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

Abstract

The invention discloses a capacity optimization calculation method of an energy storage power station considering the dynamic adjustment of the state of charge, comprising: establishing a battery charge state partition model of a wind farm energy storage system, and performing battery overcharge and overdischarge protection control on the model; The objective function of wind farm energy storage system capacity optimization is constructed with the goal of optimizing the comprehensive benefit of energy, and the constraint conditions of charging and discharging power of energy storage power station and the constraint condition of wind farm output power fluctuation level are established; the PSO algorithm is selected to optimize the objective function of energy storage system capacity Solve and calculate to determine the optimal capacity value of the wind farm energy storage system. The beneficial effects of the present invention are: the capacity optimization calculation model of the present invention comprehensively considers the configuration of the energy storage power station and the overall economy in the operation process, is beneficial to the effective combination with the field, and provides a theoretical premise and guarantee for the optimization of the energy storage capacity.

Description

Calculation method for capacity optimization of energy storage power station considering dynamic adjustment of state of charge

technical field

The invention relates to the field of power fluctuation stabilization, in particular to an energy storage power station capacity optimization calculation method considering the dynamic adjustment of the state of charge.

Background technique

As a renewable energy source, wind energy is being widely used all over the world. Due to the randomness, intermittent and uncontrollable characteristics of the wind, its output will have an impact on the stability of the grid voltage and power quality. In the face of the continuous growth of renewable energy such as wind energy, how to solve the impact of its output power fluctuations on the power grid has become an important issue facing the current power grid. Installing an energy storage system with a certain capacity and power in the wind farm can effectively smooth the fluctuation of wind power and improve the stability of the power system. However, the cost of energy storage system configuration and the effect of stabilizing wind power fluctuations are mutually restricted. Therefore, how to optimize the energy storage capacity to achieve the effectiveness and economy of stabilizing wind power fluctuations is an urgent problem to be solved at present.

In the optimal calculation of energy storage capacity, there are currently the following deficiencies: 1) The current research on energy storage systems that takes the state of charge of the energy storage system as a parameter is more reflected in the research on the level of energy storage control, while the research based on the state of charge and The optimal capacity planning of the economical energy storage system is seldom studied; 2) In the process of energy storage capacity optimization calculation, it is only considered to ensure the stable value of wind power for a long time to configure the capacity, or the wind turbine and storage The standard deviation of the output power fluctuation of the energy storage device is used as the index for optimization, or the minimization of the operating cost and investment cost is considered as the optimization goal, but the impact of insufficient charging and discharging power of the energy storage system or the state of overcharging and overdischarging on the smoothing of grid-connected power fluctuations is not taken as the target. Indicators in energy storage capacity optimization calculations.

The state of charge (SOC) refers to the ratio of its remaining capacity to its fully charged state. Its value ranges from 0 to 1. When SOC=1, it means that the battery is fully charged. When SOC=0, it means that the battery is fully discharged. In energy storage power stations, under normal circumstances, the maximum value of the state of charge in each battery pack is taken as the state of charge value of the entire energy storage system during charging; the minimum value of the state of charge in each battery pack is taken as the value of the entire energy storage system during discharge. The state of charge value of the energy system. This can effectively prevent overcharge and overdischarge of a single battery.

In the traditional energy storage capacity optimization process, the state of charge of the energy storage system is not considered. The disadvantages are: First, due to the frequent overcharge and overdischarge of the energy storage system, or the abnormal working charge for a long time State, resulting in a greatly reduced service life, greatly increased the cost of the energy storage system, which is not conducive to economic considerations; second, the overcharge and overdischarge of the energy storage system makes it difficult to control the charging and discharging power, which will cause the power injected into the grid to appear. Severe fluctuations affect the stability of the power grid.

Contents of the invention

The purpose of the present invention is to solve the above problems, and proposes an energy storage power station capacity optimization calculation method that considers the dynamic adjustment of the state of charge. .

In order to achieve the above object, the present invention adopts the following technical solutions:

A calculation method for optimizing the capacity of an energy storage power station considering the dynamic adjustment of the state of charge, comprising the following steps:

(1) Establish a partition model of the battery state of charge of the wind farm energy storage system, and perform battery overcharge and overdischarge protection control on the model.

(2) On the premise of ensuring the smooth output power, construct the capacity optimization objective function of the wind farm energy storage system with the goal of optimizing the comprehensive benefit of energy storage, and establish the charging and discharging power constraint conditions of the energy storage station and the output power fluctuation level of the wind farm Restrictions.

(3) Under the condition of satisfying the battery charge and discharge protection of the wind farm energy storage system, the PSO algorithm is selected to solve and calculate the capacity optimization objective function of the energy storage system to determine the optimal capacity value of the wind farm energy storage system.

The specific method of the step (1) is:

Set the limit classification of battery state of charge when the energy storage system is running: Q _SOCmax and Q _SOCmin are the upper limit and lower limit of the state of charge of the energy storage system respectively, [Q _SOCmin , Q _SOClow-L2 ] is the over-discharge area, [Q _{SOClow -L2} , Q _SOClow-L1 ] is the pre-over-discharge area, [Q _SOClow-L1 , Q _SOChigh-L1 ] is the normal area, [Q _SOChigh-L1 , Q _SOChigh-L2 ] is the pre-over-charge area, [Q _{SOChigh- L2} , Q _SOCmax ] is the overcharge area, Q _SOChigh-L2 and Q _SOClow-L2 are the overcharge and over discharge warning lines respectively.

