CN103779869A - Energy storage station capacity optimizing calculation method considering dynamic adjustment of electrically charged state - Google Patents

Abstract

The invention discloses an energy storage station capacity optimizing calculation method considering dynamic adjustment of an electrically charged state. The optimizing calculation method includes the steps that a wind power plant energy storage system battery electrically charged state model is set up, and battery overcharging and overdischarging protective control is performed on the model; a wind power plant energy storage system capacity optimizing objective function is built with the optimal comprehensive benefit of stored energy as an objective, and energy storage station charging and discharging power constraint conditions and wind power plant output power fluctuation horizontal constraint conditions are built; the energy storage system capacity optimizing objective function is solved through calculation by selecting a PSO algorithm to determine the optimal wind power plant energy storage system capacity value. The energy storage station capacity optimizing calculation method has the advantages that energy storage station arrangement and overall economy in the operation process are considered comprehensively for the capacity optimizing calculation model, effective combination on site is facilitated, and theoretical premises and guarantees are provided for energy storage capacity optimization.

Description

Consider the energy-accumulating power station capacity optimized calculation method that state-of-charge is dynamically adjusted

Technical field

The present invention relates to power fluctuation and stabilize field, relate in particular to a kind of energy-accumulating power station capacity optimized calculation method of considering that state-of-charge is dynamically adjusted.

Background technology

Wind energy is just worldwide utilized widely as a kind of regenerative resource.Due to randomness, intermittence and the uncontrollability of wind, it is exerted oneself can exert an influence to the aspect such as stability and the quality of power supply of line voltage.And in the face of the sustainable growth of this class regenerative resource scale of wind energy, how solving its output-power fluctuation becomes on the impact of electrical network the major issue that current electrical network faces.At the energy-storage system of wind energy turbine set configuration certain capacity and power, smooth wind power power fluctuation effectively, improves stability of power system.But how the cost of energy-storage system configuration and the but restriction mutually of effect of stabilizing wind power fluctuation, to be optimized stored energy capacitance for this reason, realizing validity and the economy of stabilizing wind power fluctuation is to need at present the problem of solution badly.

Calculate in the optimization of stored energy capacitance, exist at present following not enough: the research research that is embodied in energy storage control plane that is 1) energy-storage system of parameter take the state-of-charge of energy-storage system at present more, and energy-storage system optimum capacity planning based on state-of-charge and economy rarely has research; 2) stored energy capacitance is optimized in computational process, or only consider to ensure that in the long period wind power is that stationary value is that standard is carried out configuration capacity, or be optimized take wind-powered electricity generation unit and energy storage device output-power fluctuation standard deviation as index, or consider that operating cost and cost of investment minimize as optimization aim, all do not discharge and recharge underpower or super-charge super-discharge state to stabilizing the index of the impact of grid-connected power fluctuation in calculating as stored energy capacitance optimization using energy-storage system.

State-of-charge (SOC) refers to the capacity ratio of its residual capacity charged state complete with it.Its span 0 to 1, represents that in the time of SOC=1 battery is full of completely, represents that battery discharge is complete in the time of SOC=0.In energy-accumulating power station, under normal circumstances, when charging, get state-of-charge maximum in each battery pack state-of-charge value as whole energy-storage system; When electric discharge, get state-of-charge minimum value in each battery pack state-of-charge value as whole energy-storage system.Can effectively prevent like this super-charge super-discharge phenomenon of single battery.

Traditional about in stored energy capacitance optimizing process, do not consider the state-of-charge of energy-storage system, such deficiency is: first, because super-charge super-discharge phenomenon frequently appears in energy-storage system, or for a long time in abnormal work state-of-charge, cause greatly reduce its useful life, significantly increased the cost of energy-storage system, be unfavorable for the consideration of economy; The second, the super-charge super-discharge of energy-storage system makes to discharge and recharge power to be difficult to control, and can cause the power that injects electrical network to occur big ups and downs, affects grid stability.

Summary of the invention

Object of the present invention is exactly in order to address the above problem, and has proposed a kind of energy-accumulating power station capacity optimized calculation method of considering that state-of-charge is dynamically adjusted, and the method has realized the stored energy capacitance optimization of taking dispatching requirement, storage energy operation life-span and economy into account.

To achieve these goals, the present invention adopts following technical scheme:

Consider the energy-accumulating power station capacity optimized calculation method that state-of-charge is dynamically adjusted, comprise the following steps:

(1) set up wind energy turbine set energy-storage system battery charge state partition model, and model is carried out to the control of over-charging of battery Cross prevention.

(2) guaranteeing under the prerequisite of level and smooth power output, building wind energy turbine set energy storage system capacity optimization aim function take the comprehensive benefit optimum of energy storage as target, and setting up energy-accumulating power station and discharge and recharge power constraint condition and output power fluctuation of wind farm horizontal restraint condition.

(3) meeting under the condition of wind energy turbine set energy-storage system battery charging and discharging protection, select PSO algorithm to solve calculating to energy storage system capacity optimization aim function, determine the optimum capacity numerical value of wind energy turbine set energy-storage system.

