CN112290571B - Energy storage system smooth control method - Google Patents
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Abstract
The invention discloses a smooth control method of an energy storage system, which is used for switching a sliding window according to the fluctuation condition of real-time wind power so as to distinguish stable wind power output and sudden wind power change. The smoothing strategy can calculate the optimal solution of the fluctuation coefficient under two different working conditions and obtain a grid-connected power reference value, wherein the grid-connected power reference value is the optimal solution in the statistical sense, so that the charging and discharging depth and the accumulated capacity are minimized. In order to give consideration to the smooth control effect and the influence of sudden power change on the energy storage battery, the invention corrects the grid-connected power reference value according to the grid-connected power fluctuation limit and the SOC level of the battery, so that the energy storage output is in a reasonable range. The proposed control strategy can keep the best smooth control effect when the wind power is stably output; when the wind power suddenly changes, the change of the original wind power can be tracked to the maximum extent, the charging and discharging depth is reduced, and the safe and stable operation of the energy storage system is facilitated.
Description
Technical Field
The invention relates to a smooth control method of an energy storage system, and belongs to the technical field of energy storage control.
Background
With the rapid development of wind power generation technology, the installed capacity of wind power is continuously increased, and the permeability of wind power is also continuously improved. In order to improve the grid-connection capability of wind power and reduce the phenomena of wind abandoning and electricity limiting, an Energy Storage System (ESS) with a certain capacity is equipped for a large wind farm, and the ESS becomes an effective means for smoothing the power fluctuation of the wind power at home and abroad in recent years.
The first-order low-pass filtering algorithm is widely applied due to simple principle and high operation speed, but has a certain delay in tracking wind power change. Document [1] discloses a prediction control method for stabilizing wind power fluctuation by battery energy storage, and provides a Model Prediction Control (MPC) method, wherein fluctuation is stabilized by energy storage charge state feedback and prediction. Document [2] discloses a research on a MPC dual energy storage control strategy under the condition of wind power fluctuation stabilization, and two groups of energy storage devices in different charging and discharging states are applied to stabilize wind power fluctuation so as to reduce the problem of frequent charging and discharging of a single group of energy storage devices, but the increase of a group of energy storage devices undoubtedly brings about the increase of cost. Document [3] discloses a two-stage model combining a grid adaptive search algorithm and improved particles, considering the change of the energy storage capacity of a micro-grid in terms of the service life of a battery and economic operation, and effectively reducing the charging and discharging problems of energy storage. Document [4] discloses an energy storage system real-time smoothing control strategy with a charge state adjusting function, and wind power fluctuation is stabilized through weight adjustment and frequency bandwidth of a weighted moving average algorithm, so that power abrupt change can be effectively smoothed.
Most smoothing control methods do not distinguish sudden power changes and stable output of the wind turbine generator, and research on effective methods has important safety significance and economic value in reducing the charging and discharging depth and the battery capacity of the battery on the premise of ensuring the smoothing effect.
Disclosure of Invention
The invention aims to design a stabilizing strategy combining double sliding windows and a particle swarm algorithm, wherein the sliding windows are switched according to the fluctuation condition of real-time wind power, and the stable wind power output and the sudden wind power change are separated. The smoothing strategy can calculate the optimal solution of the fluctuation coefficient under two different working conditions and obtain a grid-connected power reference value, wherein the grid-connected power reference value is the optimal solution in the statistical sense, so that the charging and discharging depth and the accumulated capacity are minimized. In order to give consideration to the smooth control effect and the influence of sudden power change on the energy storage battery, the invention corrects the grid-connected power reference value according to the grid-connected power fluctuation limit and the SOC level of the battery, so that the energy storage output is in a reasonable range. The proposed control strategy can keep the best smooth control effect when the wind power is stably output; when the wind power suddenly changes, the change of the original wind power can be tracked to the maximum extent, the charging and discharging depth is reduced, and the safe and stable operation of the energy storage system is facilitated.