修正

使其减小；若

则维持原值。When the state of charge is in the pre-overcharge area, if

fix

make it smaller; if

but Keep the original value.

修正使其减小；若

则维持原值。When the state of charge is in the pre-over-discharge area, if

fix make it smaller; if

but Keep the original value.

维持原值。When the state of charge is in the normal area,

Keep the original value.

时，储能系统处于充电状态，

时，储能系统处于放电状态。in, is the charging and discharging power of the energy storage system at time t,

When the energy storage system is in the charging state,

When , the energy storage system is in the discharge state.

时，修正

使其减小的修正系数为：when

time, fix

The correction factor to reduce it is:

${\mathrm{<span\; class="notranslate">\; \&delta;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; lg</span>}<span\; class="notranslate">\; (</span>\frac{{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; SOC</span>}\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; SOCi</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}{{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; SOC</span>}\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; SO\; High</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; L</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}}<span\; class="notranslate">\; )</span>}$

The revised charging power of the energy storage system is:

P _ESS (t)=δ _i (t)ΔP(t)η _C .

Among them, Q _SOCi (t) is the state of charge of the energy storage system at time t, Q _SOCmax is the upper limit of the state of charge of the energy storage system, Q _SOChigh-L1 is the lower limit of the state of charge of the pre-overcharge area, and η _C is the energy storage The charging efficiency of the system, ΔP(t) is the difference between the wind farm output power P _W (t) and the grid-connected target power P _ref (t) at time t: ΔP(t)=P _W (t)-P _ref (t ).

时，修正使其减小的修正系数为：when

time, fix The correction factor to reduce it is:

${\mathrm{<span\; class="notranslate">\; \&delta;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; lg</span>}<span\; class="notranslate">\; (</span>\frac{{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; SOCi</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; SOClow</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; L</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}}{{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; SOClow</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; L</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; SOClow</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; L</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; )</span>}$

The corrected discharge power of the energy storage system is: P _ESS (t)=δ _i (t)ΔP(t)/η _D .

Among them, Q _SOCi (t) is the state of charge of the energy storage system at time t, Q _SOClow-L1 and Q _SOClow-L2 are the lower limit and upper limit of the pre-over-discharge area, respectively, η _D is the discharge efficiency of the energy storage system, ΔP( t) is the difference between the wind farm output power P _W (t) and the grid-connected target power P _ref (t) at time t: ΔP(t)=P _W (t)-P _ref (t).

The objective function for capacity optimization of the wind farm energy storage system in step (2) is:

minC＝K _L ρ _L L _LOST +K _S ρ _S L _SHORT +K _E ρ _E L _ESS +C _C

Among them, ρ _L , ρ _S , and ρ _E are the corresponding unit prices of wind farm abandonment energy loss, smooth power shortage energy loss, and energy storage system running off-line conversion; L _LOST , L _SHORT , and L _ESS are wind farm ρ _L L _LOST is the wind farm abandoned wind energy cost; ρ _S L _SHORT is the wind farm smooth power shortage energy loss cost; ρ _E L _ESS is the conversion loss energy cost of the energy storage system running beyond the line; K _L , K _S and K _E are the penalty coefficients of the operation cost; C _{C is} the input cost of the energy storage system.

The input cost C _C of the energy storage system is calculated as:

C _C =C _M +C _R +C _B ;

C _B =N _bess ρ ₁ W _O +N _bess ρ ₂ W _O m;

$\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; r</span>}{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; r</span>}<span\; class="notranslate">\; )</span>}^{{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}}}{{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; r</span>}<span\; class="notranslate">\; )</span>}^{{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}$

Among them, C _M is the maintenance cost of the energy storage system, C _R is the replacement cost of each energy storage unit of the energy storage system, C _B is the basic investment cost of the energy storage system, N _bess is the number of batteries in the energy storage system; ρ ₁ W is the installation price per unit capacity of energy storage capacity; W _O is the rated value of the optimal energy storage capacity of the wind farm; _ρ2 is the unit capacity price of energy storage capacity; m is the depreciation coefficient; r is the depreciation rate; L _m is the project life.

The calculation methods of the wind farm abandoned wind energy loss, the smooth power shortage energy loss and the converted energy of the energy storage system running over the line are respectively:

${\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; LOST</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; the\; y</span>}}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; g</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; p</span>}}{\overset{\mathrm{<span\; class="notranslate">\; q</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\frac{\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&delta;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}{{\mathrm{<span\; class="notranslate">\; \&eta;</span>}}_{\mathrm{<span\; class="notranslate">\; C</span>}}}{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; ref</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; \&Delta;t</span>}$

${\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; SHORT</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; the\; y</span>}}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; h</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; u</span>}}{\overset{\mathrm{<span\; class="notranslate">\; v</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&delta;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; \&eta;</span>}}_{\mathrm{<span\; class="notranslate">\; D.</span>}}{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; ref</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; \&Delta;t</span>}$

