CN115954909B - Power prediction deviation compensation method and system for new energy power station - Google Patents

Abstract

The invention discloses a power prediction deviation compensation method and a system for a new energy power station, wherein the method comprises the following steps: acquiring actual power generation of a power generation system, a short-term power prediction interval and an ultra-short-term power prediction interval of a power generation prediction system, a real-time SOC of an energy storage system, a preset SOC upper limit value and a preset SOC lower limit value; determining a power prediction interval to which the actual power generation power belongs according to the actual power generation power of the power generation system, a short-term power prediction interval and an ultra-short-term power prediction interval of the power generation prediction system; determining whether the energy storage system performs power prediction deviation compensation or not according to a power prediction interval to which the actual power belongs, a real-time SOC of the energy storage system, a preset SOC upper limit value and a preset SOC lower limit value; when the energy storage system performs power prediction deviation compensation, the charge and discharge power of the energy storage system is calculated according to the actual power generation power and the power prediction interval to which the actual power generation power belongs. The invention improves the accuracy of power prediction deviation compensation and simultaneously realizes the efficient utilization of the energy storage system.

Description

Power prediction deviation compensation method and system for new energy power station

Technical Field

The invention relates to the technical field of new energy power generation and energy storage, in particular to a power prediction deviation compensation method and system of a new energy power station.

Background

Because the power of the power generation system of the new energy power station has volatility and randomness, the frequent fluctuation can cause impact on a power grid, and the power grid needs to predict the power of the power generation system of the new energy power station. However, the prediction accuracy of the current prediction system cannot reach the standard under the influence of factors such as environment, and the new energy power station needs to compensate the predicted power through the charge and discharge of the standard energy storage system.

For example, chinese patent application publication No. CN112994121a discloses a new energy power generation power prediction deviation compensation method and system, and the patent application proposes that under the condition that a new energy power station is configured with an energy storage system, a short-term power prediction value P short-term and an ultra-short-term power prediction value P ultra-short term are obtained, and a confidence interval [ b ] of short-term power prediction is calculated and obtained _{Short term} ，a _{Short term} ]Confidence interval of ultra-short term power prediction [ b ] _{Ultra-short term} ，a _{Ultra-short term} ]The method comprises the steps of carrying out a first treatment on the surface of the And determining the value range of the target power Pcompensation of the energy storage system, and transmitting a value to the energy storage system. However, the patent application only selects a value in the confidence interval and transmits the value to the energy storage system, and the transmitted power is not reasonably selected in combination with the state of charge and discharge SOC of the battery of the energy storage system, so that the charge and discharge utilization rate of the energy storage system is not high; moreover, because the power of the power generation system has volatility and randomness, the power is easily generated by selecting the boundary value of the confidence interval, so that the compensation effect cannot be expected.

For example, chinese patent application publication No. CN112865157a discloses a hybrid power station and a method for compensating prediction deviation of power generated by new energy, and the patent application proposes a compensation power range required to meet the power prediction deviation compensation requirements of a first new energy power station and a second new energy power station, which are respectively recorded as a first compensation power range and a second compensation power range; and controlling the first energy storage converter to compensate the power prediction deviation of the first new energy power station in the first compensation power range, and simultaneously controlling the second energy storage converter to compensate the power prediction deviation of the second new energy power station in the second compensation power range. However, each energy storage power station in the patent application only has a single prediction interval, when two prediction intervals exist, a reasonable power distribution scheme does not exist, and the power is issued by adopting the boundary value of the compensation power range, so that the compensation effect cannot reach the expectations.

Therefore, a power prediction deviation compensation method with high power prediction accuracy, high charge and discharge utilization rate of an energy storage system and good compensation effect is needed at present.

Disclosure of Invention

In order to solve the technical problems in the background technology, the invention provides a power prediction deviation compensation method and a system for a new energy power station.

The invention provides a power prediction deviation compensation method of a new energy power station, which comprises the following steps:

acquiring power data of a target new energy power station; wherein the power data comprises: the method comprises the steps of actual power generation of a power generation system, a short-term power prediction interval and an ultra-short-term power prediction interval of the power generation prediction system, and real-time SOC, a preset SOC upper limit value and a preset SOC lower limit value of an energy storage system;

determining a power prediction interval to which the actual power generation power belongs according to the actual power generation power of the power generation system, a short-term power prediction interval and an ultra-short-term power prediction interval of the power generation prediction system;

determining whether the energy storage system needs to be charged or discharged for power prediction deviation compensation according to a power prediction interval to which the actual power generation belongs and a real-time SOC, a preset SOC upper limit value and a preset SOC lower limit value of the energy storage system;

and calculating the charge and discharge power of the energy storage system when the power prediction deviation compensation is carried out according to the actual power generation and the power prediction interval to which the actual power generation belongs.

Further, determining whether the energy storage system needs to be charged or discharged for power prediction deviation compensation according to a power prediction interval to which the actual generated power belongs and a real-time SOC, a preset SOC upper limit value and a preset SOC lower limit value of the energy storage system, specifically includes:

when P _{Hair in fact} ∈[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∈[a _{Ultra-short term} ，b _{Ultra-short term} ]In the time-course of which the first and second contact surfaces,

if SOC is _{Real time} >SOC _max The energy storage system needs multiple discharges to perform power prediction deviation compensation;

if SOC is _{Real time} < SOC _min The energy storage system needs multiple charges to perform power prediction deviation compensation;

if SOC is _min ≤ SOC _{Real time} ≤ SOC _max The energy storage system does not need to charge and discharge to carry out power prediction deviation compensation;

wherein [ a ] _{Short term} ，b _{Short term} ]A short-term power prediction interval [ a ] representing a power generation prediction system _{Ultra-short term} ，b _{Ultra-short term} ]Representing an ultra-short term power prediction interval, P, of a power generation prediction system _{Hair in fact} Representing actual power generation of power generation system, SOC _{Real time} Representing a real objectTime SOC, SOC _max Indicating a preset SOC upper limit value, SOC _min Representing a preset SOC lower limit value;

when P _{Hair in fact} ∈[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∉[a _{Ultra-short term} ，b _{Ultra-short term} ]In the time-course of which the first and second contact surfaces,

if SOC is _{Real time} >SOC _max The energy storage system needs less charging to perform power prediction deviation compensation;

if SOC is _{Real time} <SOC _min When the power prediction deviation compensation is carried out, the energy storage system needs multiple charges;

if SOC is _min ≤ SOC _{Real time} ≤ SOC _max The energy storage system is charged with the least charging power to perform power prediction deviation compensation;

when P _{Hair in fact} ∉[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∈[a _{Ultra-short term} ，b _{Ultra-short term} ]In the time-course of which the first and second contact surfaces,

if SOC is _{Real time} >SOC _max The energy storage system needs multiple discharges to perform power prediction deviation compensation;

if SOC is _{Real time} <SOC _min When the power prediction deviation compensation is carried out, the energy storage system needs less discharge;

if SOC is _min ≤ SOC _{Real time} ≤ SOC _max The energy storage system needs to discharge with the minimum discharge power to perform power prediction deviation compensation;

when P _{Hair in fact} ∉[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∉[a _{Ultra-short term} ，b _{Ultra-short term} ]In the time-course of which the first and second contact surfaces,

if SOC is _{Real time} >SOC _max The energy storage system needs less charging to perform power prediction deviation compensation;

if SOC is _{Real time} <SOC _min The energy storage system needs multiple charges to perform power prediction deviation compensation;

if SOC is _min ≤ SOC _{Real time} ≤ SOC _max The energy storage system needs to be charged with the least charging power for power pre-chargingAnd (5) measuring deviation compensation.