The invention specifically adopts the following technical scheme: the energy storage system smoothing control method is characterized by comprising the following steps:
step SS 1: initializing the capacity and the charge state of an energy storage battery;
step SS 2: setting each power output value of the energy storage system to be P at t (t is 1, 2.. n)Wind,t、PBat,t、 PGrid,t(ii) a And each power output value satisfies the relational expression:
PBat,t＝PGrid,t-PWind,t (2)
PBat,t>at 0, the energy storage system discharges; pBat,t<When 0, the energy storage system is charged; pWindRepresenting the wind power generation output power; pBatRepresenting the output power of the energy storage power supply; pGridGrid-connected power is supplied to the wind storage combined system;
step SS 3: acquiring N continuous wind power data at and after the time t, wherein an initial value N is NcJudging whether the original wind power exceeds the fluctuation range, if so, generating a current wind power sequence as follows:
if the wind power sequence is judged to be the current time, the wind power sequence is generated as follows:
step SS 4: generating an initial fluctuation coefficient population based on the current moment wind power sequence in the step SS3
Or
Step SS 5: updating the fluctuation coefficient population speed and position in the step SS 4;
step SS 6: calculating the grid-connected power of the wind storage combined system, and solving the value of a target function F;
step SS 7: updating the positions of the local extreme point and the global extreme point, judging whether the maximum iteration times is reached, and if so, obtaining the optimal fluctuation coefficient omegabestCalculating the current time grid-connected power reference value PGrid,tIf the sliding window t is t +1, the process proceeds to step SS2, and if the determination is no, the process proceeds to step SS 5;
step SS 8: the current-time grid-connected power reference value P obtained in the determination step SS7Grid,tIf the maximum power fluctuation requirement of the power grid is met, directly switching to the step SS9 if the maximum power fluctuation requirement of the power grid is met, otherwise, PGrid,t＝PGrid,t-1Δ P, Δ P being the maximum allowable ripple power value, PGrid,tRepresenting the grid-connected power at time t, PGrid,t-1Representing the grid-connected power at the t-1 moment; go to step SS 9;
step SS 9: determining a correction coefficient c according to the SOC level; calculating the actual output power of the energy storage power supply to be PBat＝c×(PGrid-PWind)。
As a preferred embodiment, step SS3 specifically includes: designing an optimal smooth control window and an optimal wind power tracking window, wherein the window widths are NcAnd NsRespectively starting an optimal smooth control window N according to the fluctuation of wind power, namely the proportion of the power difference value of a sampling time interval to the rated powercAnd an optimal wind power tracking window Ns(ii) a Based on the smooth control requirement, the relation between the optimal smooth control window and the optimal wind power tracking window width is Nc＝ηNs，η≥2。
As a preferred embodiment, step SS3 further includes: when the fluctuation range of the real-time wind power per minute is less than 1.5 percent and the fluctuation range of the real-time wind power per minute is less than 15 percent, starting an optimal smooth control window NcWhen the fluctuation range of the real-time wind power per minute is larger than 1.5 percent or the fluctuation range of the real-time wind power per minute is larger than 15 percent, starting the optimal wind power tracking window NsAnd switching the sliding window in real time by calculating the power fluctuation amplitude, specifically as follows:
and starting an optimal smooth control window N under the condition that the power fluctuation at the moment T is smallcThe real-time power sequence of the wind power plant at the moment is
Calculating P at the moment through a smooth control strategyGridThe optimal smooth control window moves to the T +1 moment; if the fluctuation of the time T is in a large range, starting an optimal wind power tracking window N immediately at the time TsThe real-time power sequence of the wind power plant at the moment is
Calculate P at that timeGridThe optimum state of charge fluctuation window moves to time T + 1.
As a preferred embodiment, step SS5 specifically includes: setting the window width as a fixed value N, taking N-1 continuous wind power data at and after T time to form a particle swarm, wherein T is the maximum iteration number, and calculating the individual optimal solution p of each iteration by updating the position and speed of the particle before reaching the maximum iteration numberbestAnd a global optimal solution Pbest。
As a preferred embodiment, step SS5 further includes: the updated formula of the particle velocity and position is shown as formula (5):
wherein δ is an inertia factor; t is the number of iterations; c. C1And c2Is a particle learning factor; r is1And r2Is [0, 1]]Random numbers within the interval;
and
respectively the speed and position of the ith particle in the t iteration;
and
the individual optimal position and the global optimal position of the t iteration are respectively; after the maximum iteration number T is reached, the global optimum position is returned, namely the global optimum solution Pbest。
As a preferred embodiment, step SS6 specifically includes: setting the window width as a fixed value N, taking N-1 continuous wind power data at and after the time t, and establishing a variable omegatThe optimization model specifically includes:
defining the coefficient of fluctuation as ωtLet Δ t be the sampling time interval, the larger the slope of the time from t to (t +. DELTA.t), the larger the change rate of the power in the period, the more obvious the fluctuation, and the fluctuation coefficient ωtSatisfies the following conditions:
as can be seen from the formula (3), the smaller the fluctuation coefficient is, the better the stabilizing effect on the wind power is, and the more stable the wind power plant grid-connected power is, but the excessive stabilization may be caused; from the perspective of engineering application considering economic cost, the scale of an energy storage system needs to be reduced as much as possible, and a proper fluctuation coefficient is selected to obtain target power meeting the wind power fluctuation index; the price of the energy storage system is in direct proportion to the energy storage capacity, and the capacity of the energy storage system is reduced as much as possible on the premise of meeting the stabilization requirement; the objective function is defined as:
n is the width of the sliding window; pGrid,t(ωt) Representing the coefficient of fluctuation omega at time ttThe varied grid-connected target power after stabilization;
as a preferred embodiment, step SS7 specifically includes: obtaining the optimal value omega of the fluctuation coefficient at the current moment based on the step SS5bestCalculating to obtain the reference value P of the current time grid-connected power through a formula (3)Grid,tAnd the grid-connected power reference value is an optimal statistical solution of the compensation power of the energy storage system, so that the charging and discharging depth of the system and the allowable capacity of the battery are minimized in a statistical sense.