$\begin{array}{c}{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; less</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; the\; y</span>}}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; k</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; x</span>}}{\overset{\mathrm{<span\; class="notranslate">\; the\; y</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; SOCi</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; SO\; High</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; L</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; W</span>}}_{\mathrm{<span\; class="notranslate">\; o</span>}}\\ <span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; the\; y</span>}}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; l</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; z</span>}}{\overset{\mathrm{<span\; class="notranslate">\; a</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; SOCi</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; SOClow</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; L</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; W</span>}}_{\mathrm{<span\; class="notranslate">\; o</span>}}\end{array}$

Among them, N _y is the time year of the research object; g, h are the total number of times that the charging and discharging process lasts for δ _i < 1 in the N _y year; p, q are the initial and end times of the g interval; u, v are the initial and end times of interval h respectively; k is the total number of times that the energy storage system’s operating state exceeds the maximum state of charge in N _y year; l is the number of times the energy storage system’s operating state is below the minimum state of charge in N _y year The total number of times; x, y are the initial and end time of interval k respectively; z, a are the initial and end time of interval l respectively, P _ref (t) is the grid-connected target power, δ _i (t) is the correction coefficient, Q _SOCi (t) is the state of charge of the energy storage system at time t, _QSOHigh-L2 and _QSOClow-L2 are the warning lines for overcharge and overdischarge respectively, W _O is the rated value of the optimal energy storage capacity of the wind farm, and _ηC is The charging efficiency of the energy storage system, η _D is the discharge efficiency of the energy storage system, and Δt is the sampling time step.

The charging and discharging power constraints of the energy storage power station in the step (2) are:

-P _D η _D ≤P _W (t)-P _ref (t)≤P _C

In the formula: P _C and P _D are the limit charge and discharge power of the energy storage system respectively, and the discharge is regarded as a negative charge process, and its magnitude is based on its absolute value; P _W (t) is the output power of the wind farm at time t, and P _ref (t) is the grid-connected target power.

The output power fluctuation level constraint of the wind farm is:

P{|ΔP _d (t)|≤ΔP _dmax }≥Λ

In the formula: ΔP _d (t) is the fluctuation value of the output power of the wind farm after being stabilized by the energy storage system; ΔP _dmax is the upper limit of the maximum allowable range of the fluctuation value; Λ is the corresponding reliability level.

The steps of solving and calculating the objective function for capacity optimization of the energy storage system are as follows:

a. Extract the wind farm operation data time window length T and its operation data P(t).

b. Determine the desired power output target value _PG , and give an initial SOC value.

c. Set the particle swarm dimension D, the maximum number of iterations M _max , the convergence accuracy C _σ , and initialize the particle swarm position x and velocity v at the same time.

d. Calculate the fitness value of each particle And compare its own particle extreme value p _i with the global example extreme value p _g , if the fitness value is smaller, then update p _i and p _g , otherwise, update the particle velocity V and position X.

e. Calculate Δσ ² to judge whether the convergence condition is satisfied, and the convergence condition is:

$\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; \&Right\; Arrow;</span><span\; class="notranslate">\; \&infin;</span>}{\mathrm{<span\; class="notranslate">\; lim</span>}}{\mathrm{<span\; class="notranslate">\; \&Delta;\&sigma;</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}$

In the formula, Δσ ² is the variation of the population or global fitness variance of the particle swarm, and C _σ is the convergence accuracy, which is a constant constant close to zero; if the convergence condition is satisfied, the optimal energy storage capacity W _O is obtained; If the convergence condition is not met, release the example again to form a new group, and repeat step d.

The beneficial effects of the present invention are:

The capacity optimization calculation model of the present invention comprehensively considers the overall economics in the configuration and operation of the energy storage power station, is beneficial to the effective combination with the field, and provides a theoretical premise and guarantee for the optimization of the energy storage capacity.

The invention uses the state of charge as an important indicator of the operating state of the energy storage system, and establishes a charge-discharge model that suppresses excessive charge-discharge by changing the charge-discharge power correction coefficient, thereby realizing an adjustment strategy for SOC in the energy storage capacity allocation process. At the same time, after the energy storage capacity is configured, the control strategy can be used for reference in the corresponding actual wind farm-energy storage joint operation system to form an actual wind farm energy storage control strategy; on this basis, the operating cost corresponding to the state of charge is introduced, A capacity optimization model of energy storage system was established with the economic index as the objective function. This model realizes the optimization of energy storage capacity taking into account dispatching demand, energy storage operation life and economy.

Description of drawings

Fig. 1 is the expected power output curve of the selected time section of the present embodiment;

Fig. 2 is the schematic diagram of the selected time section smoothing effect of the present embodiment;

FIG. 3 is a schematic diagram of the SOC curve of this embodiment.

Detailed ways:

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 the wind farm energy storage system is: when the output power of the wind turbine is greater than the grid-connected power reference value, the energy storage system charges to stabilize the output power fluctuation; when the wind turbine output power is lower than the grid-connected power reference value, the energy storage system Discharge to make up for the lack of output power, so as to smooth the output power of wind turbines and achieve the stability of wind power grid-connected power.

The difference ΔP(t) between the wind farm output power P _W (t) and the grid-connected target power P _ref (t) at time t is:

ΔP(t)＝P _W (t)-P _ref (t) (1)

Then the charging and discharging power of the energy storage system is shown in formula (2,3).