Further, after determining the power prediction interval to which the actual power generation belongs according to the actual power generation of the power generation system, the short-term power prediction interval and the ultra-short-term power prediction interval of the power generation prediction system, the method further comprises:

determining a compensation dead zone of a power prediction interval to which the actual generated power belongs;

when the energy storage system performs power prediction deviation compensation, the charging and discharging power of the energy storage system is calculated according to the actual power generation power, the power prediction interval to which the actual power generation power belongs and the compensation dead zone.

Further, determining a compensation dead zone of a power prediction interval to which the actual generated power belongs specifically includes:

the compensation dead zone is preconfigured.

Further, after the compensation dead zone is preconfigured, the method further includes:

calculating a maximum compensation dead zone according to a power prediction interval to which the actual generated power belongs;

judging whether to update the compensation dead zone according to the compensation dead zone and the maximum compensation dead zone;

if k > d, let k=d;

where k represents the compensation dead zone and d represents the maximum compensation dead zone.

Further, the calculation formula of the maximum compensation dead zone is as follows

If P _{Hair in fact} ∈[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∈[a _{Ultra-short term} ，b _{Ultra-short term} ]D=c× (a _{Short term} -b _{Ultra-short term} ）；

If P _{Hair in fact} ∈[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∉[a _{Ultra-short term} ，b _{Ultra-short term} ]D=c× (a _{Ultra-short term} -b _{Short term} ）；

If P _{Hair in fact} ∉[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∈[a _{Ultra-short term} ，b _{Ultra-short term} ]D=c× (a _{Ultra-short term} -b _{Short term} ）；

If P _{Hair in fact} ∉[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∉[a _{Ultra-short term} ，b _{Ultra-short term} ]D=c× (a _{Ultra-short term} -b _{Short term} ）；

Wherein [ a ] _{Short term} ，b _{Short term} ]A short-term power prediction interval [ a ] representing a power generation prediction system _{Ultra-short term} ，b _{Ultra-short term} ]Representing an ultra-short term power prediction interval, P, of a power generation prediction system _{Hair in fact} Represents the actual power of the power generation system, c is a preset coefficient, and 0<c<1。

Further, the calculation formula of the charge and discharge power of the energy storage system is as follows

When P _{Hair in fact} ∈[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∈[a _{Ultra-short term} ，b _{Ultra-short term} ]If SOC is at _{Real time} >SOC _max P is then _{Energy storage} =a _{Short term} -k-P _{Hair in fact} ；

If SOC is _{Real time} < SOC _min P is then _{Energy storage} =b _{Ultra-short term} +k-P _{Hair in fact} ；

If SOC is _min ≤ SOC _{Real time} ≤ SOC _max P is then _{Energy storage} =0；

Wherein [ a ] _{Short term} ，b _{Short term} ]A short-term power prediction interval [ a ] representing a power generation prediction system _{Ultra-short term} ，b _{Ultra-short term} ]Representing an ultra-short term power prediction interval, P, of a power generation prediction system _{Hair in fact} Representing the actual power generated by the power generation system, P _{Energy storage} Representing charge and discharge power, SOC of an energy storage system _{Real time} Representing real-time SOC, SOC _max Indicating a preset SOC upper limit value, SOC _min Representing a preset SOC lower limit value;

when P _{Hair in fact} ∈[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∉[a _{Ultra-short term} ，b _{Ultra-short term} ]In the time-course of which the first and second contact surfaces,

if SOC is _{Real time} >SOC _max P is then _{Energy storage} =a _{Ultra-short term} -k-P _{Hair in fact} ；

If SOC is _{Real time} <SOC _min When then P _{Energy storage} =b _{Short term} +k-P _{Hair in fact} ；

If SOC is _min ≤ SOC _{Real time} ≤ SOC _max Then

P _{Energy storage} =-MIN ( fabs (a _{Ultra-short term} -k-P _{Hair in fact} )，fabs (b _{Short term} +k-P _{Hair in fact} ) )；

Wherein fabs represent absolute values, MIN represents minimum values;

when P _{Hair in fact} ∉[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∈[a _{Ultra-short term} ，b _{Ultra-short term} ]In the time-course of which the first and second contact surfaces,

if SOC is _{Real time} >SOC _max P is then _{Energy storage} =a _{Ultra-short term} -k-P _{Hair in fact} ；

If SOC is _{Real time} <SOC _min When then P _{Energy storage} =b _{Short term} +k-P _{Hair in fact} ；

If SOC is _min ≤ SOC _{Real time} ≤ SOC _max Then

P _{Energy storage} =MIN ( fabs (a _{Ultra-short term} -k-P _{Hair in fact} )，fabs (b _{Short term} +k-P _{Hair in fact} ) )；

When P _{Hair in fact} ∉[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∉[a _{Ultra-short term} ，b _{Ultra-short term} ]In the time-course of which the first and second contact surfaces,

if SOC is _{Real time} >SOC _max P is then _{Energy storage} =a _{Ultra-short term} -k-P _{Hair in fact} ；

If SOC is _{Real time} <SOC _min P is then _{Energy storage} =b _{Short term} +k-P _{Hair in fact} ；

If SOC is _min ≤ SOC _{Real time} ≤ SOC _max Then

P _{Energy storage} = -MIN ( fabs (a _{Ultra-short term} -k-P _{Hair in fact} )，fabs (b _{Short term} +k-P _{Hair in fact} ) )。

The invention also provides a power prediction deviation compensation system of the new energy power station, which comprises the following components:

the acquisition module is used for acquiring power data of the target new energy power station; wherein the power data comprises: the method comprises the steps of actual power generation of a power generation system, a short-term power prediction interval and an ultra-short-term power prediction interval of the power generation prediction system, and real-time SOC, a preset SOC upper limit value and a preset SOC lower limit value of an energy storage system;

the predicted power interval determining module is used for determining a power prediction interval to which the actual power generation power belongs according to the actual power generation power of the power generation system, a short-term power prediction interval and an ultra-short-term power prediction interval of the power generation prediction system;

the power prediction deviation compensation determining module is used for determining whether the energy storage system needs to be charged or discharged for power prediction deviation compensation according to a power prediction interval to which the actual power generation power belongs and a real-time SOC (state of charge) of the energy storage system, a preset SOC upper limit value and a preset SOC lower limit value;

the power prediction deviation compensation calculation module is used for calculating the charge and discharge power of the energy storage system according to the actual power generation power and the power prediction interval to which the actual power generation power belongs when the energy storage system performs power prediction deviation compensation.

Further, the system also comprises a compensation dead zone determining module, wherein the compensation dead zone determining module is used for determining a compensation dead zone of a power prediction interval to which the actual generated power belongs;

the power prediction deviation compensation calculation module is used for calculating the charge and discharge power of the energy storage system according to the actual power generation power, the power prediction interval to which the actual power generation power belongs and the compensation dead zone when the energy storage system performs power prediction deviation compensation.

