阅读说明：本技术 一种适用于智慧园区的储能容量配置方法 (Energy storage capacity configuration method suitable for smart park ) 是由 凌在汛 郑景文 崔一铂 蔡万里 陈文� 熊平 康逸群 游力 向慕超 熊昊哲 于 2021-07-12 设计创作，主要内容包括：本发明提供一种适用于智慧园区的储能容量配置方法,先建立电动汽车充电负荷模型,再结合分时电价的限制,建立含电动汽车与可再生能源的智慧园区配电网数学模型,以此确定混合储能需要平抑的功率,并采用高通滤波算法对其进行分配,确定超级电容器和锂电池各自需要平抑的功率,以此建立混合储能容量配置模型,以混合储能年收益最大为目标函数,构建混合储能约束条件,采用优化算法进行求解,得到混合储能最优容量配置。本发明与现有的混合储能配置方法相比,考虑了在智慧园区中分时电价的限制,对混合储能系统需要平抑的功率进行求解并建立混合储能容量配置模型,可以较好的提高系统的经济性,实现平抑电动汽车充电和光伏发电功率波动的有效控制。(The invention provides an energy storage capacity configuration method suitable for a smart park, which comprises the steps of firstly establishing an electric automobile charging load model, then establishing a smart park power distribution network mathematical model containing electric automobiles and renewable energy sources by combining time-of-use electricity price limitation, determining power required to be stabilized by hybrid energy storage, distributing the power by adopting a high-pass filtering algorithm, determining power required to be stabilized by a super capacitor and a lithium battery respectively, establishing a hybrid energy storage capacity configuration model, establishing a hybrid energy storage constraint condition by taking the annual income maximum of the hybrid energy storage as an objective function, and solving by adopting an optimization algorithm to obtain the optimal capacity configuration of the hybrid energy storage. Compared with the existing hybrid energy storage configuration method, the method considers the time-of-use electricity price limit in the intelligent park, solves the power required to be stabilized by the hybrid energy storage system and establishes the hybrid energy storage capacity configuration model, can better improve the economical efficiency of the system, and realizes the effective control of stabilizing the charging of the electric automobile and the photovoltaic power generation power fluctuation.)

1. A hybrid energy storage capacity allocation method suitable for an intelligent park is characterized by comprising the following steps:

s1, establishing an electric vehicle charging load model and predicting the electric vehicle charging load;

s2, considering the limit of time-of-use electricity price, establishing a mathematical model of a smart park distribution network containing the electric automobile and renewable energy, and determining the power of the hybrid energy storage system to be stabilized through the electric automobile charging load data and the smart park photovoltaic power generation data predicted in the step S1;

s3, distributing the power of the hybrid energy storage system through a high-pass filtering algorithm according to the power which needs to be stabilized by the hybrid energy storage and is determined in the step S2, and determining the power which needs to be stabilized by the super capacitor and the lithium battery respectively;

s4, considering the limit of time-of-use electricity price, establishing a hybrid energy storage capacity configuration model;

s5, constructing a constraint condition of the hybrid energy storage system according to the maximum annual income as an objective function of the hybrid energy storage system;

and S6, performing model solution on the hybrid energy storage capacity configuration model established in the step S4 by combining an optimization algorithm according to the power required to be stabilized by the super capacitor and the lithium battery determined in the step S3 and the constraint conditions of the hybrid energy storage system established in the step S5, and obtaining the optimal capacity configuration of the hybrid energy storage system.

2. The method of claim 1, wherein the step S1 is implemented by considering the influence of four factors, namely initial charging time, daily mileage of the vehicle, charging power and charging duration, to create the charging load model of the electric vehicle as follows:

s11: the last trip time of the electric automobile is used as the initial charging time, the ending time of the last trip of the electric automobile meets normal distribution, and the formula is as follows:

wherein t is the initial charging time; mu.s_sIs the standard deviation of the initial charge time; sigma_sStandard deviation of initial charging time, mu_sAnd σ_sVaries from vehicle type to vehicle type;

s12: the daily mileage of the electric automobile meets the lognormal distribution, and the formula is as follows:

wherein s is the daily mileage and the unit is km; mu.s_DIs a desire for lns; sigma_DIs the standard deviation of lns.

S13: the charging time of the electric automobile is calculated from the daily driving mileage, and accords with the lognormal distribution, and the formula is as follows:

wherein, mu_tc＝ln[W₁₀₀/(100ηP_c)]+μ_DFor expected charging duration, W₁₀₀The unit of the power consumption for 100km of automobile running is (kW.h)/hundred kilometers, sigma_tc＝σ_DThe standard deviation of the charging time length is shown as eta, and the charging efficiency is shown as eta.

