CN105529728B - Energy storage schedulable capacity prediction method considering multi-source information fusion and planned output - Google Patents
Energy storage schedulable capacity prediction method considering multi-source information fusion and planned output Download PDFInfo
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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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Abstract
The invention provides an energy storage schedulable capacity prediction method considering multi-source information fusion and planned output, which comprises the following steps: (1) acquiring real-time data; (2) calculating the day-ahead predicted power, the ultra-short-term predicted power and the charge state of the energy storage system of the new energy power generation field at the current moment; (3) calculating the output value of the energy storage system at the current moment based on the running state of the new energy power generation field and the state of charge interval of the energy storage system; (4) distributing the output value of the energy storage system to each energy storage unit, and calculating the state of charge value of each energy storage unit at the end of the current time; (5) calculating the state of charge value of the energy storage system at the end of four hours in the future at the current moment; (6) and calculating the schedulable charging and discharging capacity of the energy storage system for four hours in the future. The method is suitable for predicting the dispatchable capacity of the energy storage system when new energy such as wind energy storage, light energy storage, wind energy storage and the like and energy storage combined power generation tracking power generation plan are applied.
Description
Technical Field
The invention belongs to the technical field of intelligent power grids and energy storage and conversion, and particularly relates to an energy storage schedulable capacity prediction method considering multi-source information fusion and planned output, which is particularly suitable for predicting schedulable capacity of an energy storage system at a future moment and energy management of the energy storage system when the energy storage system participates in tracking power generation planned output.
Background
In recent years, the power generation scale of new energy such as wind power and photovoltaic is continuously enlarged, but the inherent randomness and fluctuation of the new energy cause that the safety and stability of a power grid can be endangered by large-scale grid connection of the new energy. The actual power generation power of wind power, photovoltaic power and the like and the planned value in the day before often have the condition of larger error, and in order to reduce the error between the actual power generation power and the planned value in the day before, the output of the planned power generation power is participated and tracked by utilizing the charge-discharge bidirectional characteristic of energy storage to gradually become a feasible scheme.
The method can effectively improve the utilization efficiency of the stored energy and make better decisions on the charge and discharge control of the stored energy by predicting the schedulable capacity of the energy storage system, and at present, the patents, documents, technical reports and the like related to the schedulable capacity prediction of the energy storage system are few, and further research is needed.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides an energy storage schedulable capacity prediction method considering multi-source information fusion and planned output, provides a prediction method of schedulable capacity of an energy storage system when a new energy and energy storage combined power generation system tracks power generation planned output, and is suitable for predicting schedulable capacity of the energy storage system when new energy such as wind energy storage, light energy storage, wind-light energy storage and the like and energy storage combined power generation track power generation planned application.
In order to achieve the purpose of the invention, the invention adopts the following technical scheme:
an energy storage schedulable capacity prediction method considering multi-source information fusion and planned output comprises the following steps
The method comprises the following steps:
(1) acquiring real-time data;
(2) calculating the day-ahead predicted power, the ultra-short-term predicted power and the charge state of the energy storage system of the new energy power generation field at the current moment;
(3) calculating the output value of the energy storage system at the current moment based on the running state of the new energy power generation field and the state of charge interval of the energy storage system;
(4) distributing the output value of the energy storage system to each energy storage unit, and calculating the state of charge value of each energy storage unit at the end of the current time;
(5) calculating the state of charge value of the energy storage system at the end of four hours in the future at the current moment;
(6) and calculating the schedulable charging and discharging capacity of the energy storage system for four hours in the future.
Preferably, in step (1), the real-time data includes: the method comprises the steps of obtaining real-time data of each wind turbine set at the current moment from a new energy power plant monitoring system, obtaining wind power day-ahead predicted power and ultra-short-term predicted power from a wind power prediction system, and obtaining current related data of each energy storage unit from an energy storage power station monitoring system.
