CN110460075B - Hybrid energy storage output control method and system for stabilizing peak-valley difference of power grid - Google Patents

Hybrid energy storage output control method and system for stabilizing peak-valley difference of power grid Download PDF

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CN110460075B
CN110460075B CN201910772403.0A CN201910772403A CN110460075B CN 110460075 B CN110460075 B CN 110460075B CN 201910772403 A CN201910772403 A CN 201910772403A CN 110460075 B CN110460075 B CN 110460075B
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hybrid energy
storage system
peak
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CN110460075A (en
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李朝晖
饶宇飞
杨海晶
谷青发
周宁
党彬
付冬
王振华
徐鹏煜
王建波
孟高军
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State Grid Corp of China SGCC
Electric Power Research Institute of State Grid Henan Electric Power Co Ltd
Anyang Power Supply Co of State Grid Henan Electric Power Co Ltd
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State Grid Corp of China SGCC
Electric Power Research Institute of State Grid Henan Electric Power Co Ltd
Anyang Power Supply Co of State Grid Henan Electric Power Co Ltd
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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
    • H02J15/00—Systems for storing electric energy
    • 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
    • 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
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/34—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering
    • H02J7/345—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering using capacitors as storage or buffering devices
    • 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
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/10—Energy storage using batteries

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Abstract

The application relates to a hybrid energy storage output control method and a hybrid energy storage output control system for stabilizing a peak-valley difference of a power grid, aiming at the problem that a load peak-valley is not considered for a hybrid energy storage system charge-discharge power expected value, firstly, real-time detection and collection are carried out on the actually-measured power of the hybrid energy storage system for peak clipping and valley filling, a quadratic index smoothing algorithm is combined, and a peak-valley factor is considered at the same time, so that the charge-discharge power expected value of the hybrid energy storage system is solved; and then, determining the output condition of the super capacitor and the lithium battery according to the change of the expected value of the charge and discharge power of the hybrid energy storage system, and further performing optimization control on the output condition of the super capacitor and the lithium battery by adopting a global parallel optimization algorithm. The method disclosed by the invention combines the peak-valley factor, the variable coefficient index cut-fill method, the nonlinear state observation method and the global parallel optimization algorithm together, and provides effective reference for the capacity configuration of the energy storage system.

Description

Hybrid energy storage output control method and system for stabilizing peak-valley difference of power grid
Technical Field
The application belongs to the technical field of power grid hybrid energy storage, and particularly relates to a hybrid energy storage output control method and system for stabilizing a peak-valley difference of a power grid.
Background
With the increasing situation of the shortage of traditional energy in the world and the vigorous promotion of clean energy policy in the world, under the dual pressure of energy demand and environmental protection, the application of distributed renewable energy such as solar energy, wind energy and the like is increasingly paid attention by various countries. However, the random nature and uncertainty of the output of the distributed power supply which is connected to the power distribution network in a large amount bring new problems to the safe operation of the power distribution network. New energy represented by wind power is changing the world electric power and energy structures deeply, but the problems of uncertainty, randomness, intermittency, high permeability and the like of the wind power bring obstacles to wind power access and consumption, and also bring great challenges to the tide distribution, the electric energy quality, the safe and stable operation of a power grid.
In such a trend, the grid load peak-to-valley difference tends to increase, while the maximum utilization hours rapidly decrease. And renewable energy source is incorporated into the power networks and causes a series of problems to the steady operation of the electric wire netting, also cause not little difficulty to the electric power scheduling. The power grid power supply and the power transmission and distribution equipment are planned and constructed according to the peak load of the power grid, but the peak load of the power grid has short duration, so that the asset utilization rate of the power equipment is low. In this case, the peak load shifting of the power grid is very important.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the hybrid energy storage output control method and system for stabilizing the peak-valley difference of the power grid are provided for solving the problem of insufficient economical efficiency of capacity allocation of the large-scale wind power grid-connected energy storage system in the prior art.
The technical scheme adopted by the invention for solving the technical problems is as follows: a hybrid energy storage system output control method for stabilizing grid peak-valley difference comprises the following steps:
step 1, setting a power value meeting the peak-valley difference requirement of a stabilizing power grid, and setting a power grid peak-valley difference limit value;
step 2, collecting the actual measurement power of the hybrid energy storage system for peak clipping and valley filling in real time, calculating the deviation value of the actual measurement power and the set power value, and comparing the deviation value with the set peak-valley difference limit value to obtain a peak-valley factor meeting the peak clipping and valley filling requirements;
step 3, judging whether the hybrid energy storage system needs to be accessed into a power grid for peak clipping and valley filling according to the peak-valley factor, and entering the step 3 if the hybrid energy storage system needs to be accessed into the power grid for peak clipping and valley filling; otherwise, returning to the step 2 again;
step 4, according to the peak-valley factor, a quadratic exponential smoothing method is adopted to calculate a model, and the expected value of the charge-discharge power of the hybrid energy storage system is predicted;
step 5, optimizing the peak-valley factor by using a nonlinear state observation method, so as to optimize the expected value of the charge-discharge power of the hybrid energy storage system;
and 6, distributing the output power of the hybrid energy storage system according to the peak-valley factor and the expected charge-discharge power value of the hybrid energy storage system and by combining the charge state and the power constraint of the hybrid energy storage system.