The concrete grammar of described step (1) is:

The restriction classification of battery charge state: Q while setting energy-storage system operation _sOCmaxand Q _sOCminbe respectively the upper and lower bound of energy-storage system state-of-charge, [Q _sOCmin, Q _sOClow-L2] be to put region, [Q _sOClow-L2, Q _sOClow-L1] put region, [Q for pre-mistake _sOClow-L1, Q _sOChigh-L1] be normal region, [Q _sOChigh-L1, Q _sOChigh-L2] for overcharging in advance region, [Q _sOChigh-L2, Q _sOCmax] for overcharging region, Q _sOChigh-L2and Q _sOClow-L2be respectively super-charge super-discharge warning line.

State-of-charge is positioned at while overcharging region in advance, if

revise

it is reduced; If

maintain initial value.

State-of-charge is positioned in advance to be crossed while putting region, if

revise it is reduced; If

maintain initial value.

When state-of-charge is positioned at normal region,

maintain initial value.

Wherein, for t moment energy-storage system discharges and recharges power,

time, energy-storage system is in charged state,

time, energy-storage system is in discharge condition.

When

time, revise

make its correction factor reducing be:

${\mathrm{\δ}}_i\left(t\right)=\frac{1}{-\lg \left(\frac{Q_{\mathrm{SOC}\max }-Q_{\mathrm{SOCi}}\left(t\right)}{Q_{\mathrm{SOC}\max }-Q_{\mathrm{SOChigh}-L1}}\right)}$

Revised energy-storage system charge power is:

P _ESS(t)＝δ _i(t)ΔP(t)η _C。

Wherein, Q _sOCi(t) be the state-of-charge of t moment energy-storage system, Q _sOCmaxfor the upper limit of energy-storage system state-of-charge, Q _sOChigh-L1for overcharging in advance the lower limit of region state-of-charge, η _cfor the charge efficiency of energy-storage system, Δ P (t) is t moment Power Output for Wind Power Field P _w(t) with grid-connected target power P _ref(t) difference: Δ P (t)=P _w(t)-P _ref(t).

When

time, revise make its correction factor reducing be:

${\mathrm{\δ}}_i\left(t\right)=\frac{1}{-\lg \left(\frac{Q_{\mathrm{SOCi}}\left(t\right)-Q_{\mathrm{SOClow}-L2}}{Q_{\mathrm{SOClow}-L1}-Q_{\mathrm{SOClow}-L2}}\right)}$

Revised energy storage system discharges power is: P _eSS(t)=δ _i(t) Δ P (t)/η _d.

Wherein, Q _sOCi(t) be the state-of-charge of t moment energy-storage system, Q _sOClow-L1and Q _sOClow-L2be respectively and cross in advance lower limit and the upper limit of putting region, η _dfor the discharging efficiency of energy-storage system, Δ P (t) is t moment Power Output for Wind Power Field P _w(t) with grid-connected target power P _ref(t) difference: Δ P (t)=P _w(t)-P _ref(t).

In described step (2), wind energy turbine set energy storage system capacity optimization aim function is:

minC＝K _Lρ _LL _LOST+K _Sρ _SL _SHORT+K _Eρ _EL _ESS+C _C

Wherein, ρ _l, ρ _s, ρ _ebe respectively wind energy turbine set and abandon wind off-energy, level and smooth shortage of power off-energy and energy-storage system and get over the corresponding unit price of the conversion energy of line operation; L _lOST, L _sHORT, L _eSSbeing respectively wind energy turbine set abandons wind off-energy, level and smooth shortage of power off-energy and energy-storage system and gets over the conversion energy of line operation; ρ _ll _lOSTfor wind energy turbine set is abandoned wind cost of energy; ρ _sl _sHORTfor the level and smooth shortage of power off-energy of wind energy turbine set cost; ρ _el _eSSfor energy-storage system is got over the conversion off-energy cost of line operation; K _l, K _sand K _efor the penalty coefficient of operating cost; C _cthe input cost of energy-storage system.

The input cost C of energy-storage system _ccomputational methods be:

C _C＝C _M+C _R+C _B；

C _B＝N _bessρ ₁W _O+N _bessρ ₂W _Om；

$m=\frac{r{(1+r)}^{L_m}}{{(1+r)}^{L_m}-1}$

Wherein, C _mfor the maintenance cost of energy-storage system, C _rfor the displacement cost of the each energy-storage units of energy-storage system, C _bfor the capital investment cost of energy-storage system, N _bessfor the quantity of storage battery in energy-storage system; ρ ₁for stored energy capacitance unit capacity is installed price; W _ofor the rated value of the optimum stored energy capacitance of wind energy turbine set; ρ ₂for stored energy capacitance unit capacity price; M is coefficient of depreciation; R is allowance for depreciation; L _mfor age of project.

Described wind energy turbine set is abandoned wind off-energy, level and smooth shortage of power off-energy and energy-storage system and is got over the computational methods of the conversion energy of line operation and be respectively:

$L_{\mathrm{LOST}}=N_y\underset{i=1}{\overset{g}{\mathrm{\Σ}}}\underset{t=p}{\overset{q}{\mathrm{\Σ}}}\frac{1-{\mathrm{\δ}}_i\left(t\right)}{{\mathrm{\η}}_C}{P}_{\mathrm{ref}}\left(t\right)\mathrm{\Δt}$

$L_{\mathrm{SHORT}}=N_y\underset{i=1}{\overset{h}{\mathrm{\Σ}}}\underset{t=u}{\overset{v}{\mathrm{\Σ}}}(1-{\mathrm{\δ}}_i\left(t\right)){\mathrm{\η}}_D{P}_{\mathrm{ref}}\left(t\right)\mathrm{\Δt}$

