WO2017161785A1 - Method for controlling stable photovoltaic power output based on energy storage running state - Google Patents

Abstract

A method for controlling stable photovoltaic power output based on an energy storage running state, comprising: S1. selecting a research object time section window length y and running data P(t) thereof; S2. based on an optimum power output model, determining a desired output target value P_G; S3. setting the number D of particle swarm dimensions, the maximum number M_max of iterations and a convergence precision C_σ, and initializing a particle swarm position x and speed v at the same time; S4. according to a charge and discharge policy, calculating a fitness value Px_id of each particle and comparing a particle extreme value p_i thereof with a global particle extreme value p_g, if the fitness value is lower, updating p_i and p_g, and if not, updating a particle speed V_id and position X_id; and S5. calculating Δσ² and determining whether a convergence condition formula I is satisfied, if so, acquiring the optimum energy storage capacity W_o, and if not, re-releasing particles to build a new particle swarm and repeating step S4. The method can effectively suppress grid-connection power fluctuation and precisely adjust a state of charge of an energy storage system at the same time.

Description

Photovoltaic power stable output control method based on energy storage operation state Technical field

The invention relates to the technical field of photovoltaic power generation, in particular to a photovoltaic power stable output control method based on an energy storage operation state.

Background technique

As an important part of the national energy strategy, photovoltaic power generation has developed rapidly in recent years, and has delivered a large amount of clean energy to the power grid. However, due to its inherent volatility, intermittent and uncontrollable characteristics, the installed capacity and permeability of photovoltaic power generation systems are constantly improving, and it also brings many negative effects to the safe and stable operation of power systems, such as poor peak shaving capability. The impact on the power grid is large, and the rotating spare capacity needs to be increased. Therefore, the energy storage energy storage system equipped with a certain capacity for the photovoltaic electric field can effectively suppress the fluctuation of the photovoltaic output power, improve the power quality of the system, and achieve friendly access to the power grid.

In recent years, domestic and foreign scholars have carried out related research on the allocation of energy storage, and have obtained many research results. The following technical solutions are disclosed in the prior art:

Through the real-time battery SOC feedback adjustment control, the power fluctuation of the wind-light combined power generation system is achieved respectively;

The wavelet packet method is used to decompose the photovoltaic output power signal, combined with the cycle life performance of different types of energy storage, and the charge-discharge power of energy-type energy storage and power-type energy storage is adjusted in real time through fuzzy adaptive control of power-type energy storage SOC;

Based on the state of charge (SOC) of the battery energy storage system, the filter time constant of the filter is adjusted in real time by relevant rules to achieve the goal of controlling the state of charge to be stable in the normal working state;

Based on the short-term prediction error of photovoltaic output and load, the capacity estimation function of energy storage equipment is obtained by interval estimation method, and the prediction error variance of energy storage capacity under distributed configuration and centralized configuration is compared to achieve better power compensation effect;

Using the discrete Fourier transform to perform spectrum analysis on renewable energy to determine the energy storage compensation range, and then propose a capacity determination method that meets the requirements;

Taking the independent scenery diesel microgrid system as the research object, the capacity optimization model is established. The genetic algorithm is used to optimize the minimum cost. The optimal capacity configuration of each power supply in a given scheduling strategy is discussed.

The above research has made more considerations on the fluctuation of grid-connected power of photovoltaic power plants, but does not consider the influence of the operating state of the energy storage body on the fluctuation of photovoltaic power, which makes the above-mentioned methods have a large demand for energy storage capacity.

Summary of the invention

In order to overcome the deficiencies of the prior art, an object of the present invention is to provide a photovoltaic power stable output control method based on an energy storage operation state.

In order to achieve the above objective, the technical solution provided by the embodiment of the present invention is as follows:

A photovoltaic power stable output control method based on an energy storage operation state, the method comprising:

S1, selected research object time section window length y and its operation data P(t);

S2, determining a desired output target value P _G based on an optimal power output model, and giving an initial SOC value;

S3, setting the particle group dimension D, the maximum iteration number M _max , the convergence precision C _σ , and initializing the particle group position x and the velocity v;

S4. Calculate the fitness value px _{id of} each particle according to the charging and discharging strategy, and compare the self particle extreme value p _i with the global example extreme value p _g . If the fitness value is small, update p _i and p _g , If not update the particle velocity V _id and position X _id ;

S5. Calculate Δσ ² and determine whether the convergence condition is satisfied.

