阅读说明：本技术 一种微网风光储系统中混合储能容量优化配置方法 (Hybrid energy storage capacity optimal configuration method in micro-grid wind and solar energy storage system ) 是由 薄鑫 吴倩 韩笑 王梦帆 潘益 郑建勇 王洋 宋杉 王琳媛 邹盛 李辰 许偲轩 于 2019-11-28 设计创作，主要内容包括：本发明公开了一种考虑风光储系统出力波动平抑的混合储能容量优化配置方法,采用变分模态分解,将经由滑动平均滤波得到的混合储能系统参考功率分解为高频与低频两部分,分别作为混合储能系统中超级电容器和蓄电池的参考功率。利用仿真得到的大量储能系统参数与风光储系统输出功率平滑度的对应关系训练神经网络模型。以平滑效果最优与投资成本最低为目标函数,构建混合储能系统容量优化配置模型,采用遗传算法求解混合储能系统的最优容量配置方案。本发明充分利用了蓄电池和超级电容器在功率密度、能量密度等方面的互补特性,既减少了蓄电池和超级电容器配置的容量,保证了经济性,又增强了HESS对风、光输出功率波动的平抑效果。(The invention discloses a hybrid energy storage capacity optimal configuration method considering output fluctuation stabilization of a wind-solar energy storage system. And training a neural network model by utilizing the corresponding relation between a large number of energy storage system parameters obtained by simulation and the smoothness of the output power of the wind-solar energy storage system. And taking the optimal smooth effect and the lowest investment cost as objective functions, constructing a capacity optimal configuration model of the hybrid energy storage system, and solving an optimal capacity configuration scheme of the hybrid energy storage system by adopting a genetic algorithm. The invention fully utilizes the complementary characteristics of the storage battery and the super capacitor in the aspects of power density, energy density and the like, reduces the configured capacity of the storage battery and the super capacitor, ensures the economy, and enhances the stabilizing effect of HESS on the fluctuation of wind and light output power.)

1. A hybrid energy storage capacity optimal configuration method in a micro-grid wind and light storage system is characterized by comprising the following steps:

step 1) determining grid-connected reference power of a wind-solar energy storage system by adopting a sliding average method;

step 2) determining a boundary frequency through variation modal decomposition and Hilbert transform, and formulating a control strategy of the hybrid energy storage system according to the boundary frequency and energy storage charge state constraint;

step 3) establishing an evaluation index system of wind and light output power fluctuation characteristics and wind and light storage system output power fluctuation degree;

step 4) training a neural network model through the corresponding relation between the energy storage parameters obtained by simulation and the evaluation index system;

and 5) constructing a capacity configuration model of the hybrid energy storage system by taking the optimal smoothing effect and the lowest investment construction cost of the hybrid energy storage system as objective functions, and solving the model by combining a genetic algorithm to obtain the optimal capacity configuration of the hybrid energy storage system.

2. The optimal configuration method for the hybrid energy storage capacity in the microgrid wind-solar energy storage system according to claim 1, characterized in that the step 1) specifically comprises the following steps:

step 1-1) selecting a sliding time window with the length of N, collecting N wind and light synthesized output power data each time, calculating an arithmetic mean value to replace a central point value of the sliding time window, discarding the data collected firstly when newly collecting data and placing the data at the tail of the sliding time window, and calculating the grid-connected reference power P of the wind-solar-energy-storage combined power generation system at the time t according to the formula (1)_out(t)，

Wherein N represents the sliding time window length, P_G(t) represents the photosynthetic output power, k satisfies:

step 1-2) calculating the reference power P of the hybrid energy storage system according to the formula (2)_HESS(t)，

P_HESS(t)＝P_out(t)-P_G(t) (2)。

3. The optimal configuration method for the hybrid energy storage capacity in the microgrid wind-solar energy storage system according to claim 2, wherein the determination of the demarcation frequency in the step 2) is specifically as follows:

presetting a decomposition scale as k, and performing variational modal decomposition on HESS reference power to obtain k central frequencies respectively omega_kNatural modal component u of_k(t) see formula (3),

subjecting each natural modal component to Hilbert transform to obtain corresponding k instantaneous frequency-time curves f_k(t)；

Set current curve f_i+1(t) has a frequency lower than f_mWhen, the corresponding time is t_l(1, …, j), corresponding to a power u_l(t_l) (ii) a Curve f_i(t) has a frequency higher than f_mWhen, the corresponding time is t_h(h 1, …, k), corresponding to a power of u_h(t_h) Defining a modal aliasing energy E according to equation (3)_overlap，

Note f_i+1(t) has a maximum value of f_max，f_i(t) has a minimum value of f_minFrom f_minStarting to take value as f_maxAdding 0.0001Hz each time as a temporary frequency division for the end point, calculating corresponding modal aliasing energy, and when the modal aliasing energy is minimum, the corresponding frequency is the dividing frequency f of the high frequency and the low frequency_m。

4. The optimal configuration method for the hybrid energy storage capacity in the microgrid wind-solar energy storage system according to claim 2, wherein the step 2) of formulating a control strategy and energy storage body of the hybrid energy storage system according to the demarcation frequency and the energy storage state of charge constraints comprises the following steps:

step 2-1) defining charging and discharging power P of battery and super capacitor_B0(t)、P_C0(t) are respectively:

step 2-2) setting the output power of the energy storage system to meet the following constraint:

-P_B,e≤P_B(t)≤P_B,e

-P_C,e≤P_C(t)≤P_C,e

the remaining capacity of the energy storage device should satisfy the following constraints:

E_B,min≤E_B(t)≤E_B,max

E_C,min≤E_C(t)≤E_C,max

wherein the content of the first and second substances,

E_B,min＝E_B·SOC_B,min

E_B,max＝E_B·SOC_B,max

E_C,min＝E_C·SOC_C,min

E_C,max＝E_C·SOC_C,max

wherein: SOC_B,min，SOC_B,max，SOC_C,min，SOC_C,maxThe upper limit and the lower limit of the charge state allowed by the storage battery and the super capacitor respectively; e_B、E_CRated capacities of a storage battery and a super capacitor are respectively set;

the actual charging and discharging power of the energy storage system is as follows:

P_B0≥0

P_B0＜0

P_C0≥0

P_C0＜0

calculating the actual output power of the wind-solar-energy-storage combined power generation system at the time t according to the formula (4) as follows:

P_out,r(t)＝P_G(t)+P_B(t)+P_C(t) (4)。

5. the optimal configuration method for the hybrid energy storage capacity in the microgrid wind-solar energy storage system according to claim 1, characterized in that the power fluctuation characteristic index in the step 3) is as follows:

P_G,max＝max[P_G(t)],t＝0,…,288

P_G,min＝min[P_G(t)],t＝0,…,288

ΔP_G,10min,max＝max[max|P_G(m)-P_G(n)|]n-m is greater than 0 and less than or equal to 2, and m is 0

ΔP_G,60min,max＝max[max|P_G(m)-P_G(n)|]N-m ≦ 12, 0 < n-m, and m ═ 0

Wherein the content of the first and second substances,

the evaluation index system of the wind-solar energy storage system output power fluctuation degree comprises a short-term power fluctuation evaluation index and a long-term power fluctuation evaluation index, short-term power fluctuation evaluation indexes rLOS10 and rLOS60 which reflect the proportion of the active power fluctuation which does not meet the requirements within 10min and 60min in one day are calculated in the step 3) according to the formula (5) and the formula (6),

and x₁₀(i)、x₆₀(i) Satisfies the following conditions:

in the formula, Δ P10 and Δ P60 are the maximum variation of the active power output by the wind-light storage system within 10min and 60min respectively; and the delta P10max and the delta P60max are limit values of the maximum variation of the active power within 10min and 60min respectively.

6. The optimal configuration method for the hybrid energy storage capacity in the microgrid wind-solar energy storage system according to claim 5, wherein the step 4) specifically comprises the following steps:

step 4-1) establishing a neural network model comprising n energy storage parameters, fluctuation characteristics-power smoothness short-term neural network units and n groups of power fluctuation characteristic indexes of n days of wind and light output power;

step 4-2) after the parameters of the energy storage system are input, the parameters and n groups of power fluctuation characteristics are jointly introduced into an energy storage parameter, fluctuation characteristic-power smoothness short-term neural network model to obtain an n-day short-term power fluctuation evaluation index r_LOS10And r_LOS60；

Step 4-3) calculating a long-term power fluctuation evaluation index E according to the formula (7), taking the energy storage system parameters as input and the long-term power fluctuation evaluation index E as output, obtaining an energy storage parameter-power smoothness long-term neural network model through neural network learning,

wherein the content of the first and second substances,

r_LOS＝(r_LOS10+r_LOS60)/2

in the formula, R is a limiting standard of short-term fluctuation evaluation indexes of the wind-solar energy storage system output active power; e represents the proportion of days not meeting the smoothness requirement to the total number of days n.

7. The optimal configuration method for the hybrid energy storage capacity in the microgrid wind-solar energy storage system according to claim 1, characterized in that the objective function established in the step 5) is according to the formula (8):

min F＝f₁+f₂(8)

wherein the content of the first and second substances,

in the formula (8), Y is a limit value of the long-term power fluctuation evaluation index E; u is an arbitrary large value; k₁、K₂、K₃、K₄The unit power cost and the unit capacity cost of the power of the storage battery and the super capacitor are respectively; g_maxIs K₁P_B,e+K₂P_C,e+K₃E_B+K₄E_CMaximum value of (d); f. of₁For the stabilizing effect of the hybrid energy storage system on wind and light output power fluctuation, when E is more than Y, the long-term power fluctuation index does not meet the limiting requirement, and the energy storage configuration scheme at the moment is directly omitted, wherein f1 is U, and U is more than K1PB, E + K2PC, E + K3EB + K4 EC; f. of₂Investment cost for the hybrid energy storage system.

Technical Field

The invention belongs to the field of microgrid energy storage capacity optimal configuration, and particularly relates to a hybrid energy storage capacity optimal configuration method considering output fluctuation stabilization of a wind-solar energy storage system.

Background

In recent years, global attention to energy sustainability and environmental issues caused by traditional power generation methods has provided a driving force for the development of renewable energy. The microgrid is an important mode for accessing renewable energy power generation equipment into a power grid, and the safety and the stability of the operation of the microgrid are influenced by the randomness and the intermittency of the renewable energy power generation. An energy storage device with a certain capacity is arranged in the micro-grid, so that the output fluctuation of renewable energy sources can be stabilized, the electric energy quality and the operation stability of the micro-grid are ensured, and the energy utilization rate is improved. The energy storage technology applied to the microgrid at present can be divided into energy type energy storage and power type energy storage, and the two types of energy storage have strong complementarity on the technical and economic performances such as energy density, power density, cycle life and price. Therefore, in order to stabilize the fluctuation of the wind power and the photovoltaic output power and simultaneously consider the operation economy of the system, a Hybrid Energy Storage System (HESS) integrated by the two types of energy storage is generally adopted.