Further, according to the output control method of the hybrid energy storage system of the present invention, in step 2 and step 3, (1) when Δ p (t) is greater than or equal to 0, Δ p (t) is a deviation value between the measured power and the set power value:
if Δ p (t) is not less than 0 and less than Det, λ (t) is 1, Det is a set power grid peak-valley difference limit value, λ (t) is a peak-valley factor, and at this time, the power grid peak-valley difference is low, and peak clipping and valley filling are not required;
if it is
Figure BDA0002173998320000029
Then λ (t) ═ Det/Δ p (t);
Figure BDA0002173998320000021
the peak clipping maximum value of the energy storage system;
if it is
Figure BDA0002173998320000022
Then
Figure BDA0002173998320000023
(2) When Δ P (t) < 0:
if-Det is less than or equal to Δ p (t) < 0, λ (t) ═ 1, and peak clipping and valley filling are not needed for the power grid;
if it is
Figure BDA0002173998320000024
Then λ (t) ═ Det/Δ p (t),
Figure BDA0002173998320000025
filling the valley for energy storage systemA maximum value;
if it is
Figure BDA0002173998320000026
Then
Figure BDA0002173998320000027
Further, according to the output control method of the hybrid energy storage system of the present invention, in step 4, a quadratic exponential smoothing method calculation model is:
Figure BDA0002173998320000028
Figure BDA0002173998320000031
wherein the content of the first and second substances,
Figure BDA0002173998320000032
the charging and discharging expected value of the hybrid energy storage system at the t +1 th moment is obtained;
at、btis a smoothing coefficient;
λ (t) is the peak-to-valley factor;
Figure BDA0002173998320000033
smoothing the cut-and-fill value at the time t for the first exponential;
Figure BDA0002173998320000034
smoothing the cut-and-fill value at the time t for the second exponential;
Figure BDA0002173998320000035
the clipping value at time t-1 is twice exponentially smoothed.
Further, according to the output control method of the hybrid energy storage system of the present invention, in step 5, the method for optimizing the peak-valley factor includes:
(1) calculating the expected value of the charging and discharging power of the hybrid energy storage system at the t-th moment
Figure BDA0002173998320000036
Measured value P of time tbess,t＝ytThe sum of the squares of the fitting errors between, i.e.:
Figure BDA0002173998320000037
(2) optimizing the peak-to-valley factor to obtain the minimum value of the fitting error sum of squares:
according to the calculation model of the quadratic exponential smoothing method,
Figure BDA0002173998320000038
is a function f (λ (t)) on λ (t), i.e.:
Figure BDA0002173998320000039
performing iterative operation on f (lambda (t)) by adopting a nonlinear state observation method, specifically:
①λ(t)＝λ(t-1)+ωtPt t＝1,2,…,k
wherein the content of the first and second substances,
Figure BDA00021739983200000310
q (λ (t)) < Q (λ (t-1)), and Q (λ (t)) ═ FT(λ(t))·F(λ(t))；
ωtIs a step size factor;
f (λ (t)) is a column vector function with respect to F (λ (t));
Jt(λ (t)) is the jacobian matrix of F (λ (t));
and secondly, judging whether | Q (lambda (t)) -Q (lambda (t-1)) | < epsilon or not for the set precision epsilon >0, if so, determining the output peak-valley factor lambda (t) as an optimal value, and otherwise, continuing the step I.
Further, according to the output control method of the hybrid energy storage system of the present invention, in step 6, the energy distribution of the hybrid energy storage system is as follows:
when the SOC isBAT(t)<SOCBAT_minAnd SOCSC(t)<SOCSC_minThe method comprises the following steps:
the hybrid energy storage system can only operate in a charging state, if the hybrid energy storage system is charged, the storage battery is charged preferentially, and the rest energy is borne by the super capacitor;
when SOCBAT(t)<SOCBAT_minAnd SOCSC_min<SOCSC(t)<SOCSC_maxThe method comprises the following steps:
if the battery is in a charging state, the storage battery is charged preferentially;
if the storage battery is in a discharging state, the storage battery stops running, and the super capacitor undertakes a discharging task;
taken as SOCBAT(t)<SOCBAT_minAnd SOCSC(t)>SOCSC_maxThe method comprises the following steps:
if the charging state is the charging state, the storage battery is charged, and the super capacitor stops running;
if the discharge state is the discharge state, the storage battery stops running, and the super capacitor undertakes all discharge tasks;
when SOCBAT _ min < SOCBAT (t) < SOCBAT _ max and soccc (t) < soccc _ min:
if the charging state is the charging state, the super capacitor is charged preferentially, and the rest energy is borne by the storage battery;
if the discharge state is achieved, the super capacitor stops running, and the storage battery undertakes the discharge task;
when SOC is reachedBAT_min<SOCBAT(t)<SOCBAT_maxAnd SOCSC(t)>SOCSC_maxThe method comprises the following steps:
if the charging state is the charging state, the storage battery undertakes a charging task;
if the discharge state is achieved, the super capacitor is discharged preferentially;
when SOC isBAT(t)>SOCBAT_maxAnd SOCSC(t)<SOCSC_minThe method comprises the following steps:
if the charging state is the charging state, the super capacitor is charged;
if the discharge state is achieved, the super capacitor stops running, and the storage battery undertakes the discharge task;
when SOC is reachedBAT(t)>SOCBAT_maxAnd SOCSC_min<SOCSC(t)<SOCSC_maxThe method comprises the following steps:
if the charging state is the charging state, the super capacitor undertakes a charging task;
if the battery is in a discharging state, the battery is discharged preferentially;
when SOCBAT(t)>SOCBAT_maxAnd SOCSC(t)>SOCSC_maxThe method comprises the following steps:
the hybrid energy storage system can only operate in a discharging state, if the hybrid energy storage system discharges, the storage battery is preferentially discharged to be lower than the upper limit value of the state of charge, and the rest energy is borne by the super capacitor.