If yes, obtain the optimal energy storage capacity W _O ; if not, re-release the example to form a new ethnic group, and repeat step S4, where Δσ ² is the variation of the population or global fitness variance of the particle group, C _σ is A constant that is close to zero.

As a further improvement of the invention, in the optimal power output model in step S1:

The difference ΔP(t) between the photovoltaic power output power P _P (t) and the grid-connected target power P _ref (t) at time t is ΔP(t)=P _P (t)-P _ref (t);

When the energy storage system is charging:

For the charging and discharging power of the energy storage system at time t, η _C is the charging efficiency of the energy storage system;

When the energy storage system is in a discharged state:

The charging and discharging power of the energy storage system at time t, η _D is the discharge efficiency of the energy storage system.

As a further improvement of the invention, in the optimal power output model in step S1:

When the energy storage system is in a state of charge: P _ESS (t) = δ _i (t) ΔP(t) η _C ,

For the charging and discharging power of the energy storage system at time t, η _C is the charging efficiency of the energy storage system, and δ _i (t) is the correction coefficient of the charging and discharging power at time t;

When the energy storage system is in a discharged state: P _ESS (t) = δ _i (t) ΔP(t) / η _D ,

The charging and discharging power of the energy storage system at time t, η _D is the discharge efficiency of the energy storage system, and δ _i (t) is the correction coefficient of the charging and discharging power at time t.

As a further improvement of the present invention, the energy storage system is classified according to the limitation of the operating SOC, including: pre-discharge area [Q _SOClow-L2 , Q _SOClow-L1 ], normal area [Q _SOClow-L1 , Q _SOChigh-L1 ] , pre-charge area [Q _SOChigh-L1 , Q _SOChigh-L2 ].

As a further improvement of the present invention, the charge-discharge power correction coefficient δ _i (t) at the time t is specifically:

In the pre-charged area, when charging

δ _i (t) is 1 in the discharge state;

In the normal region, δ _i (t) is 1 in both the state of charge and the state of discharge;

In the pre-discharge area, δ _i (t) is 1 in the state of charge, and in the state of discharge

As a further improvement of the present invention, in the charging and discharging strategy in the step S4, the goal of the energy storage capacity optimization is:

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

Where ρ _L , ρ _S , and ρ _E are respectively the corresponding unit price of the photovoltaic power plant's abandoned light loss energy, the smooth power shortage loss energy, and the converted energy of the energy storage system crossing the line; ρ _L L _LOST is the photovoltaic power plant abandoned light energy cost; ρ _S L _SHORT is the energy cost lost by the smooth power shortage of the photovoltaic power station; ρ _E L _ESS is the converted energy cost of the energy storage system crossing the line; K _L , K _S and K _E are the penalty coefficients of the running cost; C _C energy storage The input cost of the system.

As a further improvement of the present invention, in the charging and discharging strategy in the step S4, the photovoltaic power plant abandoned light loss energy, the smooth power shortage loss energy and the energy storage system cross-line operation conversion energy are respectively:

In the formula, N _y is the time year of the research object; g and h are the total number of times during the charging and discharging process in the year of _{y i} <1 to adjust the operation interval; p and q are the initial and end times of the g interval respectively; u, v is the initial and final time interval h; K exceeds the maximum number of which is on the state of charge is N _y annual energy storage system in the operating state; L is N _y annual energy storage system in the operating state is located lower than the minimum state The total number of times; x and y are the initial and end times of the k interval; z and a are the initial and end times of the l interval, respectively.

As a further improvement of the present invention, in the charging and discharging strategy in the step S4, the constraint conditions include:

Charge and discharge power constraints:

-P _D η _D ≤P _W (t)-P _ref (t)≤P _C , P _D and P _C are the ultimate charge and discharge power of the energy storage system, respectively;

PV power plant output power fluctuation level constraints:

P{|ΔP _d (t)| ≤ ΔP _{d max} } ≥ Λ, ΔP _d (t) ΔP _d (t) is the fluctuation value of the photovoltaic power plant output power after being stabilized by the energy storage system, and ΔP _{d max} is the maximum fluctuation value Allow the upper limit of the range, which is the corresponding level of credibility;

Charge constraint:

with

They are the minimum and maximum charge of the energy storage unit.