There have been research achievements aiming at reasonably configuring the capacities of two types of energy storage in the HESS: based on a first-order low-pass filtering method and wind-limiting data statistical characteristic analysis, the wind power fluctuation stabilizing effect and the wind power absorption capacity are considered, and an optimal capacity configuration scheme is selected with the maximum HESS comprehensive income as a target; the method comprises the steps that an HESS primary power distribution strategy is formulated based on spectrum analysis of microgrid net load power, charging and discharging constraints and charge state constraints of stored energy are introduced, secondary correction is conducted on the power distribution strategy, and an HESS capacity optimization model is constructed with the aim of lowest annual comprehensive cost of HESS; processing wind power by adopting a wavelet packet decomposition technology, reasonably distributing power inside the hybrid energy storage, and establishing a hybrid energy storage capacity economic optimization model considering the service life loss of a battery; the HESS power distribution method for adaptively adjusting the filtering order of an Empirical Mode Decomposition (EMD) according to the charge state of the energy storage system is provided, and the stability of the charge state of the energy storage system is maintained while wind power fluctuation is effectively stabilized; based on the requirement of photovoltaic grid connection on power fluctuation, an HESS charging and discharging control strategy for smoothing photovoltaic power fluctuation by adopting Ensemble Empirical Mode Decomposition (EEMD) is designed.

At present, methods such as a low-pass filtering method, wavelet packet decomposition, EMD decomposition and EEMD decomposition technologies are commonly adopted to distribute the power of two types of stored energy in the HESS and configure the capacity of the two types of stored energy. The low-pass filtering method has simple principle and high operation speed, but generates certain time delay. The accuracy of the wavelet packet decomposition is susceptible to the selected basis functions and is computationally complex. Modal components with similar frequencies decomposed by EMD are easy to generate modal aliasing phenomenon, and influence power distribution. EEMD greatly improves the modal aliasing problem, but the data computation is too large. Compared with the method, the Variable Mode Decomposition (VMD) has the advantages of no time delay, high operation efficiency, difficult noise interference and capability of solving the problem of mode aliasing in the EMD.

Disclosure of Invention

The invention aims to overcome the defects of the prior art, provides a hybrid energy storage capacity optimal configuration method considering the output fluctuation stabilization of a wind-solar energy storage system, fully utilizes the complementary characteristics of a storage battery and a super capacitor in the aspects of power density, energy density and the like, reduces the configured capacity of the storage battery and the super capacitor, ensures the economy, and enhances the stabilization effect of HESS on the wind and light output power fluctuation, and is realized by the following technical scheme:

the optimal configuration method for the hybrid energy storage capacity in the micro-grid wind and light storage system comprises the following steps:

step 1) determining grid-connected reference power of a wind-solar energy storage system by adopting a sliding average method;

step 2) determining a boundary frequency through variation modal decomposition and Hilbert transform, and formulating a control strategy of the hybrid energy storage system according to the boundary frequency and energy storage charge state constraint;

step 3) establishing an evaluation index system of wind and light output power fluctuation characteristics and wind and light storage system output power fluctuation degree;

step 4) training a neural network model through the corresponding relation between the energy storage parameters obtained by simulation and the evaluation index system;

and 5) constructing a capacity configuration model of the hybrid energy storage system by taking the optimal smoothing effect and the lowest investment construction cost of the hybrid energy storage system as objective functions, and solving the model by combining a genetic algorithm to obtain the optimal capacity configuration of the hybrid energy storage system.

The method for optimally configuring the hybrid energy storage capacity in the microgrid wind and light storage system is further designed in that the step 1) specifically comprises the following steps:

step 1-1) selecting a sliding time window with the length of N, collecting N wind and light synthesized output power data each time, calculating an arithmetic mean value to replace a central point value of the sliding time window, discarding the data collected firstly when newly collecting data and placing the data at the tail of the sliding time window, and calculating the grid-connected reference power P of the wind-solar-energy-storage combined power generation system at the time t according to the formula (1)_out(t)，

P_out(t)＝(P_G(t-(k-1))+P_G(t-(k-2))+...+P_G(t)+...+P_G(t+k))/N (1)

P_G(t)＝P_PV(t)+P_WG(t)

Wherein N represents the sliding time window length, P_G(t) represents the photosynthetic output power, k satisfies:

step 1-2) calculating the reference power P of the hybrid energy storage system according to the formula (2)_HESS(t)，

P_HESS(t)＝P_out(t)-P_G(t) (2)

The method for optimally configuring the hybrid energy storage capacity in the microgrid wind and light storage system is further designed in that the step 2) of determining the boundary frequency specifically comprises the following steps:

presetting a decomposition scale as k, and performing variational modal decomposition on HESS reference power to obtain k central frequencies respectively omega_kNatural modal component u of_k(t) see formula (3),

subjecting each natural modal component to Hilbert transform to obtain corresponding k instantaneous frequency-time curves f_k(t)；