Further, according to the output control method of the hybrid energy storage system of the present invention, in step 6, the rule for performing energy distribution on the hybrid energy storage system is as follows: dividing the deviation value delta P (t) of the power into a high-frequency part and a low-frequency part, and executing a high-frequency component in a power instruction of the hybrid energy storage system by the super capacitor; the storage battery is responsible for bearing low-frequency components for a long time, and can adjust the charge state of the super capacitor in real time and respond to a high-frequency part of a next power instruction in real time.
Further, according to the output control method of the hybrid energy storage system of the present invention, in step 6, when the output of the hybrid energy storage system is subjected to energy distribution, the power and the state of charge of the storage battery and the super capacitor are optimized by combining multi-objective optimization control, and finally, the optimized output condition of the hybrid energy storage system is output, so that the output optimization control of the hybrid energy storage system is realized.
Further, according to the output control method of the hybrid energy storage system, the multi-objective optimization control method comprises the following steps: and establishing a hybrid energy storage system optimization control model, and optimizing parameters in the hybrid energy storage system optimization control model by adopting a global optimization algorithm to obtain an optimal charging and discharging power expected value and a charge state of the hybrid energy storage system, so as to realize the optimal control of the internal energy of the hybrid energy storage system.
Further, according to the output control method of the hybrid energy storage system of the present invention, the hybrid energy storage system optimization control model is:
Figure BDA0002173998320000051
constraint conditions are as follows:
Figure BDA0002173998320000052
in the formula, minF (X) represents a multi-objective optimization function;
minimum value minF (X) indicates that each target in the target function F (X) is equally minimized (f)1、f2)。
f1Representing a limit on the magnitude of the battery power;
f2representing the current SOC value and SOC of the minimized super capacitormedA difference of (2)
SOCmedA moderate level of supercapacitor state of charge;
PBAT,Ethe rated power of the storage battery;
PBAT(t) is the charging and discharging power of the storage battery
MSCThe energy storage capacity of the super capacitor;
PSC(t) is the charge and discharge power of the super capacitor;
SOCSC(t-1) is the charge state of the super capacitor at the time of t-1;
Figure BDA0002173998320000061
the charge state upper and lower limits of the super capacitor are set;
Figure BDA0002173998320000062
the upper and lower limits of the charge state of the storage battery;
t is the running time.
The application also provides a hybrid energy storage system control system that outputs power, hybrid energy storage system control system that outputs power includes:
the power state real-time monitoring equipment is used for acquiring the actual measurement power of the hybrid energy storage system for peak clipping and valley filling in real time;
the energy storage switching control unit is used for determining a peak-valley factor and judging whether the hybrid energy storage system needs to be connected to a power grid or not according to the peak-valley factor;
the power calculation unit is used for calculating the expected charge-discharge power value of the hybrid energy storage system by adopting a quadratic exponential smoothing algorithm according to the peak-valley factor;
the charge state detection device is used for detecting the charge states of a storage battery and a super capacitor in the hybrid energy storage system;
the control decision unit is used for determining the output conditions of the storage battery and the super capacitor according to the charge state of the hybrid energy storage system and the expected charge-discharge power value of the hybrid energy storage system, and issuing a power distribution instruction to the power distribution unit;
and the power distribution unit is used for performing energy distribution on the output of the storage battery and the super capacitor in the hybrid energy storage system according to the power distribution instruction of the control decision unit.
The invention has the beneficial effects that: the hybrid energy storage system is matched with the wind power to output power, so that on one hand, the intermittence and randomness of the wind power output can be reduced, the output power of the wind power is stabilized, and the impact on a power grid is reduced; on the other hand, the energy cross-time scheduling is realized, the peak clipping and valley filling effects are achieved, and the operation of the power distribution network is optimized.
By combining the secondary and nonlinear state observation methods, a capacity allocation scheme capable of coordinating economic benefits can be obtained, the operation period is shortened, and the algorithm calculation efficiency is improved.
The peak-valley factor, the quadratic exponential smoothing algorithm, the nonlinear state observation method and the global parallel optimization algorithm are combined together, and effective reference is provided for the capacity configuration of the energy storage system.
Drawings
The technical solution of the present application is further explained below with reference to the drawings and the embodiments.
FIG. 1 is a flow chart of a control method of an embodiment of the present application;
FIG. 2 is a schematic diagram illustrating a value of a valley-peak factor according to an embodiment of the present disclosure;
fig. 3 is a block diagram of hybrid energy storage system output control according to an embodiment of the present application.