The invention has the following beneficial effects:

The invention considers that the photovoltaic power is affected by natural conditions and has strong volatility, and uses the energy storage to realize the smooth output control of the photovoltaic power. Through the charge and discharge power correction coefficient, the effective adjustment of the energy storage state is realized, thereby avoiding excessive charge and discharge, thereby effectively prolonging the energy storage operation life and reducing the system operation cost while fully suppressing the photovoltaic output fluctuation.

DRAWINGS

FIG. 1 is a schematic flow chart of a photovoltaic power stable output control method based on an energy storage operation state according to the present invention.

2 is a graph of a desired output cross-section of a selected time section in accordance with an embodiment of the present invention.

FIG. 3 is a schematic diagram showing the effect of the selected time section leveling effect in an embodiment of the present invention.

4 is a schematic diagram of a SOC curve in accordance with an embodiment of the present invention.

detailed description

In order to make those skilled in the art better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. The embodiments are only a part of the embodiments of the invention, and not all of the embodiments. Base All other embodiments obtained by those skilled in the art without creative efforts shall fall within the scope of the present invention.

In the prior art, the energy storage operation strategy for suppressing photovoltaic power fluctuations does not consider the state of charge of the energy storage system. Obviously, the energy storage system frequently exhibits overcharge and over discharge, or is in an abnormal working state for a long time. As a result, its service life is greatly reduced, the cost of the energy storage system is greatly increased, which is not conducive to economic considerations. Second, the overcharge and overdischarge of the energy storage system makes the charge and discharge power difficult to control, resulting in severe power injection into the grid. Fluctuations affect the stability of the grid. The state of charge is the ratio of the capacity of its remaining capacity to its fully charged state. When SOC=1, the battery is fully charged, and when SOC=0, it indicates that the battery is completely discharged. In the energy storage power station, in general, the maximum state of charge in each battery pack is taken as the state of charge of the entire energy storage system during charging; the minimum state of charge in each battery pack is taken as the entire storage during discharge. The state of charge of the system. This can effectively prevent overcharging and overdischarging of a single battery.

The invention adjusts the energy storage charging and discharging system to adjust the energy storage device to always work in the normal working range, and simultaneously considers the energy storage state and the photovoltaic power output stability, can simultaneously stabilize the grid power fluctuation and accurately adjust the energy storage system load. Electrical state.

As shown in FIG. 1 , a photovoltaic power stable output control method based on an energy storage operation state of the present invention includes:

S1, selected research object time section window length y and its operation data P(t);

S2, determining a desired output target value P _G based on an optimal power output model, and giving an initial SOC value;

S3, setting the particle group dimension D, the maximum iteration number M _max , the convergence precision C _σ , and initializing the particle group position x and the velocity v;

S4. Calculate the fitness value px _{id of} each particle according to the charging and discharging strategy, and compare the self particle extreme value p _i with the global example extreme value p _g . If the fitness value is small, update p _i and p _g , If not update the particle velocity V _id and position X _id ;

S5. Calculate Δσ ² and determine whether the convergence condition is satisfied.

If yes, obtain the optimal energy storage capacity W _O ; if not, re-release the example to form a new ethnic group, and repeat step S4, where Δσ ² is the variation of the population or global fitness variance of the particle group, C _σ is A constant that is close to zero.

The invention adjusts the energy storage and discharge system to adjust the energy storage device to always work in the normal working range. The method simultaneously considers the energy storage state and the photovoltaic power output stability, and can effectively stabilize the grid power fluctuation and accurately adjust the energy storage. System state of charge.

The energy storage strategy of the photovoltaic power storage system is: when the photovoltaic power output power is greater than the grid-connected power reference value, the energy storage system is charged to stabilize the output power fluctuation; when the photovoltaic power output power is less than the grid-connected power reference value, the energy storage system Discharge to compensate for the lack of output power, in order to smooth the output power of photovoltaic power, to achieve the stability of photovoltaic power grid-connected power.

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

ΔP(t)=P _P (t)-P _ref (t) (1)

Then, the charge and discharge power of the energy storage system is as shown in equations (2) and (3).

