CN114725968A - Wind power fluctuation smooth control method based on dynamic adjustment SOC state - Google Patents

Wind power fluctuation smooth control method based on dynamic adjustment SOC state Download PDF

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CN114725968A
CN114725968A CN202210400809.8A CN202210400809A CN114725968A CN 114725968 A CN114725968 A CN 114725968A CN 202210400809 A CN202210400809 A CN 202210400809A CN 114725968 A CN114725968 A CN 114725968A
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energy storage
storage battery
soc
charging
power
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林莉
林雨露
贾源琦
范米
王静芝
谭惠丹
曹雅裴
孔宪宇
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Chongqing University
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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
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • 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
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
    • 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
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • 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
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/0047—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with monitoring or indicating devices or circuits
    • H02J7/0048—Detection of remaining charge capacity or state of charge [SOC]
    • 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
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/0068—Battery or charger load switching, e.g. concurrent charging and load supply
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)

Abstract

The invention provides a wind power fluctuation smooth control method based on a dynamic adjustment SOC state, which comprises the following steps: s1, dividing the time of one day into n moments according to a set time step; s2, collecting an SOC value of an energy storage battery in the wind power system at the kth moment, and determining a charging and discharging saturation capacity value of the energy storage battery; s3, constructing a prediction power sequence of the energy storage battery in the timing domain based on the charging and discharging saturation capacity value of the energy storage battery at the kth moment; s4, taking the first item in the predicted power sequence of the energy storage battery obtained through prediction as energy storage reference power, and controlling the energy storage battery to charge and discharge based on the reference power; s5, let k equal to k +1, and return to step S2; the method can accurately predict the power in the future setting time domain of the wind power based on the charging and discharging saturation capacity value of the energy storage battery at the current moment, and control the charging and discharging of the energy storage battery based on the predicted value, so that the wind power stabilizing capacity when the energy storage output level is higher is improved.