$\begin{array}{c}{L}_{\mathrm{LESS}}=N_y\underset{i=1}{\overset{k}{\mathrm{\Σ}}}\underset{t=x}{\overset{y}{\mathrm{\Σ}}}(Q_{\mathrm{SOCi}}\left(t\right)-Q_{\mathrm{SOChigh}-L2})W_O\\ +N_y\underset{i=1}{\overset{l}{\mathrm{\Σ}}}\underset{t=z}{\overset{a}{\mathrm{\Σ}}}(Q_{\mathrm{SOCi}}\left(t\right)-Q_{\mathrm{SOClow}-L2})W_O\end{array}$

Wherein, N _yfor year time of research object; G, h are N _yin year, charge and discharge process continues δ _ithe total degree in < 1 adjust operation interval; P, q are respectively the initial and end time in g interval; U, v are respectively the initial and end time in h interval; K is N _yin year, energy-storage system running status is positioned at the total degree that exceeds maximum state-of-charge; L is N _yin year, energy-storage system running status is positioned at the total degree lower than minimum state-of-charge; X, y are respectively the initial and end time in k interval; Z, a are respectively the initial and end time in l interval, P _ref(t) be grid-connected target power, δ _i(t) be correction factor, Q _sOCi(t) be the state-of-charge of t moment energy-storage system, Q _sOChigh-L2and Q _sOClow-L2be respectively super-charge super-discharge warning line, W _ofor the rated value of the optimum stored energy capacitance of wind energy turbine set, η _cfor the charge efficiency of energy-storage system, η _dfor the discharging efficiency of energy-storage system, Δ t is sampling time step-length.

The middle energy-accumulating power station of described step (2) discharges and recharges power constraint condition and is:

-P _Dη _D≤P _W(t)-P _ref(t)≤P _C

In formula: P _cand P _dthe limit that is respectively energy-storage system discharges and recharges power, regards electric discharge as negative charging process, and its size is as the criterion with its absolute value; P _w(t) be t moment Power Output for Wind Power Field, P _ref(t) be grid-connected target power.

Described output power fluctuation of wind farm horizontal restraint is:

P{|ΔP _d(t)|≤ΔP _dmax}≥Λ

In formula: Δ P _d(t) be the undulating value of Power Output for Wind Power Field after energy-storage system is stabilized; Δ P _dmaxfor the permitted maximum range upper limit of undulating value; Λ is corresponding confidence level.

The step that described energy storage system capacity optimization aim function solves calculating is:

A. extract wind energy turbine set service data time window length T and service data P(t thereof).

B. determine and expect power stage desired value P _g, and given initial SOC value.

C., population dimension D is set, maximum iteration time M _max, convergence precision C _σ, initialization population position x and speed v simultaneously.

D. calculate the fitness value of each particle and by himself particle extreme value p _iand overall example extreme value p _grelatively, if fitness value is less, upgrade p _iand p _g, otherwise, upgrade particle rapidity V and position X.

E. calculate Δ σ ²judge whether to meet the condition of convergence, the described condition of convergence is:

$\underset{t\&RightArrow;\&infin;}{\lim }{\mathrm{\&Delta;\&sigma;}}^2=C_{\mathrm{\&sigma;}}$

Δ σ in formula ²for the colony of population or the variable quantity of overall fitness variance, C _σfor convergence precision, this convergence precision is the permanent number close to zero; If meet the condition of convergence, obtain best stored energy capacitance W _o; If do not meet the condition of convergence, again discharge example and set up new group, and repeat steps d.

The invention has the beneficial effects as follows:

Capacity of the present invention is optimized computation model and has been considered the macroeconomy in energy-accumulating power station configuration and running, is conducive to and effective combination at scene, for the optimization of stored energy capacitance provides theoretical premise and guarantee.

The important indicator of the present invention using state-of-charge as energy-storage system running status, discharges and recharges corrected coefficient of power by change and sets up the charging and recharging model that inhibition excessively charges and discharge, and has realized the adjustment strategy of SOC in stored energy capacitance layoutprocedure.This control strategy, after stored energy capacitance configuration, can be used for reference in corresponding actual wind energy turbine set-energy storage combined operation system simultaneously, forms actual wind energy turbine set energy storage control strategy; On this basis, introduce operating cost corresponding to state-of-charge, built and set up energy storage system capacity Optimized model take economic index as target function, this model realization take the stored energy capacitance optimization of dispatching requirement, storage energy operation life-span and economy into account.

Accompanying drawing explanation

Fig. 1 is that power stage curve is expected in the present embodiment cross section seclected time;

Fig. 2 is that effect schematic diagram is stabilized in the present embodiment cross section seclected time;

Fig. 3 is the present embodiment SOC curve synoptic diagram.

Embodiment:

Below in conjunction with accompanying drawing and embodiment, the present invention will be further described:

1 state-of-charge partition model

The energy storage strategy of wind energy turbine set energy-storage system is: in the time that wind-powered electricity generation unit power output is greater than grid-connected value and power reference, energy-storage system charges to stabilize output-power fluctuation; In the time that wind-powered electricity generation unit power output is less than grid-connected value and power reference, energy storage system discharges, to make up the deficiency of power output, with the power output of this smooth wind power unit, realizes the stability of wind-electricity integration power.

