If H2≤h<H3, the working state of the energy storage system in the target time interval is set to the third-level working state;

[0051] If H3≤h<H4, the working state of the energy storage system in the target time interval is set to the fourth-level working state;

[0052] If H4≤h<H5, the working state of the energy storage system within the target time interval is set to the fifth-level working state.

[0053] In some embodiments of the present application, the secondary control strategy includes:

[0054] If [p (ti) -p]>0;

[0055] Obtaining the state evaluation value h of the energy storage system within the target time interval;

[0056] If 0≤h

[0057] If H1≤h<H4, the working state of the energy storage system in the target time interval is set to the secondary working state;

[0058] ​​​If H4≤h<H5, the working state of the energy storage system within the target time interval is set to the fifth-level working state.

[0059] In some embodiments of the present application, the secondary control strategy further includes:

[0060] If [p (ti) -p]<0;

[0061] Obtaining the state evaluation value h of the energy storage system within the target time interval;

[0062] If 0≤h

[0063] If H1≤h<H4, the working state of the energy storage system in the target time interval is set to the fourth-level working state;

[0064] If H4≤h<H5, the working state of the energy storage system within the target time interval is set to the fifth-level working state.

[0065] Compared with the prior art, the energy storage system collaborative optimization method based on photovoltaic power prediction in the embodiment of the present application has the following beneficial effects:

[0066] The energy storage system's safe energy storage capacity is set according to the periodic fluctuation evaluation value. At the same time, multiple time intervals are established within a single adjustment cycle to dynamically adjust the working state of the energy storage system to avoid overcharging and discharging of the energy storage system and increase the service life of the energy storage system.

[0067] The expected photovoltaic power in each time interval is generated according to the preset power prediction model, and the working state of the energy storage system in each time interval is adjusted according to the actual photovoltaic power parameters to control the output or storage of electric energy, improve the efficiency of smoothing photovoltaic output fluctuations, and ensure the stable operation of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 It is a flow chart of a method for collaborative optimization of an energy storage system based on photovoltaic power prediction in a preferred embodiment of the present application. DETAILED DESCRIPTION

[0069] The specific implementation methods of the present application are further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present application but are not intended to limit the scope of the present application.

[0070] ​In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0071] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0072] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0073] like Figure 1 As shown, a method for collaborative optimization of an energy storage system based on photovoltaic power prediction in a preferred embodiment of the present application includes:

[0074] S101: Generate a fluctuation evaluation value of the current adjustment cycle according to historical operating parameters;

[0075] S102: setting the energy storage capacity of the energy storage system in the current regulation cycle according to the fluctuation evaluation value;

[0076] S103: setting a current adjustment cycle to establish multiple time intervals according to the fluctuation evaluation value, and setting a control strategy in each time interval according to the expected photovoltaic power in each time interval.

[0077] Specifically, when generating fluctuation evaluation values ​​based on historical operating parameters, it includes:

[0078] Obtain the photovoltaic operation parameters and environmental parameters in the previous regulation cycle of the current regulation time node;

[0079] Generate a fluctuation evaluation value f of the current adjustment time node according to a preset evaluation model;

[0080]

[0081] Among them, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; n1 is the number of time intervals in the previous adjustment cycle; ci is the actual photovoltaic power in the i-th time interval in the previous adjustment cycle; c′i is the expected photovoltaic power in the i-th time interval in the previous adjustment cycle; Y(i) is the selection coefficient, if ci-c′i>0, then Y(i)=0; if ci-c′i<0, then Y(i)=-1; θ is the number of environmental evaluation indicators; βi is the influencing factor of the i-th environmental indicator; ki is the reference value of the i-th environmental evaluation indicator in the previous adjustment cycle.

[0082] Specifically, environmental evaluation indicators include but are not limited to light intensity, light duration, cloud cover, presence of haze, ambient temperature, wind speed and other environmental parameters that may affect photovoltaic power generation.