Set current curve f_i+1(t) has a frequency lower than f_mWhen, the corresponding time is t_l(1, …, j), corresponding to a power u_l(t_l) (ii) a Curve f_i(t) has a frequency higher than f_mWhen, the corresponding time is t_h(h 1, …, k), corresponding to a power of u_h(t_h) Defining a modal aliasing energy E according to equation (3)_overlap，

Note f_i+1(t) has a maximum value of f_max，f_i(t) has a minimum value of f_minFrom f_minStarting to take value as f_maxAdding 0.0001Hz each time as a temporary frequency division for the end point, calculating corresponding modal aliasing energy, and when the modal aliasing energy is minimum, the corresponding frequency is the dividing frequency f of the high frequency and the low frequency_m。

The method for optimally configuring the hybrid energy storage capacity in the microgrid wind and light storage system is further designed in that the step 2) of formulating a control strategy and energy storage of the hybrid energy storage system according to the demarcation frequency and the constraint of the energy storage state of charge specifically comprises the following steps:

step 2-1) defining charging and discharging power P of battery and super capacitor_B0(t)、P_C0(t) are respectively:

step 2-2) setting the output power of the energy storage system to meet the following constraint:

-P_B,e≤P_B(t)≤P_B,e

-P_C,e≤P_C(t)≤P_C,e

the remaining capacity of the energy storage device should satisfy the following constraints:

E_B,min≤E_B(t)≤E_B,max

E_C,min≤E_C(t)≤E_C,max

wherein the content of the first and second substances,

E_B,min＝E_B·SOC_B,min

E_B,max＝E_B·SOC_B,max

E_C,min＝E_C·SOC_C,min

E_C,max＝E_C·SOC_C,max

wherein: SOC_B,min，SOC_B,max，SOC_C,min，SOC_C,maxThe upper limit and the lower limit of the charge state allowed by the storage battery and the super capacitor respectively; e_B、E_CRated capacities of a storage battery and a super capacitor are respectively set;

the actual charging and discharging power of the energy storage system is as follows:

P_B0≥0

P_B0＜0

P_C0≥0

P_C0＜0

calculating the actual output power of the wind-solar-energy-storage combined power generation system at the time t according to the formula (4) as follows:

P_out,r(t)＝P_G(t)+P_B(t)+P_C(t) (4)

the method for optimally configuring the hybrid energy storage capacity in the microgrid wind and light storage system is further designed in that the power fluctuation characteristic indexes in the step 3) are as follows:

P_G,max＝max[P_G(t)],t＝0,...,288

P_G,min＝min[P_G(t)],t＝0,...,288

ΔP_G,10min,max＝max[max|P_G(m)-P_G(n)|]n-m is greater than 0 and less than or equal to 2, and m is 0

ΔP_G,60min,max＝max[max|P_G(m)-P_G(n)|]N-m ≦ 12, 0 < n-m, and m ═ 0

Wherein the content of the first and second substances,

the average value of the wind and light synthesized output power is obtained; p_G,max and P_G,minRespectively the maximum value and the minimum value of the wind and light synthesis output power; sigma is the standard deviation of the output power of the wind and light synthesis; delta P_G,10min,maxAnd Δ P_G,60min,maxMaximum values of maximum power change within 10min and 60min respectively; delta

And Δ

Average values of power variation values within 10min and 60min respectively;

the evaluation index system of the wind-solar energy storage system output power fluctuation degree comprises a short-term power fluctuation evaluation index and a long-term power fluctuation evaluation index, short-term power fluctuation evaluation indexes rLOS10 and rLOS60 which reflect the proportion of the active power fluctuation which does not meet the requirements within 10min and 60min in one day are calculated in the step 3) according to the formula (5) and the formula (6),

and x₁₀(i)、x₆₀(i) Satisfies the following conditions:

in the formula, Δ P10 and Δ P60 are the maximum variation of the active power output by the wind-light storage system within 10min and 60min respectively; and the delta P10max and the delta P60max are limit values of the maximum variation of the active power within 10min and 60min respectively.

The method for optimally configuring the hybrid energy storage capacity in the microgrid wind and light storage system is further designed in that the step 4) specifically comprises the following steps:

step 4-1) establishing a neural network model comprising n energy storage parameters, fluctuation characteristics-power smoothness short-term neural network units and n groups of power fluctuation characteristic indexes of n days of wind and light output power;

step 4-2) after the parameters of the energy storage system are input, the parameters and n groups of power fluctuation characteristics are jointly introduced into an energy storage parameter, fluctuation characteristic-power smoothness short-term neural network model to obtain an n-day short-term power fluctuation evaluation index r_LOS10And r_LOS60；

Step 4-3) calculating a long-term power fluctuation evaluation index E according to the formula (7), taking the energy storage system parameters as input and the long-term power fluctuation evaluation index E as output, obtaining an energy storage parameter-power smoothness long-term neural network model through neural network learning,

wherein the content of the first and second substances,

r_LOS＝(r_LOS10+r_LOS60)/2

in the formula, R is a limiting standard of short-term fluctuation evaluation indexes of the wind-solar energy storage system output active power; e represents the proportion of days not meeting the smoothness requirement to the total number of days n.