T moment Power Output for Wind Power Field P _w(t) with grid-connected target power P _ref(t) difference DELTA P (t) is:

ΔP(t)＝P _W(t)-P _ref(t) （1）

Energy-storage system discharge and recharge power suc as formula shown in (2,3).

Energy-storage system is in the time of charged state:

$P_{\mathrm{ESS}}^{\mathrm{ref}}\left(t\right)=\mathrm{\&Delta;P}\left(t\right){\mathrm{\&eta;}}_C---\left(2\right)$

Energy-storage system is in the time of discharge condition:

$P_{\mathrm{ESS}}^{\mathrm{ref}}\left(t\right)=\mathrm{\&Delta;P}\left(t\right)/{\mathrm{\&eta;}}_D---\left(3\right)$

In formula:

for t moment energy-storage system discharges and recharges power; When

time, energy-storage system charging,

time, energy storage system discharges; η _cfor the charge efficiency of energy-storage system, generally get 0.65～0.85.

Energy-accumulating power station discharges and recharges power instruction should consider current SOC level and the power instruction size of current time, and, when SOC is positioned at normal range of operation, the power that discharges and recharges of energy-accumulating power station remains unchanged; In the time that SOC gets over line to non-normal working scope, need to adjust and discharge and recharge power in time, prevent super-charge super-discharge phenomenon.

The restriction classification of SOC while setting energy-storage system operation.Wherein, Q _sOCmaxand Q _sOCminbe respectively the upper and lower bound of energy-storage system state-of-charge, [Q _sOCmin, Q _sOClow-L2] be to put region, [Q _sOClow-L2, Q _sOClow-L1] put region, [Q for pre-mistake _sOClow-L1, Q _sOChigh-L1] be normal region, [Q _sOChigh-L1, Q _sOChigh-L2] for overcharging in advance region, [Q _sOChigh-L2, Q _sOCmax] for overcharging region, be more than the traffic coverage of the different state-of-charges of energy-storage system, wherein Q _sOChigh-L2and Q _sOClow-L2super-charge super-discharge warning line respectively.

2 super-charge super-discharge protections are controlled

The change of energy-storage system state-of-charge traffic coverage will cause the correspondence adjustment of corrected coefficient of power, changes the power that discharges and recharges of energy-storage system by corrected coefficient of power, to reach the operation of controlling in advance energy-storage system, avoids it to reach the state of super-charge super-discharge.Concrete control strategy is as shown in table 1.

Table 1 corrected coefficient of power control law

Analyze known: higher when energy-storage system state-of-charge, be positioned at while overcharging region in advance, represent that energy storage is tending towards saturated.If be under charged state

it is right to need

control in advance, through type (4) Modulating Power correction factor, revises it is reduced, and the speed raising to alleviate its state-of-charge, prevents that the state overcharging from appearring in energy-storage system; If be under discharge condition

maintain initial value.Vice versa, on the low side when energy-storage system state-of-charge, is positioned at and crosses in advance while putting region, if be in discharge condition

through type (5) Modulating Power correction factor, revises

it is reduced, and the speed reducing to slow down its state-of-charge, prevents that the state of deep discharge from appearring in energy-storage system.If be under charged state

maintain initial value.In the time that energy-storage system state-of-charge is positioned at normal region, maintain correction factor constant, it is is normally discharged and recharged.

${\mathrm{\&delta;}}_i\left(t\right)=\frac{1}{-\lg \left(\frac{Q_{\mathrm{SOC}\max }-Q_{\mathrm{SOCi}}\left(t\right)}{Q_{\mathrm{SOC}\max }-Q_{\mathrm{SOChigh}-L1}}\right)}---\left(4\right)$

${\mathrm{\&delta;}}_i\left(t\right)=\frac{1}{-\lg (\frac{Q_{\mathrm{SOCi}}\left(t\right)-Q_{\mathrm{SOClow}-L2}}{Q_{\mathrm{SOClow}-L1}-Q_{\mathrm{SOClow}-L2}}}---\left(5\right)$

In formula, δ _i(t), for the t moment discharges and recharges corrected coefficient of power, in the time that energy-storage system is positioned at normal region, value is 1; Q _sOC(t) be the state-of-charge of t moment energy-storage system.Here adopt logarithm barrier function, when state-of-charge approaches Q _sOCmaxor Q _sOClow-L2time, because logarithmic function convergence is strong, can reduce faster δ _i(t), play better in advance and to control the effect that discharges and recharges power, effectively avoid the state-of-charge of energy-storage system to reach and overcharge or cross the state of putting.

It should be noted that, the corrected coefficient of power control method that the present invention proposes reaches Q at energy-storage system state-of-charge _sOChigh-L2time, δ _i(t) minimum value is not 0, and its object is to guarantee making full use of of stored energy capacitance, still can continue charging; And state-of-charge reaches Q _sOClow-L2time by δ _i(t) be modified to zero, can strictly control like this lowest capacity of energy-storage system, thoroughly avoid energy-storage system to operate in and put region, reduce the life consumption of energy-storage system.

Thus, the energy-storage system after can being adjusted discharges and recharges power.