[0083] Specifically, in the above embodiment, all parameters in the model are normalized by adding a first fixed coefficient and a second fixed coefficient, so that each parameter is within the same value range.

[0084] Specifically, when the current adjustment period is set according to the fluctuation evaluation value to establish multiple time intervals, it includes:

[0085] According to the fluctuation evaluation value f of the current adjustment time node, the number of time intervals n of the current adjustment cycle is set;

[0086] A time interval sequence T is established, T=(t1, t2...ti...tn), wherein ti is the i-th time interval in the current adjustment period; and n is the number of adjustment periods in the current time interval.

[0087] Specifically, the greater the fluctuation evaluation value, the greater the possibility of fluctuations in the current photovoltaic power, the greater the number of time intervals that should be set, and the shorter the duration of the corresponding single time interval, thereby improving the control accuracy of the working state of the energy storage system in each time interval, smoothing the efficiency of photovoltaic output fluctuations and ensuring the stable operation of the system.

[0088] In a preferred embodiment of the present application, setting the energy storage capacity of the energy storage system in the current regulation cycle includes:

[0089] The primary energy storage capacity A1 and the secondary energy storage capacity A2 are set according to the energy storage system parameters, wherein A1<A2;

[0090] Set the correction coefficient r according to the fluctuation evaluation value f of the current adjustment time node;

[0091] The energy storage capacity interval A of the energy storage system in the current regulation cycle is generated according to the correction coefficient r, where A=(r*A1, A2).

[0092] Specifically, when setting the correction coefficient r, it includes:

[0093] Presetting a first fluctuation evaluation value interval (F1, F2), a second fluctuation evaluation value interval (F2, F3) and a third fluctuation evaluation value interval (F3, F4);

[0094] If the fluctuation evaluation value f is within the preset first fluctuation evaluation value interval, the correction coefficient r is set to the preset first correction coefficient r1, that is, r=r1;

[0095] If the fluctuation evaluation value f is within the preset second fluctuation evaluation value interval, the correction coefficient r is set to the preset second correction coefficient r2, that is, r=1;

[0096] If the fluctuation evaluation value f is within the preset third fluctuation evaluation value interval, the correction coefficient r is set to the preset third correction coefficient r3, that is, r=r3, and r1<1<r3.

[0097] Specifically, a correction coefficient is set according to the fluctuation evaluation value, so as to dynamically adjust the safe energy storage capacity of the energy storage system, thereby constraining the energy storage state of the energy storage system, avoiding excessive charging and discharging of the energy storage system, and increasing the service life of the energy storage system.

[0098] It can be understood that in the above embodiment, the safe energy storage capacity of the energy storage system is set according to the periodic fluctuation evaluation value, and multiple time intervals are established in a single adjustment cycle to dynamically adjust the working state of the energy storage system to avoid excessive charging and discharging of the energy storage system and increase the service life of the energy storage system.

[0099] In a preferred embodiment of the present application, when setting the control strategy in each time interval, it includes:

[0100] Generate the expected photovoltaic power in each time interval according to the preset power prediction model;

[0101] Establish the expected photovoltaic power P, P = (p1, p2...pi...pn), where pi is the expected photovoltaic power in the i-th time interval;

[0102] According to the time interval sequence T, the i-th time interval is sequentially set as the target time interval;

[0103] Get the actual photovoltaic power P at the start time of the target time interval (ti) ;

[0104] Generate a power fluctuation value f in a target time interval;

[0105] f=Q3*[p (ti) -p] / k;

[0106] Among them, p is the expected photovoltaic power at the target time node; k is the photovoltaic installed capacity; Q3 is the preset third fixed coefficient;

[0107] Preset a first power fluctuation value threshold F1;

[0108] If f<F1, the control strategy of the target time interval is set as the first-level control strategy;

[0109] If f≧F1, the control strategy for the target time interval is set as the secondary control strategy.