When the energy storage system is charging:

${\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; ESS</span>}}^{\mathrm{<span\; class="notranslate">\; ref</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; \&Delta;P</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; \&eta;</span>}}_{\mathrm{<span\; class="notranslate">\; C</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>$

When the energy storage system is in discharge state:

${\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; ESS</span>}}^{\mathrm{<span\; class="notranslate">\; ref</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; \&Delta;P</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; \&eta;</span>}}_{\mathrm{<span\; class="notranslate">\; D.</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; )</span>$

为t时刻储能系统充放电功率；当

时，储能系统充电，

时，储能系统放电；η_C为储能系统的充电效率，一般取0.65～0.85。In the formula:

is the charging and discharging power of the energy storage system at time t; when

When the energy storage system is charged,

When , the energy storage system discharges; η _C is the charging efficiency of the energy storage system, generally 0.65-0.85.

The charging and discharging power command of the energy storage station should consider the current SOC level and the power command at the current moment, that is, when the SOC is within the normal operating range, the charging and discharging power of the energy storage station remains unchanged; when the SOC crosses the line to the abnormal operating range , it is necessary to adjust the charging and discharging power in time to prevent overcharging and overdischarging.

Set the limit classification of SOC when the energy storage system is running. Among them, Q _SOCmax and Q _SOCmin are the upper limit and lower limit of the state of charge of the energy storage system respectively, [Q _SOCmin , Q _SOClow-L2 ] is the over-discharge area, [Q _SOClow-L2 , Q _SOClow-L1 ] is the pre-over-discharge area , [Q _SOClow-L1 , Q _SOChigh-L1 ] is the normal area, [Q _SOChigh-L1 , Q _SOChigh-L2 ] is the pre-overcharge area, [Q _SOChigh-L2 , Q _SOCmax ] is the overcharge area, the above is the reserve The operating range of different states of charge of the energy system, where Q _SOChigh-L2 and Q _SOClow-L2 are respectively overcharge and overdischarge warning lines.

2 Overcharge and overdischarge protection control

The change of the operating interval of the state of charge of the energy storage system will trigger the corresponding adjustment of the power correction coefficient, and the charging and discharging power of the energy storage system can be changed through the power correction coefficient to achieve pre-control of the operation of the energy storage system and avoid its overcharging and over-discharging. state. The specific control strategy is shown in Table 1.

Table 1 Power correction factor control rules

需对

进行预先控制，通过式（4）调整功率修正系数，修正使其减小，以缓解其荷电状态升高的速度，防止储能系统出现过度充电的状态；若处在放电状态下

则维持原值。反之亦然，当储能系统荷电状态偏低，即位于预过放区域时，若处在放电状态

通过式（5）调整功率修正系数，修正

使其减小，以减缓其荷电状态降低的速度，防止储能系统出现深度放电的状态。若处在充电状态下

则维持原值。当储能系统荷电状态位于正常区域时，维持修正系数不变，使其正常充放电。The analysis shows that when the state of charge of the energy storage system is relatively high, that is, in the pre-overcharge area, it means that the energy storage system tends to be saturated. If in charging state

Need to be right

Carry out pre-control, adjust the power correction coefficient through formula (4), and correct Make it smaller to slow down the rate of increase of its state of charge and prevent the energy storage system from being overcharged; if it is in a state of discharge

then maintain the original value. Vice versa, when the state of charge of the energy storage system is low, that is, in the pre-over-discharge area, if it is in the discharge state

Adjust the power correction coefficient through formula (5), correct

Make it smaller to slow down the rate of its state of charge reduction and prevent the energy storage system from appearing in a state of deep discharge. If in charging state

then maintain the original value. When the state of charge of the energy storage system is in the normal area, the correction coefficient remains unchanged to make it charge and discharge normally.

${\mathrm{<span\; class="notranslate">\; \&delta;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; lg</span>}<span\; class="notranslate">\; (</span>\frac{{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; SOC</span>}\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; SOCi</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}{{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; SOC</span>}\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; SO\; High</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; L</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 4</span>}<span\; class="notranslate">\; )</span>$

${\mathrm{<span\; class="notranslate">\; \&delta;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; lg</span>}<span\; class="notranslate">\; (</span>\frac{{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; SOCi</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; SOClow</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; L</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}}{{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; SOClow</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; L</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; SOClow</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; L</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 5</span>}<span\; class="notranslate">\; )</span>$

In the formula, _δi (t) is the correction coefficient of charging and discharging power at time t, and the value is 1 when the energy storage system is in the normal area; Q _SOC (t) is the state of charge of the energy storage system at time t. The logarithmic barrier function is used here. When the state of charge is close to Q _SOCmax or Q _SOClow-L2 , due to the strong convergence of the logarithmic function, δ _i (t) can be reduced faster, and it can better control the charging and discharging power in advance. Effectively prevent the state of charge of the energy storage system from reaching an overcharge or overdischarge state.

It should be noted that the power correction coefficient control method proposed in the present invention is when the state of charge of the energy storage system reaches Q _SOHigh-L2 , the minimum value of δ _i (t) is not 0, and its purpose is to ensure full utilization of the energy storage capacity, It can still continue to charge; and when the state of charge reaches Q _SOClow-L2, δ _i (t) has been corrected to zero, which can strictly control the minimum capacity of the energy storage system, completely avoid the operation of the energy storage system in the over-discharge area, and reduce the storage capacity. system life loss.

Thus, the adjusted charging and discharging power of the energy storage system can be obtained.