Preferably, the step (2) comprises the steps of:
step 2-1, calculating the day-ahead predicted power and the ultra-short-term predicted power of the new energy power plant at the current moment:
the power of the new energy power generation field is the sum of the power values of the new energy motor sets:
in the formula, Pf、PufRespectively predicting power of the new energy power generation field in the day ahead and predicting power of the new energy power generation field in the ultra-short period; pfi、PufiRespectively predicting the day-ahead predicted power and the ultra-short-term predicted power of the wind turbine generator i, wherein N is the total number of the wind turbine generators;
step 2-2, calculating the state of charge value of the energy storage system:
in the formula, SOC is the state of charge value of the energy storage system; SOCiIs the state of charge value of the energy storage unit i; eNiThe capacity of the energy storage unit i is shown, and M is the total number of the energy storage units.
Preferably, the step (3) comprises the following steps:
3-1, determining the current wind power state based on the wind power ultra-short-term predicted power at the current moment and the wind power day-ahead predicted power data;
setting three generated power prediction characteristic values, including: predicted upper limit characteristic value P of generated powerfb(t), current generated power prediction value Pf(t) predicted lower limit characteristic value P of generated Powerfs(t)，
Predicted power upper limit value: pfb(t)＝Pf(t)+Plimit
Predicted power lower limit value: pfs(t)＝Pf(t)-Plimit
Wherein: plimitTaking 0.25 as alpha x Cap, wherein Cap is the installed capacity of the new energy generator set;
the three generated power prediction characteristic values divide (0, ∞) into three intervals: puf(t)＜Pfs(t)、Pfs(t)≤Puf(t)≤Pfb(t)、Puf(t)＞Pfb(t), each interval corresponds to a power generation state, and is named as a power generation state A, B, C respectively, wherein Puf(t) the ultra-short-term predicted power value of the wind power at the moment t;
step 3-2, setting four control coefficients SOClow、a1、a2And SOChighAnd satisfy SOClow＜a1＜a2＜SOChighAnd setting the current state of charge (SOC) of the energy storage system to be 0,1 according to the four control coefficients]Is divided into five intervals in sequence, 0 is more than or equal to SOC (t) < SOClow、SOClow≤SOC(t)＜a1、a1≤SOC(t)＜a2、a2≤SOC(t)＜SOChigh、SOChighSOC (t) is less than or equal to 1 and is respectively named as intervals I, II, III, IV and V;
and 3-3, determining the output value of the energy storage system at the current moment according to a calculation rule based on the power generation state and the charge state interval at the current moment.
Preferably, in step 3-3, the calculation rule is:
when the power generation state is A and the SOC is in the interval I, the output value of the energy storage system is 0;
when the power generation state is A and the SOC is in the intervals II and III, the output value of the energy storage system is Pfs(t)-Puf(t)；
When the power generation state is A and the SOC is in the intervals IV and V, the output value of the energy storage system is (P)fs(t)-Puf(t),Pfb(t)-Puf(t))；
When the power generation state is B and the SOC is in the interval I, the output value of the energy storage system is- (P)uf(t)-Pfs(t))；
When the power generation state is B and the SOC is in the interval II, the output value of the energy storage system is (0, P)uf(t)-Pfs(t))；
When the power generation state is B and the SOC is in the interval III, the output value of the energy storage system is 0;
when the power generation state is B and the SOC is in the interval IV, the output value of the energy storage system is (0, P)fb(t)-Puf(t))；
When the power generation state is B and the SOC is in the interval V, the output value of the energy storage system is Pfb(t)-Puf(t)；
When the power generation state is C and the SOC is in the intervals I and II, the output value of the energy storage system is- (P)uf(t)-Pfb(t),Puf(t)-Pfs(t))；
When the power generation state is C and the SOC is in intervals III and IV, the output value of the energy storage system is- (P)uf(t)-Pfb(t))；
When the power generation state is C and the SOC is in the interval IV, the output value of the energy storage system is 0;
the determination of the output value of the energy storage system simultaneously satisfies the following constraint conditions:
-Pmax≤PES≤Pmax
SOClow≤SOC(t)≤SOChigh
wherein P isESIs the output value of the energy storage system; pmaxIs the energy storage system maximum output power.