The method for optimally configuring the hybrid energy storage capacity in the microgrid wind and light storage system is further designed in that an objective function is established according to an equation (8) in the step 5):

minF＝f₁+f₂(8)

wherein the content of the first and second substances,

in the formula (8), Y is a limit value of the long-term power fluctuation evaluation index E; u is an arbitrary large value; k₁、K₂、K₃、K₄The unit power cost and the unit capacity cost of the power of the storage battery and the super capacitor are respectively; g_maxIs K₁P_B,e+K₂P_C,e+K₃E_B+K₄E_CMaximum value of (d); f. of₁For the stabilizing effect of the hybrid energy storage system on wind and light output power fluctuation, when E is more than Y, the long-term power fluctuation index does not meet the limiting requirement, and the energy storage configuration scheme at the moment is directly omitted, wherein f1 is U, and U is more than K1PB, E + K2PC, E + K3EB + K4 EC; f. of₂Investment cost for the hybrid energy storage system.

The invention has the advantages of

The optimal configuration method for the hybrid energy storage capacity in the micro-grid wind and light storage system considers optimal smoothing effect and lowest investment cost, and is suitable for reasonably configuring the capacity configuration of energy storage in HESS.

According to the method for optimally configuring the hybrid energy storage capacity in the micro-grid wind and light storage system, complementary characteristics of the storage battery and the super capacitor in the aspects of power density, energy density and the like are fully utilized, the configured capacity of the storage battery and the super capacitor is reduced, the economy is ensured, and the stabilizing effect of HESS on wind and light output power fluctuation is enhanced.

Drawings

Fig. 1 is a flow chart of a hybrid energy storage capacity optimal configuration method in a micro-grid wind and light storage system.

Fig. 2 is a schematic diagram of a wind-solar energy storage system based on a direct-current networking mode.

Fig. 3 is a diagram of a HESS control strategy.

FIG. 4 is a schematic diagram of a short-term neural network model.

FIG. 5 is a long-term neural network model diagram.

FIG. 6 is a schematic diagram of a wind-solar hybrid output power.

FIG. 7 is a graph of neural network training errors.

FIG. 8 is a graph of wind-solar hybrid output power before and after smoothing. Wherein, fig. 8a is a diagram of the wind-solar combined output power before and after the stabilization based on the EMD decomposition, and fig. 8b is a diagram of the wind-solar combined output power before and after the stabilization based on the VMD decomposition.

Fig. 9 is a HESS output power graph. Fig. 9a is a graph of HESS output power based on EMD decomposition and fig. 9b is a graph of HESS output power based on VMD decomposition.

Detailed Description

The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.

As shown in fig. 1, according to the optimal configuration method for the hybrid energy storage capacity in the microgrid wind-solar energy storage system, the variable mode decomposition is adopted, and the reference power of the hybrid energy storage system obtained through the moving average filtering is decomposed into a high-frequency part and a low-frequency part which are respectively used as the reference power of a super capacitor and a storage battery in the hybrid energy storage system. And training a neural network model by utilizing the corresponding relation between a large number of energy storage system parameters obtained by simulation and the smoothness of the output power of the wind-solar energy storage system. And taking the optimal smooth effect and the lowest investment cost as objective functions, constructing a capacity optimal configuration model of the hybrid energy storage system, and solving an optimal capacity configuration scheme of the hybrid energy storage system by adopting a genetic algorithm.

The method comprises the following implementation steps:

step 1) determining grid-connected reference power of a wind-solar energy storage system by adopting a sliding average method;

compared with an alternating-current networking mode, the topological structure of the wind and light storage system in the microgrid in the embodiment has the advantages that the direct-current bus-based networking mode is simple in structure, convenient to control and obvious in advantage, so that the capacity configuration of the hybrid energy storage system is researched on the basis of the typical wind and light storage system structure based on the direct-current networking mode shown in fig. 2.

As shown in fig. 2, the wind-solar energy storage system of the embodiment mainly includes a wind generating set, a photovoltaic generating set, a hybrid energy storage system composed of a storage battery and a super capacitor, and a converter. The converter realizes AC-DC conversion, and the hybrid energy storage system plays a role in stabilizing wind and light output power fluctuation.

In fig. 2, ppv (t) and pwg (t) are output powers at time t of the photovoltaic generator set and the wind generator set, respectively; PB (t) and PC (t) are the charge and discharge power of the storage battery and the super capacitor at the time t respectively, and are positive during discharge and negative during charge.

In order to better smooth the fluctuation of wind and photosynthetic output power, improve the overall performance of the energy storage system and reduce the cost of energy storage configuration, a storage battery and a super capacitor are selected to form a hybrid energy storage system by utilizing the complementary characteristics of power type and energy type energy storage in the aspects of energy density, power density, cycle life and the like. The storage battery is used for stabilizing low-frequency fluctuation with high energy, and the super capacitor is used for stabilizing high-frequency fluctuation with low energy.

When the wind and light synthesized output power is larger than the grid-connected reference power obtained by a moving average method, the hybrid energy storage system is charged; and when the wind and light combined output power is smaller than the grid-connected reference power obtained by the moving average method, the hybrid energy storage system discharges. The state of charge versus charge and discharge power for the battery and supercapacitor can be expressed as:

in the above formula, Δ t is a sampling time interval; SOC_B(t)、SOC_C(t)、SOC_B(t+Δt)、SOC_C(t + Δ t) is the charge of the battery and the supercapacitor at time t and time t + Δ t, respectivelyAn electrical state; e_B、E_CCapacity of battery and super capacitor, respectively η_Bc、η_Cc、η_Bd、η_CdRespectively the charging efficiency and the discharging efficiency of the storage battery and the super capacitor.