When the energy storage system is charging:

P _ESS (t) = δ _i (t) ΔP (t) η _C (6)

When the energy storage system is in discharge state:

_PESS (t)＝ _δi (t)ΔP(t)/ _ηD (7)

In formula (6) and formula (7): P _ESS (t) is the charge and discharge power of the energy storage system adjusted by the power correction coefficient at time t. When P _ESS (t) > 0, the energy storage system is charged, and P _ESS When (t)<0, the energy storage system is discharged.

Energy Storage Capacity Planning

The goal of wind farm energy storage capacity optimization is to adjust the mutual constraint relationship between input cost and operation cost on the premise of reducing the fluctuation of wind power output power, and to minimize the input cost and operation cost of energy storage on the premise of ensuring smooth output power. The cost realizes the optimization of the operation benefit of the energy storage system of the wind farm.

1 Objective function

Wind farms with different energy storage capacities have different smoothing effects on wind farm power fluctuations. On the premise of ensuring that the output power fluctuation requirements of wind farms are met, considering the constraints of energy storage capacity input costs and operating costs, the comprehensive benefits of energy storage Achieving optimality is the goal. Among them, the input cost C _C of the energy storage system includes the maintenance cost C _M of the energy storage system, the replacement cost of each energy storage unit of the energy storage system (only when the service life of the energy storage unit is less than the project life) C _R and the energy storage The basic investment cost C _B of the system.

C _C ＝C _M +C _R +C _B (8)

C _B ＝N _bess ρ ₁ W _O +N _bess ρ ₂ W _O m (9)

In the formula: N _bess is the number of batteries in the energy storage system; ρ ₁ is the installation price per unit capacity of the energy storage capacity; W _O is the rated value of the optimal energy storage capacity of the wind farm; ρ ₂ is the price per unit capacity of the energy storage capacity; m is the depreciation factor, which is defined as:

$\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; r</span>}{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; r</span>}<span\; class="notranslate">\; )</span>}^{{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}}}{{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; r</span>}<span\; class="notranslate">\; )</span>}^{{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 10</span>}<span\; class="notranslate">\; )</span>$

In the formula: r is the depreciation rate; L _m is the project life.

The operating cost includes the wind farm curtailment loss cost caused by the adjustment of the power correction coefficient, the smoothing power shortage loss cost, and the conversion loss cost of the energy storage system running off-line, all of which change due to changes in energy storage capacity.

Because the output power of wind farms has annual periodicity, the annual output power of wind farms is taken as the research object of energy storage capacity optimization, and the converted energy of the wind farm’s abandoned wind energy loss, smooth power shortage energy loss, and energy storage system over-the-line operation are as follows: Formula (11), formula (12), formula (13):

${\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; LOST</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; the\; y</span>}}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; g</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; p</span>}}{\overset{\mathrm{<span\; class="notranslate">\; q</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\frac{\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&delta;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}{{\mathrm{<span\; class="notranslate">\; \&eta;</span>}}_{\mathrm{<span\; class="notranslate">\; C</span>}}}{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; ref</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; \&Delta;t</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 11</span>}<span\; class="notranslate">\; )</span>$

${\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; SHORT</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; the\; y</span>}}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; h</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; u</span>}}{\overset{\mathrm{<span\; class="notranslate">\; v</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&delta;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; \&eta;</span>}}_{\mathrm{<span\; class="notranslate">\; D.</span>}}{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; ref</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; \&Delta;t</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 12</span>}<span\; class="notranslate">\; )</span>$

$\begin{array}{c}{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; less</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; the\; y</span>}}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; k</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; x</span>}}{\overset{\mathrm{<span\; class="notranslate">\; the\; y</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; SOCi</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; SO\; High</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; L</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; W</span>}}_{\mathrm{<span\; class="notranslate">\; o</span>}}\\ <span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; the\; y</span>}}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; l</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; z</span>}}{\overset{\mathrm{<span\; class="notranslate">\; a</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; SOCi</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; SOClow</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; L</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span>\end{array}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 13</span>}<span\; class="notranslate">\; )</span>$

In the formula: N _y is the time year of the research object; g, h are the total number of times that the charging and discharging process lasts for δ _i < 1 in the N _y year; p, q are the initial and end times of the g interval; u, v are the initial and end times of interval h respectively; k is the total number of times that the energy storage system operating state exceeds the maximum state of charge in the year N _y ; l is the operating state of the energy storage system in the year N _y is below the minimum state of charge The total number of times; x, y are the initial and end time of k interval respectively; z, a are the initial and end time of l interval respectively.

The goal of wind farm energy storage capacity optimization is to

minC＝K _L ρ _L L _LOST +K _S ρ _S L _SHORT +K _E ρ _E L _ESS +C _C (14)

In the formula: ρ _L , ρ _S , ρ _E are the corresponding unit prices of the energy lost in wind farm abandonment, the energy lost in smooth power shortage, and the converted energy of the energy storage system running off-line, respectively; ρ _L L _LOST is the cost of wind power abandoned in the wind farm ; ρ _S L _SHORT is the energy cost of the wind farm smooth power shortage loss; ρ _E L _ESS is the converted energy loss cost of the energy storage system running off-line; K _L , K _S and K _E are the penalty coefficients of the operation cost; C _C storage Energy system input cost.