Preferably, the step (4) comprises the following steps:
step 4-1, distributing the output value of the energy storage system to each energy storage unit,
if P isES＞0：
Wherein P isbatiThe output value of the energy storage unit i is obtained;
verification PbatiWhether or not it is [0, P ]maxi]In the range of wherein PmaxiThe maximum output limit of the energy storage unit i is determined by the characteristics of the energy storage unit; if not, P is setbati＝PmaxiThe remaining points within range are updated as follows:
in which W is PbatiSatisfy [0, P ]maxi]Number of points within range;
if P isES＜0：
Verification PbatiWhether or not it is in [ -P ]maxi,0]Within the range, if not, P is setbati＝-PmaxiThe remaining points within range are updated as follows:
in which H is PbatiSatisfies [ -P [)maxi,0]Number of points within range;
step 4-2, calculating the charge state values of all the energy storage units at the end of the current time,
calculating the SOC value at the end of the t time by using the following recursion relation: when P is presentbatiWhen (t) is less than or equal to 0:
SOCi(t)＝(1-σsdr)SOCi(t-1)-Pbati(t)ΔtηC/ENi
when P is presentbati(t) > 0:
SOCi(t)＝(1-σsdr)SOCi(t-1)-Pbati(t)Δt/ηDENi
in the formula: SOCi(t) is the state of charge value of the energy storage unit at the end of time t; sigmasdrIs the self-discharge rate of the energy storage system; etaCAnd ηDThe charging efficiency and the discharging efficiency of the energy storage system are respectively obtained; Δ t is the calculation window duration, min; eNiIs the rated capacity of the energy storage unit.
Preferably, the step (5) comprises the steps of:
step 5-1, calculating the state of charge value of the energy storage system at the current moment:
wherein SOC (t) is a state of charge value of the energy storage system at the time t; SOCi(t) is the state of charge value of the energy storage unit i at the moment t; eNiIs the capacity of the energy storage unit i;
and 5-2, circularly calculating the output value of the energy storage system in the future four hours according to the steps and the state of charge value of the energy storage system at the end of each time, and finally calculating the state of charge value at the end of the current four hours in the future.
Preferably, in the step (6), the formula for calculating the schedulable charging capacity of the energy storage system for the next four hours is as follows:
EdisC(t)＝SOCaf(t)*EN
the formula for calculating the schedulable discharge capacity of the energy storage system for four hours in the future is as follows:
EdisD(t)＝(1-SOCaf(t))*EN
in the formula EdisC(t) schedulable of the energy storage System for the next four hours at that timeCharge capacity, unit: MW; edisD(t) is the discharge capacity which can be scheduled by the energy storage system in the next four hours at the moment, unit: MW; SOCaf(t) is the state of charge value of the energy storage system at the end of four hours in the future at that time, ENIs the capacity value of the energy storage system.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a prediction method for the scheduling capacity of an energy storage system when a new energy and energy storage combined power generation system tracks the planned output of power generation by utilizing the multi-source information of new energy power generation (wind power generation and photovoltaic power generation) and the energy storage system, comprehensively considering ultra-short-term predicted power, power predicted characteristic value, the state of charge of the energy storage system and the like, and considering the analysis and fusion of the multi-source information. The method is suitable for predicting the dispatchable capacity of the energy storage system when new energy such as wind energy storage, light energy storage, wind energy storage and the like and energy storage combined power generation tracking power generation plan are applied, and can provide a reference basis for the optimal control and energy management of the energy storage system.