[0110] Specifically, a first power fluctuation value threshold is set according to historical operating parameters and control requirements. When the real-time power fluctuation value is less than the first power fluctuation value threshold, it means that the photovoltaic output power fluctuation in the current time interval is small. At this time, the corresponding working state can be set according to the real-time storage capacity of the energy storage system.

[0111] Specifically, when the real-time power fluctuation value is greater than the first power fluctuation value threshold, it means that the photovoltaic output power fluctuation in the current time interval is large, and the working state of the energy storage system needs to be adjusted to smooth the efficiency of photovoltaic output fluctuations and ensure the stable operation of the system.

[0112] In a preferred embodiment of the present application, the primary control strategy includes:

[0113] Obtain the initial energy value B of the energy storage system at the start time node of the target time interval;

[0114] Generate the storage energy h of the energy storage system within the target time interval;

[0115]

[0116] Wherein, j is the duration of the target time interval; Y(x) is the function of the output power of the energy storage system changing with time in the target time interval;

[0117] The working state of the energy storage system within the target time interval is set according to the storage energy h.

[0118] Specifically, the function Y(x) can be constructed based on parameters such as the expected photovoltaic power in the target time interval and the grid demand power.

[0119] Specifically, when setting the working state of the energy storage system within the target time interval, it includes:

[0120] According to the energy storage capacity interval A, a first energy storage threshold H1, a second energy storage threshold H2, a third energy storage threshold H3, a fourth energy storage threshold H4 and a fifth energy storage threshold H5 are set;

[0121] Among them, 0

[0122] If 0≤h

[0123] If H1≤h<H2, the working state of the energy storage system in the target time interval is set to the secondary working state;

[0124] If H2≤h<H3, the working state of the energy storage system in the target time interval is set to the third-level working state;

[0125] If H3≤h<H4, the working state of the energy storage system in the target time interval is set to the fourth-level working state;

[0126] If H4≤h<H5, the working state of the energy storage system within the target time interval is set to the fifth-level working state.

[0127] Specifically, the fifth energy storage threshold refers to the maximum energy storage capacity of the energy storage system, and the second energy storage threshold and the third energy storage threshold are the optimal energy storage ranges of the energy storage system.

[0128] Specifically, the first-level working state is charging only and not discharging, the second-level working state is charging first and discharging is limited, the third-level working state is normal charging and discharging, the fourth-level working state is discharging first and charging is limited, and the fifth-level working state is discharging only and not charging.

[0129] It is understandable that in the above embodiment, the expected photovoltaic power in each time interval is generated according to the preset power prediction model, and the working state of the energy storage system in each time interval is adjusted in combination with the actual photovoltaic power parameters to control the output or storage of electric energy, avoid excessive charging and discharging of the energy storage system, and increase the service life of the energy storage system. The efficiency of smoothing the fluctuation of photovoltaic output is improved to ensure the stable operation of the system.

[0130] In a preferred embodiment of the present application, the secondary control strategy includes:

[0131] If [p (ti) -p]>0;

[0132] Obtaining the state evaluation value h of the energy storage system within the target time interval;

[0133] If 0≤h

[0134] If H1≤h<H4, the working state of the energy storage system in the target time interval is set to the secondary working state;

[0135] ​​​If H4≤h<H5, the working state of the energy storage system within the target time interval is set to the fifth-level working state.

[0136] Specifically, when the photovoltaic output power fluctuates greatly and shows a downward trend, the working state of the energy storage system needs to be corrected to prioritize charging and limit discharge, so as to better improve the efficiency of smoothing photovoltaic output fluctuations and ensure the stability of the power grid.

[0137] Specifically, the secondary control strategy also includes:

[0138] If [p (ti) -p]<0;

[0139] Obtaining the state evaluation value h of the energy storage system within the target time interval;

[0140] If 0≤h

[0141] If H1≤h<H4, the working state of the energy storage system in the target time interval is set to the fourth-level working state;

[0142] If H4≤h<H5, the working state of the energy storage system within the target time interval is set to the fifth-level working state.