As a preferred embodiment, step SS8 specifically includes: defining a sampling period as 1 minute, and determining the fluctuation rate of the wind power grid-connected power within 1 minute:
△PGrid,1min％＝|PGrid,t-PGrid,t-1|/Pr (7)
wherein, PGrid,tRepresenting the grid-connected power at time t, PGrid,t-1Represents the grid-connected power at time t-1, PrRated power for the wind farm; define the wind power fluctuation rate for 10 minutes:
△PGrid,10min％＝|max(PGrid,t～t+9)-min(PGrid,t～t+9)|/Pr (8)
wherein, max (P)Grid,t～t+9) And min (P)Grid,t～t+9) Respectively representing the maximum value and the minimum value of grid-connected power within 10 minutes; from the definition of the wind power fluctuation rate of 10 minutes, the power fluctuation of 10 minutes is accumulated by the power fluctuation of 1 minute, so that the wind power fluctuation of 1 minute in the wind power integration is constrained to be considered firstly; the maximum fluctuating power constraint is:
(1) the fluctuation amplitude per minute is lower than 2% of the rated power of the system;
(2) the fluctuation amplitude of every 10 minutes is lower than 20% of the rated power of the system;
when the obtained grid-connected target power is not in the allowable fluctuation range, carrying out corresponding correction according to the grid-connected power at the last moment:
PGrid,t＝PGrid,t-1+△p (9)
where Δ p is the maximum allowable ripple power value.
As a preferred embodiment, step SS9 specifically includes: the actual battery output power is adjusted in real time by considering the current battery charge state and the charge and discharge instruction, and the actual energy storage battery output is defined as:
PBat＝c×(PGrid-PWind) (10)
and c is a charge and discharge correction coefficient of the energy storage system.
As a preferred embodiment, step SS9 specifically includes: and (3) discharging state: when the SOC is at an extremely low charge level of 10%, the power correction coefficient c is 0, and the energy storage battery stops discharging; when the SOC is larger than the minimum allowable discharge depth and smaller than the optimal regulation SOC value (50%), the power correction coefficient c is uniformly increased from 0 to 1; when the SOC is larger than 50%, the power correction coefficient c is 1, and the energy storage battery can discharge according to the current discharging instruction;
the charging state is as follows: when the SOC is positioned at 90% of the maximum allowable charging depth of the battery, the power correction coefficient c is 0, and the charging power of the battery is enabled to be zero; when the SOC is larger than the optimal adjustment SOC value (50%) and smaller than the maximum allowable charging depth of the battery, the power correction coefficient is uniformly reduced from 1 to 0; when the SOC is less than 50%, the power correction coefficient c is 1, and the energy storage battery can be charged according to the charging instruction at the current moment.
The invention achieves the following beneficial effects: (1) the invention fully considers the fluctuation characteristic of original wind power, utilizes the capability of fast searching and avoiding falling into local optimum of the particle swarm algorithm to process the dynamic target optimization problem, and provides a novel modeling method combining double sliding windows and particle swarm. (2) An optimal smooth control window is selected in the stable wind power output stage, so that the smooth control effect is ensured when the power fluctuation is small; when the wind power suddenly changes, the optimal wind power tracking window is switched to, the rapid following performance of the original wind power can be guaranteed, and the generation of deep charging and discharging is reduced. (3) For the grid-connected power reference value obtained by the calculation of the optimization model, the grid-connected maximum power fluctuation limitation and the energy storage system charge state adjustment are assisted, the deviation of the residual electric quantity of the energy storage battery to the extreme direction is restrained, and the phenomenon that the SOC regulator cannot continuously operate in the energy storage power station due to low electric quantity or high electric quantity is avoided.