Drawings
Figure 1 is a system diagram of a new energy generator set and an energy storage unit provided by the invention,
FIG. 2 is a flow chart of an energy storage dispatchable capacity prediction method considering multi-source information fusion and planned output according to the present invention,
figure 3 is a graph of the ratio of schedulable discharge capacity of the energy storage system provided by the present invention,
fig. 4 is a graph of the scheduled charge capacity occupancy ratio for an energy storage system provided by the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
As shown in fig. 1, the system diagram of a new energy generator set and an energy storage system is shown, the new energy generator set includes a wind power generator set and a photovoltaic generator set, the wind power generator set is connected with a wind power plant monitoring system and a wind power prediction system, the photovoltaic generator set is connected with a photovoltaic power station monitoring system and a photovoltaic power station prediction system, the energy storage system includes M energy storage units, each energy storage unit is connected with a bidirectional converter, and the energy storage system is connected with an energy storage power station monitoring system.
As shown in fig. 2, the method for controlling a multi-type energy storage system to improve the planned output capacity of the tracking power generation in this embodiment mainly includes the following steps:
step A, acquiring wind power day-ahead predicted power, ultra-short-term predicted power and current related data of each energy storage unit of each wind turbine generator at the current moment;
b, calculating the day-ahead predicted power, the ultra-short-term predicted power and relevant data of the energy storage system of the new energy power plant at the current moment;
step C, calculating the output value of the energy storage system at the current moment based on the wind power state and the charge state interval of the energy storage system;
step D, distributing the output values of the energy storage system among the energy storage units, and calculating the charge state values of the energy storage units at the end of the current moment;
e, calculating the state of charge value of the energy storage system at the end of four hours in the future at the current moment;
and F, calculating the schedulable discharge capacity and the schedulable charge capacity of the energy storage system in the next four hours.
In step B, the method for calculating the data related to the current wind farm and the energy storage system is as follows:
firstly, the day-ahead predicted power and the ultra-short-term predicted power of the wind power plant at the current moment are calculated:
the power of the wind power plant is the sum of the power values of all the wind power units:
in the formula, Pf、PufRespectively predicting the day-ahead predicted power and the ultra-short-term predicted power of the wind power plant; pfi、PufiAre respectively asAnd (3) predicting the day-ahead predicted power and the ultra-short-term predicted power of the wind turbine generator i, wherein N is the total number of the wind turbine generators.
Then, the state of charge value of the energy storage system is calculated:
in the formula, SOC is the state of charge value of the energy storage system; SOCiIs the state of charge value of the energy storage unit i; eNiThe capacity of the energy storage unit i is shown, and M is the total number of the energy storage units.
In step C, the calculation method of the force output value of the energy storage system is as follows:
firstly, determining the current wind power state based on the wind power ultra-short-term predicted power at the current moment and the predicted power data in the day ahead of the wind power;
setting three generated power prediction characteristic values, including: predicted upper limit characteristic value P of generated powerfb(t), current generated power prediction value Pf(t) predicted lower limit characteristic value P of generated Powerfs(t)。
Predicted power upper limit value: pfb(t)＝Pf(t)+Plimit
Predicted power lower limit value: pfs(t)＝Pf(t)-Plimit
Wherein: plimitα × Cap, α is 0.25; and the Cap is the installed capacity of the new energy generator set.
The three generated power prediction characteristic values divide (0, ∞) into three intervals: puf(t)＜Pfs(t)、Pfs(t)≤Puf(t)≤Pfb(t)、Puf(t)＞Pfb(t), each interval corresponds to a power generation state, and is named as a power generation state A, B, C respectively, wherein Puf(t) the ultra-short-term predicted power value of the wind power at the moment t;
secondly, determining a current state of charge interval based on the current state of charge value of the energy storage system;
setting four control coefficients SOClow、a1、a2And SOChighAnd satisfy SOClow＜a1＜a2＜SOChighWherein SOC islowAnd SOChighDetermined by the characteristics of the energy storage system itself. Setting the current state of charge (SOC) of the energy storage system to be 0,1 according to the four control coefficients]Is divided into five intervals in sequence, 0 is more than or equal to SOC (t) < SOClow、SOClow≤SOC(t)＜a1、a1≤SOC(t)＜a2、a2≤SOC(t)＜SOChigh、SOChighSOC (t) is less than or equal to 1 and is respectively named as intervals I, II, III, IV and V;
thirdly, determining the output value of the energy storage system at the current moment according to the calculation rule shown in the specification based on the power generation state and the charge state interval at the current moment;
finally, the determination of the output value of the energy storage system simultaneously meets the following constraint conditions:
-Pmax≤PES≤Pmax
SOClow≤SOC(t)≤SOChigh
wherein P isESIs the output value of the energy storage system; pmaxIs the energy storage system maximum output power.