Detailed Description
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
The technical solutions of the present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Control method embodiment
The embodiment provides a method for controlling output of a hybrid energy storage system for stabilizing grid peak-valley difference, as shown in fig. 1, including:
step 1, collecting actually measured power P of a hybrid energy storage system for peak clipping and valley filling in real timebess,tCalculating the measured power Pbess,tAnd comparing the deviation value delta P (t) with a set deviation value delta P (t) of the power value meeting the peak-valley difference requirement of the stabilizing power grid, and comparing the deviation value delta P (t) with a set peak-valley difference limit value Det to obtain a peak-valley factor lambda (t) meeting the peak clipping and valley filling requirements.
As shown in fig. 2, the values of λ (t) are specifically as follows:
(1) when Δ P (t) ≧ 0:
if 0 ≦ Δ p (t) < Det, λ (t) ═ 1;
if it is
Figure BDA0002173998320000071
Then λ (t) ═ Det/Δ p (t);
Figure BDA0002173998320000072
the peak clipping maximum value of the energy storage system;
if it is
Figure BDA0002173998320000073
Then
Figure BDA0002173998320000074
(2) When Δ P (t) < 0:
if-Det ≦ Δ p (t) < 0, λ (t) ═ 1;
if it is
Figure BDA0002173998320000081
Then λ (t) ═ Det/Δ p (t),
Figure BDA0002173998320000082
filling the maximum value of the energy storage system;
if it is
Figure BDA0002173998320000083
Then
Figure BDA0002173998320000084
Step 2, judging whether the hybrid energy storage system needs to be accessed into a power grid for peak clipping and valley filling according to the peak-valley factor, and entering step 3 if the hybrid energy storage system needs to be accessed into the power grid for peak clipping and valley filling; otherwise, returning to the step 1 again;
according to step 1, if λ (t) is 1, the grid peak-to-valley difference is low, and no peak clipping and valley filling are required.
And 3, according to the peak-valley factor, adopting a quadratic exponential smoothing method to calculate a model, and predicting the expected value of the charge-discharge power of the hybrid energy storage system.
Inputting the peak-valley factor lambda (t) into a quadratic exponential smoothing method calculation model, and simultaneously combining a primary charge-discharge power expected value and an actually measured power Pbess,tAnd solving to obtain the expected value of the charge and discharge power of the hybrid energy storage system.
The quadratic exponential smoothing method calculation model is as follows:
Figure BDA0002173998320000085
Figure BDA0002173998320000086
wherein the content of the first and second substances,
Figure BDA0002173998320000087
the charging and discharging expected value of the hybrid energy storage system at the t +1 th moment is obtained;
at、btis a smoothing coefficient;
λ (t) is the peak-to-valley factor;
Figure BDA0002173998320000088
the cut-and-fill value at the time t is subjected to exponential smoothing once, wherein the cut-and-fill value refers to a power value for peak clipping and valley filling;
Figure BDA0002173998320000089
smoothing the cut-and-fill value at the time t for the second exponential;
Figure BDA0002173998320000091
the clipping value at time t-1 is twice exponentially smoothed.
The secondary exponential smoothing method determination method comprises the following steps: without discarding the first cut-and-fill value
Figure BDA0002173998320000092
Binding of peak-to-valley factor lambda (t) pairs
Figure BDA0002173998320000093
Gradually weakening is carried out, namely, along with the continuous increase of the time t, the weight which gradually converges to zero is given, and a more accurate cut-and-fill value is obtained; the principle is that the index cut-filling value of any period of the second time
Figure BDA0002173998320000094
Are the cut and fill values of the first time of the book
Figure BDA0002173998320000095
And the second previous period index cut-fill value
Figure BDA0002173998320000096
Weighted average of (2).
And 4, optimizing the peak-valley factor by using a nonlinear state observation method, so as to optimize the expected value of the charge-discharge power of the hybrid energy storage system.
The method specifically comprises the following steps:
1) expected value of charging/discharging power at time t
Figure BDA0002173998320000097
And measured value P at time tbess,t＝ytThe fitting error square sum between the following specific forms:
Figure BDA0002173998320000098
setting function
Figure BDA0002173998320000099
Calculating a model by a quadratic exponential smoothing method to obtain:
Figure BDA00021739983200000910
i.e., f (λ (t)) is a function of λ (t) that transforms the problem of optimizing the peak-to-valley factor λ (t) into a problem of solving the sum of squares of the fitting errors and the minimum.
F(λ(t))＝[f(λ(1)),f(λ(2)),…,f(λ(k))]TIs a column vector function for f (λ (t)) whose inverse relationship is expressed in the form:
Q(λ(t))＝FT(λ(t))F(λ(t))
2) since F (λ (t)) is of arbitrary order, F (λ (t)) can be microminiature, and the jacobian matrix for F (λ (t)) is:
Figure BDA00021739983200000911
wherein, JtThe j-th column (1. ltoreq. j. ltoreq.k) of (λ (t)) is expressed as follows:
Figure BDA0002173998320000101
according to the nonlinear state observation method, which is a search algorithm with relaxation properties, the following steps are successively used for f (λ (i)) to iterate:
①λ(t)＝λ(t-1)+ωtPt t＝1,2,…,k
wherein the content of the first and second substances,
Figure BDA0002173998320000102
satisfies the following conditions: q (λ (t)) < Q (λ (t-1)), and Q (λ (t)) ═ FT(λ(t))F(λ(t))
ωtIs a step size factor;
and secondly, judging whether | Q (lambda (t)) -Q (lambda (t-1)) | < epsilon or not for the set precision epsilon >0, if so, determining the output peak-valley factor lambda (t) as an optimal value, and if not, continuing to obtain the first step.