Because renewable energy power generation has strong intermittence and randomness, the fluctuation of the output power of wind and light synthesis is large, and the grid-connected requirement cannot be met. The wind-solar hybrid power generation system adopts a moving average method to smooth wind and light synthesis output power and calculates grid-connected reference power of the wind-solar hybrid power generation system.

Selecting a sliding time window with the length of N, collecting N wind and light synthesized output power data each time, and solving the arithmetic mean value of the N wind and light synthesized output power data to replace the numerical value of the center point of the time window [17 ]. Each time a new acquisition of data is placed at the end of the time window, an oldest acquisition is discarded. Therefore, the grid-connected reference power of the wind-solar-storage combined power generation system at the time t can be represented as:

wherein the content of the first and second substances,

P_G(t)＝P_PV(t)+P_WG(t) (7)

taking the difference between grid-connected reference power Pout (t) of the wind-solar-storage combined power generation system and wind-light synthesized output power PG (t) as the reference power of the hybrid energy storage system:

P_HESS(t)＝P_out(t)-P_G(t) (8)

step 2) determining a boundary frequency by utilizing VMD and Hilbert transform, and formulating an HESS control strategy according to the boundary frequency and energy storage state of charge constraints;

the VMD-Hilbert-based HESS power distribution method in the step 2) comprises the following steps: reference power P of hybrid energy storage system_HESSReference power P from accumulator_B0And a reference power P of the super capacitor_C0And (4) forming. Book (I)The embodiment adopts a variation mode decomposition method and a Hilbert transform method, carries out reasonable power distribution according to the characteristics of the storage battery and the super capacitor, and realizes the stabilization of the output power fluctuation of the wind and light synthesis.

VMD is a novel signal processing method proposed by konstatin in 2014, which is essentially noise reduction of nano-filter and can convert the modal estimation problem into completely non-recursive and quasi-orthogonal signal decomposition. The VMD decomposition process is not easily affected by noise, and when a proper decomposition mode number k is preset, the mode aliasing phenomenon can be effectively relieved, so that the VMD decomposition method has more advantages than the traditional EMD decomposition method.

Presetting a decomposition scale as k, performing VMD decomposition on HESS reference power to obtain k central frequencies respectively omega_kNatural modal component u of_k(t)：

Hilbert transformation is carried out on each natural modal component to obtain corresponding k instantaneous frequency-time curves f_k(t) and curve f in VMD decomposition_i+1(t) frequency is generally higher than curve f_i(t) of (d). According to the above description, the accumulator is responsible for damping low frequency fluctuations and the supercapacitor is responsible for damping high frequency fluctuations. If in two adjacent curves f_i(t) and f_i+1(t) dividing the frequency f by an interval_mCurve f_i+1(t) medium frequency lower than f_mThe corresponding power of the part is distributed to the storage battery, so that the charging and discharging times of the storage battery are increased, and the service life is reduced; curve f_i(t) medium frequencies above f_mThe corresponding power of the part of (2) is distributed to the super capacitor, and the capacity of the super capacitor is increased. Thus finding the appropriate crossover frequency f_mSo that is in contact with f_mCurve f of immediate vicinity_iAnd (t) and fi +1(t) modal aliasing are minimum, and the method is crucial to making a proper HESS power distribution strategy.

Frequency of the curve fi +1(t) is lower than f_mWhen, the corresponding time is t_l(1, …, j), corresponding to a power u_l(tl); curve f_i(t) has a frequency higher than f_mWhen, toThe time is t_h(h 1, …, k), corresponding to a power of u_h(th). Defining the product of the absolute value of the power corresponding to the modal aliasing part and the corresponding time as modal aliasing energy, and expressing the following expression:

note f_i+1(t) has a maximum value of f_maxThe minimum value of fi (t) is f_min. From f_minStarting to take value as f_maxFor the end point, 0.0001Hz is added each time, and the corresponding modal aliasing energy is calculated as the temporary frequency division frequency. The frequency corresponding to the minimum modal aliasing energy is the dividing frequency f of the high frequency and the low frequency_m。

The charge and discharge power of the battery and the supercapacitor can be represented as:

wherein each symbol has the same meaning as above.

The HESS control strategy considering the capacity and power constraints in the step 2): through the moving average filtering and the VMD decomposition, the reference output power of the storage battery and the super capacitor is calculated, however, in practice, the constraints such as the charge state of the energy storage system and the like should be considered.

HESS control strategy As shown in FIG. 3, the rated power of the storage battery is P_B,eRated power of the super capacitor is P_C,e；E_B,max，E_C,max，E_B,min，E_C,minThe upper limit and the lower limit of the residual electric quantity of the storage battery and the super capacitor are respectively set; p_B(t) and P_CAnd (t) actual charging and discharging power of the storage battery and the super capacitor at the moment t respectively.

The output power of the energy storage system should satisfy the following constraints:

-P_B,e≤P_B(t)≤P_B,e(13)

-P_C,e≤P_C(t)≤P_C,e(14)

the remaining capacity of the energy storage device should satisfy the following constraints:

E_B,min≤E_B(t)≤E_B,max(15)

E_C,min≤E_C(t)≤E_C,max(16)

wherein the content of the first and second substances,

in the formula, SOC_B,max，SOC_C,max，SOC_B,min，SOC_C,minThe upper and lower limits of the allowed charge states of the storage battery and the super capacitor are respectively set; EB, EC are the rated capacities of the battery and the super capacitor, respectively.