When the energy storage system is charging:

When the energy storage system is in a discharged state:

In the formula:

Charging and discharging power for the energy storage system at time t;

When the energy storage system is charged,

When the energy storage system is discharged; η _C is the charging efficiency of the energy storage system, generally taking 0.65 to 0.85, and η _D is the discharge efficiency of the energy storage system, generally taking about 0.95.

The charge and discharge power command of the energy storage station should consider the current SOC level and the power command size at the current time, that is, when the SOC is in the normal working range, the charge and discharge power of the energy storage power station remains unchanged; When crossing the line to the abnormal working range, it is necessary to adjust the charging and discharging power in time to prevent overcharge and overdischarge.

Set the limit classification of the SOC when the energy storage system is running. Where Q _SOCmax and Q _SOCmin are the upper and lower limits of the state of charge of the energy storage system, respectively, [Q _SOCmin , Q _SOClow-L2 ] is the _overdraft area, and [Q _SOClow-L2 , Q _SOClow-L1 ] is the pre- _overdraft 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, and the above is the reserve The operating range of the system with different state of charge, wherein Q _SOChigh-L2 and Q _SOClow-L2 are respectively overcharged and over _-alarmed .

The change of the operating range of the energy storage system's state of charge will trigger the corresponding adjustment of the power correction coefficient, and the power correction coefficient will be used to change the charging and discharging power of the energy storage system to achieve the pre-control operation of the energy storage system to avoid overcharge and over discharge. status. The specific control strategy is shown in Table 1.

Table 1 power correction coefficient control rules

When the state of charge of the energy storage system is too high, that is, when it is located in the pre-charged area, it indicates that the energy storage tends to be saturated. If it is under charge

Need to

Perform pre-control, adjust power correction factor by formula (4), and correct

Reducing it to alleviate the speed at which its state of charge rises, preventing the state of overcharging of the energy storage system; 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, when it is in the pre-discharge area, if it is in the discharge state

Adjust the power correction factor by equation (5), correct

It is reduced to slow down the state of its charge state and prevent the state of deep discharge of the energy storage system. If it is under charge

Then maintain the original value. When the state of charge of the energy storage system is in the normal area, the correction coefficient is maintained unchanged, so that it is normally charged and discharged. among them,

In the formula, δ _i (t) is the charge/discharge power correction coefficient at time t, and is 1 when the energy storage system is in the normal region; Q _SOC (t) is the state of charge of the energy storage system at time t. The present invention adopts a logarithmic barrier function. When the state of charge is close to Q _SOCmax or Q _SOClow-L2 , the logarithmic function is highly convergent, and δ _i (t) can be lowered more quickly, and the charge and discharge can be better controlled in advance. The role of power effectively prevents the state of charge of the energy storage system from reaching an overcharge or overdischarge state.

It should be noted that, in the power correction coefficient control method proposed by the present invention, when the state of charge of the energy storage system reaches Q _SOChigh-L2 , the minimum value of δ _i (t) is not 0, and the purpose is to ensure full utilization of the energy storage capacity. The charging can still be continued; 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 avoiding the energy storage system operating in the over-discharge area and reducing the storage. The lifetime loss of the system.

Thereby, the charge and discharge power of the adjusted 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 a discharged state:

P _ESS (t)=δ _i (t)ΔP(t)/η _D (7)

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

The goal of optimizing the energy storage capacity of photovoltaic power plants is to ensure the mutual control of the input power and operating costs under the premise of reducing the fluctuation of photovoltaic power output power. Under the premise of ensuring smooth output power, the input cost of the lowest energy storage and The operating cost optimizes the operational efficiency of the photovoltaic power storage system.

The power fluctuations obtained by different energy storage capacities of photovoltaic power plants have different effects. Under the premise of ensuring the fluctuation of photovoltaic power output power, the comprehensive benefit of energy storage is the most important for the relationship between energy storage capacity input cost and operating cost. Excellent target. Among them, the input cost C _C of the energy storage system includes the maintenance cost C _M of the energy storage system, and 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 engineering life) C _R and energy storage The basic investment cost of the system is C _B .

C _C =C _M +C _R +C _B (8)

C _M =YN _bess ρ (9)

C _B =N _bess ρ ₁ W _O +N _bess ρ ₂ W _O m (10)

Where: Y is the working time; N _bess is the number of batteries in the energy storage system; ρ is the storage capacity unit capacity maintenance price; ρ ₁ is the storage capacity unit capacity installation price; W _O is the optimal energy storage capacity of the photovoltaic power station Rated value; ρ ₂ is the energy storage capacity per unit capacity price; m is the depreciation coefficient, which is defined as:

Where: r is the depreciation rate; L _m is the engineering year.