In formula (13), the conversion loss cost of the energy storage system running over-the-line includes two parts: first, when the energy storage system is running in an over-charged state, the energy storage system is not in a reasonable operating state and affects the conversion of its own life cycle Second, when the state of charge of the energy storage system is too low, the energy storage system is not in a reasonable operating state and affects the converted cost of its own life cycle.

2 Constraints

Charge and discharge power constraints:

-P _D η _D ≤ P _W (t) -P _ref (t) ≤ P _C (14)

In the formula: P _D and P _C are the limit charge and discharge power of the energy storage system, respectively, and the discharge is regarded as a negative charge process, and its magnitude is based on its absolute value.

Constraints include wind farm output power fluctuation level constraints:

P{|ΔP _d (t)|≤ΔP _dmax }≥Λ (15)

In the formula: ΔP _d (t) ΔP _d (t) is the fluctuation value of the output power of the wind farm after being stabilized by the energy storage system; ΔP _dmax is the upper limit of the maximum allowable range of the fluctuation value; Λ is the corresponding reliability level.

3 solution method

The particle swarm optimization (PSO) algorithm is an intelligent swarm computing method with the advantages of simple calculation and good convergence performance. It has been widely used to solve various numerical optimization problems, but it still has low search accuracy when solving some complex optimization problems. And the defect that it is easy to fall into a local optimal solution. For this reason, the present invention considers the PSO algorithm to solve the stochastic optimization problem of the present invention including dynamic boundary conditions and containing multiple random variables. The specific method is as follows:

Step 1: Select the time section window length y of the research object and its operating data P(t);

Step 2: Determine the expected output target value _PG based on the optimal power output model, and give the initial SOC equivalent;

Step 3: Set the particle swarm dimension D, the maximum number of iterations M _max , the convergence accuracy C _σ , and initialize the particle swarm position x and velocity x at the same time;

并将其自身粒子极值p_i及全局例子极值p_g比较，若适应度值较小，则更新p_i及p_g，若否更新粒子速度V_id及位置X_id；Step 4: According to the charging and discharging strategy of the present invention, set parameters such as c1, c2, ω, V _min , V _max, etc., and calculate the fitness value of each particle in combination with formula (11-15)

And compare its own particle extremum p _i with the global example extremum p _g , if the fitness value is small, then update p _i and p _g , if not, update the particle velocity V _id and position X _id ;

Step 5: Calculate Δσ ² to judge whether the convergence condition is satisfied, and the search convergence condition is:

$\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; \&Right\; Arrow;</span><span\; class="notranslate">\; \&infin;</span>}{\mathrm{<span\; class="notranslate">\; lim</span>}}{\mathrm{<span\; class="notranslate">\; \&Delta;\&sigma;</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 16</span>}<span\; class="notranslate">\; )</span>$

In the formula, Δσ ² is the variation of the population or global fitness variance of the particle swarm, and C _σ is a constant constant close to zero. If yes, obtain the optimal energy storage capacity W _O ; if not, release examples to form a new group, and repeat step (4).

The installed capacity of a wind farm is 90MW, the wind power data in 2011 is selected, the collection frequency is 5min, and the stabilization target value is shown in Figure 1.

According to the charging and discharging power adjustment strategy and the energy storage capacity optimization calculation model of the invention, the output curve of smooth fluctuation is obtained as shown in Figure 2, and the calculation results of the method of the present invention are shown in Table 2.

Table 2 calculation results

Analyzing the above calculation example, it can be obtained that in terms of capacity planning, the method of the present invention effectively realizes the optimization of energy storage capacity; in terms of stabilizing power offset, the method of the present invention is similar to the conventional method, with a slight increase. The reason is that the power correction factor adjustment strategy The probability of abandoning wind or stabilizing insufficient energy is improved; in terms of exceeding the limit value operation, the present invention greatly reduces the value of N, and the reduction rate reaches 96.2%, and the effect is obvious. Investigate the change of SOC in the process of obtaining the optimal capacity of the energy storage power station, as shown in Figure 3. It can be seen that in the method of the present invention, the SOC does not run beyond the limit value in this section, which effectively guarantees the service life of the ESS.

To sum up, it can be concluded that the capacity optimization calculation model of the present invention comprehensively considers the overall economics during the configuration and operation of the energy storage power station, which is beneficial to the effective combination with the field. The above theoretical research provides a theoretical premise and guarantee for the optimization of energy storage capacity. At the same time, the analysis of actual data examples verifies the above conclusions.

Although the specific implementation of the present invention has been described above in conjunction with the accompanying drawings, it does not limit the protection scope of the present invention. Those skilled in the art should understand that on the basis of the technical solution of the present invention, those skilled in the art do not need to pay creative work Various modifications or variations that can be made are still within the protection scope of the present invention.