And 5, combining power constraints (P) of the storage battery and the super capacitor according to the fluctuation condition of the peak-valley factor and the expected charge-discharge power value of the hybrid energy storage systemBAT_lim(t) and PSC_limAnd (t)), performing energy distribution on the output of the hybrid energy storage system.
Expected value of charging and discharging power of hybrid energy storage system
Figure BDA0002173998320000103
Charging and discharging power P from super capacitorBAT(t) and the charging and discharging power P of the storage batterySC(t) composition, i.e.:
Figure BDA0002173998320000104
in the charge-discharge process of the storage battery and the super capacitor, the charge-discharge power of the storage battery and the super capacitor cannot exceed the maximum charge-discharge power, the hybrid energy storage system must be ensured to work in a normal state, and adverse effects caused by overcharge and overdischarge of the hybrid energy storage system are prevented, namely:
|PBAT(t)|≤|PBAT_lim(t)|
|PSC(t)|≤|PSC_lim(t)|
PBAT_lim(t) and PSC_limAnd (t) the maximum allowable charge and discharge power of the storage battery and the super capacitor at the time t respectively, and the maximum allowable charge and discharge power are determined by the rated charge and discharge power and the residual energy of the energy storage device.
The internal energy coordination control rule of the hybrid energy storage is as follows: the super capacitor executes high-frequency components in a power instruction of hybrid energy storage; the storage battery is responsible for bearing low-frequency components for a long time, and can adjust the charge state of the capacitor in real time and respond to a high-frequency part of a next command in real time.
The method divides the deviation value delta P (t) of the real-time actual measurement power of the hybrid energy storage system and the set power value meeting the peak-valley requirement of the stabilizing power grid into a high-frequency part and a low-frequency part. Having a filter transfer function of
Figure BDA0002173998320000111
Where τ is the filter time constant. For convenient optimization calculation, the filter is converted from a frequency domain to a time domain, and the power of the storage battery and the power of the super capacitor at the moment t are obtained through the frequency division effect of the filter
Figure BDA0002173998320000112
Figure BDA0002173998320000113
Wherein λ is a filter coefficient and has a relation with a filter time constant of
Figure BDA0002173998320000114
The lambda value range is 0-1, the smaller the lambda is, the flatter the power output of the storage battery is, and the more severe the power fluctuation of the super capacitor is; if λ is largerAt this time, the flatness of the output power of the storage battery is reduced, the output power is higher overall, and accordingly, the task of the super capacitor load is reduced and the fluctuation output is also reduced. It follows from this that:
the energy distribution of the hybrid energy storage can be determined by controlling the filter coefficient, and the filter coefficient is in direct proportion to the power of the storage battery and in inverse proportion to the output of the super capacitor.
Considering the characteristics of large discharge power of the super capacitor and high energy density of the storage battery, and combining the maximum allowable charge and discharge power of the storage battery and the super capacitor, the energy distribution of the embodiment is specifically as follows:
when the SOC isBAT(t)<SOCBAT_minAnd SOCSC(t)<SOCSC_minThe method comprises the following steps:
the super capacitor of the hybrid energy storage system and the SOC state of the storage battery are both lower than the lower limit value and can only be charged. In a charging state, the SOC level of the storage battery is firstly improved according to the principle that the storage battery is charged and discharged preferentially, the rest energy is borne by the super capacitor, and the power calculation formula is as follows:
Figure BDA0002173998320000115
when SOCBAT(t)<SOCBAT_minAnd SOCSC_min<SOCSC(t)<SOCSC_maxThe method comprises the following steps:
if the SOC of the storage battery is lower than the lower limit value and the storage battery is charged preferentially in a charging state, the SOC of the storage battery is lower than the lower limit value, and if the storage battery is in the charging state, the storage battery is charged preferentially
Figure BDA0002173998320000116
If the storage battery is in a discharging state, the storage battery stops running and the super capacitor undertakes the discharging task, then
Figure BDA0002173998320000121
Taken as SOCBAT(t)<SOCBAT_minAnd SOCSC(t)>SOCSC_maxThe method comprises the following steps:
the SOC of the storage battery is lower than the lower limit value, and the SOC of the super capacitor is higher than the upper limit value.
If the charging state is the charging state, the storage battery is charged, and the super capacitor stops running, then:
Figure BDA0002173998320000122
if the super capacitor is in a discharging state and takes over all discharging tasks, then
Figure BDA0002173998320000123
When SOCBAT_min<SOCBAT(t)<SOCBAT_maxAnd SOCSC(t)<SOCSC_minThe method comprises the following steps:
in the state, the SOC of the storage battery is in a normal operation range, and the SOC of the super capacitor is lower than a lower limit value. If the charging state is the charging state, the super capacitor is charged preferentially, and the rest energy is borne by the storage battery
Figure BDA0002173998320000124
If the discharge state is adopted, the super capacitor stops running, and the storage battery discharges
Figure BDA0002173998320000125
When SOC is reachedBAT_min<SOCBAT(t)<SOCBAT_maxAnd SOCSC(t)>SOCSC_maxThe method comprises the following steps:
the SOC of the storage battery is in a normal operation range, and the SOC of the super capacitor is higher than an upper limit value.