The operating cost includes the cost of light loss from the photovoltaic power station caused by the adjustment of the power correction factor, the cost of smoothing the power shortage and the cost of the conversion of the energy storage system across the line, all of which vary due to changes in energy storage capacity.

Because the output power of photovoltaic power plants has an annual periodicity, the annual output power of photovoltaic power plants is the research object of energy storage capacity optimization. The photovoltaic power plant abandoned light loss energy, smooth power shortage loss energy and storage. The conversion energy of the system can be operated as shown in equations (11), (12), and (13):

Where: N _y is the time year of the study object; g, h is the total number of times during the charging and discharging process in the year of _{y i} <1 to adjust the operation interval; p and q are the initial and end times of the g interval respectively; u, v is the initial and final time interval h; K exceeds the maximum number of which is on the state of charge is N _y annual energy storage system in the operating state; L is N _y annual energy storage system in the operating state is located lower than the minimum state The total number of times; x and y are the initial and end times of the k interval; z and a are the initial and end times of the l interval, respectively.

The goal of optimizing the energy storage capacity of photovoltaic power plants is:

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

Where: ρ _L , ρ _S , ρ _E are the corresponding unit price of photovoltaic power plant abandoned light loss energy, smooth power shortage loss energy and energy conversion system cross-line operation; ρ _L L _LOST is photovoltaic power plant abandoned light energy cost ; ρ _S L _SHORT is the energy cost of the smooth power shortage of the photovoltaic power station; ρ _E L _ESS is the converted energy cost of the energy storage system crossing the line; K _L , K _S and K _E are the penalty coefficients of the operating cost; C _C storage The input cost of the system.

In equation (13), the cost of conversion loss of the energy storage system across the line consists of two parts: First, when the energy storage system is operating in an excessively high state, the energy storage system is not in a reasonable operating state and affects its life cycle. Cost; 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 conversion cost of its life cycle.

The constraints in the charge and discharge strategy of the present invention include:

Charge and discharge power constraints:

-P _D η _D ≤P _W (t)-P _ref (t)≤P _C (15)

Where: P _D and P _C are the ultimate charge and discharge power of the energy storage system, respectively, and the discharge is regarded as a negative charging process, the size of which is based on its absolute value.

Constraints include photovoltaic power plant output power fluctuation level constraints:

P{|ΔP _d (t)| ≤ ΔP _{d max} } ≥ Λ (16)

Where: ΔP _d (t) ΔP _d (t) is the fluctuation value of the photovoltaic power plant output power after being stabilized by the energy storage system; ΔP _{d max} is the upper limit of the maximum allowable range of the fluctuation value; Λ is the corresponding credibility level.

Charge constraint:

In the formula:

with

They are the minimum and maximum charge of the energy storage unit.

In order to verify the effectiveness of the method of the present invention, the optimal energy storage capacity is calculated based on the actual operational data of a photovoltaic power plant in Qinghai. This embodiment considers the PSO algorithm to solve the stochastic optimization problem of the present invention including dynamic boundary conditions and containing a plurality of random variables. The specific model solving steps are:

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

Step 2: Determine a desired output target value PG based on the optimal power output model, and give an initial SOC equivalent;

Step 3: Set the particle swarm dimension D, the maximum iteration number M _max , the convergence precision C _σ , and initialize the particle swarm position x and velocity v;

Step 4: According to the charge and discharge strategy of the present invention, parameters such as c ₁ , c ₂ , ω, V _min , and V _max are set, and the fitness value px _{id of} each particle is calculated according to the formula (11-17), and the particle size of the particle itself is Comparing the value p _i with the global example extreme value p _g , if the fitness value is small, updating p _i and p _g , if not updating the particle velocity V _id and the position X _id ;

Step 5: Calculate Δσ ^{2 to} determine whether the convergence condition is satisfied. The search convergence condition is:

Where Δσ ² is the amount of change in the population or global fitness variance of the particle population, and C _σ is a constant constant close to zero. If yes, obtain the optimal energy storage capacity W _O ; if not, re-release the example to form a new ethnic group and repeat step (4).