3. The method of claim 1, wherein the step S2 is performed by taking into account a time-of-use electricity price limit to establish a mathematical model of the smart park power distribution network including electric vehicles and renewable energy sources, and the mathematical model is as follows:

the hybrid energy storage system is used for stabilizing the power fluctuation of photovoltaic power generation and electric vehicle charging load in the power distribution network, ensuring the balance of supply and demand of the power distribution network, ensuring the stable operation of the power distribution network and ensuring the power P of the hybrid energy storage system_HESSAs follows:

P_HESS＝P_sc+P_b

P_HESS＝P_EV-P_PV-P_grid

in the formula: p_sc、P_bThe charging and discharging power of the super capacitor and the lithium battery respectively; p_EVCharging power for the electric vehicle; p_PVFor photovoltaic power generation, P_gridTransmitting power for the power grid;

considering the limit of time-of-use electricity price, the power P is transmitted to the power grid at low electricity price_gridSet to maximum, transmit power P to the network at the price of electricity_gridThe setting is low, and the power P is transmitted to the power grid when the price of electricity is high_gridSet to minimum, charging power P of electric vehicle_EVLess than the photovoltaic power generation power P_PVTransmitting power P to the power grid_gridIs set to 0.

4. The method of claim 1, wherein the step S3 is as follows:

power P of hybrid energy storage system by adopting high-pass filter_HESSFiltering to obtain itHigh-frequency fluctuation component as active instruction P of energy storage of super capacitor_scAnd then taking the residual power instruction after the high-pass filtering as an active instruction P of the energy storage of the lithium battery_bThe following relationship exists for hybrid energy storage system power allocation:

wherein s is a differential operator; t is_fThe filtering time constant is determined according to the power fluctuation frequency band required to be stabilized by the super capacitor energy storage system and is usually from the second level to the minute level.

5. The method of claim 1, wherein the step S4 is modeled in consideration of a time-of-use electricity price limit, and comprises:

considering the limit of time-of-use electricity price, the hybrid energy storage is charged at low electricity price, the hybrid energy storage is discharged at electricity price low price and electricity price high price, and the two energy storages should meet the limit of the state of charge when being charged or discharged, so the limit of discharging from the maximum state of charge to the minimum state of charge should be met when being discharged at electricity price high price and electricity price low price, and the calculation formulas of the rated capacities of the two energy storage devices are as follows:

wherein the content of the first and second substances,rated capacity of super capacitor, t is initial discharge time of stored energy, delta t_pFor the time period of electricity price average, Δ t_hA time period of high price of electricity; SOC_sc,maxAnd SOC_sc,minMaximum and minimum values of the state of charge of the supercapacitor;

wherein the content of the first and second substances,is the rated capacity, SOC, of the lithium battery_b，maxAnd an SOCb, and a non-volatile memory,_minthe maximum and minimum values of the state of charge of the lithium battery.

6. The method of claim 1, wherein the step S5 is as follows:

s51: the benefit of the energy storage system is that the energy storage system buys power at low electricity prices and sells power at high electricity prices, and the direct profit of the hybrid energy storage system is expressed as follows:

wherein, Δ t₁Represents the charging and discharging time of the super capacitor at the ith moment, delta t₂The charging and discharging time of the lithium battery at the ith moment is represented, the energy storage at the same moment can only maintain the charging or discharging state, and the charging and discharging cannot be carried out simultaneously; r_iThe price of electricity at the ith moment is shown, and k represents the number of days;

s52: the construction cost of the hybrid energy storage system is mainly related to the capacity of the hybrid energy storage system, and the calculation formula is as follows:

C₁＝α_scP_sc+α_bP_b+β_scQ_sc+β_bQ_b

wherein, C₁The construction cost of the hybrid energy storage system; alpha is alpha_scUnit power unit price of the super capacitor; p_scCharging and discharging power for the super capacitor; alpha is alpha_bThe unit power unit price of the lithium battery; p_bThe charge and discharge power of the lithium battery; beta is a_scUnit price per unit capacity of the stage capacitor; q_scThe capacity of the super capacitor; beta is a_bUnit price per unit capacity of the lithium battery; q_bThe capacity of the lithium battery;

s53: the hybrid energy storage system needs maintenance cost from the beginning to the whole process of scrapping, and the calculation formula is as follows:

wherein epsilon_scMaintaining a cost factor for the operation of the supercapacitor; epsilon_bThe cost coefficient of the operation and maintenance of the lithium battery is obtained;

s54: the objective function of the hybrid energy storage system is obtained as follows:

wherein r is the current rate and l is the energy storage service life;

s55: to ensure that the energy storage system can operate properly, the following constraints should be satisfied:

(1) energy conservation constraint, and the power needs to meet the supply and demand balance at any time;

(2) output power constraint, wherein the photovoltaic power, the super capacitor and the lithium battery are reasonably set according to respective limitations;

(3) the state of charge (SOC) constraint is that the upper limit and the lower limit of the SOC are reasonably set according to the characteristics of energy storage, so that the battery life is prevented from being influenced by over-charge and over-discharge;

in summary, the constraint conditions are:

wherein, P_sc,cmax,P_b,cmax，P_sc，dmax，P_b，dmaxThe maximum charging and discharging power of the super capacitor and the storage battery respectively; p_PV，maxThe maximum output of the photovoltaic system; SOC_max，SOC_minIs the maximum minimum state of charge of the battery.

7. The method of claim 1, wherein the optimization algorithm in step S6 is an adaptive weight particle swarm algorithm.

Technical Field

The invention relates to the technical field of electric power, in particular to an energy storage capacity configuration method suitable for an intelligent park.

Background

With the deterioration of global climate environment, people pay more attention to the use of new energy. Solar energy is one of representatives of new energy, and is rapidly developed due to the advantages of environmental friendliness, high safety and reliability, convenience in installation and the like. The popularization and the application to photovoltaic power generation have been strengthened in current wisdom garden, but photovoltaic power generation's output receives temperature and illumination intensity's influence great, and its electricity generation output is extremely unstable to influence the electric energy quality and the power supply reliability of electric wire netting. Secondly, with the development of economy and technology, the number of electric vehicles is rapidly increased, so that a large number of electric vehicle charging stations are connected into a power grid, great influence is brought to the power quality of the power grid and a user side, and the problems of power unbalance of the power grid, equipment aging and the like are caused. To above-mentioned problem, can be equipped with the energy storage in the wisdom garden, the effect of utilizing the peak clipping of energy storage to fill the valley reduces the electric automobile and charges the impact and the photovoltaic power generation power output unstability of bringing adverse effect for the electric wire netting to the electric wire netting.

The types of energy storage systems are mainly: energy type energy storage, power type energy storage and hybrid energy storage. The hybrid energy storage can combine the advantages of two kinds of energy storage, and has a good stabilizing effect on photovoltaic power generation and electric automobile charging. However, the energy storage cost is high, the prepared energy storage capacity cannot be used when the capacity is too large or too small, the economy of the system is reduced when the capacity is too large, and the stable operation requirement of the system is difficult to meet when the capacity is too small, so that the finding of the optimal capacity configuration of the energy storage in the power distribution network of the intelligent park is extremely important.

Disclosure of Invention

The invention aims to provide an energy storage capacity configuration method suitable for an intelligent park, determine an optimal energy storage configuration scheme of the intelligent park and provide technical support for energy storage planning of the intelligent park.

The purpose of the invention is realized by the following technical scheme:

a hybrid energy storage capacity allocation method suitable for an intelligent park comprises the following steps:

s1, establishing an electric vehicle charging load model and predicting the electric vehicle charging load;

s2, considering the limit of time-of-use electricity price, establishing a mathematical model of a smart park distribution network containing the electric automobile and renewable energy, and determining the power of the hybrid energy storage system to be stabilized through the electric automobile charging load data and the smart park photovoltaic power generation data predicted in the step S1;

s3, distributing the power of the hybrid energy storage system through a high-pass filtering algorithm according to the power which needs to be stabilized by the hybrid energy storage and is determined in the step S2, and determining the power which needs to be stabilized by the super capacitor and the lithium battery respectively;

s4, considering the limit of time-of-use electricity price, establishing a hybrid energy storage capacity configuration model;

s5, constructing a constraint condition of the hybrid energy storage system according to the maximum annual income as an objective function of the hybrid energy storage system;

and S6, performing model solution on the hybrid energy storage capacity configuration model established in the step S4 by combining an optimization algorithm according to the power required to be stabilized by the super capacitor and the lithium battery determined in the step S3 and the constraint conditions of the hybrid energy storage system established in the step S5, and obtaining the optimal capacity configuration of the hybrid energy storage system.