Claims (10)

1. A calculation method for optimizing the capacity of an energy storage power station considering the dynamic adjustment of the state of charge, characterized in that it comprises the following steps: (1) Establish a partition model of the battery state of charge of the wind farm energy storage system, and perform battery overcharge and overdischarge protection control on the model; (2) On the premise of ensuring the smooth output power, construct the capacity optimization objective function of the wind farm energy storage system with the goal of optimizing the comprehensive benefit of energy storage, and establish the charging and discharging power constraint conditions of the energy storage station and the output power fluctuation level of the wind farm Restrictions; (3) Under the condition of satisfying the battery charge and discharge protection of the wind farm energy storage system, the PSO algorithm is selected to solve and calculate the capacity optimization objective function of the energy storage system to determine the optimal capacity value of the wind farm energy storage system. 2. An energy storage power station capacity optimization calculation method considering dynamic adjustment of state of charge according to claim 1, characterized in that, the specific method of the step (1) is: Set the limit classification of battery state of charge when the energy storage system is running: Q _SOCmax and Q _SOCmin are the upper limit and lower limit of the state of charge of the energy storage system respectively, [Q _SOCmin , Q _SOClow-L2 ] is the over-discharge area, [Q _{SOClow -L2} , Q _SOClow-L1 ] is the pre-over-discharge area, [Q _SOClow-L1 , Q _SOChigh-L1 ] is the normal area, [Q _SOChigh-L1 , Q _SOChigh-L2 ] is the pre-over-charge area, [Q _{SOChigh- L2} , Q _SOCmax ] is the overcharge area, Q _SOChigh-L2 and Q _SOClow-L2 are the overcharge and over discharge warning lines respectively; When the state of charge is in the pre-overcharge area, if

fix

make it smaller; if

but maintain the original value; When the state of charge is in the pre-over-discharge area, if

fix make it smaller; if

but

maintain the original value; When the state of charge is in the normal area,

maintain the original value; in,

is the charging and discharging power of the energy storage system at time t,

When the energy storage system is in the charging state, When , the energy storage system is in the discharge state. 3. A method for calculating the capacity optimization of energy storage power stations considering the dynamic adjustment of state of charge as claimed in claim 2, characterized in that,

time, fix

The correction factor to reduce it is: ${\mathrm{<span\; class="notranslate">\; \&delta;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; lg</span>}<span\; class="notranslate">\; (</span>\frac{{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; SOC</span>}\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; SOCi</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}{{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; SOC</span>}\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; SO\; High</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; L</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ;</span>}$ The revised charging power of the energy storage system is: P _ESS (t) = δ _i (t) ΔP (t) η _C ; Among them, Q _SOCi (t) is the state of charge of the energy storage system at time t, Q _SOCmax is the upper limit of the state of charge of the energy storage system, Q _SOChigh-L1 is the lower limit of the state of charge of the pre-overcharge area, and η _C is the energy storage The charging efficiency of the system, ΔP(t) is the difference between the wind farm output power P _W (t) and the grid-connected target power P _ref (t) at time t: ΔP(t)=P _W (t)-P _ref (t ). 4. A method for calculating the capacity optimization of an energy storage power station considering the dynamic adjustment of the state of charge as claimed in claim 2, wherein when