[0143] Specifically, when the photovoltaic output power fluctuates greatly and shows an upward trend, the working state of the energy storage system needs to be corrected to prioritize discharge and limit charging, so as to better improve the efficiency of smoothing photovoltaic output fluctuations and ensure the stability of the power grid. This will better ensure the stability of the energy storage system.

[0144] According to the first concept of the present application, the energy storage system is dynamically adjusted to set the safe energy storage capacity of the energy storage system according to the periodic fluctuation evaluation value, and multiple time intervals are established within a single adjustment cycle to dynamically adjust the working state of the energy storage system to avoid excessive charging and discharging of the energy storage system and increase the service life of the energy storage system.

[0145] According to the second concept of the present application, the expected photovoltaic power in each time interval is generated according to the preset power prediction model, and the working state of the energy storage system in each time interval is adjusted in combination with the actual photovoltaic power parameters to control the output or storage of electric energy, improve the efficiency of smoothing photovoltaic output fluctuations, and ensure the stable operation of the system.

[0146] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and substitutions can be made without departing from the technical principles of the present application. These improvements and substitutions should also be regarded as the scope of protection of the present application.​

Claims

1. A collaborative optimization method for energy storage system based on photovoltaic power prediction, characterized in that: include: Generate the fluctuation evaluation value of the current adjustment cycle based on the historical operating parameters; The energy storage capacity of the energy storage system in the current regulation cycle is set according to the fluctuation evaluation value; The current regulation cycle is set according to the fluctuation evaluation value to establish multiple time intervals, and the control strategy in each time interval is set according to the expected photovoltaic power in each time interval.

2. The method for collaborative optimization of energy storage system based on photovoltaic power prediction according to claim 1, characterized in that: When generating fluctuation evaluation values ​​based on historical operating parameters, it includes: Obtain the photovoltaic operation parameters and environmental parameters in the previous regulation cycle of the current regulation time node; Generate a fluctuation evaluation value f of the current adjustment time node according to a preset evaluation model; Among them, e1 is the preset first weight coefficient; e2 is the preset second weight coefficient; Q1 is the preset first fixed coefficient; Q2 is the preset second fixed coefficient; n1 is the number of time intervals in the previous adjustment cycle; ci is the actual photovoltaic power in the i-th time interval in the previous adjustment cycle; c′i is the expected photovoltaic power in the i-th time interval in the previous adjustment cycle; Y(i) is the selection coefficient, if ci-c′i>0, then Y(i)=0; if ci-c′i<0, then Y(i)=-1; θ is the number of environmental evaluation indicators; βi is the influencing factor of the i-th environmental indicator; ki is the reference value of the i-th environmental evaluation indicator in the previous adjustment cycle.

3. The method for collaborative optimization of energy storage system based on photovoltaic power prediction according to claim 2, characterized in that: When the current adjustment period is set according to the fluctuation evaluation value to establish multiple time intervals, it includes: According to the fluctuation evaluation value f of the current adjustment time node, the number of time intervals n of the current adjustment cycle is set; A time interval sequence T is established, T=(t1, t2...ti...tn), wherein ti is the i-th time interval in the current adjustment period; and n is the number of adjustment periods in the current time interval.

4. The method for collaborative optimization of energy storage system based on photovoltaic power prediction according to claim 3, characterized in that: Setting the energy storage capacity of the energy storage system in the current regulation cycle includes: The primary energy storage capacity A1 and the secondary energy storage capacity A2 are set according to the energy storage system parameters, wherein A1<A2; Set the correction coefficient r according to the fluctuation evaluation value f of the current adjustment time node; The energy storage capacity interval A of the energy storage system in the current regulation cycle is generated according to the correction coefficient r, where A=(r*A1, A2).