Description

Wind power fluctuation smooth control method based on dynamic adjustment SOC state
Technical Field
The invention relates to a wind power control method, in particular to a wind power fluctuation smooth control method based on a dynamic adjustment SOC state.
Background
The wind power generation is a novel power generation mode with the best development scale and commercial development prospect due to the relatively mature technology and relatively low cost, and in recent years, the wind power is rapidly developed and plays a certain role in environmental protection, energy conservation and emission reduction, but the wind power shows obvious fluctuation, and along with the increase of the wind power permeability, the influence on the stability and the safety of a power grid is brought. The energy storage technology has the characteristics of quick response, flexible operation and the like, can flexibly and quickly throughput power, quickly adjust electric energy to stabilize wind power fluctuation, and accordingly reduces the influence of the wind power fluctuation on a power grid. On the other hand, the ability of energy storage to stabilize wind power is limited by the capacity of energy storage, and during the charging and discharging process, the situation that the electric quantity is too large and further charging cannot be performed or the electric quantity is too small and discharging cannot be performed exists. At present, no effective method for solving the technical problems is available.
Therefore, in order to solve the above technical problems, it is necessary to provide a new technical means.
Disclosure of Invention
In view of the above, the present invention provides a wind power fluctuation smoothing control method based on a dynamic SOC state, which can accurately predict power in a future setting time domain of wind power based on a charge-discharge saturation capacity value of an energy storage battery at a current time, perform charge-discharge control of the energy storage battery based on a predicted value, improve a wind power stabilizing capacity when an energy storage output level is high, and speed for recovering the output level faster when the energy storage output level is low, and can reduce life loss of the energy storage battery, and improve reliability and economy of the whole wind power system.
The invention provides a wind power fluctuation smooth control method based on a dynamic adjustment SOC state, which comprises the following steps:
s1, dividing the time of one day into n moments according to a set time step;
s2, collecting an SOC value of an energy storage battery in the wind power system at the kth moment, and determining a charging and discharging saturation capacity value of the energy storage battery;
s3, constructing a prediction power sequence of the energy storage battery in the timing domain based on the charging and discharging saturation capacity value of the energy storage battery at the kth moment;
s4, taking the first item in the predicted power sequence of the energy storage battery obtained through prediction as energy storage reference power, and controlling the energy storage battery to charge and discharge based on the reference power;
s5, let k be k +1, and return to step S2.
Further, step S2 specifically includes:
the SOC of the energy storage battery is divided into a charging and discharging saturation interval and a charging and discharging unsaturation interval, wherein:
the unsaturated charging and discharging interval comprises [0, a ], [ a, b ] and (b, 1 ];
when the SOC of the energy storage battery is in the [0, a) interval, the SOC is in a charging and discharging unsaturated interval, and the discharging capacity of the energy storage battery is insufficient at the moment;
when the SOC of the energy storage battery is in the [ a, b ] interval, the charging and discharging saturation interval is set, and the energy storage energy is abundant;
when the SOC of the energy storage battery is in the (b, 1) interval, the SOC is in a charging and discharging unsaturated interval, and the charging capacity of the energy storage battery is insufficient at the moment;
constructing a calculation model of the charge and discharge saturation capacity value:
Figure BDA0003600092720000021
wherein: gamma is the SOC state of the current energy storage battery, rho (gamma) is the charging and discharging saturation capacity value of the energy storage battery, a is the discharging capacity saturation operation boundary, b is the charging capacity saturation operation boundary, and a + b is 1, a>0，b>0; r is a critical value of charge-discharge saturation capacity; i isThe charge and discharge saturation capacity decreases in a gradient manner.
Further, step S3 specifically includes:
constructing a discrete time state space equation:
Figure BDA0003600092720000031
wherein: pESFor the power of the energy storage cell, PgFor wind-power grid-connected power, PwFor the original wind power, SOCESFor the state of charge of the energy storage cell, TsTime interval between moments, QESIs the capacity of the energy storage battery;
constructing a rolling optimization function model:
Figure BDA0003600092720000032
wherein: time ρ (k) is k is determined by SOCES(k) Calculating the charging and discharging saturation capacity value of the energy storage battery,
Pw_raterated power for wind power; delta PgFor wind power grid-connected power increment, the expression is delta Pg＝Pg(k)-Pg(k-1)；
And converting the rolling optimization function model into a quadratic programming form for solving.
Further, the rolling optimization function model includes the following constraints:
the method comprises the following steps of energy storage power constraint, energy storage SOC constraint and wind power fluctuation rate constraint:
Figure BDA0003600092720000033
wherein, PES_maxThe maximum value of the power of the energy storage battery; SOC (system on chip)ES_min、SOCES_maxRespectively the minimum and maximum limit values of the SOC of the energy storage battery; delta is the grid-connection fluctuation limit value of unit time.
The invention has the beneficial effects that: according to the invention, the power in the future set time domain of the wind power can be accurately predicted based on the charge-discharge saturation capacity value of the energy storage battery at the current moment, the charge-discharge control of the energy storage battery is carried out based on the predicted value, the wind power stabilizing capacity when the energy storage output level is higher is improved, the speed of recovering the output level is higher when the energy storage output level is low is increased, the service life loss of the energy storage battery can be reduced, and the reliability and the economical efficiency of the whole wind power system are improved.
Drawings