If the battery is in a charging state, the battery is not charged, and the storage battery bears the charging task, the following steps are performed:
Figure BDA0002173998320000126
if the discharge state is reached, the super capacitor is discharged first, and the storage battery is second, the
Figure BDA0002173998320000127
When SOC isBAT(t)>SOCBAT_maxAnd SOCSC(t)<SOCSC_minThe method comprises the following steps:
the SOC of the storage battery is higher than the upper limit value, and the SOC of the super capacitor is lower than the lower limit value.
If the charging state is reached, the storage battery stops operating, and the super capacitor is charged, then the following steps are carried out:
Figure BDA0002173998320000131
if the discharge state is adopted, the super capacitor stops running, and the storage battery discharges, then:
Figure BDA0002173998320000132
when SOC is reachedBAT(t)>SOCBAT_maxAnd SOCSC_min<SOCSC(t)<SOCSC_maxThe method comprises the following steps:
the SOC of the storage battery is higher than the upper limit value, and the SOC of the super capacitor is normal.
If the charging state is reached, the storage battery stops operating, and the super capacitor is charged
Figure BDA0002173998320000133
If the battery is in a discharge state and the battery is discharged preferentially, the method comprises
Figure BDA0002173998320000134
When SOCBAT(t)>SOCBAT_maxAnd SOCSC(t)>SOCSC_maxThe method comprises the following steps:
the SOC of the storage battery and the SOC of the super capacitor are higher than the upper limit value, and the hybrid energy storage system can only discharge.
If the storage battery is in a discharging state, the storage battery is preferentially discharged to enable the storage battery to be lower than the SOC upper limit value, and the rest energy is borne by the super capacitor, then
Figure BDA0002173998320000135
As a further optimization, the embodiment performs multi-objective optimization control on the energy distribution of the hybrid energy storage system, including:
firstly, establishing an optimal control model of a hybrid energy storage system
Figure BDA0002173998320000136
In the formula (f)1The output power amplitude of the storage battery at each sampling point is limited, and overcharge and overdischarge are avoided; f. of2The requirement of charging or discharging at the next moment can be met, and the SOC of the super capacitor is kept at a moderate level; SOCmedThe SOC is a moderate level, and generally takes about 0.5; pBAT,EThe rated power of the storage battery; mSCThe energy storage capacity of the super capacitor.
Constraint conditions
Figure BDA0002173998320000141
In the formula (I), the compound is shown in the specification,
Figure BDA0002173998320000142
the charge state upper and lower limits of the super capacitor are set;
Figure BDA0002173998320000143
the upper and lower limits of the charge state of the storage battery; t is the running time.
② mixing PBAT(t)、PSC(t)、SOCSC(t)、SOCBATAnd (t) as a variable, obtaining the optimal charging and discharging power expected value and the optimal charge state of the hybrid energy storage system by using a global optimization algorithm for the hybrid energy storage system optimization control model, and realizing the optimal control of the internal energy of the hybrid energy storage system.
The control system comprises:
as shown in fig. 3, the output control system of the hybrid energy storage system of the present embodiment includes:
the power state real-time monitoring equipment is used for acquiring the actual measurement power of the hybrid energy storage system for peak clipping and valley filling in real time;
the energy storage switching control unit is used for determining a peak-valley factor and judging whether the hybrid energy storage system needs to be connected to a power grid or not according to the peak-valley factor;
the power calculation unit is used for calculating the expected charge-discharge power value of the hybrid energy storage system by adopting a quadratic exponential smoothing algorithm according to the peak-valley factor;
the control decision unit is used for determining the output conditions of the storage battery and the super capacitor according to the charge state of the hybrid energy storage system and the expected charge-discharge power value of the hybrid energy storage system, and issuing a power instruction to the power distribution unit;
and the power distribution unit is used for distributing the energy of the output of the storage battery and the super capacitor in the hybrid energy storage system according to the power distribution instruction issued by the control decision unit.
As a further optimization, the embodiment further provides a multi-objective optimization control unit for performing optimization control on the energy of the storage battery and the super capacitor in the hybrid energy storage system. The specific optimization control mode is the same as the control method embodiment.
In order to solve the problem of insufficient economical efficiency of capacity allocation of the energy storage system of large-scale wind power integration, the method considers the power grid peak-valley difference caused by wind power integration, and obtains the energy storage system power expected value meeting the stabilization target requirement and the self state of the energy storage system by utilizing a secondary smoothing index algorithm and combining a peak-valley factor according to real-time load power data needing peak clipping and valley filling.
Meanwhile, the peak-valley factor is optimized by adopting a nonlinear state observation method, the Jacobian matrix is calculated by calculating the sum of squares of errors between the load power and the expected value and forming a corresponding column vector function, and the optimal solution of the peak-valley factor is calculated by an iterative process.