time, fix The correction factor to reduce it is: ${\mathrm{<span\; class="notranslate">\; \&delta;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; lg</span>}<span\; class="notranslate">\; (</span>\frac{{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; SOCi</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; SOClow</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; L</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}}{{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; SOClow</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; L</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; SOClow</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; L</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ;</span>}$ The revised discharge power of the energy storage system is: P _ESS (t) = δ _i (t) ΔP (t) / η _D ; Among them, Q _SOCi (t) is the state of charge of the energy storage system at time t, Q _SOClow-L1 and Q _SOClow-L2 are the lower limit and upper limit of the pre-over-discharge area, respectively, η _D is the discharge efficiency of the energy storage system, ΔP( t) is the difference between the wind farm output power P _W (t) and the grid-connected target power P _ref (t) at time t: ΔP(t)=P _W (t)-P _ref (t). 5. A calculation method for optimizing the capacity of an energy storage power station considering the dynamic adjustment of the state of charge as claimed in claim 1, wherein the objective function for capacity optimization of the wind farm energy storage system in step (2) is: minC＝K _L ρ _L L _LOST +K _S ρ _S L _SHORT +K _E ρ _E L _ESS +C _C Among them, ρ _L , ρ _S , and ρ _E are the corresponding unit prices of wind farm abandoned wind energy loss, smooth power shortage energy loss, and converted energy of energy storage system running off-line, respectively; L _LOST , L _SHORT , and L _ESS are wind farm ρ _L L _LOST is the wind farm abandoned wind energy cost; ρ _S L _SHORT is the wind farm smooth power shortage energy loss cost; ρ _E L _ESS is the conversion loss energy cost of the energy storage system running beyond the line; K _L , K _S and K _E are the penalty coefficients of the operation cost; C _{C is} the input cost of the energy storage system. 6. A calculation method for optimizing the capacity of an energy storage power station considering the dynamic adjustment of the state of charge as claimed in claim 5, wherein the calculation method for the input cost _C of the energy storage system is: C _C =C _M +C _R +C _B ; C _B =N _bess ρ ₁ W _O +N _bess ρ ₂ W _O m; $\mathrm{<span\; class="notranslate">\; m</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; r</span>}{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; r</span>}<span\; class="notranslate">\; )</span>}^{{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}}}{{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; r</span>}<span\; class="notranslate">\; )</span>}^{{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ;</span>$ Among them, C _M is the maintenance cost of the energy storage system, C _R is the replacement cost of each energy storage unit of the energy storage system, C _B is the basic investment cost of the energy storage system, N _bess is the number of batteries in the energy storage system; ρ ₁ W is the installation price per unit capacity of energy storage capacity; W _O is the rated value of the optimal energy storage capacity of the wind farm; _ρ2 is the unit capacity price of energy storage capacity; m is the depreciation coefficient; r is the depreciation rate; L _m is the project life. 7. A method for calculating the capacity optimization of an energy storage power station considering the dynamic adjustment of the state of charge as claimed in claim 5, wherein the wind farm abandons wind energy loss, smoothing power shortage energy loss, and energy storage system crossing the line The calculation methods of the running converted energy are as follows: ${\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; LOST</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; the\; y</span>}}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; g</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; p</span>}}{\overset{\mathrm{<span\; class="notranslate">\; q</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\frac{\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&delta;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}{{\mathrm{<span\; class="notranslate">\; \&eta;</span>}}_{\mathrm{<span\; class="notranslate">\; C</span>}}}{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; ref</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; \&Delta;t</span>}$ ${\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; SHORT</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; the\; y</span>}}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; h</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; u</span>}}{\overset{\mathrm{<span\; class="notranslate">\; v</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&delta;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; \&eta;</span>}}_{\mathrm{<span\; class="notranslate">\; D.</span>}}{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; ref</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; \&Delta;t</span>}$ $\begin{array}{c}{\mathrm{<span\; class="notranslate">\; L</span>}}_{\mathrm{<span\; class="notranslate">\; less</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; the\; y</span>}}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; k</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; x</span>}}{\overset{\mathrm{<span\; class="notranslate">\; the\; y</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; SOCi</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; SO\; High</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; L</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; W</span>}}_{\mathrm{<span\; class="notranslate">\; o</span>}}\\ <span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; the\; y</span>}}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; l</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; z</span>}}{\overset{\mathrm{<span\; class="notranslate">\; a</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; SOCi</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; Q</span>}}_{\mathrm{<span\; class="notranslate">\; SOClow</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; L</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; W</span>}}_{\mathrm{<span\; class="notranslate">\; o</span>}}\end{array}$ Among them, N _y is the time year of the research object; g, h are the total number of times that the charging and discharging process lasts for δ _i < 1 in the N _y year; p, q are the initial and end times of the g interval; u, v are the initial and end times of interval h respectively; k is the total number of times that the energy storage system’s operating state exceeds the maximum state of charge in N _y year; l is the number of times the energy storage system’s operating state is below the minimum state of charge in N _y year The total number of times; x, y are the initial and end time of interval k respectively; z, a are the initial and end time of interval l respectively, P _ref (t) is the grid-connected target power, δ _i (t) is the correction coefficient, Q _SOCi (t) is the state of charge of the energy storage system at time t, _QSOHigh-L2 and _QSOClow-L2 are the warning lines for overcharge and overdischarge respectively, W _O is the rated value of the optimal energy storage capacity of the wind farm, and _ηC is The charging efficiency of the energy storage system, η _D is the discharge efficiency of the energy storage system, and Δt is the sampling time step. 8. A calculation method for optimizing the capacity of an energy storage power station considering the dynamic adjustment of the state of charge as claimed in claim 1, characterized in that, the constraint condition of charging and discharging power of the energy storage power station in the step (2) is: -P _D η _D ≤P _W (t)-P _ref (t)≤P _C In the formula: P _C and P _D are the limit charge and discharge power of the energy storage system respectively, and the discharge is regarded as a negative charge process, and its magnitude is based on its absolute value; P _W (t) is the output power of the wind farm at time t, and P _ref (t) is the grid-connected target power. 9. The method for calculating the capacity optimization of energy storage power stations considering the dynamic adjustment of state of charge according to claim 1, characterized in that, in the step (2), the output power fluctuation level constraint of the wind farm is: P{|ΔP _d (t)|≤ΔP _dmax }≥Λ In the formula: ΔP _d (t) is the fluctuation value of the output power of the wind farm after being stabilized by the energy storage system; ΔP _dmax is the upper limit of the maximum allowable range of the fluctuation value; Λ is the corresponding reliability level. 10. A method for calculating the capacity optimization of an energy storage power station considering the dynamic adjustment of the state of charge as claimed in claim 1, wherein the step of solving and calculating the capacity optimization objective function of the energy storage system is as follows: a. Extract wind farm operation data time window length T and its operation data P(t); b. Determine the desired power output target value _PG , and give the initial SOC value; c. Set the particle swarm dimension D, the maximum number of iterations M _max , the convergence accuracy C _σ , and initialize the particle swarm position x and velocity v at the same time; d. Calculate the fitness value of each particle

And compare its own particle extreme value p _i with the global example extreme value p _g , if the fitness value is small, then update p _i and p _g , otherwise, update the particle velocity V and position X; e. Calculate Δσ ² to judge whether the convergence condition is satisfied, and the convergence condition is: $\underset{\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; \&Right\; Arrow;</span><span\; class="notranslate">\; \&infin;</span>}{\mathrm{<span\; class="notranslate">\; lim</span>}}{\mathrm{<span\; class="notranslate">\; \&Delta;\&sigma;</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}$ In the formula, Δσ ² is the variation of the population or global fitness variance of the particle swarm, and C _σ is the convergence accuracy, which is a constant constant close to zero; if the convergence condition is satisfied, the optimal energy storage capacity W _O is obtained; If the convergence condition is not met, release the example again to form a new group, and repeat step d.

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