From equations (1) to (4) and equations (11) to (17), the actual charge-discharge power of the energy storage system can be obtained as follows:

P_B0≥0

P_B0＜0

P_C0≥0

P_C0＜0

at this point, the power and capacity constraints of the energy storage device result in the actual output power of the HESS being different from the HESS reference power. Therefore, the actual output power of the wind-solar-energy-storage combined power generation system at the time t is as follows:

P_out,r(t)＝P_G(t)+P_B(t)+P_C(t) (22)

step 3) establishing an evaluation index system of wind and light output power fluctuation characteristics and wind and light storage system output power fluctuation degree;

as can be seen from equation (22), the smoothing effect of HESS on wind and light power depends not only on the power and capacity of the disposed storage battery and supercapacitor, but also on the combined output power of wind and light. Because the output of wind power and photovoltaic is influenced by weather change, the random output is strong, and for different wind and light synthesis output power every day, the configured energy storage capacity is different inevitably in order to achieve the same smoothing effect. Therefore, the following 8 indexes are defined to characterize the fluctuation characteristics of the output power of the photosynthetic by the daily wind. Wherein the sampling time interval delta t is 5min, namely 289 times of wind and photosynthetic output power are collected in one day.

The power fluctuation characteristics in one day are as follows:

in the formula (I), the compound is shown in the specification,

the average value of the wind and light synthesized output power is obtained; p_G,maxAnd P_G,minRespectively the maximum value and the minimum value of the wind and light synthesis output power; sigma is the standard deviation of the output power of the wind and light synthesis; delta P_G,10min,maxAnd Δ P_G,60min,maxMaximum values of maximum power change within 10min and 60min respectively; deltaAnd Δ

Average values of power variation values within 10min and 60min respectively

For the wind of a certain dayAnd synthesizing output power by light, and simulating by adopting the HESS control strategy under different energy storage system parameter combinations to obtain different wind and light power smoothing effects. In order to quantify the smoothing effect of HESS on wind and light power, so as to establish the corresponding relation between the energy storage parameters, the power fluctuation indexes and the smoothing effect, and define the short-term fluctuation evaluation index r_LOS10And r_LOS60And respectively reflecting the proportion of the fluctuation of the active power which does not meet the requirement within 10min and 60min in one day. Thus r_LOS10And r_LOS60The smaller the size, the better the smoothing effect of the configured HESS on wind and light power fluctuation is. The short-term power fluctuation evaluation index is as follows:

and the number of the first and second electrodes,

in the formula: delta P₁₀And Δ P₆₀The maximum variation of the active power output by the wind-light storage system within 10min and 60min respectively; delta P_10maxAnd Δ P_60maxThe maximum variation of the active power is limited within 10min and 60min respectively.

The present embodiment adopts a three-layer BP neural network model, as shown in fig. 4. Training the neural network, and fitting the corresponding relation among the energy storage parameters, the fluctuation characteristics and the smoothness indexes. Therefore, corresponding output data r can be obtained directly by inputting parameters and power fluctuation characteristic indexes of the energy storage system_LOS10And r_LOS60And a complex simulation process is avoided, and the calculation efficiency is improved.

Step 4) training a neural network model by utilizing the corresponding relation between the energy storage parameters obtained by simulation and the evaluation index system;

in order to study the influence of different energy storage parameters on the smoothing effect of wind and light output power in a longer period of time (n days), an energy storage parameter-power smoothness long-term neural network model shown in fig. 4 is constructed, wherein the energy storage parameter-power smoothness long-term neural network model comprises n energy storage parameters, fluctuation characteristics-power smoothness short-term neural network models and n groups of power fluctuation characteristic indexes of wind and light output power in n days.

Defining a long-term power fluctuation evaluation index E:

wherein the content of the first and second substances,

r_LOS＝(r_LOS10+r_LOS60)/2 (30)

in the formula: r is a limiting standard of short-term fluctuation evaluation indexes of the wind-solar energy storage system output active power; e represents the proportion of days not meeting the smoothness requirement to the total number of days n, and the smaller E indicates the better long-term smoothing effect of the configured HESS on wind, light output power.

After the parameters of the energy storage system are input, the parameters and n groups of power fluctuation characteristics are jointly brought into an energy storage parameter, fluctuation characteristic-power smoothness short-term neural network model, and an n-day short-term power fluctuation evaluation index r can be obtained_LOS10And r_LOS60The corresponding index E can be calculated according to equations (28) to (30). And (3) obtaining an energy storage parameter-power smoothness long-term neural network model by learning a plurality of corresponding relations obtained by taking the energy storage system parameters as input and the long-term power fluctuation evaluation index E as output through a neural network.