In light of the foregoing description of the preferred embodiments according to the present application, it is to be understood that various changes and modifications may be made without departing from the spirit and scope of the invention. The technical scope of the present application is not limited to the contents of the specification, and must be determined according to the scope of the claims.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

Claims (7)

1. A hybrid energy storage output control method for stabilizing the peak-valley difference of a power grid is characterized by comprising the following steps:
step 1, setting a power value meeting the peak-valley difference requirement of a stabilizing power grid, and setting a power grid peak-valley difference limit value;
step 2, collecting the actual measurement power of the hybrid energy storage system for peak clipping and valley filling in real time, calculating the deviation value of the actual measurement power and the set power value, and comparing the deviation value with the set peak-valley difference limit value to obtain a peak-valley factor meeting the peak clipping and valley filling requirements;
step 3, judging whether the hybrid energy storage system needs to be accessed into a power grid for peak clipping and valley filling according to the peak-valley factor, and entering step 4 if the hybrid energy storage system needs to be accessed into the power grid for peak clipping and valley filling; otherwise, returning to the step 2 again;
step 4, according to the peak-valley factor, a quadratic exponential smoothing method is adopted to calculate a model, and the expected value of the charge-discharge power of the hybrid energy storage system is predicted;
step 5, optimizing the peak-valley factor by using a nonlinear state observation method, so as to optimize the expected value of the charge-discharge power of the hybrid energy storage system;
step 6, according to the peak-valley factor and the expected charging and discharging power value of the hybrid energy storage system, and in combination with the state of charge and the power constraint of the hybrid energy storage system, performing energy distribution on the output of the hybrid energy storage system;
in the step 6, when the output of the hybrid energy storage system is subjected to energy distribution, the power and the charge state of the storage battery and the super capacitor are optimized by combining multi-objective optimization control, and finally the output condition of the optimized hybrid energy storage system is output, so that the optimization control of the output of the hybrid energy storage system is realized;
the multi-objective optimization control method comprises the following steps: establishing a hybrid energy storage system optimization control model, and optimizing parameters in the hybrid energy storage system optimization control model by adopting a global optimization algorithm to obtain an optimal charging and discharging power expected value and a charge state of the hybrid energy storage system, so as to realize the optimization control of the internal energy of the hybrid energy storage system;
the hybrid energy storage system optimization control model is as follows:
Figure FDA0003498191970000011
constraint conditions are as follows:
Figure FDA0003498191970000021
in the formula, min F (X) represents a multi-objective optimization function;
f1representing a limit on the magnitude of the battery power;
f2representing the current SOC value and SOC of the minimized super capacitormedA difference of (a);
SOCmeda moderate level of supercapacitor state of charge;
PBAT,Ethe rated power of the storage battery;
PBAT(t) is the charging and discharging power of the storage battery
MSCThe energy storage capacity of the super capacitor;
PSC(t) is the charge and discharge power of the super capacitor;
SOCSC(t-1) is the charge state of the super capacitor at the time of t-1;
Figure FDA0003498191970000022
the charge state upper and lower limits of the super capacitor are set;
Figure FDA0003498191970000023
the upper and lower limits of the charge state of the storage battery;
t is the running time.
2. The hybrid energy storage output control method according to claim 1, wherein in step 2 and step 3, (1) when Δ p (t) is greater than or equal to 0, Δ p (t) is a deviation value between the measured power and the set power value:
if Δ p (t) is not less than 0 and less than Det, λ (t) is 1, Det is a set power grid peak-valley difference limit value, λ (t) is a peak-valley factor, and at this time, the power grid peak-valley difference is low, and peak clipping and valley filling are not required;
if it is
Figure FDA0003498191970000024
Then λ (t) ═ Det/Δ p (t);
Figure FDA0003498191970000025
the peak clipping maximum value of the energy storage system;
if it is
Figure FDA0003498191970000026
Then
Figure FDA0003498191970000027
(2) When Δ P (t) < 0:
if-Det is less than or equal to Δ p (t) < 0, λ (t) ═ 1, and peak clipping and valley filling are not needed for the power grid;
if it is
Figure FDA0003498191970000031
Then λ (t) ═ Det/Δ p (t),
Figure FDA0003498191970000032
filling the maximum value of the energy storage system;
if it is
Figure FDA0003498191970000033
Then
Figure FDA00034981919700000312
3. The hybrid energy storage output control method according to claim 1, wherein in step 4, the quadratic exponential smoothing method calculation model is:
Figure FDA0003498191970000034
Figure FDA0003498191970000035
wherein the content of the first and second substances,
Figure FDA0003498191970000036
the charging and discharging expected value of the hybrid energy storage system at the t +1 th moment is obtained;
at、btis a smoothing coefficient;
λ (t) is the peak-to-valley factor;
Figure FDA0003498191970000037
smoothing the cut-and-fill value at the time t for the first exponential;
Figure FDA0003498191970000038
smoothing the cut-and-fill value at the time t for the second exponential;
Figure FDA0003498191970000039
the clipping value at time t-1 is twice exponentially smoothed.