In step D, the calculation method for distributing the output values of the energy storage system among the energy storage units and calculating the state of charge values of the energy storage units at the end of the current time is as follows:
firstly, distributing the output force values of the energy storage system among the energy storage units:
if P isES> 0, i.e. the energy storage system discharges:
wherein P isbatiIs the output value of the energy storage unit i.
AuthenticationPbatiWhether or not it is [0, P ]maxi]Within the range, if not, P is setbati＝Pmaxi. The remaining points in range are updated as follows, where PmaxiThe maximum output limit of the energy storage unit i is determined by the characteristics of the energy storage unit.
Wherein W is PbatiSatisfy [0, P ]maxi]Number of points in the range.
If P isES< 0, i.e. energy storage system charging:
verification PbatiWhether or not it is in [ -P ]maxi,0]Within the range, if not, P is setbati＝-Pmaxi. The remaining points within range are updated as follows:
wherein H is PbatiSatisfies [ -P [)maxi,0]Number of points in the range.
And then, calculating the charge state values of the energy storage units at the end of the current time:
calculating the SOC value at the end of the t time by using the following recursion relation: when P is presentbatiWhen (t) is less than or equal to 0:
SOCi(t)＝(1-σsdr)SOCi(t-1)-Pbati(t)ΔtηC/ENi
when P is presentbati(t) > 0:
SOCi(t)＝(1-σsdr)SOCi(t-1)-Pbati(t)Δt/ηDENi
in the formula: SOCi(t) is the state of charge value of the energy storage unit at the end of time t; sigmasdrFor energy storage systemsSelf-discharge rate of (d); etaCAnd ηDThe charging efficiency and the discharging efficiency of the energy storage system are respectively obtained; Δ t is the calculation window duration, min; eNiIs the rated capacity of the energy storage unit.
In step E, the method for calculating the state of charge value of the energy storage system at the end of four hours in the future at the current time is as follows:
firstly, calculating the state of charge value of the energy storage system at the current moment:
wherein SOC (t) is a state of charge value of the energy storage system at the time t; SOCi(t) is the state of charge value of the energy storage unit i at the moment t; eNiIs the capacity of the energy storage unit i.
Then, circularly calculating the output value of the energy storage system for four hours in the future according to the steps and the state of charge value of the energy storage system at the end of each time, and finally calculating the state of charge value at the end of four hours in the future at the current time;
in step F, the method for calculating the schedulable discharge capacity and the schedulable charge capacity of the energy storage system for the four hours in the future is as follows:
EdisC(t)＝SOCaf(t)*EN
EdisD(t)＝(1-SOCaf(t))*EN
in the formula, EdisC(t) is the schedulable charging capacity, MW, of the energy storage system four hours in the future at that moment; edisD(t) is the discharge capacity, MW, of the energy storage system which can be scheduled in the next four hours at the moment; SOCaf(t) is the state of charge value at the end of four hours in the future at that time; eNIs the capacity value of the energy storage system.