Energy-storage system is in the time of charged state:

P _ESS(t)＝δ _i(t)ΔP(t)η _C （6）

Energy-storage system is in the time of discharge condition:

P _ESS(t)＝δ _i(t)ΔP(t)/η _D （7）

In formula (6), formula (7): P _eSS(t) discharge and recharge power for the energy-storage system of t moment after corrected coefficient of power adjustment, work as P _eSS(t) when > 0, energy-storage system charging, P _eSS(t) when < 0, energy storage system discharges.

Stored energy capacitance planning

The target of wind energy turbine set stored energy capacitance optimization is to guarantee to reduce under the prerequisite of wind power output power fluctuation, regulate the mutual restricting relation between input cost and operating cost, guaranteeing under the prerequisite of level and smooth power output, realizing the on-road efficiency optimization of wind energy turbine set energy-storage system with the input cost of minimum energy storage and operating cost.

1 target function

Wind energy turbine set configures the wind energy turbine set power fluctuation that different stored energy capacitances obtains and stabilizes effect difference, meet under the prerequisite of output power fluctuation of wind farm requirement in assurance, for the restricting relation of stored energy capacitance input cost and operating cost, reach optimum as target take the comprehensive benefit of energy storage.Wherein, the input cost C of energy-storage system _ccomprise the maintenance cost C of energy-storage system _m, displacement cost (only considering in the time that be less than age of project the useful life of the energy-storage units) C of the each energy-storage units of energy-storage system _rcapital investment cost C with energy-storage system _b.

C _C＝C _M+C _R+C _B (8)

C _B＝N _bessρ ₁W _O+N _bessρ ₂W _Om (9)

In formula: N _bessfor the quantity of storage battery in energy-storage system; ρ ₁for stored energy capacitance unit capacity is installed price; W _ofor the rated value of the optimum stored energy capacitance of wind energy turbine set; ρ ₂for stored energy capacitance unit capacity price; M is coefficient of depreciation, and it is defined as:

$m=\frac{r{(1+r)}^{L_m}}{{(1+r)}^{L_m}-1}---\left(10\right)$

In formula: r is allowance for depreciation; L _mfor age of project.

Operating cost comprises the wind energy turbine set causing because of corrected coefficient of power adjustment and abandons windage loss mistake cost, and level and smooth shortage of power loss cost and energy-storage system are got over the conversion loss cost of line operation, and three is all because the variation of stored energy capacitance changes.

Because Power Output for Wind Power Field has year periodically, the research object of optimizing using annual Power Output for Wind Power Field as stored energy capacitance, its wind energy turbine set is abandoned conversion energy that wind off-energy, level and smooth shortage of power off-energy and energy-storage system get over line operation respectively suc as formula shown in (11), formula (12), formula (13):

$L_{\mathrm{LOST}}=N_y\underset{i=1}{\overset{g}{\mathrm{\&Sigma;}}}\underset{t=p}{\overset{q}{\mathrm{\&Sigma;}}}\frac{1-{\mathrm{\&delta;}}_i\left(t\right)}{{\mathrm{\&eta;}}_C}{P}_{\mathrm{ref}}\left(t\right)\mathrm{\&Delta;t}---\left(11\right)$

$L_{\mathrm{SHORT}}=N_y\underset{i=1}{\overset{h}{\mathrm{\&Sigma;}}}\underset{t=u}{\overset{v}{\mathrm{\&Sigma;}}}(1-{\mathrm{\&delta;}}_i\left(t\right)){\mathrm{\&eta;}}_D{P}_{\mathrm{ref}}\left(t\right)\mathrm{\&Delta;t}---\left(12\right)$

$\begin{array}{c}{L}_{\mathrm{LESS}}=N_y\underset{i=1}{\overset{k}{\mathrm{\&Sigma;}}}\underset{t=x}{\overset{y}{\mathrm{\&Sigma;}}}(Q_{\mathrm{SOCi}}\left(t\right)-Q_{\mathrm{SOChigh}-L2})W_O\\ +N_y\underset{i=1}{\overset{l}{\mathrm{\&Sigma;}}}\underset{t=z}{\overset{a}{\mathrm{\&Sigma;}}}(Q_{\mathrm{SOCi}}\left(t\right)-Q_{\mathrm{SOClow}-L2})\end{array}---\left(13\right)$

In formula: N _yfor year time of research object; G, h are N _yin year, charge and discharge process continues δ _ithe total degree in < 1 adjust operation interval; P, q are respectively the initial and end time in g interval; U, v are respectively the initial and end time in h interval; K is N _yin year, energy-storage system running status is positioned at the total degree that exceeds maximum state-of-charge; L is N _yin year, energy-storage system running status is positioned at the total degree lower than minimum state-of-charge; X, y are respectively the initial and end time in k interval; Z, a are respectively the initial and end time in l interval.

The target of wind energy turbine set stored energy capacitance optimization is

minC＝K _Lρ _LL _LOST+K _Sρ _SL _SHORT+K _Eρ _EL _ESS+C _C （14）

In formula: ρ _l, ρ _s, ρ _ebe respectively wind energy turbine set and abandon wind off-energy, level and smooth shortage of power off-energy and energy-storage system and get over the corresponding unit price of the conversion energy of line operation; ρ _ll _lOSTfor wind energy turbine set is abandoned wind cost of energy; ρ _sl _sHORTfor the level and smooth shortage of power off-energy of wind energy turbine set cost; ρ _el _eSSfor energy-storage system is got over the conversion off-energy cost of line operation; K _l, K _sand K _efor the penalty coefficient of operating cost; C _cthe input cost of energy-storage system.