Further, the compensation dead zone determination module includes:

and the compensation dead zone pre-configuration sub-module is used for pre-configuring the compensation dead zone.

Further, the compensation dead zone determination module further includes:

the maximum compensation dead zone calculation sub-module is used for calculating the maximum compensation dead zone according to the power prediction interval to which the actual generated power belongs;

the compensation dead zone updating sub-module is used for judging whether to update the compensation dead zone according to the compensation dead zone and the maximum compensation dead zone;

if k > d, let k=d; where k represents the compensation dead zone and d represents the maximum compensation dead zone.

Further, the calculation formula of the maximum compensation dead zone is as follows

If P _{Hair in fact} ∈[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∈[a _{Ultra-short term} ，b _{Ultra-short term} ]D=c× (a _{Short term} -b _{Ultra-short term} ）；

If P _{Hair in fact} ∈[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∉[a _{Ultra-short term} ，b _{Ultra-short term} ]D=c× (a _{Ultra-short term} -b _{Short term} ）；

If P _{Hair in fact} ∉[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∈[a _{Ultra-short term} ，b _{Ultra-short term} ]D=c× (a _{Ultra-short term} -b _{Short term} ）；

If P _{Hair in fact} ∉[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∉[a _{Ultra-short term} ，b _{Ultra-short term} ]D=c× (a _{Ultra-short term} -b _{Short term} ）；

Wherein [ a ] _{Short term} ，b _{Short term} ]A short-term power prediction interval [ a ] representing a power generation prediction system _{Ultra-short term} ，b _{Ultra-short term} ]Representing an ultra-short term power prediction interval, P, of a power generation prediction system _{Hair in fact} Represents the actual power of the power generation system, c is a preset coefficient, and 0<c<1。

Further, the calculation formula of the charge and discharge power of the energy storage system is as follows

When P _{Hair in fact} ∈[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∈[a _{Ultra-short term} ，b _{Ultra-short term} ]If SOC is at _{Real time} >SOC _max ，

Then P _{Energy storage} =a _{Short term} -k-P _{Hair in fact} ；

If SOC is _{Real time} < SOC _min P is then _{Energy storage} =b _{Ultra-short term} +k-P _{Hair in fact} ；

If SOC is _min ≤ SOC _{Real time} ≤ SOC _max P is then _{Energy storage} =0；

Wherein [ a ] _{Short term} ，b _{Short term} ]A short-term power prediction interval [ a ] representing a power generation prediction system _{Ultra-short term} ，b _{Ultra-short term} ]Representing an ultra-short term power prediction interval, P, of a power generation prediction system _{Hair in fact} Representing the actual power generated by the power generation system, P _{Energy storage} Representing charge and discharge power, SOC of an energy storage system _{Real time} Representing real-time SOC, SOC _max Indicating a preset SOC upper limit value, SOC _min Representing a preset SOC lower limit value;

when P _{Hair in fact} ∈[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∉[a _{Ultra-short term} ，b _{Ultra-short term} ]In the time-course of which the first and second contact surfaces,

if SOC is _{Real time} >SOC _max P is then _{Energy storage} =a _{Ultra-short term} -k-P _{Hair in fact} ；

If SOC is _{Real time} <SOC _min When then P _{Energy storage} =b _{Short term} +k-P _{Hair in fact} ；

If SOC is _min ≤ SOC _{Real time} ≤ SOC _max Then

P _{Energy storage} = -MIN ( fabs (a _{Ultra-short term} -k-P _{Hair in fact} )，fabs (b _{Short term} +k-P _{Hair in fact} ) )；

Wherein fabs represent absolute values, MIN represents minimum values;

when P _{Hair in fact} ∉[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∈[a _{Ultra-short term} ，b _{Ultra-short term} ]In the time-course of which the first and second contact surfaces,

if SOC is _{Real time} >SOC _max P is then _{Energy storage} =a _{Ultra-short term} -k-P _{Hair in fact} ；

If SOC is _{Real time} <SOC _min When then P _{Energy storage} =b _{Short term} +k-P _{Hair in fact} ；

If SOC is _min ≤ SOC _{Real time} ≤ SOC _max Then

P _{Energy storage} = MIN ( fabs (a _{Ultra-short term} -k-P _{Hair in fact} )，fabs (b _{Short term} +k-P _{Hair in fact} ) )；

When P _{Hair in fact} ∉[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∉[a _{Ultra-short term} ，b _{Ultra-short term} ]In the time-course of which the first and second contact surfaces,

if SOC is _{Real time} >SOC _max P is then _{Energy storage} =a _{Ultra-short term} -k-P _{Hair in fact} ；

If SOC is _{Real time} <SOC _min P is then _{Energy storage} =b _{Short term} +k-P _{Hair in fact} ；

If SOC is _min ≤ SOC _{Real time} ≤ SOC _max Then

P _{Energy storage} = -MIN ( fabs (a _{Ultra-short term} -k-P _{Hair in fact} )，fabs (b _{Short term} +k-P _{Hair in fact} ) )。

According to the power prediction deviation compensation method and system for the new energy power station, disclosed by the invention, the accuracy of overall power prediction is improved through the short-term power prediction interval and the ultra-short-term power prediction interval of the power generation prediction system, and the power prediction deviation compensation is carried out by selecting a proper power prediction interval through the combination of the short-term power prediction interval and the ultra-short-term power prediction interval of the power generation prediction system and the real-time SOC (state of charge) of the energy storage system, the preset SOC upper limit value and the preset SOC lower limit value, so that the compensation precision of the power prediction deviation compensation is improved, the efficient utilization of the energy storage system is realized, and the service life of the energy storage system is prolonged.

Drawings

Fig. 1 is an overall block diagram of a new energy power station in the present invention.

Fig. 2 is a flow chart of a power prediction bias compensation method for a new energy power station according to an embodiment of the invention.

Fig. 3 is a schematic diagram of a power prediction interval to which an actual generated power belongs in an embodiment of the present invention.

Fig. 4 is a schematic diagram of a power prediction interval to which an actual generated power belongs in another embodiment of the present invention.

Fig. 5 is a schematic diagram of a power prediction interval to which an actual generated power belongs in another embodiment of the present invention.

Fig. 6 is a schematic diagram of a power prediction interval to which an actual generated power belongs in another embodiment of the present invention.

Fig. 7 is a flowchart of a power prediction bias compensation method of a new energy power station according to another embodiment of the present invention.

Description of the embodiments

It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other. The invention will be described in detail below with reference to the drawings in connection with embodiments.

As shown in fig. 1 and fig. 2, the power prediction deviation compensation method for the new energy power station provided by the invention comprises the following steps:

acquiring power data of a target new energy power station; wherein the power data comprises: the method comprises the steps of actual power generation of a power generation system, a short-term power prediction interval and an ultra-short-term power prediction interval of a power generation prediction system, a real-time SOC of an energy storage system, a preset SOC upper limit value and a preset SOC lower limit value;

determining a power prediction interval to which the actual power generation power belongs according to the actual power generation power of the power generation system, a short-term power prediction interval and an ultra-short-term power prediction interval of the power generation prediction system;

determining whether the energy storage system needs to be charged or discharged for power prediction deviation compensation according to a power prediction interval to which the actual power generation belongs and a real-time SOC, a preset SOC upper limit value and a preset SOC lower limit value of the energy storage system;

when the energy storage system performs power prediction deviation compensation, the charge and discharge power of the energy storage system is calculated according to the actual power generation power and the power prediction interval to which the actual power generation power belongs.