4. The hybrid energy storage output control method of claim 3, wherein in step 5, the peak-to-valley factor is optimized by:
(1) calculating the expected value of the charging and discharging power of the hybrid energy storage system at the t-th moment
Figure FDA00034981919700000310
Measured value P of time tbess,t＝ytThe sum of squared fitting errors between Q (λ (t)), i.e.:
Figure FDA00034981919700000311
(2) optimizing the peak-to-valley factor to obtain the minimum value of the fitting error sum of squares:
according to the calculation model of the quadratic exponential smoothing method,
Figure FDA0003498191970000041
is a function f (λ (t)) on λ (t), i.e.:
Figure FDA0003498191970000042
(t is 2,3, …, k), and f (λ (t)) is iteratively calculated by a nonlinear state observation method, specifically:
①λ(t)＝λ(t-1)+ωtPt t＝1,2,…,k
wherein the content of the first and second substances,
Figure FDA0003498191970000043
q (λ (t)) < Q (λ (t-1)), and Q (λ (t)) ═ FT(λ(t))·F(λ(t))；
ωtIs a step size factor;
f (λ (t)) is a column vector function with respect to F (λ (t));
Jt(λ (t)) is the jacobian matrix of F (λ (t));
and secondly, judging whether | Q (lambda (t)) -Q (lambda (t-1)) | < epsilon or not for the set precision epsilon >0, if so, determining the output peak-valley factor lambda (t) as an optimal value, and otherwise, continuing the step I.
5. The hybrid energy storage output control method according to claim 1, wherein in step 6, the energy distribution of the hybrid energy storage system is as follows:
when the SOC isBAT(t)<SOCBAT_minAnd SOCSC(t)<SOCSC_minThe method comprises the following steps:
the hybrid energy storage system can only operate in a charging state, if the hybrid energy storage system is charged, the storage battery is charged preferentially, and the rest energy is borne by the super capacitor;
when SOCBAT(t)<SOCBAT_minAnd SOCSC_min<SOCSC(t)<SOCSC_maxThe method comprises the following steps:
if the battery is in a charging state, the storage battery is charged preferentially;
if the storage battery is in a discharging state, the storage battery stops running, and the super capacitor undertakes a discharging task;
taken as SOCBAT(t)<SOCBAT_minAnd SOCSC(t)>SOCSC_maxThe method comprises the following steps:
if the charging state is the charging state, the storage battery is charged, and the super capacitor stops running;
if the discharge state is the discharge state, the storage battery stops running, and the super capacitor undertakes all discharge tasks;
when SOCBAT_min<SOCBAT(t)<SOCBAT_maxAnd SOCSC(t)<SOCSC_minThe method comprises the following steps:
if the charging state is the charging state, the super capacitor is charged preferentially, and the rest energy is borne by the storage battery;
if the discharge state is achieved, the super capacitor stops running, and the storage battery undertakes the discharge task;
when SOC is reachedBAT_min<SOCBAT(t)<SOCBAT_maxAnd SOCSC(t)>SOCSC_maxThe method comprises the following steps:
if the charging state is the charging state, the storage battery undertakes a charging task;
if the discharge state is achieved, the super capacitor is discharged preferentially;
when SOC isBAT(t)>SOCBAT_maxAnd SOCSC(t)<SOCSC_minThe method comprises the following steps:
if the charging state is the charging state, the super capacitor is charged;
if the discharge state is achieved, the super capacitor stops running, and the storage battery undertakes the discharge task;
when SOC is reachedBAT(t)>SOCBAT_maxAnd SOCSC_min<SOCSC(t)<SOCSC_maxThe method comprises the following steps:
if the charging state is the charging state, the super capacitor undertakes a charging task;
if the battery is in a discharging state, the storage battery is preferentially discharged;
when SOCBAT(t)>SOCBAT_maxAnd SOCSC(t)>SOCSC_maxThe method comprises the following steps:
the hybrid energy storage system can only operate in a discharging state, if the hybrid energy storage system discharges, the storage battery is preferentially discharged to be lower than the upper limit value of the state of charge, and the rest energy is borne by the super capacitor.
6. The hybrid energy storage output control method according to claim 1, wherein in step 6, the energy distribution rule for the hybrid energy storage system is as follows: dividing the deviation value delta P (t) of the power into a high-frequency part and a low-frequency part, and executing a high-frequency component in a power instruction of the hybrid energy storage system by the super capacitor; the storage battery is responsible for bearing low-frequency components for a long time, and can adjust the charge state of the super capacitor in real time and respond to a high-frequency part of a next power instruction in real time.
7. A hybrid energy storage output control system employing the control method of any one of claims 1-6, wherein the hybrid energy storage output control system comprises:
the power state real-time monitoring equipment is used for acquiring the actual measurement power of the hybrid energy storage system for peak clipping and valley filling in real time;
the energy storage switching control unit is used for determining a peak-valley factor and judging whether the hybrid energy storage system needs to be connected to a power grid or not according to the peak-valley factor;
the power calculation unit is used for calculating the expected charge-discharge power value of the hybrid energy storage system by adopting a quadratic exponential smoothing algorithm according to the peak-valley factor;
the charge state detection device is used for detecting the charge states of a storage battery and a super capacitor in the hybrid energy storage system;
the control decision unit is used for deciding the output of the storage battery and the super capacitor according to the charge states of the storage battery and the super capacitor and the expected charge-discharge power value;
the energy coordination control unit is used for carrying out internal energy coordination control on the storage battery and the super capacitor according to the instruction of the control decision unit and determining the specific output conditions of the storage battery and the super capacitor;
and the power distribution unit is used for distributing the energy of the output of the storage battery and the super capacitor in the hybrid energy storage system according to the instruction of the energy coordination control unit.
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