Drawings
FIG. 1 is a schematic view of a combined wind and storage system of the present invention;
FIG. 2 is a comparison of the smoothing strategy calculation results 1 of the present invention;
FIG. 3 is a comparison of smoothing strategy calculation results 2 of the present invention;
FIG. 4 is a functional plot of power correction coefficients for the charge and discharge operating conditions of the present invention;
FIG. 5 is a comparison graph of the SOC regulator state of charge trend and the power correction factor variation trend of the present invention;
FIG. 6 is a flow chart of an energy storage system smoothing control method of the present invention;
FIG. 7 is a graph comparing power fluctuations for different window width smoothing strategies in accordance with the present invention;
FIG. 8 is a state of charge trend graph for different window width smoothing strategies in accordance with the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
Example 1: 3.1 wind-storage combined system
The basic idea of utilizing the energy storage system to stabilize the wind power is to counteract, absorb or release the generated power fluctuation through the energy storage system, and compensate the wind power fluctuation so as to achieve the purpose of smoothing the output power of the intermittent power supply. Fig. 1 is a schematic structural diagram of a wind storage combined system. PWindRepresenting the wind power generation output power; p isBatRepresenting the output power of the energy storage power supply; pGridAnd grid-connected power is supplied to the wind storage combined system. The system shown in fig. 1 can be known from the energy balance theorem:
PWind+PBat-PGrid＝0 (1)
in order to meet the grid-connected requirement, the energy storage system adjusts the output power of the energy storage system according to the real-time output of the wind power plant, PBat>0 denotes the energy storage system discharge, PBat<0 represents energy storage system charging.
3.2 smooth control strategy based on double sliding windows and particle swarm optimization
3.2.1 basic control strategy
Energy storageThe system needs to absorb or release the difference value between the wind power and the wind power grid-connected power in time, so that the power quality of the power grid is improved. Setting each power output value of the system to be P at t (t is 1, 2.. n)Wind,t、PBat,t、PGrid,t. And each power output value satisfies the relation:
PBat,t＝PGrid,t-PWind,t (2)
PBat,t>at 0, the energy storage system discharges; pBat,t<And 0, charging the energy storage system. By designing a smooth control strategy, the charging and discharging times and the charging and discharging depth of the energy storage battery are reduced, the service life of the battery can be prolonged, and the safe continuous operation and cost control of an energy storage system are facilitated.
The slope of the wind power generation power curve directly reflects the fluctuation condition of the power. Defining the coefficient of fluctuation as ωtLet Δ t be the sampling time interval, and the larger the slope at time t to (t + Δt) means that the larger the rate of change of the power at that time interval, the more pronounced the fluctuation. Coefficient of fluctuation omegatSatisfies the following conditions:
according to the formula, the smaller the fluctuation coefficient is, the better the stabilizing effect on the wind power is, and the more stable the wind power plant grid-connected power is, but the over-stabilization may be caused. From the perspective of engineering application considering economic cost, the scale of an energy storage system needs to be reduced as much as possible, and a proper fluctuation coefficient is selected to obtain target power meeting the wind power fluctuation index. Generally, the price of the energy storage system is in a direct proportion relation with the energy storage capacity, and the capacity of the energy storage system is reduced as much as possible on the premise of meeting the stabilizing requirement. The objective function is defined as:
n is the width of the sliding window; pGrid,t(ωt) Representing the coefficient of fluctuation omega at time ttAfter settling of the changeAnd (5) grid-connected target power.
The invention combines a sliding window and a Particle Swarm Optimization (PSO) to correct the fluctuation coefficient omegatAnd performing dynamic optimization, and calculating the minimum energy storage output in the window width. Setting the window width as a fixed value N, taking N-1 continuous wind power data at and after the time t, and establishing a variable omega shown as a formula (3) and a formula (4)tThe optimization model of (1). The particle swarm optimization is used as a random search algorithm, and has the advantages of high search speed and capability of avoiding being trapped in local optimization in dynamic target optimization processing. The main algorithm flow is as follows: setting a particle swarm composed of n particles, wherein T is the maximum iteration number, and calculating the individual optimal solution p of each iteration by updating the positions and the speeds of the particles before the maximum iteration number is reachedbestAnd a global optimal solution Pbest. The update formula of the particle velocity and position is shown in formula (5).
Wherein δ is an inertia factor; t is the number of iterations; c. C1And c2Is a particle learning factor; r is1And r2Is [0, 1]]Random numbers within the interval;
and
respectively the speed and position of the ith particle in the t iteration;
and
the individual and global optimal positions of the t-th iteration are respectively. After the maximum iteration time T is reached, returning to a global optimal position, namely a global optimal solution, and obtaining the optimal value omega of the fluctuation coefficient at the current momentbestThen, the current time can be calculated and obtained through the formula (3)Grid-connected power reference value PGrid,tAnd the grid-connected power reference value is an optimal statistical solution of the compensation power of the energy storage system, so that the charging and discharging depth of the system and the allowable capacity of the battery are minimized in a statistical sense.