According to the invention, the accuracy of overall power prediction is improved through the short-term power prediction interval and the ultra-short-term power prediction interval of the power generation prediction system, and the power prediction deviation compensation is carried out by selecting a proper power prediction interval through the combination of the short-term power prediction interval and the ultra-short-term power prediction interval of the power generation prediction system and the real-time SOC (state of charge) of the energy storage system, the preset SOC upper limit value and the preset SOC lower limit value, so that the compensation precision of the power prediction deviation compensation is improved, the efficient utilization of the energy storage system is realized, and the service life of the energy storage system is prolonged.

In this embodiment, determining whether the energy storage system needs to be charged or discharged to perform power prediction bias compensation according to a power prediction interval to which the actual generated power belongs and a real-time SOC, a preset SOC upper limit value, and a preset SOC lower limit value of the energy storage system specifically includes:

when P _{Hair in fact} ∈[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∈[a _{Ultra-short term} ，b _{Ultra-short term} ]In the time-course of which the first and second contact surfaces,

if SOC is _{Real time} >SOC _max The energy storage system needs multiple discharges to perform power prediction deviation compensation;

if SOC is _{Real time} < SOC _min The energy storage system needs multiple charges to perform power prediction deviation compensation;

if SOC is _min ≤ SOC _{Real time} ≤ SOC _max The energy storage system does not need to charge and discharge to carry out power prediction deviation compensation; wherein [ a ] _{Short term} ，b _{Short term} ]A short-term power prediction interval [ a ] representing a power generation prediction system _{Ultra-short term} ，b _{Ultra-short term} ]Representing an ultra-short term power prediction interval, P, of a power generation prediction system _{Hair in fact} Representing actual power generation of power generation system, SOC _{Real time} Representing real-time SOC, SOC _max Indicating a preset SOC upper limit value, SOC _min Representing a preset SOC lower limit value;

when P _{Hair in fact} ∈[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∉[a _{Ultra-short term} ，b _{Ultra-short term} ]In the time-course of which the first and second contact surfaces,

if SOC is _{Real time} >SOC _max The energy storage system needs less charging to perform power prediction deviation compensation;

if SOC is _{Real time} <SOC _min When the power prediction deviation compensation is carried out, the energy storage system needs multiple charges;

if SOC is _min ≤ SOC _{Real time} ≤ SOC _max The energy storage system is charged with the least charging power to perform power prediction deviation compensation;

when P _{Hair in fact} ∉[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∈[a _{Ultra-short term} ，b _{Ultra-short term} ]In the time-course of which the first and second contact surfaces,

if SOC is _{Real time} >SOC _max The energy storage system needs multiple discharges to perform power prediction deviation compensation;

if SOC is _{Real time} <SOC _min When the power prediction deviation compensation is carried out, the energy storage system needs less discharge;

if SOC is _min ≤ SOC _{Real time} ≤ SOC _max The energy storage system needs to discharge with the minimum discharge power to perform power prediction deviation compensation;

when P _{Hair in fact} ∉[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∉[a _{Ultra-short term} ，b _{Ultra-short term} ]In the time-course of which the first and second contact surfaces,

if SOC is _{Real time} >SOC _max The energy storage system needs less charging to perform power prediction deviation compensation;

if SOC is _{Real time} <SOC _min The energy storage system needs multiple charges to perform power prediction deviation compensation;

if SOC is _min ≤ SOC _{Real time} ≤ SOC _max The energy storage system needs to be charged with the minimum charge power for power prediction bias compensation.

In actual use, the power is transmitted by the short-term power prediction interval and the boundary value of the ultra-short-term power prediction interval adopted by the power generation prediction system in the prior art, so that the compensation effect cannot be expected due to power fluctuation, the compensation precision is low, and the like. In order to solve this problem, as shown in fig. 7, in the present embodiment, after determining a power prediction section to which the actual power generation is to belong, from the actual power generation of the power generation system, a short-term power prediction section and an ultra-short-term power prediction section of the power generation prediction system, the present embodiment further includes:

determining a compensation dead zone of a power prediction interval to which the actual generated power belongs;

when the energy storage system performs power prediction deviation compensation, the charging and discharging power of the energy storage system is calculated according to the actual power generation power, the power prediction interval to which the actual power generation power belongs and the compensation dead zone.

In a further embodiment, determining a compensation dead zone of a power prediction interval to which the actual generated power belongs specifically includes:

the compensation dead zone is preconfigured.

By the arrangement, on the basis of calculating the charge and discharge power of the energy storage system when the power prediction deviation compensation is carried out by combining the short-term power prediction interval and the ultra-short-term power prediction interval of the power generation prediction system with the real-time SOC state of the energy storage system, the compensation dead zone is adopted to compensate the prediction error, the problem that the compensation precision is low due to the energy storage loss and other problems when the compensation boundary value is adopted for issuing is solved, and the compensation precision of the power prediction deviation compensation is improved.

Specifically, the compensation dead zone is configured by the upper computer or is a fixed value. For example, the compensation dead band is 10% of the stored energy rated power of the energy storage system.

In order to further avoid compensation errors caused by loss or fluctuation of the energy storage system after issuing the boundary value of the selected compensation interval, in a further embodiment, after pre-configuring the compensation dead zone, the method further includes:

calculating a maximum compensation dead zone according to a power prediction interval to which the actual generated power belongs;

judging whether to update the compensation dead zone according to the compensation dead zone and the maximum compensation dead zone;

if k > d, updating the compensation dead zone according to the maximum compensation dead zone, and enabling k=d;

where k represents the compensation dead zone and d represents the maximum compensation dead zone.

It should be appreciated that the compensation dead zone k is not updated when k.ltoreq.d.

In a further embodiment, the calculation of the maximum compensation dead zone d is as follows

As shown in FIG. 3, if P _{Hair in fact} ∈[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∈[a _{Ultra-short term} ，b _{Ultra-short term} ]，

D=c× (a _{Short term} -b _{Ultra-short term} ）；

As shown in FIG. 4, if P _{Hair in fact} ∈[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∉[a _{Ultra-short term} ，b _{Ultra-short term} ]，

D=c× (a _{Ultra-short term} -b _{Short term} ）；

As shown in FIG. 5, if P _{Hair in fact} ∉[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∈[a _{Ultra-short term} ，b _{Ultra-short term} ]，

D=c× (a _{Ultra-short term} -b _{Short term} ）；

As shown in FIG. 6, if P _{Hair in fact} ∉[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∉[a _{Ultra-short term} ，b _{Ultra-short term} ]，

D=c× (a _{Ultra-short term} -b _{Short term} ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein [ a ] _{Short term} ，b _{Short term} ]A short-term power prediction interval [ a ] representing a power generation prediction system _{Ultra-short term} ，b _{Ultra-short term} ]Representing an ultra-short term power prediction interval, P, of a power generation prediction system _{Hair in fact} Represents the actual power of the power generation system, c is a preset coefficient, and 0<c<1。

By the arrangement, the compensation dead zone of the power prediction interval can be adjusted in real time according to the power prediction interval to which the actual generated power belongs, so that compensation errors caused by loss or fluctuation of the energy storage system after the boundary value of the compensation interval is selected are effectively avoided, and the compensation precision of power prediction deviation compensation is greatly improved.