As shown in fig. 3, which is a graph of the proportion of the discharge capacity schedulable by the energy storage system for 20 hours, and as shown in fig. 4, which is a graph of the proportion of the charge capacity schedulable by the energy storage system for 20 hours.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (5)
1. An energy storage schedulable capacity prediction method considering multi-source information fusion and planned output is characterized by comprising the following steps:
(1) acquiring real-time data;
(2) calculating the day-ahead predicted power, the ultra-short-term predicted power and the charge state of the energy storage system of the new energy power generation field at the current moment;
(3) calculating the output value of the energy storage system at the current moment based on the running state of the new energy power generation field and the state of charge interval of the energy storage system;
(4) distributing the output value of the energy storage system to each energy storage unit, and calculating the state of charge value of each energy storage unit at the end of the current time;
(5) calculating the state of charge value of the energy storage system at the end of four hours in the future at the current moment;
(6) calculating the schedulable charging and discharging capacity of the energy storage system for four hours in the future;
the step (2) comprises the following steps:
step 2-1, calculating the day-ahead predicted power and the ultra-short-term predicted power of the new energy power plant at the current moment:
the power of the new energy power generation field is the sum of the power values of the new energy motor sets:
in the formula, Pf、PufRespectively predicting power of the new energy power generation field in the day ahead and predicting power of the new energy power generation field in the ultra-short period; pfi、PufiRespectively predicting the day-ahead predicted power and the ultra-short-term predicted power of the wind turbine generator i; n is the total number of the wind turbine generators;
step 2-2, calculating the state of charge value of the energy storage system:
in the formula, SOC is the state of charge value of the energy storage system; SOCiIs the state of charge value of the energy storage unit i; eNiIs the capacity of the energy storage unit i; m is the total number of the energy storage units;
the step (3) comprises the following steps:
3-1, determining the current wind power state based on the wind power ultra-short-term predicted power at the current moment and the wind power day-ahead predicted power data;
setting three generated power prediction characteristic values, including: predicted upper limit characteristic value P of generated powerfb(t), current generated power prediction value Pf(t) predicted lower limit characteristic value P of generated Powerfs(t)，
Predicted power upper limit value: pfb(t)＝Pf(t)+Plimit
Predicted power lower limit value: pfs(t)＝Pf(t)-Plimit
Wherein: plimitTaking 0.25 as alpha x Cap, wherein Cap is the installed capacity of the new energy generator set;
the three generated power prediction characteristic values divide (0, ∞) into three intervals: puf(t)＜Pfs(t)、Pfs(t)≤Puf(t)≤Pfb(t)、Puf(t)＞Pfb(t), each interval corresponds to a power generation state, and is named as a power generation state A, B, C respectively, wherein Puf(t) the ultra-short-term predicted power value of the wind power at the moment t;
step 3-2, setting four controlsSystem coefficient SOClow、a1、a2And SOChighAnd satisfy SOClow＜a1＜a2＜SOChighAnd setting the current state of charge (SOC) of the energy storage system to be 0,1 according to the four control coefficients]Is divided into five intervals in sequence, 0 is more than or equal to SOC (t) < SOClow、SOClow≤SOC(t)＜a1、a1≤SOC(t)＜a2、a2≤SOC(t)＜SOChigh、SOChighSOC (t) is less than or equal to 1 and is respectively named as intervals I, II, III, IV and V;
3-3, determining the output value of the energy storage system at the current moment according to a calculation rule based on the power generation state and the state of charge interval at the current moment;
in step 3-3, the calculation rule is:
when the power generation state is A and the SOC is in the interval I, the output value of the energy storage system is 0;
when the power generation state is A and the SOC is in the intervals II and III, the output value of the energy storage system is Pfs(t)-Puf(t)；
When the power generation state is A and the SOC is in the intervals IV and V, the output value of the energy storage system is (P)fs(t)-Puf(t),Pfb(t)-Puf(t))；
When the power generation state is B and the SOC is in the interval I, the output value of the energy storage system is- (P)uf(t)-Pfs(t))；
When the power generation state is B and the SOC is in the interval II, the output value of the energy storage system is (0, P)uf(t)-Pfs(t))；
When the power generation state is B and the SOC is in the interval III, the output value of the energy storage system is 0;
when the power generation state is B and the SOC is in the interval IV, the output value of the energy storage system is (0, P)fb(t)-Puf(t))；
When the power generation state is B and the SOC is in the interval V, the output value of the energy storage system is Pfb(t)-Puf(t)；
When the power generation state is C and the SOC is in the intervals I and II, the output value of the energy storage system is- (P)uf(t)-Pfb(t),Puf(t)-Pfs(t))；
When the power generation state is C and the SOC is in intervals III and IV, the output value of the energy storage system is- (P)uf(t)-Pfb(t))；
When the power generation state is C and the SOC is in the interval IV, the output value of the energy storage system is 0;
the determination of the output value of the energy storage system simultaneously satisfies the following constraint conditions:
-Pmax≤PES≤Pmax
SOClow≤SOC(t)≤SOChigh
wherein P isESIs the output value of the energy storage system; pmaxIs the energy storage system maximum output power.