3.2.2 double sliding Window analysis method
In the analysis, the larger the sliding window width N adopted by the smoothing control strategy is, the better the smoothing effect is, but the too large window width causes the rapid following performance of the smoothing power curve to the original power to be deteriorated, and the required charge-discharge power of the energy storage battery deviates from the actual power curve. Fig. 2 shows the calculation result of the smoothing strategy when the window width N is too large. The problem of deep charge and discharge of the energy storage battery is accompanied when the wind power sudden change condition occurs in the region 1 and the region 2, and the deep charge and discharge of the energy storage of the storage battery is an important factor for reducing the service life of the storage battery; if the energy storage mode of the storage battery is adopted, the electrochemical reaction rate is limited, and when the load power suddenly changes, the target power cannot be absorbed or released quickly[10]It is difficult to meet the dynamic requirements of the system.
The smaller the sliding window width N is, the worse the smoothing effect is, although the small window width can ensure the fast following performance of the smoothing power curve to the original power curve, the smoothing control system loses the smoothing effect on the original wind power. Fig. 3 shows the calculation result of the smoothing strategy when the window width N is too small. The difference between the original wind power and the stabilized power is small in the region 1 and the region 2, and the energy storage battery still approaches to be not operated when the wind power suddenly changes, so that the smoothing function is lost.
Through the analysis, the width of the sliding window is an important factor influencing the smooth effect of the control strategy, and the window cannot meet the actual field application when being too large or too small. Literature documents[11]A novel double-sliding-window analysis method is provided, and the method can effectively analyze the trend change of time series data and eliminate the influence of isolated mutation data. The invention introduces a double sliding window method into a smooth control strategy.
In order to balance the deep charge and discharge problems caused by smooth control effect and wind power sudden change, two sliding windows with different widths need to be designed: optimum flatA sliding control window and an optimal wind power tracking window, the window width is NcAnd Ns. As shown in equation (6), when the power fluctuation is small, the use of a sliding window with a large width can ensure that the smooth control effect is optimal. The wind power fluctuation is defined as the ratio of the power difference value of the sampling time interval to the rated power, and the optimal smooth control window N is started when the real-time wind power fluctuation amplitude per minute is less than 1.5 percent (can be adjusted according to the smooth control requirement) and the 10-minute fluctuation amplitude is less than 15 percent (can be adjusted according to the smooth control requirement)c. When the fluctuation is large, the sliding window with a small width can maintain the rapid following performance of the stabilizing power curve to the original power, and the fluctuation of the battery charge state in a reasonable range is ensured. Starting an optimal wind power tracking window N when the fluctuation amplitude per minute is more than 1.5 percent or the fluctuation amplitude per 10 minutes is more than 15 percents. In order to meet the requirements of smooth control, the relation between the width of the optimal smooth control window and the width of the optimal wind power tracking window is Nc＝ηNs，η≥2。
And starting an optimal smooth control window N under the condition that the power fluctuation at the moment T is smallcThe real-time power sequence of the wind power plant at the moment is
Calculating P at the moment through a smooth control strategyGridThe optimal smooth control window moves to the T +1 moment; if the fluctuation of the time T is in a large range, starting an optimal wind power tracking window N immediately at the time TsThe real-time power sequence of the wind power plant at the moment is
Calculate P at that timeGridThe optimum state of charge fluctuation window moves to time T + 1. And switching the sliding window in real time by calculating the power fluctuation amplitude.
3.3 wind Power correction and SOC regulator design
3.3.1 wind Power correction
According to the technical regulation of accessing wind power plants to a power system in China, in order to avoid the impact of intermittent power jump on a power grid, the power fluctuation of grid-connected wind is regulated to meet the national standard of the climbing rate of the wind power plants. Table 1 shows the technical specifications of the wind power fluctuation amount in China.
TABLE 1 maximum limit of wind power variation
Tab.1 The maximum variation of wind power
The invention defines the sampling period as 1 minute, and defines the fluctuation rate of the wind power grid-connected power within 1 minute:
△PGrid,1min％＝|PGrid,t-PGrid,t-1|/Pr (7)
wherein, PGrid,tRepresenting the grid-connected power at time t, PGrid,t-1Represents the grid-connected power at time t-1, PrThe rated power of the wind power plant. Define the wind power fluctuation rate for 10 minutes:
△PGrid,10min％＝|max(PGrid,t～t+9)-min(PGrid,t～t+9)|/Pr (8)
wherein, max (P)Grid,t～t+9) And min (P)Grid,t～t+9) The maximum and minimum values of grid-connected power in 10 minutes are shown respectively. From the definition of the 10-minute wind power fluctuation rate, the 10-minute power fluctuation is accumulated by the 1-minute power fluctuation, so the 1-minute wind power fluctuation in the wind power integration is constrained to be considered firstly. The maximum fluctuating power constraint is:
(1) the fluctuation range per minute is lower than 2% of the rated power of the system;
(2) the fluctuation range of every 10 minutes is lower than 20% of the rated power of the system.