In formula (13), the conversion loss cost that energy-storage system is got over line operation comprises 2 parts: the one, and in the time that energy-storage system operates in too high state-of-charge, energy-storage system does not affect the conversion cost of self life cycle in reasonable running status; The 2nd, in the time that energy-storage system state-of-charge is too low, energy-storage system does not affect the conversion cost of self life cycle in reasonable running status.

2 constraintss

Discharge and recharge power constraint:

-P _Dη _D≤P _W(t)-P _ref(t)≤P _C （14）

In formula: P _dand P _cthe limit that is respectively energy-storage system discharges and recharges power, regards electric discharge as negative charging process, and its size is as the criterion with its absolute value.

Constraints comprises output power fluctuation of wind farm horizontal restraint:

P{|ΔP _d(t)|≤ΔP _dmax}≥Λ （15）

In formula: Δ P _d(t) Δ P _d(t) be the undulating value of Power Output for Wind Power Field after energy-storage system is stabilized; Δ P _dmaxfor the permitted maximum range upper limit of undulating value; Λ is corresponding confidence level.

3 method for solving

Population (PSO) algorithm is that one has calculating simply, the intelligent group computational methods of the advantages such as good convergence, be widely used in solving all kinds of Numerical Optimizations, but in the time solving some of complex optimization problem, still do not existed search precision high and be easily absorbed in the defect of locally optimal solution.For this reason, the present invention considers the stochastic optimization problems that PSO algorithm comprises dynamic boundary condition and contains multiple stochastic variables to solve the present invention.Concrete mode is as follows:

Step 1: selected research object time cross-section length of window y and service data P(t thereof);

Step 2: determine desired output desired value P based on best power output model _g, and given initial SOC equivalence;

Step 3: population dimension D is set, maximum iteration time M _max, convergence precision C _σ, initialization population position x and speed x simultaneously;

Step 4: discharge and recharge strategy according to the present invention, c1, c2, ω, V are set _min, V _maxetc. parameter, convolution (11-15) calculates the fitness value of each particle

and by himself particle extreme value p _iand overall example extreme value p _grelatively, if fitness value is less, upgrade p _iand p _g, upgrade if not particle rapidity V _idand position X _id;

Step 5: calculate Δ σ ²judge whether to meet the condition of convergence, the search condition of convergence is:

$\underset{t\&RightArrow;\&infin;}{\lim }{\mathrm{\&Delta;\&sigma;}}^2=C_{\mathrm{\&sigma;}}---\left(16\right)$

Δ σ in formula ²for the colony of population or the variable quantity of overall fitness variance, C _σfor the permanent number close to zero.If so, obtain best stored energy capacitance W _o; If not, again discharge example and set up new group, and repeating step (4).

Certain wind energy turbine set installed capacity 90MW, chooses wind power data in 2011, and frequency acquisition is 5min, stabilizes desired value as shown in Figure 1.

Discharge and recharge power according to literary composition invention and adjust strategy and stored energy capacitance optimization computation model, obtain stabilizing fluctuation curve of output as shown in Figure 2, the inventive method result of calculation as shown in Table 2.

Form 2 result of calculations

Analyzing above-mentioned example can obtain, capacity planning aspect, and the inventive method has effectively realized the optimization of stored energy capacitance; Stabilize power offset aspect, the inventive method and conventional method are close, slightly increase, and its reason is that corrected coefficient of power adjustment strategy has promoted the probability of abandoning wind or stabilizing not enough energy; More limiting value operation aspect, the present invention significantly reduces the numerical value of N, and it decreases by 96.2%, successful.Investigate the changing condition of SOC in the optimum procurement of reserve capacity process of energy-accumulating power station, as shown in Figure 3.Can find out, in the inventive method SOC in the more limiting value operation of this section, effective guarantee the useful life of ESS.

To sum up can obtain, capacity of the present invention is optimized computation model and has been considered the macroeconomy in energy-accumulating power station configuration and running, is conducive to the effective combination with scene.The optimization that above-mentioned theory research is stored energy capacitance provides theoretical premise and guarantee.Meanwhile, real data sample calculation analysis has been verified above-mentioned conclusion.

By reference to the accompanying drawings the specific embodiment of the present invention is described although above-mentioned; but not limiting the scope of the invention; one of ordinary skill in the art should be understood that; on the basis of technical scheme of the present invention, those skilled in the art do not need to pay various modifications that creative work can make or distortion still in protection scope of the present invention.

Claims (10)

1. consider the energy-accumulating power station capacity optimized calculation method that state-of-charge is dynamically adjusted, it is characterized in that, comprise the following steps:

(1) set up wind energy turbine set energy-storage system battery charge state partition model, and model is carried out to the control of over-charging of battery Cross prevention;

(2) guaranteeing under the prerequisite of level and smooth power output, building wind energy turbine set energy storage system capacity optimization aim function take the comprehensive benefit optimum of energy storage as target, and setting up energy-accumulating power station and discharge and recharge power constraint condition and output power fluctuation of wind farm horizontal restraint condition;

(3) meeting under the condition of wind energy turbine set energy-storage system battery charging and discharging protection, select PSO algorithm to solve calculating to energy storage system capacity optimization aim function, determine the optimum capacity numerical value of wind energy turbine set energy-storage system.