The invention measures the effectiveness of the method of the invention from the index parameters such as the optimal capacity W _O , the leveling power offset χ, the SOC extreme value limit number N, and the SOC process curve. The installed capacity of the photovoltaic power station is 9MW, the acquisition frequency is 5min, and the target value is as shown in Figure 2.

According to the charging and discharging power adjustment strategy and the energy storage capacity optimization calculation model of the present invention, the smoothing fluctuation output curve is obtained as shown in FIG. 3, and the calculation result of the method of the present invention is shown in Table 2.

Table 2 calculation results

The above embodiment can be analyzed. In terms of capacity planning, the method of the present invention effectively realizes the optimization of the energy storage capacity; in terms of the power offset, the method of the present invention is similar to the conventional method and slightly increased, and the reason is the power correction coefficient adjustment strategy. The probability of abandoning or stabilizing the insufficient energy is improved; in terms of the limit value operation, the invention greatly reduces the value of N, and the decrease is 96.2%, and the effect is obvious. Investigate the change of SOC in the process of optimal capacity acquisition of energy storage power station, as shown in Figure 4. It can be seen that the SOC in the method of the invention does not operate at the limit value, which effectively guarantees the service life of the ESS.

In summary, the capacity optimization calculation model of the present invention comprehensively considers the overall economics of the configuration and operation of the energy storage power station, and is beneficial to the effective combination with the site. The above theoretical research provides theoretical premise and guarantee for the optimization of energy storage capacity. At the same time, the actual data example analysis verified the above conclusions.

It can be seen from the above technical solutions that the present invention has the following beneficial effects:

The invention considers that the photovoltaic power is affected by natural conditions and has strong volatility, and uses the energy storage to realize the smooth output control of the photovoltaic power. Through the charge and discharge power correction coefficient, the effective adjustment of the energy storage state is realized, thereby avoiding excessive charge and discharge, thereby effectively prolonging the energy storage operation life and reducing the system operation cost while fully suppressing the photovoltaic output fluctuation. In this control strategy, the technical director further explored the optimal capacity of photovoltaic power plant configuration energy storage, and provided important theoretical support for the optimal operation of the optical storage system. The actual operation data of photovoltaic power station in Qinghai area is used for calculation. The results show that the photovoltaic power can be output smoothly, and the fluctuation range of the stored energy state can be controlled, which can effectively avoid excessive charging and discharging, which indicates that the method has strong feasibility and application. Sex.

It is apparent to those skilled in the art that the present invention is not limited to the details of the above-described exemplary embodiments, and the present invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the present embodiments are to be considered as illustrative and not restrictive, and the scope of the invention is defined by the appended claims instead All changes in the meaning and scope of equivalent elements are included in the present invention. Any reference signs in the claims should not be construed as limiting the claim.

In addition, it should be understood that although the description is described in terms of embodiments, not every embodiment includes only one independent technical solution. The description of the specification is merely for the sake of clarity, and those skilled in the art should regard the specification as a whole. The technical solutions in the respective embodiments may also be combined as appropriate to form other embodiments that can be understood by those skilled in the art.

Claims (8)

1. A photovoltaic power stable output control method based on an energy storage operation state, wherein the method comprises:

   S1, selected research object time section window length y and its operation data P(t);

   S2, determining a desired output target value P _G based on an optimal power output model, and giving an initial SOC value;

   S3, setting the particle group dimension D, the maximum iteration number M _max , the convergence precision C _σ , and initializing the particle group position x and the velocity v;

   S4. Calculate the fitness value px _{id of} each particle according to the charging and discharging strategy, and compare the self particle extreme value p _i with the global example extreme value p _g . If the fitness value is small, update p _i and p _g , If not update the particle velocity V _id and position X _id ;

   S5. Calculate Δσ ² and determine whether the convergence condition is satisfied.