When the obtained grid-connected target power is not in the allowable fluctuation range, carrying out corresponding correction according to the grid-connected power at the last moment:
PGrid,t＝PGrid,t-1+△p (9)
where Δ p is the maximum allowable ripple power value.
3.3.2 SOC regulator design
In order to prolong the service life of the energy storage battery, the current battery charge state and the current charge and discharge instruction should be considered in real time during the charge and discharge process of the battery. When the charge state of the battery is below 10%, the energy storage of the battery is insufficient, and the energy storage power station cannot operate continuously; when the charge state of the battery is more than 90%, the stored energy of the battery tends to be saturated, and the redundant output of the wind power plant is not favorably absorbed subsequently. In order to overcome the problem of deep charge and discharge, a proper control strategy is adopted to maintain the SOC level between 10% and 90%. As shown in fig. 4, the SOC regulator designed in the present invention takes into account the current battery state of charge and the charge/discharge command to adjust the actual battery output power in real time, and defines the actual energy storage battery output as:
PBat＝c×(PGrid-PWind) (10)
and c is a charge and discharge correction coefficient of the energy storage system.
In order to maintain the SOC at a reasonable level, when the SOC is large, the energy storage system tends to be saturated, and the charging is fully discharged or less charged or even stopped; when the SOC is small, the stored energy is insufficient, and charging or discharging is sufficiently performed or is reduced or even stopped. The SOC regulator is designed under two working conditions of a discharging state and a charging state[12]。
And (3) discharging state: when the SOC is at an extremely low charge level of 10%, the power correction coefficient c is 0, and the energy storage battery stops discharging; when the SOC is larger than the minimum allowable discharge depth and smaller than the optimal regulation SOC value (50%), the power correction coefficient c is uniformly increased from 0 to 1; when the SOC is greater than 50%, the power correction coefficient c is 1, and the energy storage battery can be discharged according to the current discharge command.
And (3) charging state: when the SOC is positioned at 90% of the maximum allowable charging depth of the battery, the power correction coefficient c is 0, and the charging power of the battery is enabled to be zero; when the SOC is larger than the optimal adjustment SOC value (50%) and smaller than the maximum allowable charging depth of the battery, the power correction coefficient is uniformly reduced from 1 to 0; when the SOC is less than 50%, the power correction coefficient c is 1, and the energy storage battery can be charged according to the charging instruction at the current moment.
And constructing a smooth control strategy by adopting a method of combining double sliding windows and particle swarm. Fig. 5 shows the state of charge trend with and without the SOC regulator, which can stabilize the battery state of charge around 50% and suppress the SOC curve from shifting to the two poles. The SOC is maintained to be about 0.5 through an effective method, the maximum battery tolerance can be provided for the next-stage smooth control, and the influence of overcharge and overdischarge on the energy storage battery is effectively reduced. By combining the above analysis, the energy storage system real-time smooth control flow taking wind power fluctuation and battery state of charge into account is shown in fig. 6.
FIG. 7 is a comparison of power fluctuations using different window width smoothing strategies. FIG. 7a is a comparison graph of smooth power fluctuations over a 1 day period, with the red boxed area enlarged as FIG. 7 b. Through the partial enlarged view, when the power fluctuation is large and even the power suddenly changes in the area 1 and the area 2, the smoothing strategy of the over-wide window is obviously deviated from the original power, although a better smoothing effect can be obtained, the problem of deep charging and discharging is generated along with the problem; the smoothing strategy of the over-narrow window is matched with the original power curve, and the smoothing effect is basically lost; and a double-sliding-window control strategy is adopted to switch to the optimal wind power tracking window when the power is suddenly changed, so that better tracking and smoothing effects are achieved, and the problem of deep charging and discharging of the energy storage battery can be reduced. In the stage of stable wind power output in the region 3, smooth curves of the control strategies adopting the double sliding windows and the over-wide window are basically consistent, because the control strategy adopting the double sliding windows can be switched to the optimal smooth control window in the stage so as to keep the optimal smooth control effect.