Of course, in other embodiments, 0.3.ltoreq.c.ltoreq.0.7. In one embodiment, c is 0.5.

In a further embodiment, the energy storage system charge and discharge power is calculated as follows

As shown in FIG. 3, when P _{Hair in fact} ∈[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∈[a _{Ultra-short term} ，b _{Ultra-short term} ]In the time-course of which the first and second contact surfaces,

if SOC is _{Real time} >SOC _max P is then _{Energy storage} =a _{Short term} -k-P _{Hair in fact} ；

If SOC is _{Real time} < SOC _min P is then _{Energy storage} =b _{Ultra-short term} +k-P _{Hair in fact} ；

If SOC is _min ≤ SOC _{Real time} ≤ SOC _max P is then _{Energy storage} = 0；

Wherein [ a ] _{Short term} ，b _{Short term} ]A short-term power prediction interval [ a ] representing a power generation prediction system _{Ultra-short term} ，b _{Ultra-short term} ]Representing an ultra-short term power prediction interval, P, of a power generation prediction system _{Hair in fact} Representing the actual power generated by the power generation system, P _{Energy storage} Representing charge and discharge power, SOC of an energy storage system _{Real time} Representing real-time SOC, SOC _max Indicating a preset SOC upper limit value, SOC _min Representing a preset SOC lower limit value;

as shown in FIG. 4, when P _{Hair in fact} ∈[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∉[a _{Ultra-short term} ，b _{Ultra-short term} ]In the time-course of which the first and second contact surfaces,

if SOC is _{Real time} >SOC _max P is then _{Energy storage} =a _{Ultra-short term} -k-P _{Hair in fact} ；

If SOC is _{Real time} <SOC _min When then P _{Energy storage} =b _{Short term} +k-P _{Hair in fact} ；

If SOC is _min ≤ SOC _{Real time} ≤ SOC _max Then

P _{Energy storage} =-MIN ( fabs (a _{Ultra-short term} -k-P _{Hair in fact} )，fabs (b _{Short term} +k-P _{Hair in fact} ) )；

Wherein fabs represent absolute values, MIN represents minimum values;

as shown in FIG. 5, when P _{Hair in fact} ∉[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∈[a _{Ultra-short term} ，b _{Ultra-short term} ]In the time-course of which the first and second contact surfaces,

if SOC is _{Real time} >SOC _max P is then _{Energy storage} =a _{Ultra-short term} -k-P _{Hair in fact} ；

If SOC is _{Real time} <SOC _min When then P _{Energy storage} =b _{Short term} +k-P _{Hair in fact} ；

If SOC is _min ≤ SOC _{Real time} ≤ SOC _max Then

P _{Energy storage} =MIN ( fabs (a _{Ultra-short term} -k-P _{Hair in fact} )，fabs (b _{Short term} +k-P _{Hair in fact} ) )；

As shown in FIG. 6, when P _{Hair in fact} ∉[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∉[a _{Ultra-short term} ，b _{Ultra-short term} ]In the time-course of which the first and second contact surfaces,

if SOC is _{Real time} >SOC _max P is then _{Energy storage} =a _{Ultra-short term} -k-P _{Hair in fact} ；

If SOC is _{Real time} <SOC _min P is then _{Energy storage} =b _{Short term} +k-P _{Hair in fact} ；

If SOC is _min ≤ SOC _{Real time} ≤ SOC _max Then

P _{Energy storage} = -MIN ( fabs (a _{Ultra-short term} -k-P _{Hair in fact} )，fabs (b _{Short term} +k-P _{Hair in fact} ) )。

By the arrangement, the charge and discharge efficiency of the energy storage system can be improved as much as possible under the condition of meeting the compensation precision.

It should be appreciated that P _{Energy storage} Is a signed scalar that is positive when the energy storage system is discharged and negative when charged.

The invention also provides a power prediction deviation compensation system of the new energy power station, which comprises the following components:

the acquisition module is used for acquiring power data of the target new energy power station; wherein the power data comprises: the method comprises the steps of actual power generation of a power generation system, a short-term power prediction interval and an ultra-short-term power prediction interval of a power generation prediction system, a real-time SOC of an energy storage system, a preset SOC upper limit value and a preset SOC lower limit value;

the predicted power interval determining module is used for determining a power prediction interval to which the actual power generation power belongs according to the actual power generation power of the power generation system, a short-term power prediction interval and an ultra-short-term power prediction interval of the power generation prediction system;

the power prediction deviation compensation determining module is used for determining whether the energy storage system is charged or discharged to perform power prediction deviation compensation according to a power prediction interval to which the actual power generation power belongs and a real-time SOC (state of charge) of the energy storage system, a preset SOC upper limit value and a preset SOC lower limit value;

the power prediction deviation compensation calculation module is used for calculating the charge and discharge power of the energy storage system according to the actual power generation power and the power prediction interval to which the actual power generation power belongs when the energy storage system performs power prediction deviation compensation.

In the embodiment, the short-term power prediction interval and the ultra-short-term power prediction interval of the power generation prediction system are combined with the real-time SOC, the preset SOC upper limit value and the preset SOC lower limit value of the energy storage system to select a proper power prediction interval for power prediction deviation compensation, so that the utilization rate of the energy storage system is improved, and the service life of the energy storage system is prolonged.

In this embodiment, the power generation system further includes a compensation dead zone determining module, where the compensation dead zone determining module is configured to determine a compensation dead zone of a power prediction interval to which the actual generated power belongs;

the power prediction deviation compensation calculation module is used for calculating the charge and discharge power of the energy storage system according to the actual power generation power, the power prediction interval to which the actual power generation power belongs and the compensation dead zone when the energy storage system performs power prediction deviation compensation.

According to the embodiment, on the basis of calculating the charge and discharge power of the energy storage system when the power prediction deviation compensation is carried out by combining the short-term power prediction interval and the ultra-short-term power prediction interval of the power generation prediction system with the real-time SOC state of the energy storage system, the compensation dead zone is adopted to compensate the prediction error, the problem that the compensation precision is low due to the fact that the compensation boundary value is issued due to the loss of energy storage and the like is solved, and the compensation precision of the power prediction deviation compensation is improved.

In a further embodiment, the compensation dead zone determination module includes:

and the compensation dead zone pre-configuration sub-module is used for pre-configuring the compensation dead zone.

In order to further improve the compensation accuracy of the power prediction bias compensation, in a further embodiment, the method further includes:

the maximum compensation dead zone calculation sub-module is used for calculating the maximum compensation dead zone according to the power prediction interval to which the actual generated power belongs;

the compensation dead zone updating sub-module is used for judging whether to update the compensation dead zone according to the compensation dead zone and the maximum compensation dead zone;

if k > d, let k=d; where k represents the compensation dead zone and d represents the maximum compensation dead zone.