   

   If yes, obtain the optimal energy storage capacity W _O ; if not, re-release the example to form a new ethnic group, and repeat step S4, where Δσ ² is the variation of the population or global fitness variance of the particle group, C _σ is A constant that is close to zero.
2. The photovoltaic power stable output control method based on an energy storage operation state according to claim 1, wherein in the optimal power output model in the step S1:

   The difference ΔP(t) between the photovoltaic power output power P _P (t) and the grid-connected target power P _ref (t) at time t is ΔP(t)=P _P (t)-P _ref (t);

   When the energy storage system is charging:

   

   For the charging and discharging power of the energy storage system at time t, η _C is the charging efficiency of the energy storage system;

   When the energy storage system is in a discharged state:

   

   The charging and discharging power of the energy storage system at time t, η _D is the discharge efficiency of the energy storage system.
3. The photovoltaic power stable output control method based on an energy storage operation state according to claim 2, wherein in the optimal power output model in the step S1:

   When the energy storage system is in a state of charge: P _ESS (t) = δ _i (t) ΔP(t) η _C ,

   

   For the charging and discharging power of the energy storage system at time t, η _C is the charging efficiency of the energy storage system, and δ _i (t) is the correction coefficient of the charging and discharging power at time t;

   When the energy storage system is in a discharged state: P _ESS (t) = δ _i (t) ΔP(t) / η _D ,

   

   The charging and discharging power of the energy storage system at time t, η _D is the discharge efficiency of the energy storage system, and δ _i (t) is the correction coefficient of the charging and discharging power at time t.
4. The photovoltaic power stable output control method based on an energy storage operation state according to claim 3, wherein the energy storage system is classified according to a limitation of a runtime SOC, including: a pre-discharge area [Q _SOClow-L2 , Q _SOClow-L1 ], normal area [Q _SOClow-L1 , Q _SOChigh-L1 ], pre-charged area [Q _SOChigh-L1 , Q _SOChigh-L2 ].
5. The photovoltaic power stable output control method based on the energy storage operation state according to claim 4, wherein the charge/discharge power correction coefficient δ _i (t) at the time t is specifically:

   In the pre-charged area, when charging

   

   δ _i (t) is 1 in the discharge state;

   In the normal region, δ _i (t) is 1 in both the state of charge and the state of discharge;

   In the pre-discharge area, δ _i (t) is 1 in the state of charge, and in the state of discharge

   
6. The photovoltaic power stable output control method based on the energy storage operation state according to claim 1, wherein in the charging and discharging strategy in the step S4, the energy storage capacity optimization target is:

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

   Where ρ _L , ρ _S , and ρ _E are respectively the corresponding unit price of the photovoltaic power plant's abandoned light loss energy, the smooth power shortage loss energy, and the converted energy of the energy storage system crossing the line; ρ _L L _LOST is the photovoltaic power plant abandoned light energy cost; ρ _S L _SHORT is the energy cost lost by the smooth power shortage of the photovoltaic power station; ρ _E L _ESS is the converted energy cost of the energy storage system crossing the line; K _L , K _S and K _E are the penalty coefficients of the running cost; C _C energy storage The input cost of the system.
7. The photovoltaic power stable output control method based on the energy storage operation state according to claim 6, wherein in the charging and discharging strategy in the step S4, the photovoltaic power plant loses light loss energy and smooths power shortage The energy lost to the loss of energy and the energy storage system is:

   

   

   

   In the formula, N _y is the time year of the research object; g and h are the total number of times during the charging and discharging process in the year of _{y i} <1 to adjust the operation interval; p and q are the initial and end times of the g interval respectively; u, v is the initial and final time interval h; K exceeds the maximum number of which is on the state of charge is N _y annual energy storage system in the operating state; L is N _y annual energy storage system in the operating state is located lower than the minimum state The total number of times; x and y are the initial and end times of the k interval; z and a are the initial and end times of the l interval, respectively.
8. The photovoltaic power stable output control method based on the energy storage operation state according to claim 1, wherein in the charging and discharging strategy in the step S4, the constraint condition comprises:

   Charge and discharge power constraints:

   -P _D η _D ≤P _W (t)-P _ref (t)≤P _C , P _D and P _C are the ultimate charge and discharge power of the energy storage system, respectively;

   PV power plant output power fluctuation level constraints:

   P{|ΔP _d (t)| ≤ ΔP _dmax } ≥ Λ, ΔP _d (t) ΔP _d (t) is the fluctuation value of the photovoltaic power plant output power after being stabilized by the energy storage system, and ΔP _dmax is the maximum allowable range of the fluctuation value The upper limit is the corresponding level of credibility;

   Charge constraint:

   

   with

   

   They are the minimum and maximum charge of the energy storage unit.

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