FIG. 8 shows the state of charge trend for different window widths. When the window width is too wide, the smooth control effect is optimal, but the charge state fluctuation is maximum, and the level of 0.2 low electric quantity is reached. When the control strategy adopts a single window with too wide width, the energy storage capacity required to be configured by the energy storage system is higher, and the SOC regulator has the risk of incapability of continuously operating the energy storage power station due to low electric quantity or high electric quantity. When the window width is too narrow, the charge state fluctuation is minimum, but the stabilization effect of the window on the original power is the worst in the analysis, and the smoothing effect is lost. When the double sliding windows are adopted, the charging and discharging depth and the battery capacity configuration of the energy storage battery are slightly higher than the extreme condition that the single sliding window is too narrow. As shown in table 2, the use of the dual sliding windows sacrifices less energy storage capacity, but better control effect can be obtained, and the smoothing and wind power tracking effects- + -/"of the wind power steady output stage and the wind power sudden change stage are well balanced.
TABLE 2 State of Charge comparison for different window Width smoothing strategies
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (10)
1. The energy storage system smoothing control method is characterized by comprising the following steps:
step SS 1: initializing the capacity and the charge state of an energy storage battery;
step SS 2: setting each power output value of the energy storage system at the time t as PWind,t、PBat,t、PGrid,tT is 1,2, …, n; and each power output value satisfies the relation:
PBat,t＝PGrid,t-PWind,t (2)
PBat,twhen the voltage is higher than 0, the energy storage system discharges; pBat,tWhen the voltage is less than 0, the energy storage system is charged; pWindRepresenting the wind power generation output power; p isBatRepresenting the output power of the energy storage power supply; pGridGrid-connected power is supplied to the wind storage combined system;
step SS 3: acquiring N continuous wind power data at and after the time t, wherein an initial value N is NcJudging whether the original wind power exceeds the fluctuation range, if so, judging that the original wind power exceeds the fluctuation rangeGenerating a wind power sequence at the current moment as follows:
if the wind power sequence is judged to be not the current moment, generating a wind power sequence at the current moment as follows:
step SS 4: generating an initial fluctuation coefficient population based on the current moment wind power sequence in the step SS3
Or
Step SS 5: updating the fluctuation coefficient population speed and position in the step SS 4;
step SS 6: calculating the grid-connected power of the wind storage combined system, and solving the value of a target function F;
the objective function is defined as:
n is the width of the sliding window; pGrid,t(ωt) Representing the coefficient of fluctuation omega at time ttThe varied grid-connected target power after stabilization;
step SS 7: updating the local extreme point and the global extreme point, judging whether the maximum iteration times is reached, and if so, obtaining the optimal fluctuation coefficient omegabestCalculating the current time grid-connected power reference value PGrid,tIf the sliding window t is t +1, the process proceeds to step SS2, and if the determination is no, the process proceeds to step SS 5;
step SS 8: the current-time grid-connected power reference value P obtained in the determination step SS7Grid,tIf the maximum power fluctuation requirement of the power grid is met, directly switching to the step SS9 if the maximum power fluctuation requirement of the power grid is met, otherwise, PGrid,t＝PGrid,t-1+ Δ P, Δ P being the maximum allowable ripple power value, PGrid,tRepresenting the grid-connected power at time t, PGrid,t-1Representing grid-connected power at the t-1 moment; go to step SS 9;
step SS 9: determining a correction coefficient c according to the SOC level; calculating the actual output power of the energy storage power supply to be PBat＝c×(PGrid-PWind)。
2. The energy storage system smoothing control method according to claim 1, wherein the step SS3 specifically comprises: designing an optimal smooth control window and an optimal wind power tracking window, wherein the window widths are NcAnd NsRespectively starting an optimal smooth control window N according to the fluctuation of wind power, namely the proportion of the power difference value of a sampling time interval to the rated powercAnd an optimal wind power tracking window Ns(ii) a Based on the smooth control requirement, the relation between the optimal smooth control window and the optimal wind power tracking window width is Nc＝ηNs，η≥2。
3. The energy storage system smoothing control method according to claim 2, wherein step SS3 further includes: when the real-time wind power per minute fluctuation amplitude is less than 1.5 percent and the 10 minute fluctuation amplitude is less than 15 percent, starting the optimal smooth control window NcWhen the fluctuation range of the real-time wind power per minute is larger than 1.5 percent or the fluctuation range of the real-time wind power per minute is larger than 15 percent, starting the optimal wind power tracking window NsAnd switching the sliding window in real time by calculating the power fluctuation amplitude, specifically as follows:
and starting an optimal smooth control window N under the condition that the power fluctuation at the moment T is smallcThe real-time power sequence of the wind power plant at the moment is
Calculating P at the moment through a smooth control strategyGridThe optimal smooth control window moves to the T +1 moment; if the fluctuation of the time T is in a large range, starting an optimal wind power tracking window N immediately at the time TsThe real-time power sequence of the wind power plant at the moment is
Calculating P of the timeGridThe optimum state of charge fluctuation window moves to time T + 1.