5. The method for collaborative optimization of energy storage system based on photovoltaic power prediction according to claim 4, characterized in that: When setting the correction factor r, include: Presetting a first fluctuation evaluation value interval (F1, F2), a second fluctuation evaluation value interval (F2, F3) and a third fluctuation evaluation value interval (F3, F4); If the fluctuation evaluation value f is within the preset first fluctuation evaluation value interval, the correction coefficient r is set to the preset first correction coefficient r1, that is, r=r1; If the fluctuation evaluation value f is within the preset second fluctuation evaluation value interval, the correction coefficient r is set to the preset second correction coefficient r2, that is, r=1; If the fluctuation evaluation value f is within the preset third fluctuation evaluation value interval, the correction coefficient r is set to the preset third correction coefficient r3, that is, r=r3, and r1<1<r3.

6. The method for collaborative optimization of energy storage system based on photovoltaic power prediction according to claim 4, characterized in that: When setting the control strategy for each time interval, include: Generate the expected photovoltaic power in each time interval according to the preset power prediction model; Establish the expected photovoltaic power P, P = (p1, p2...pi...pn), where pi is the expected photovoltaic power in the i-th time interval; According to the time interval sequence T, the i-th time interval is sequentially set as the target time interval; Get the actual photovoltaic power P at the start time of the target time interval (ti) ; Generate a power fluctuation value f in a target time interval; f=Q3*[p (ti) -p] / k; Among them, p is the expected photovoltaic power at the target time node; k is the photovoltaic installed capacity; Q3 is the preset third fixed coefficient; Preset a first power fluctuation value threshold F1; If f<F1, the control strategy of the target time interval is set as the first-level control strategy; If f≧F1, the control strategy for the target time interval is set as the secondary control strategy.

7. The method for collaborative optimization of energy storage system based on photovoltaic power prediction according to claim 6, characterized in that: The primary control strategy includes: Obtain the initial energy value B of the energy storage system at the start time node of the target time interval; Generate the storage energy h of the energy storage system within the target time interval; Wherein, j is the duration of the target time interval; Y(x) is the function of the output power of the energy storage system changing with time in the target time interval; The working state of the energy storage system within the target time interval is set according to the storage energy h.

8. The method for collaborative optimization of energy storage system based on photovoltaic power prediction according to claim 7, characterized in that: When setting the working state of the energy storage system within the target time interval, it includes: According to the energy storage capacity interval A, a first energy storage threshold H1, a second energy storage threshold H2, a third energy storage threshold H3, a fourth energy storage threshold H4 and a fifth energy storage threshold H5 are set; Among them, 0<H1<H2<H3<H4<H5; and H1=r*A1; H4=A2; If 0≤h<H1, the working state of the energy storage system in the target time interval is set to the first-level working state; If H1≤h<H2, the working state of the energy storage system in the target time interval is set to the secondary working state; If H2≤h<H3, the working state of the energy storage system in the target time interval is set to the third-level working state; If H3≤h<H4, the working state of the energy storage system in the target time interval is set to the fourth-level working state; If H4≤h<H5, the working state of the energy storage system within the target time interval is set to the fifth-level working state.

9. The method for collaborative optimization of energy storage system based on photovoltaic power prediction according to claim 8, characterized in that: Secondary control strategies include: If [p (ti) -p]>0; Obtaining the state evaluation value h of the energy storage system within the target time interval; If 0≤h<H1, the working state of the energy storage system in the target time interval is set to the first-level working state; If H1≤h<H4, the working state of the energy storage system in the target time interval is set to the secondary working state; If H4≤h<H5, the working state of the energy storage system within the target time interval is set to the fifth-level working state.

10. The method for collaborative optimization of energy storage system based on photovoltaic power prediction according to claim 8, characterized in that: Secondary control strategies also include: If [p (ti) -p]<0; Obtaining the state evaluation value h of the energy storage system within the target time interval; If 0≤h<H1, the working state of the energy storage system in the target time interval is set to the first-level working state; If H1≤h<H4, the working state of the energy storage system in the target time interval is set to the fourth-level working state; If H4≤h<H5, the working state of the energy storage system within the target time interval is set to the fifth-level working state.

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