It should be appreciated that the compensation dead zone is not updated when k.ltoreq.d.

In a further embodiment, the maximum compensation dead zone d is calculated as follows

As shown in FIG. 3, if P _{Hair in fact} ∈[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∈[a _{Ultra-short term} ，b _{Ultra-short term} ]，

D=c× (a _{Short term} -b _{Ultra-short term} ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein [ a ] _{Short term} ，b _{Short term} ]A short-term power prediction interval [ a ] representing a power generation prediction system _{Ultra-short term} ，b _{Ultra-short term} ]Representing an ultra-short term power prediction interval, P, of a power generation prediction system _{Hair in fact} Represents the actual power of the power generation system, c is a preset coefficient, and 0<c<1；

As shown in FIG. 4, if P _{Hair in fact} ∈[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∉[a _{Ultra-short term} ，b _{Ultra-short term} ]，

D=c× (a _{Ultra-short term} -b _{Short term} ）；

As shown in FIG. 5, if P _{Hair in fact} ∉[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∈[a _{Ultra-short term} ，b _{Ultra-short term} ]，

D=c× (a _{Ultra-short term} -b _{Short term} ）；

As shown in FIG. 6, if P _{Hair in fact} ∉[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∉[a _{Ultra-short term} ，b _{Ultra-short term} ]，

D=c× (a _{Ultra-short term} -b _{Short term} ）。

In a further embodiment, when P _{Hair in fact} ∈[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∈[a _{Ultra-short term} ，b _{Ultra-short term} ]In the time-course of which the first and second contact surfaces,

if SOC is _{Real time} >SOC _max The energy storage system needs multiple discharges to perform power prediction deviation compensation;

if SOC is _{Real time} < SOC _min The energy storage system needs multiple charges to perform power prediction deviation compensation;

if SOC is _min ≤ SOC _{Real time} ≤ SOC _max The energy storage system does not need to charge and discharge to carry out power prediction deviation compensation; wherein [ a ] _{Short term} ，b _{Short term} ]A short-term power prediction interval [ a ] representing a power generation prediction system _{Ultra-short term} ，b _{Ultra-short term} ]Representing an ultra-short term power prediction interval, P, of a power generation prediction system _{Hair in fact} Representing actual power generation of power generation system, SOC _{Real time} Representing real-time SOC, SOC _max Indicating a preset SOC upper limit value, SOC _min Representing a preset SOC lower limit value;

when P _{Hair in fact} ∈[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∉[a _{Ultra-short term} ，b _{Ultra-short term} ]In the time-course of which the first and second contact surfaces,

if SOC is _{Real time} >SOC _max The energy storage system needs less charging to perform power prediction deviation compensation;

if SOC is _{Real time} <SOC _min When the power prediction deviation compensation is carried out, the energy storage system needs multiple charges;

if SOC is _min ≤ SOC _{Real time} ≤ SOC _max The energy storage system is charged with the least charging power to perform power prediction deviation compensation;

when P _{Hair in fact} ∉[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∈[a _{Ultra-short term} ，b _{Ultra-short term} ]In the time-course of which the first and second contact surfaces,

if SOC is _{Real time} >SOC _max The energy storage system needs multiple discharges to perform power prediction deviation compensation;

if SOC is _{Real time} <SOC _min When the power prediction deviation compensation is carried out, the energy storage system needs less discharge;

if SOC is _min ≤ SOC _{Real time} ≤ SOC _max The energy storage system needs to discharge with the minimum discharge power to perform power prediction deviation compensation;

when P _{Hair in fact} ∉[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∉[a _{Ultra-short term} ，b _{Ultra-short term} ]In the time-course of which the first and second contact surfaces,

if SOC is _{Real time} >SOC _max The energy storage system needs less charging to perform power prediction deviation compensation;

if SOC is _{Real time} <SOC _min The energy storage system needs multiple charges to perform power prediction deviation compensation;

if SOC is _min ≤ SOC _{Real time} ≤ SOC _max The energy storage system needs to be charged with the minimum charge power for power prediction bias compensation.

In a further embodiment, the energy storage system's charge-discharge power is calculated as follows:

as shown in FIG. 3, when P _{Hair in fact} ∈[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∈[a _{Ultra-short term} ，b _{Ultra-short term} ]If SOC is at _{Real time} >SOC _max P is then _{Energy storage} =a _{Short term} -k-P _{Hair in fact} ；

If SOC is _{Real time} < SOC _min P is then _{Energy storage} =b _{Ultra-short term} +k-P _{Hair in fact} ；

If SOC is _min ≤ SOC _{Real time} ≤ SOC _max P is then _{Energy storage} =0；

Wherein [ a ] _{Short term} ，b _{Short term} ]A short-term power prediction interval [ a ] representing a power generation prediction system _{Ultra-short term} ，b _{Ultra-short term} ]Representing an ultra-short term power prediction interval, P, of a power generation prediction system _{Hair in fact} Representing the actual power generated by the power generation system, P _{Energy storage} Representing charge and discharge power, SOC of an energy storage system _{Real time} Representing real-time SOC, SOC _max Indicating a preset SOC upper limit value, SOC _min Representing a preset SOC lower limit value;

as shown in FIG. 4, when P _{Hair in fact} ∈[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∉[a _{Ultra-short term} ，b _{Ultra-short term} ]In the time-course of which the first and second contact surfaces,

if SOC is _{Real time} >SOC _max P is then _{Energy storage} =a _{Ultra-short term} -k-P _{Hair in fact} ；

If SOC is _{Real time} <SOC _min When then P _{Energy storage} =b _{Short term} +k-P _{Hair in fact} ；

If SOC is _min ≤ SOC _{Real time} ≤ SOC _max Then

P _{Energy storage} =-MIN ( fabs (a _{Ultra-short term} -k-P _{Hair in fact} )，fabs (b _{Short term} +k-P _{Hair in fact} ) )；

Wherein fabs represent absolute values, MIN represents minimum values;

as shown in FIG. 5, when P _{Hair in fact} ∉[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∈[a _{Ultra-short term} ，b _{Ultra-short term} ]In the time-course of which the first and second contact surfaces,

if SOC is _{Real time} >SOC _max P is then _{Energy storage} =a _{Ultra-short term} -k-P _{Hair in fact} ；

If SOC is _{Real time} <SOC _min When then P _{Energy storage} =b _{Short term} +k-P _{Hair in fact} ；

If SOC is _min ≤ SOC _{Real time} ≤ SOC _max Then

P _{Energy storage} =MIN ( fabs (a _{Ultra-short term} -k-P _{Hair in fact} )，fabs (b _{Short term} +k-P _{Hair in fact} ) )；

As shown in FIG. 6, when P _{Hair in fact} ∉[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∉[a _{Ultra-short term} ，b _{Ultra-short term} ]In the time-course of which the first and second contact surfaces,

if SOC is _{Real time} >SOC _max P is then _{Energy storage} =a _{Ultra-short term} -k-P _{Hair in fact} ；

If SOC is _{Real time} <SOC _min P is then _{Energy storage} =b _{Short term} +k-P _{Hair in fact} ；

If SOC is _min ≤ SOC _{Real time} ≤ SOC _max Then

P _{Energy storage} = -MIN ( fabs (a _{Ultra-short term} -k-P _{Hair in fact} )，fabs (b _{Short term} +k-P _{Hair in fact} ) )。

The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should make equivalent substitutions or modifications according to the technical scheme of the present invention and the inventive concept thereof, and should be covered by the scope of the present invention.