2. a kind of energy-accumulating power station capacity optimized calculation method of considering that state-of-charge is dynamically adjusted as claimed in claim 1, is characterized in that, the concrete grammar of described step (1) is:

The restriction classification of battery charge state: Q while setting energy-storage system operation _sOCmaxand Q _sOCminbe respectively the upper and lower bound of energy-storage system state-of-charge, [Q _sOCmin, Q _sOClow-L2] be to put region, [Q _sOClow-L2, Q _sOClow-L1] put region, [Q for pre-mistake _sOClow-L1, Q _sOChigh-L1] be normal region, [Q _sOChigh-L1, Q _sOChigh-L2] for overcharging in advance region, [Q _sOChigh-L2, Q _sOCmax] for overcharging region, Q _sOChigh-L2and Q _sOClow-L2be respectively super-charge super-discharge warning line;

State-of-charge is positioned at while overcharging region in advance, if

revise

it is reduced; If

maintain initial value;

State-of-charge is positioned in advance to be crossed while putting region, if

revise it is reduced; If

maintain initial value;

When state-of-charge is positioned at normal region,

maintain initial value;

Wherein,

for t moment energy-storage system discharges and recharges power,

time, energy-storage system is in charged state, time, energy-storage system is in discharge condition.

3. a kind of energy-accumulating power station capacity optimized calculation method of considering that state-of-charge is dynamically adjusted as claimed in claim 2, is characterized in that,

time, revise

make its correction factor reducing be:

${\mathrm{\&delta;}}_i\left(t\right)=\frac{1}{-\lg \left(\frac{Q_{\mathrm{SOC}\max }-Q_{\mathrm{SOCi}}\left(t\right)}{Q_{\mathrm{SOC}\max }-Q_{\mathrm{SOChigh}-L1}}\right);}$

Revised energy-storage system charge power is:

P _ESS(t)＝δ _i(t)ΔP(t)η _C；

Wherein, Q _sOCi(t) be the state-of-charge of t moment energy-storage system, Q _sOCmaxfor the upper limit of energy-storage system state-of-charge, Q _sOChigh-L1for overcharging in advance the lower limit of region state-of-charge, η _cfor the charge efficiency of energy-storage system, Δ P (t) is t moment Power Output for Wind Power Field P _w(t) with grid-connected target power P _ref(t) difference: Δ P (t)=P _w(t)-P _ref(t).

4. a kind of energy-accumulating power station capacity optimized calculation method of considering that state-of-charge is dynamically adjusted as claimed in claim 2, is characterized in that, when

time, revise make its correction factor reducing be:

${\mathrm{\&delta;}}_i\left(t\right)=\frac{1}{-\lg \left(\frac{Q_{\mathrm{SOCi}}\left(t\right)-Q_{\mathrm{SOClow}-L2}}{Q_{\mathrm{SOClow}-L1}-Q_{\mathrm{SOClow}-L2}}\right);}$

Revised energy storage system discharges power is: P _eSS(t)=δ _i(t) Δ P (t)/η _d;

Wherein, Q _sOCi(t) be the state-of-charge of t moment energy-storage system, Q _sOClow-L1and Q _sOClow-L2be respectively and cross in advance lower limit and the upper limit of putting region, η _dfor the discharging efficiency of energy-storage system, Δ P (t) is t moment Power Output for Wind Power Field P _w(t) with grid-connected target power P _ref(t) difference: Δ P (t)=P _w(t)-P _ref(t).

5. a kind of energy-accumulating power station capacity optimized calculation method of considering that state-of-charge is dynamically adjusted as claimed in claim 1, is characterized in that, in described step (2), wind energy turbine set energy storage system capacity optimization aim function is:

minC＝K _Lρ _LL _LOST+K _Sρ _SL _SHORT+K _Eρ _EL _ESS+C _C

Wherein, ρ _l, ρ _s, ρ _ebe respectively wind energy turbine set and abandon wind off-energy, level and smooth shortage of power off-energy and energy-storage system and get over the corresponding unit price of the conversion energy of line operation; L _lOST, L _sHORT, L _eSSbeing respectively wind energy turbine set abandons wind off-energy, level and smooth shortage of power off-energy and energy-storage system and gets over the conversion energy of line operation; ρ _ll _lOSTfor wind energy turbine set is abandoned wind cost of energy; ρ _sl _sHORTfor the level and smooth shortage of power off-energy of wind energy turbine set cost; ρ _el _eSSfor energy-storage system is got over the conversion off-energy cost of line operation; K _l, K _sand K _efor the penalty coefficient of operating cost; C _cthe input cost of energy-storage system.

6. a kind of energy-accumulating power station capacity optimized calculation method of considering that state-of-charge is dynamically adjusted as claimed in claim 5, is characterized in that the input cost C of energy-storage system _ccomputational methods be:

C _C＝C _M+C _R+C _B；

C _B＝N _bessρ ₁W _O+N _bessρ ₂W _Om；

$m=\frac{r{(1+r)}^{L_m}}{{(1+r)}^{L_m}-1};$

Wherein, C _mfor the maintenance cost of energy-storage system, C _rfor the displacement cost of the each energy-storage units of energy-storage system, C _bfor the capital investment cost of energy-storage system, N _bessfor the quantity of storage battery in energy-storage system; ρ ₁for stored energy capacitance unit capacity is installed price; W _ofor the rated value of the optimum stored energy capacitance of wind energy turbine set; ρ ₂for stored energy capacitance unit capacity price; M is coefficient of depreciation; R is allowance for depreciation; L _mfor age of project.