The invention is further described below with reference to the following figures and examples:
FIG. 1 is a flow chart of the present invention.
FIG. 2 is a schematic structural diagram of the wind power and energy storage combined system.
Fig. 3 is a diagram illustrating the stabilizing effect of the prediction control method of the present invention.
Fig. 4 is a schematic diagram illustrating the effect of adjusting the energy storage output level according to the present invention.
Detailed Description
The invention is further described in detail below:
fig. 2 is a schematic structural diagram of a wind power energy storage combined system to which the method of the present invention is applied, the stored energy is connected with an alternating current bus at a wind power grid-connection position through a DC/DC and DC/AC converter to realize free control of power, and a fan is connected to the alternating current bus through the AC/DC and DC/AC converters to reduce a cluster effect of wind power.
The invention provides a wind power fluctuation smooth control method based on a dynamic adjustment SOC state, which comprises the following steps:
s1, dividing the time of one day into n moments according to a set time step; wherein, generally, 1 minute is taken as a step length for one day, and the one day is divided into 1440 moments;
s2, collecting an SOC value of an energy storage battery in the wind power system at the kth moment, and determining a charging and discharging saturation capacity value of the energy storage battery;
s3, constructing a prediction power sequence of the energy storage battery in the timing domain based on the charging and discharging saturation capacity value of the energy storage battery at the kth moment;
s4, taking the first item in the predicted power sequence of the energy storage battery obtained through prediction as energy storage reference power, and controlling the energy storage battery to charge and discharge based on the reference power;
s5, let k be k +1, and return to step S2. By the method, the power in the future set time domain of the wind power can be accurately predicted based on the charge-discharge saturation capacity value of the energy storage battery at the current moment, the charge-discharge control of the energy storage battery is performed based on the predicted value, the wind power stabilizing capacity when the energy storage output level is high is improved, the speed of recovering the output level is higher when the energy storage output level is low, the service life loss of the energy storage battery can be reduced, and the reliability and the economical efficiency of the whole wind power system are improved.
In this embodiment, step S2 specifically includes:
the SOC of the energy storage battery is divided into a charging and discharging saturation interval and a charging and discharging unsaturation interval, wherein:
the unsaturated charging and discharging interval comprises [0, a ], [ a, b ] and (b, 1 ];
when the SOC of the energy storage battery is in the [0, a) interval, the SOC is in a charging and discharging unsaturated interval, and the discharging capacity of the energy storage battery is insufficient at the moment;
when the SOC of the energy storage battery is in the [ a, b ] interval, the charging and discharging saturation interval is set, and the energy storage energy is abundant;
when the SOC of the energy storage battery is in the (b, 1) interval, the SOC is in a charging and discharging unsaturated interval, and the charging capacity of the energy storage battery is insufficient at the moment;
constructing a calculation model of the charge and discharge saturation capacity value:
Figure BDA0003600092720000051
wherein: gamma is the SOC state of the current energy storage battery, rho (gamma) is the charging and discharging saturation capacity value of the energy storage battery, a is the discharging capacity saturation operation boundary, b is the charging capacity saturation operation boundary, and a + b is 1, a>0，b>0; r is a critical value of charge-discharge saturation capacity; and I is the charge-discharge saturation capacity reduction gradient.
In this embodiment, step S3 specifically includes:
constructing a discrete time state space equation:
Figure BDA0003600092720000052
wherein: pESFor the power of energy-storage cells, PgFor wind-power grid-connected power, PwFor the original wind power, SOCESFor the state of charge of the energy storage cell, TsTime interval between moments, QESIs the capacity of the energy storage battery;
constructing a rolling optimization function model:
Figure BDA0003600092720000053
wherein: time ρ (k) is k is determined by SOCES(k) Calculating the charge and discharge saturation capacity value of the energy storage battery,
Pw_raterated power for wind power; delta PgFor wind power grid-connected power increment, the expression is delta Pg＝Pg(k)-Pg(k-1)；
Converting the rolling optimization function model into a quadratic programming form to solve; the solution is performed by using a quadratic programming method to form an existing algorithm, and the principle and process thereof are not described herein.
Wherein: the rolling optimization function model comprises the following constraints:
the method comprises the following steps of energy storage power constraint, energy storage SOC constraint and wind power fluctuation rate constraint:
Figure BDA0003600092720000061
wherein, PES_maxThe maximum value of the power of the energy storage battery; SOCES_min、SOCES_maxRespectively representing the minimum and maximum limits of the SOC of the energy storage battery; delta is the grid-connection fluctuation limit value of unit time.
As shown in fig. 3: fig. 3 is a diagram showing the effect of the method of the present invention in stabilizing wind power fluctuation, where the abscissa is time in minutes, the ordinate is power in megawatts, the dotted line is the output power of the wind power system before the stabilization, and the solid line is the grid-connected power of the whole system after the stabilization. It can be seen that the power fluctuation after the stabilization is significantly reduced.
As shown in fig. 4: fig. 4 is a schematic diagram of the effect of adjusting the energy storage output level by the method of the present invention, and it can be seen that, under the condition that the initial SOCs are different, the SOC of the energy storage can gradually return to about 0.5 along with the time and finally keep relatively stable change at about 0.5, which indicates that the control strategy herein can dynamically adjust the return speed of the SOC according to the interval where the energy storage SOC is located, and improve the dynamic output level of the energy storage.
Finally, the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all of them should be covered in the claims of the present invention.