Claims (8)

1. The power prediction deviation compensation method of the new energy power station is characterized by comprising the following steps of:

acquiring power data of a target new energy power station; wherein the power data comprises: the method comprises the steps of actual power generation of a power generation system, a short-term power prediction interval and an ultra-short-term power prediction interval of the power generation prediction system, and real-time SOC, a preset SOC upper limit value and a preset SOC lower limit value of an energy storage system;

determining a power prediction interval to which the actual power generation power belongs according to the actual power generation power of the power generation system, a short-term power prediction interval and an ultra-short-term power prediction interval of the power generation prediction system;

determining a compensation dead zone of a power prediction interval to which the actual generated power belongs;

determining whether the energy storage system needs to be charged or discharged for power prediction deviation compensation according to a power prediction interval to which the actual power generation belongs and a real-time SOC, a preset SOC upper limit value and a preset SOC lower limit value of the energy storage system;

when the energy storage system performs power prediction deviation compensation, calculating the charge and discharge power of the energy storage system according to the actual power generation, the power prediction interval to which the actual power generation belongs and the compensation dead zone;

the calculation formula of the charge and discharge power of the energy storage system is as follows

When P _{Hair in fact} ∈[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∈[a _{Ultra-short term} ，b _{Ultra-short term} ]If SOC is at _{Real time} > SOC _max P is then _{Energy storage} =a _{Short term} -k-P _{Hair in fact} ；

If SOC is _{Real time} < SOC _min P is then _{Energy storage} =b _{Ultra-short term} +k-P _{Hair in fact} ；

If SOC is _min ≤ SOC _{Real time} ≤ SOC _max P is then _{Energy storage} =0；

Wherein [ a ] _{Short term} ，b _{Short term} ]A short-term power prediction interval [ a ] representing a power generation prediction system _{Ultra-short term} ，b _{Ultra-short term} ]Representing an ultra-short term power prediction interval, P, of a power generation prediction system _{Hair in fact} Representing the actual power generated by the power generation system, P _{Energy storage} Representing charge and discharge power, SOC of an energy storage system _{Real time} Representing real-time SOC, SOC _max Indicating a preset SOC upper limit value, SOC _min Representing a preset SOC lower limit value, and k represents a compensation dead zone;

when P _{Hair in fact} ∈[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∉[a _{Ultra-short term} ，b _{Ultra-short term} ]In the time-course of which the first and second contact surfaces,

if SOC is _{Real time} > SOC _max P is then _{Energy storage} =a _{Ultra-short term} -k-P _{Hair in fact} ；

If SOC is _{Real time} < SOC _min When then P _{Energy storage} =b _{Short term} +k-P _{Hair in fact} ；

If SOC is _min ≤ SOC _{Real time} ≤ SOC _max Then

P _{Energy storage} = -MIN ( fabs (a _{Ultra-short term} -k-P _{Hair in fact} )，fabs (b _{Short term} +k-P _{Hair in fact} ) )；

Wherein fabs represent absolute values, MIN represents minimum values;

when P _{Hair in fact} ∉[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∈[a _{Ultra-short term} ，b _{Ultra-short term} ]In the time-course of which the first and second contact surfaces,

if SOC is _{Real time} > SOC _max P is then _{Energy storage} =a _{Ultra-short term} -k-P _{Hair in fact} ；

If SOC is _{Real time} < SOC _min When then P _{Energy storage} =b _{Short term} +k-P _{Hair in fact} ；

If SOC is _min ≤ SOC _{Real time} ≤ SOC _max Then

P _{Energy storage} = MIN ( fabs (a _{Ultra-short term} -k-P _{Hair in fact} )，fabs (b _{Short term} +k-P _{Hair in fact} ) )；

When P _{Hair in fact} ∉[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∉[a _{Ultra-short term} ，b _{Ultra-short term} ]In the time-course of which the first and second contact surfaces,

if SOC is _{Real time} > SOC _max P is then _{Energy storage} =a _{Ultra-short term} -k-P _{Hair in fact} ；

If SOC is _{Real time} < SOC _min P is then _{Energy storage} =b _{Short term} +k-P _{Hair in fact} ；

If SOC is _min ≤ SOC _{Real time} ≤ SOC _max Then

P _{Energy storage} = -MIN ( fabs (a _{Ultra-short term} -k-P _{Hair in fact} )，fabs (b _{Short term} +k-P _{Hair in fact} ) )。

2. The power prediction bias compensation method of a new energy power station according to claim 1, wherein determining whether the energy storage system needs to be charged or discharged for power prediction bias compensation according to a power prediction interval to which the actual generated power belongs and a real-time SOC, a preset SOC upper limit value and a preset SOC lower limit value of the energy storage system, specifically comprises:

when P _{Hair in fact} ∈[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∈[a _{Ultra-short term} ，b _{Ultra-short term} ]In the time-course of which the first and second contact surfaces,

if SOC is _{Real time} > SOC _max The energy storage system needs multiple discharges to perform power prediction deviation compensation;

if SOC is _{Real time} < SOC _min The energy storage system needs multiple charges to perform power prediction deviation compensation;

if SOC is _min ≤ SOC _{Real time} ≤ SOC _max The energy storage system does not need to charge and discharge to carry out power prediction deviation compensation;

wherein [ a ] _{Short term} ，b _{Short term} ]A short-term power prediction interval [ a ] representing a power generation prediction system _{Ultra-short term} ，b _{Ultra-short term} ]Representing an ultra-short term power prediction interval, P, of a power generation prediction system _{Hair in fact} Representing actual power generation of power generation system, SOC _{Real time} Representing real-time SOC, SOC _max Indicating a preset SOC upper limit value, SOC _min Representing a preset SOC lower limit value;

when P _{Hair in fact} ∈[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∉[a _{Ultra-short term} ，b _{Ultra-short term} ]In the time-course of which the first and second contact surfaces,

if SOC is _{Real time} > SOC _max The energy storage system needs less charging to perform power prediction deviation compensation;

if SOC is _{Real time} < SOC _min When the power prediction deviation compensation is carried out, the energy storage system needs multiple charges;

if SOC is _min ≤ SOC _{Real time} ≤ SOC _max The energy storage system is charged with the least charging power to perform power prediction deviation compensation;

when P _{Hair in fact} ∉[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∈[a _{Ultra-short term} ，b _{Ultra-short term} ]In the time-course of which the first and second contact surfaces,

if SOC is _{Real time} > SOC _max The energy storage system needs multiple discharges to perform power prediction deviation compensation;

if SOC is _{Real time} < SOC _min When the power prediction deviation compensation is carried out, the energy storage system needs less discharge;

if SOC is _min ≤ SOC _{Real time} ≤ SOC _max The energy storage system needs to discharge with the minimum discharge power to perform power prediction deviation compensation;

when P _{Hair in fact} ∉[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∉[a _{Ultra-short term} ，b _{Ultra-short term} ]In the time-course of which the first and second contact surfaces,

if SOC is _{Real time} > SOC _max The energy storage system needs less charging to perform power prediction deviation compensation;

if SOC is _{Real time} < SOC _min The energy storage system needs multiple charges to perform power prediction deviation compensation;

if SOC is _min ≤ SOC _{Real time} ≤ SOC _max The energy storage system needs to be charged with the minimum charge power for power prediction bias compensation.