2. The prediction method according to claim 1, wherein in the step (1), the real-time data comprises: the method comprises the steps of obtaining real-time data of each wind turbine set at the current moment from a new energy power plant monitoring system, obtaining wind power day-ahead predicted power and ultra-short-term predicted power from a wind power prediction system, and obtaining current related data of each energy storage unit from an energy storage power station monitoring system.
3. The prediction method according to claim 1, wherein the step (4) comprises the steps of:
step 4-1, distributing the output value of the energy storage system to each energy storage unit,
if P isES＞0：
Wherein P isbatiThe output value of the energy storage unit i is obtained;
verification PbatiWhether or not it is [0, P ]maxi]In the range of wherein PmaxiThe maximum output limit of the energy storage unit i is determined by the characteristics of the energy storage unit; if not, P is setbati＝PmaxiThe remaining points within range are updated as follows:
in which W is PbatiSatisfy [0, P ]maxi]Number of points within range;
if P isES＜0：
Verification PbatiWhether or not it is in [ -P ]maxi,0]Within the range, if not, P is setbati＝-PmaxiThe remaining points within range are updated as follows:
in which H is PbatiSatisfies [ -P [)maxi,0]Number of points within range;
step 4-2, calculating the charge state values of all the energy storage units at the end of the current time,
calculating the SOC value at the end of the t time by using the following recursion relation: when P is presentbatiWhen (t) is less than or equal to 0:
SOCi(t)＝(1-σsdr)SOCi(t-1)-Pbati(t)ΔtηC/ENi
when P is presentbati(t) > 0:
SOCi(t)＝(1-σsdr)SOCi(t-1)-Pbati(t)Δt/ηDENi
in the formula: SOCi(t) is the state of charge value of the energy storage unit at the end of time t; sigmasdrIs the self-discharge rate of the energy storage system; etaCAnd ηDThe charging efficiency and the discharging efficiency of the energy storage system are respectively obtained; Δ t is the calculation window duration, min; eNiFor energy storage cellsi, capacity of the memory.
4. The prediction method according to claim 1, wherein the step (5) comprises the steps of:
step 5-1, calculating the state of charge value of the energy storage system at the current moment:
wherein SOC (t) is a state of charge value of the energy storage system at the time t; SOCi(t) is the state of charge value of the energy storage unit i at the moment t; eNiIs the capacity of the energy storage unit i;
and 5-2, circularly calculating the output value of the energy storage system in the future four hours according to the steps and the state of charge value of the energy storage system at the end of each time, and finally calculating the state of charge value at the end of the current four hours in the future.
5. The prediction method according to claim 1, wherein in the step (6), the formula for calculating the schedulable charge capacity of the energy storage system for the next four hours is as follows:
EdisC(t)＝SOCaf(t)*EN
the formula for calculating the schedulable discharge capacity of the energy storage system for four hours in the future is as follows:
EdisD(t)＝(1-SOCaf(t))*EN
in the formula EdisC(t) is the schedulable charging capacity of the energy storage system for the next four hours at the moment, unit: MW; edisD(t) is the discharge capacity which can be scheduled by the energy storage system in the next four hours at the moment, unit: MW; SOCaf(t) is the state of charge value of the energy storage system at the end of four hours in the future at that time, ENIs the capacity value of the energy storage system.
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