4. The energy storage system smoothing control method according to claim 1, wherein the step SS5 specifically comprises: setting the window width as a fixed value N, taking N-1 continuous wind power data at and after T time to form a particle swarm, wherein T is the maximum iteration number, and calculating the individual optimal solution p of each iteration by updating the position and speed of the particle before reaching the maximum iteration numberbestAnd a global optimal solution Pbest。
5. The energy storage system smoothing control method according to claim 4, wherein the step SS5 further comprises: the updated formula of the particle velocity and position is shown as formula (5):
wherein δ is an inertia factor; t is the number of iterations; c. C1And c2Is a particle learning factor; r is1And r2Is [0, 1]]Random numbers within the interval;
and
respectively the speed and position of the ith particle in the t iteration;
and
the individual optimal position and the global optimal position of the t iteration are respectively; after the maximum iteration number T is reached, the global optimum position is returned, namely the global optimum solution Pbest。
6. The energy storage system smoothing control method according to claim 1, wherein the step SS6 specifically comprises: setting the window width as a fixed value N, taking continuous N-1 wind power data at and after the time t, and establishing a variable omegatThe optimization model specifically includes:
defining the fluctuation coefficient as omegatLet Δ t be the sampling time interval, the larger the slope of time t to (t + Δ t), the larger the change rate of the power in the period, the more obvious the fluctuation, and the fluctuation coefficient ωtSatisfies the following conditions:
7. the energy storage system smoothing control method according to claim 1, wherein the step SS7 specifically comprises: obtaining the optimal value omega of the fluctuation coefficient at the current moment based on the step SS5bestAnd calculating to obtain a current time grid-connected power reference value P through a formula (3)Grid,tAnd the grid-connected power reference value is an optimal statistical solution of the compensation power of the energy storage system, so that the charging and discharging depth of the system and the allowable capacity of the battery are minimized in a statistical sense.
8. The energy storage system smoothing control method according to claim 1, wherein the step SS8 specifically comprises: defining a sampling period to be 1 minute, and defining the fluctuation rate of the wind power grid-connected power within 1 minute:
ΔPGrid,1min％＝|PGrid,t-PGrid,t-1|/Pr (7)
wherein, PGrid,tRepresenting the grid-connected power at time t, PGrid,t-1Represents the grid-connected power at time t-1, PrRated power for the wind farm; define the wind power fluctuation rate for 10 minutes:
ΔPGrid,10min％＝|max(PGrid,t～t+9)-min(PGrid,t～t+9)|/Pr (8)
wherein, max (P)Grid,t～t+9) And min (P)Grid,t～t+9) Respectively representing the maximum value and the minimum value of grid-connected power within 10 minutes; from the definition of the wind power fluctuation rate of 10 minutes, the power fluctuation of 10 minutes is accumulated by the power fluctuation of 1 minute, so that the wind power fluctuation of 1 minute in the wind power integration is constrained to be considered firstly; the maximum fluctuating power constraint is:
(1) the fluctuation amplitude per minute is lower than 2% of the rated power of the system;
(2) the fluctuation amplitude of every 10 minutes is lower than 20% of the rated power of the system;
when the obtained grid-connected target power is not in the allowable fluctuation range, carrying out corresponding correction according to the grid-connected power at the last moment:
PGrid,t＝PGrid,t-1+Δp (9)
where Δ p is the maximum allowable fluctuation power value.
9. The energy storage system smoothing control method according to claim 1, wherein the step SS9 specifically comprises: the actual battery output power is adjusted in real time by considering the current battery charge state and the charge and discharge instruction, and the actual energy storage battery output is defined as:
PBat＝c×(PGrid-PWind) (10)
and c is a charge and discharge correction coefficient of the energy storage system.
10. The energy storage system smoothing control method according to claim 9, wherein the step SS9 specifically includes: and (3) discharging state: when the SOC is at an extremely low charge level of 10%, the power correction coefficient c is 0, and the energy storage battery stops discharging; when the SOC is greater than the minimum allowable discharge depth and less than the optimal adjustment SOC value by 50%, the power correction coefficient c is uniformly increased from 0 to 1; when the SOC is larger than 50%, the power correction coefficient c is 1, and the energy storage battery can discharge according to the current discharge instruction;
the charging state is as follows: when the SOC is positioned at 90% of the maximum allowable charging depth of the battery, the power correction coefficient c is 0, and the charging power of the battery is enabled to be zero; when the SOC is more than the optimal adjustment SOC value by 50% and is less than the maximum allowable charging depth of the battery, the power correction coefficient is uniformly reduced from 1 to 0; when the SOC is less than 50%, the power correction coefficient c is 1, and the energy storage battery can be charged according to the charging instruction at the current moment.
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