7. a kind of energy-accumulating power station capacity optimized calculation method of considering that state-of-charge is dynamically adjusted as claimed in claim 5, it is characterized in that, described wind energy turbine set is abandoned wind off-energy, level and smooth shortage of power off-energy and energy-storage system and is got over the computational methods of the conversion energy of line operation and be respectively:

$L_{\mathrm{LOST}}=N_y\underset{i=1}{\overset{g}{\mathrm{\&Sigma;}}}\underset{t=p}{\overset{q}{\mathrm{\&Sigma;}}}\frac{1-{\mathrm{\&delta;}}_i\left(t\right)}{{\mathrm{\&eta;}}_C}{P}_{\mathrm{ref}}\left(t\right)\mathrm{\&Delta;t}$

$L_{\mathrm{SHORT}}=N_y\underset{i=1}{\overset{h}{\mathrm{\&Sigma;}}}\underset{t=u}{\overset{v}{\mathrm{\&Sigma;}}}(1-{\mathrm{\&delta;}}_i\left(t\right)){\mathrm{\&eta;}}_D{P}_{\mathrm{ref}}\left(t\right)\mathrm{\&Delta;t}$

$\begin{array}{c}{L}_{\mathrm{LESS}}=N_y\underset{i=1}{\overset{k}{\mathrm{\&Sigma;}}}\underset{t=x}{\overset{y}{\mathrm{\&Sigma;}}}(Q_{\mathrm{SOCi}}\left(t\right)-Q_{\mathrm{SOChigh}-L2})W_O\\ +N_y\underset{i=1}{\overset{l}{\mathrm{\&Sigma;}}}\underset{t=z}{\overset{a}{\mathrm{\&Sigma;}}}(Q_{\mathrm{SOCi}}\left(t\right)-Q_{\mathrm{SOClow}-L2})W_O\end{array}$

Wherein, N _yfor year time of research object; G, h are N _yin year, charge and discharge process continues δ _ithe total degree in < 1 adjust operation interval; P, q are respectively the initial and end time in g interval; U, v are respectively the initial and end time in h interval; K is N _yin year, energy-storage system running status is positioned at the total degree that exceeds maximum state-of-charge; L is N _yin year, energy-storage system running status is positioned at the total degree lower than minimum state-of-charge; X, y are respectively the initial and end time in k interval; Z, a are respectively the initial and end time in l interval, P _ref(t) be grid-connected target power, δ _i(t) be correction factor, Q _sOCi(t) be the state-of-charge of t moment energy-storage system, Q _sOChigh-L2and Q _sOClow-L2be respectively super-charge super-discharge warning line, W _ofor the rated value of the optimum stored energy capacitance of wind energy turbine set, η _cfor the charge efficiency of energy-storage system, η _dfor the discharging efficiency of energy-storage system, Δ t is sampling time step-length.

8. a kind of energy-accumulating power station capacity optimized calculation method of considering that state-of-charge is dynamically adjusted as claimed in claim 1, is characterized in that, the middle energy-accumulating power station of described step (2) discharges and recharges power constraint condition and is:

-P _Dη _D≤P _W(t)-P _ref(t)≤P _C

In formula: P _cand P _dthe limit that is respectively energy-storage system discharges and recharges power, regards electric discharge as negative charging process, and its size is as the criterion with its absolute value; P _w(t) be t moment Power Output for Wind Power Field, P _ref(t) be grid-connected target power.

9. a kind of energy-accumulating power station capacity optimized calculation method of considering that state-of-charge is dynamically adjusted as claimed in claim 1, is characterized in that, in described step (2), output power fluctuation of wind farm horizontal restraint is:

P{|ΔP _d(t)|≤ΔP _dmax}≥Λ

In formula: Δ P _d(t) be the undulating value of Power Output for Wind Power Field after energy-storage system is stabilized; Δ P _dmaxfor the permitted maximum range upper limit of undulating value; Λ is corresponding confidence level.

10. a kind of energy-accumulating power station capacity optimized calculation method of considering that state-of-charge is dynamically adjusted as claimed in claim 1, is characterized in that, the step that described energy storage system capacity optimization aim function solves calculating is:

A. extract wind energy turbine set service data time window length T and service data P(t thereof);

B. determine and expect power stage desired value P _g, and given initial SOC value;

C., population dimension D is set, maximum iteration time M _max, convergence precision C _σ, initialization population position x and speed v simultaneously;

D. calculate the fitness value of each particle

and by himself particle extreme value p _iand overall example extreme value p _grelatively, if fitness value is less, upgrade p _iand p _g, otherwise, upgrade particle rapidity V and position X;

E. calculate Δ σ ²judge whether to meet the condition of convergence, the described condition of convergence is:

$\underset{t\&RightArrow;\&infin;}{\lim }{\mathrm{\&Delta;\&sigma;}}^2=C_{\mathrm{\&sigma;}}$

Δ σ in formula ²for the colony of population or the variable quantity of overall fitness variance, C _σfor convergence precision, this convergence precision is the permanent number close to zero; If meet the condition of convergence, obtain best stored energy capacitance W _o; If do not meet the condition of convergence, again discharge example and set up new group, and repeat steps d.

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