Claims (4)

1. A wind power fluctuation smooth control method based on a dynamic adjustment SOC state is characterized in that: the method comprises the following steps:
s1, dividing the time of one day into n moments according to a set time step;
s2, collecting an SOC value of an energy storage battery in the wind power system at the kth moment, and determining a charging and discharging saturation capacity value of the energy storage battery;
s3, constructing a prediction power sequence of the energy storage battery in the timing domain based on the charging and discharging saturation capacity value of the energy storage battery at the kth moment;
s4, taking the first item in the predicted power sequence of the energy storage battery obtained through prediction as energy storage reference power, and controlling the energy storage battery to charge and discharge based on the reference power;
s5, let k be k +1, and return to step S2.
2. The wind power fluctuation smoothing control method based on the dynamic adjustment SOC state according to claim 1, characterized in that: in step S2, the method specifically includes:
the SOC of the energy storage battery is divided into a charging and discharging saturation interval and a charging and discharging unsaturation interval, wherein:
the unsaturated charging and discharging interval comprises [0, a ], [ a, b ] and (b, 1 ];
when the SOC of the energy storage battery is in the [0, a) interval, the SOC is in a charging and discharging unsaturated interval, and the discharging capacity of the energy storage battery is insufficient at the moment;
when the SOC of the energy storage battery is in the [ a, b ] interval, the charging and discharging saturation interval is set, and the energy storage energy is abundant;
when the SOC of the energy storage battery is in the (b, 1) interval, the SOC is in a charging and discharging unsaturated interval, and the charging capacity of the energy storage battery is insufficient at the moment;
constructing a calculation model of the charge and discharge saturation capacity value:
Figure FDA0003600092710000011
wherein: gamma is the SOC state of the current energy storage battery, rho (gamma) is the charging and discharging saturation capacity value of the energy storage battery, a is the discharging capacity saturation operation boundary, b is the charging capacity saturation operation boundary, and a + b is 1, a>0，b>0; r is a critical value of charge-discharge saturation capacity; and I is the charge-discharge saturation capacity reduction gradient.
3. The wind power fluctuation smoothing control method based on the dynamic adjustment SOC state as claimed in claim 2, wherein: step S3 specifically includes:
constructing a discrete time state space equation:
Figure FDA0003600092710000021
wherein: pESFor the power of energy-storage cells, PgFor wind-power grid-connected power, PwFor the original wind power, SOCESFor storing energyState of charge of the pool, TsTime interval between moments, QESIs the capacity of the energy storage battery;
constructing a rolling optimization function model:
Figure FDA0003600092710000022
wherein: the time rho (k) is k is determined by SOCES(k) Calculating the charge and discharge saturation capacity value of the energy storage battery,
Pw_raterated power for wind power; delta PgFor wind power grid-connected power increment, the expression is delta Pg＝Pg(k)-Pg(k-1)；
And converting the rolling optimization function model into a quadratic programming form for solving.
4. The wind power fluctuation smoothing control method based on the dynamic adjustment SOC state according to claim 3, characterized in that: the rolling optimization function model comprises the following constraints:
the method comprises the following steps of energy storage power constraint, energy storage SOC constraint and wind power fluctuation rate constraint:
Figure FDA0003600092710000023
wherein, PES_maxThe maximum value of the power of the energy storage battery; SOCES_min、SOCES_maxRespectively representing the minimum and maximum limits of the SOC of the energy storage battery; delta is the grid-connection fluctuation limit value of unit time.
CN202210400809.8A 2022-04-17 2022-04-17 Wind power fluctuation smooth control method based on dynamic adjustment SOC state Pending CN114725968A (en)

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