And step 5) constructing an HESS capacity configuration model by taking the optimal smoothing effect and the lowest HESS investment construction cost as objective functions, and solving the model by combining a genetic algorithm to obtain the HESS optimal capacity configuration

The method considers the stabilizing effect and the economy of the HESS on the fluctuation of the wind and light output power, and the following objective functions are formulated:

minF＝f₁+f₂(31)

wherein the content of the first and second substances,

in the formula, Y is a limit value of the long-term power fluctuation evaluation index E; u is an arbitrary large value; k₁、K₂、K₃、K₄The unit power cost and the unit capacity cost of the power of the storage battery and the super capacitor are respectively; g_maxIs K₁P_B,e+K₂P_C,e+K₃E_B+K₄E_CMaximum value of (d); f. of₁For the stabilizing effect of the hybrid energy storage system on wind and light output power fluctuation, when E is more than Y, the long-term power fluctuation index does not meet the limiting requirement, and the energy storage configuration scheme at the moment is directly omitted, wherein f1 is U, and U is more than K1PB, E + K2PC, E + K3EB + K4 EC; f. of₂Investment cost for the hybrid energy storage system.

And solving the HESS optimal capacity configuration by adopting a genetic algorithm, wherein the specific process is not described herein.

The present embodiment is further described below by way of an example.

Taking active power data output all year round in 2018 of a certain 8.4MW photovoltaic power station and a certain 18MW wind power station as an example, a hybrid energy storage system consisting of a storage battery and a super capacitor is adopted to stabilize wind and photosynthetic output power. The wind and light combined output power curve is shown in fig. 6, the sampling interval is 5min, and the relevant parameters of the hybrid energy storage system are shown in table 1.

TABLE 1 hybrid energy storage System parameters

A total of 1000 sets of parameters of different storage battery capacities, super capacitor capacities and sliding time window lengths are selected, and 36 days of 5, 10 and 25 days each month are simulated by adopting the HESS control strategy shown in FIG. 3. Wherein the nominal power P of the accumulator is known_B,eIs 1.2MW, rated power P of the super capacitor_C,eIs 2.5MW, and is taken as Δ P_10maxAnd Δ P_60maxAt 0.7MW and 2 MW. And training an energy storage parameter, fluctuation characteristics and power smoothness short-term neural network model by using 36000 groups of input and output data obtained by simulation, wherein the number of neurons in a hidden layer is 23. Part of the neural network training results and errors are shown in table 2.

1000 groups of different energy storage parameters and daily wind-solar output power fluctuation characteristics are combined and input into a trained short-term neural network model to obtain 365 r groups all year round_LOS10And r_LOS60And substituting equations (28) to (30), and calculating a corresponding long-term power fluctuation evaluation index E under the energy storage parameter combination, wherein R is 0.1. And taking the 1000 groups of corresponding data as sample data, and training an energy storage parameter-power smoothness long-term neural network model. After 1239 steps of training, the training error is less than 0.0001, and the neural network training error curve is shown in fig. 7.

TABLE 2 partial neural network training results and errors

TABLE 3 HESS Capacity configuration results and corresponding objective function values

Method of producing a composite material	EB/MW·h	EC/MW·h	N	E	f2Ten thousand yuan
						VMD	5.608	0.329	29	0.002 3	2 004.1
EMD	5.975	0.546	29	0.006 4	2 626.7

As can be seen from table 2, when the HESS is power-distributed by VMD decomposition, the configured capacity of the super capacitor and the battery capacity are respectively reduced by 39.74% and 6.14% compared with those of the battery when EMD decomposition is used. Because the cost of the unit capacity of the super capacitor is higher, the HESS investment cost can be effectively reduced by reducing the capacity of the super capacitor, and as shown in Table 2, when VMD decomposition is adopted, the investment cost is reduced by 23.70 percent compared with that when EMD decomposition is adopted. In addition, comparing the magnitude of the index E when the two methods are adopted, it can be known that the HESS capacity can be more reasonably configured by VMD decomposition than EMD decomposition, thereby obtaining a better fluctuation stabilizing effect.

Taking day 26/3 as an example, the smoothing effect of the wind-solar hybrid output power after the HESS is power-distributed by adopting two different methods, namely VMD and EMD, is compared, and the smoothing result is shown in fig. 8.

It can be seen from fig. 8 that the two methods are respectively adopted, the fluctuation of the wind and light synthesized output power is effectively suppressed, but the stabilizing effect when the EMD decomposition is adopted is slightly inferior to that when the VMD decomposition is adopted. In order to further compare the effects of VMD decomposition and EMD decomposition on HESS capacity configuration results and wind and light power fluctuation suppression effects, charge and discharge power curves of the storage battery and the super capacitor in the hybrid energy storage system were plotted as shown in fig. 8.

As can be seen from fig. 9, both EMD decomposition and VMD decomposition decompose the HESS reference power into two parts, i.e., high frequency and low frequency, where the super capacitor takes on the high frequency component and the storage battery takes on the low frequency component. And comparing output power curves of the super capacitor and the storage battery under the two methods, wherein when the HESS control strategy based on VMD decomposition is adopted, the output power of the super capacitor is smaller than that of the super capacitor when the HESS control strategy based on EMD decomposition is adopted, and the characteristics of high power density and low energy density of the super capacitor are better met.

The EMD decomposition has the mode aliasing problem, so that the capacity of a configured super capacitor is increased, the charging and discharging times of a storage battery are increased, the service life is shortened, the mode aliasing problem in the EMD decomposition is solved by the VMD decomposition, the fluctuation component in the wind and light output power is better decomposed, the complementary characteristics of the storage battery and the super capacitor are fully utilized by the HESS control strategy, and the aims of improving the stabilizing effect and reducing the investment cost are fulfilled.

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.