3. The power prediction bias compensation method of a new energy power station according to claim 1, wherein determining a compensation dead zone of a power prediction interval to which an actual generated power belongs specifically includes:

the compensation dead zone is preconfigured.

4. The power prediction bias compensation method of a new energy power station according to claim 3, further comprising, after the pre-configuring the compensation dead zone:

calculating a maximum compensation dead zone according to a power prediction interval to which the actual generated power belongs;

judging whether to update the compensation dead zone according to the compensation dead zone and the maximum compensation dead zone;

if k > d, let k=d;

where k represents the compensation dead zone and d represents the maximum compensation dead zone.

5. The power prediction bias compensation method for a new energy power station according to claim 4, wherein the calculation formula of the maximum compensation dead zone is as follows

If P _{Hair in fact} ∈[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∈[a _{Ultra-short term} ，b _{Ultra-short term} ]D=c× (a _{Short term} -b _{Ultra-short term} ）；

If P _{Hair in fact} ∈[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∉[a _{Ultra-short term} ，b _{Ultra-short term} ]D=c× (a _{Ultra-short term} -b _{Short term} ）；

If P _{Hair in fact} ∉[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∈[a _{Ultra-short term} ，b _{Ultra-short term} ]D=c× (a _{Ultra-short term} -b _{Short term} ）；

If P _{Hair in fact} ∉[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∉[a _{Ultra-short term} ，b _{Ultra-short term} ]D=c× (a _{Ultra-short term} -b _{Short term} ）；

Wherein c is a predetermined coefficient, and 0< c <1.

6. The utility model provides a power prediction deviation compensation system of new forms of energy power station which characterized in that includes:

the acquisition module is used for acquiring power data of the target new energy power station; wherein the power data comprises: the method comprises the steps of actual power generation of a power generation system, a short-term power prediction interval and an ultra-short-term power prediction interval of the power generation prediction system, and real-time SOC, a preset SOC upper limit value and a preset SOC lower limit value of an energy storage system;

the predicted power interval determining module is used for determining a power prediction interval to which the actual power generation power belongs according to the actual power generation power of the power generation system, a short-term power prediction interval and an ultra-short-term power prediction interval of the power generation prediction system;

the compensation dead zone determining module is used for determining a compensation dead zone of a power prediction interval to which the actual generated power belongs;

the power prediction deviation compensation determining module is used for determining whether the energy storage system needs to be charged or discharged for power prediction deviation compensation according to a power prediction interval to which the actual power generation power belongs and a real-time SOC (state of charge) of the energy storage system, a preset SOC upper limit value and a preset SOC lower limit value;

the power prediction deviation compensation calculation module is used for calculating the charge and discharge power of the energy storage system according to the actual power generation power, the power prediction interval to which the actual power generation power belongs and the compensation dead zone when the energy storage system performs power prediction deviation compensation;

the power prediction deviation compensation calculation module is used for calculating the charge and discharge power of the energy storage system according to the following calculation formula:

when P _{Hair in fact} ∈[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∈[a _{Ultra-short term} ，b _{Ultra-short term} ]If SOC is at _{Real time} > SOC _max P is then _{Energy storage} =a _{Short term} -k-P _{Hair in fact} ；

If SOC is _{Real time} < SOC _min P is then _{Energy storage} =b _{Ultra-short term} +k-P _{Hair in fact} ；

If SOC is _min ≤ SOC _{Real time} ≤ SOC _max P is then _{Energy storage} =0；

Wherein [ a ] _{Short term} ，b _{Short term} ]A short-term power prediction interval [ a ] representing a power generation prediction system _{Ultra-short term} ，b _{Ultra-short term} ]Representing an ultra-short term power prediction interval, P, of a power generation prediction system _{Hair in fact} Representing the actual power generated by the power generation system, P _{Energy storage} Representing charge and discharge power, SOC of an energy storage system _{Real time} Representing real-time SOC，SOC _max Indicating a preset SOC upper limit value, SOC _min Representing a preset SOC lower limit value;

when P _{Hair in fact} ∈[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∉[a _{Ultra-short term} ，b _{Ultra-short term} ]In the time-course of which the first and second contact surfaces,

if SOC is _{Real time} > SOC _max P is then _{Energy storage} =a _{Ultra-short term} -k-P _{Hair in fact} ；

If SOC is _{Real time} < SOC _min When then P _{Energy storage} =b _{Short term} +k-P _{Hair in fact} ；

If SOC is _min ≤ SOC _{Real time} ≤ SOC _max Then

P _{Energy storage} =-MIN ( fabs (a _{Ultra-short term} -k-P _{Hair in fact} )，fabs (b _{Short term} +k-P _{Hair in fact} ) )；

Wherein fabs represent absolute values, MIN represents minimum values;

when P _{Hair in fact} ∉[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∈[a _{Ultra-short term} ，b _{Ultra-short term} ]In the time-course of which the first and second contact surfaces,

if SOC is _{Real time} > SOC _max P is then _{Energy storage} =a _{Ultra-short term} -k-P _{Hair in fact} ；

If SOC is _{Real time} < SOC _min When then P _{Energy storage} =b _{Short term} +k-P _{Hair in fact} ；

If SOC is _min ≤ SOC _{Real time} ≤ SOC _max Then

P _{Energy storage} =MIN ( fabs (a _{Ultra-short term} -k-P _{Hair in fact} )，fabs (b _{Short term} +k-P _{Hair in fact} ) )；

When P _{Hair in fact} ∉[a _{Short term} ，b _{Short term} ]And P is _{Hair in fact} ∉[a _{Ultra-short term} ，b _{Ultra-short term} ]In the time-course of which the first and second contact surfaces,

if SOC is _{Real time} > SOC _max P is then _{Energy storage} =a _{Ultra-short term} -k-P _{Hair in fact} ；

If SOC is _{Real time} < SOC _min P is then _{Energy storage} =b _{Short term} +k-P _{Hair in fact} ；

If SOC is _min ≤ SOC _{Real time} ≤ SOC _max Then

P _{Energy storage} = -MIN ( fabs (a _{Ultra-short term} -k-P _{Hair in fact} )，fabs (b _{Short term} +k-P _{Hair in fact} ) )。

7. The power prediction bias compensation system of a new energy plant of claim 6, wherein the compensation dead zone determination module comprises:

and the compensation dead zone pre-configuration sub-module is used for pre-configuring the compensation dead zone.

8. The power prediction bias compensation system of a new energy plant of claim 7, wherein the compensation dead zone determination module further comprises:

the maximum compensation dead zone calculation sub-module is used for calculating the maximum compensation dead zone according to the power prediction interval to which the actual generated power belongs;

the compensation dead zone updating sub-module is used for judging whether to update the compensation dead zone according to the compensation dead zone and the maximum compensation dead zone;

if k > d, let k=d;

where k represents the compensation dead zone and d represents the maximum compensation dead zone.

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