Further, in step S1, the electric vehicle charging load model is established in consideration of the influence of the four factors, i.e., the initial charging time, the daily driving mileage of the vehicle, the charging power, and the charging duration, specifically as follows:

s11: the last trip time of the electric automobile is used as the initial charging time, the ending time of the last trip of the electric automobile meets normal distribution, and the formula is as follows:

wherein t is the initial charging time; mu.s_sIs the standard deviation of the initial charge time; sigma_sStandard deviation of initial charging time, mu_sAnd σ_sVaries from vehicle type to vehicle type;

s12: the daily mileage of the electric automobile meets the lognormal distribution, and the formula is as follows:

wherein s is the daily mileage and the unit is km; mu.s_DIs a desire for lns; sigma_DIs the standard deviation of lns.

S13: the charging time of the electric automobile is calculated from the daily driving mileage, and accords with the lognormal distribution, and the formula is as follows:

wherein, mu_tc＝ln[W₁₀₀/(100ηP_c)]+μ_DFor expected charging duration, W₁₀₀The unit of the power consumption for 100km of automobile running is (kW.h)/hundred kilometers, sigma_tc＝σ_DThe standard deviation of the charging time length is shown as eta, and the charging efficiency is shown as eta.

Further, in step S2, considering the limit of the time-of-use electricity price, a mathematical model of the smart park distribution network including the electric vehicle and the renewable energy is established, which specifically includes the following steps:

the hybrid energy storage system is used for stabilizing the power fluctuation of photovoltaic power generation and electric vehicle charging load in the power distribution network, ensuring the balance of supply and demand of the power distribution network, ensuring the stable operation of the power distribution network and ensuring the power P of the hybrid energy storage system_HESSAs follows:

P_HESS＝P_sc+P_b

P_HESS＝P_EV-P_PV-P_grid

in the formula: p_sc、P_bThe charging and discharging power of the super capacitor and the lithium battery respectively; p_EVCharging power for the electric vehicle; p_PVFor photovoltaic power generation, P_gridTransmitting power for the power grid;

considering the limit of time-of-use electricity price, the power P is transmitted to the power grid at low electricity price_gridSet to maximum, transmit power P to the network at the price of electricity_gridThe setting is low, and the power P is transmitted to the power grid when the price of electricity is high_gridSet to minimum, charging power P of electric vehicle_EVLess than the photovoltaic power generation power P_PVTransmitting power P to the power grid_gridIs set to 0.

Further, step S3 is specifically as follows:

power P of hybrid energy storage system by adopting high-pass filter_HESSFiltering to obtain high-frequency fluctuation component as active instruction P of energy storage of super capacitor_scAnd then the high-pass filtered residual power instruction is used asActive command P for storing energy for lithium battery_bThe following relationship exists for hybrid energy storage system power allocation:

wherein s is a differential operator; t is_fThe filtering time constant is determined according to the power fluctuation frequency band which needs to be stabilized by the super capacitor energy storage system and is usually from the second level to the minute level;

due to the low energy density of the super capacitor and the high energy density of the lithium battery, the time constant T is set when the total power of the hybrid energy storage is less than 0_fThe setting is small, storing most of the energy in the lithium battery.

Further, step S4 is modeled in consideration of the time-of-use electricity price limit, specifically as follows:

considering the limit of time-of-use electricity price, the hybrid energy storage is charged at low electricity price, the hybrid energy storage is discharged at electricity price low price and electricity price high price, and the two energy storages should meet the limit of the state of charge when being charged or discharged, so the limit of discharging from the maximum state of charge to the minimum state of charge should be met when being discharged at electricity price high price and electricity price low price, and the calculation formulas of the rated capacities of the two energy storage devices are as follows:

wherein the content of the first and second substances,rated capacity of super capacitor, t is initial discharge time of stored energy, delta t_pFor the time period of electricity price average, Δ t_hA time period of high price of electricity; SOC_sc,maxAnd SOC_sc,minBeing state of charge of a supercapacitorA maximum value and a minimum value;

wherein the content of the first and second substances,is the rated capacity, SOC, of the lithium battery_b，maxAnd SOCb_,minMaximum and minimum values of the state of charge of a lithium battery

Further, step S5 is specifically as follows:

s51: the benefit of the energy storage system is that the energy storage system buys power at low electricity prices and sells power at high electricity prices, and the direct profit of the hybrid energy storage system is expressed as follows:

wherein, Δ t₁Represents the charging and discharging time of the super capacitor at the ith moment, delta t₂The charging and discharging time of the lithium battery at the ith moment is represented, the energy storage at the same moment can only maintain the charging or discharging state, and the charging and discharging cannot be carried out simultaneously; r_iThe price of electricity at the ith moment is shown, and k represents the number of days;

s52: the construction cost of the hybrid energy storage system is mainly related to the capacity of the hybrid energy storage system, and the calculation formula is as follows:

C₁＝α_scP_sc+α_bP_b+β_scQ_sc+β_bQ_b

wherein, C₁The construction cost of the hybrid energy storage system; alpha is alpha_scUnit power unit price of the super capacitor; p_scCharging and discharging power for the super capacitor; alpha is alpha_bThe unit power unit price of the lithium battery; p_bThe charge and discharge power of the lithium battery; beta is a_scUnit price per unit capacity of the stage capacitor; q_scThe capacity of the super capacitor; beta is a_bUnit price per unit capacity of lithium battery；Q_bThe capacity of the lithium battery;

s53: the hybrid energy storage system needs maintenance cost from the beginning to the whole process of scrapping, and the calculation formula is as follows:

wherein epsilon_scMaintaining a cost factor for the operation of the supercapacitor; epsilon_bThe cost coefficient of the operation and maintenance of the lithium battery is obtained;

s54: the objective function of the hybrid energy storage system is obtained as follows:

wherein r is the current rate and l is the energy storage service life;

s55: to ensure that the energy storage system can operate properly, the following constraints should be satisfied:

(1) energy conservation constraint, and the power needs to meet the supply and demand balance at any time;

(2) output power constraint, wherein the photovoltaic power, the super capacitor and the lithium battery are reasonably set according to respective limitations;

(3) the state of charge (SOC) constraint is that the upper limit and the lower limit of the SOC are reasonably set according to the characteristics of energy storage, so that the battery life is prevented from being influenced by over-charge and over-discharge;

in summary, the constraint conditions are:

wherein, P_sc，cmax,P_b,cmax，P_sc,dmax,P_b,dmaxRespectively the maximum charge and discharge of the super capacitor and the storage battery

Power; p_PV，maxThe maximum output of the photovoltaic system; SOC_max，SOC_minIs the maximum minimum state of charge of the battery.

Further, the optimization algorithm in step S6 is an adaptive weight particle swarm algorithm.

The hybrid energy storage capacity configuration method suitable for the intelligent park, provided by the invention, considers the limit of time-of-use electricity price, and has the following beneficial effects:

1) the method takes the time-of-use electricity price and load peak limitations into consideration, and configures the hybrid energy storage, so that the hybrid energy storage not only meets the instability stabilization requirement, but also meets the income requirement, and has better economy;

2) the invention considers the complementary performance of power type and energy type energy storage, and can effectively improve the stabilizing effect of energy storage.

Drawings

FIG. 1 is a flow chart of a method for configuring energy storage capacity for a smart campus according to an embodiment of the present invention;

FIG. 2 is a flow chart of the method for predicting the charging load of the electric vehicle according to the embodiment of the invention;

FIG. 3 is a graph of the predicted result of the charging load of the electric vehicle according to the embodiment of the invention;

FIG. 4 is a diagram illustrating a smart park power distribution network architecture according to an embodiment of the present invention;

FIG. 5 is a graph of the power required for a hybrid energy storage system in accordance with an embodiment of the present invention;

FIG. 6 is a high pass filtering algorithm diagram according to an embodiment of the present invention;

FIG. 7 is a graph of the power required to stabilize a supercapacitor and lithium battery in accordance with an embodiment of the present invention;

FIG. 8 is a flow chart of an adaptive weight particle swarm algorithm according to an embodiment of the present invention;

FIG. 9 is a flowchart of a fitness algorithm according to an embodiment of the present invention;

FIG. 10 is a graph illustrating the variation of the optimal yield of the hybrid energy storage system according to an embodiment of the present invention;

FIG. 11 is a graph illustrating an exemplary change in energy storage state of charge.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.

Referring to fig. 1, the embodiment of the invention provides an energy storage capacity configuration method suitable for a smart park, which includes the steps of firstly establishing an electric vehicle charging load model, then establishing a smart park power distribution network mathematical model containing an electric vehicle and renewable energy sources by combining time-of-use electricity price limitation, determining power required to be stabilized by hybrid energy storage, distributing the power by adopting a high-pass filtering algorithm, determining power required to be stabilized by a super capacitor and a lithium battery, and establishing a hybrid energy storage capacity configuration model. And constructing a hybrid energy storage constraint condition by taking the maximum annual yield of hybrid energy storage as an objective function, and solving by adopting a self-adaptive weight particle swarm algorithm to obtain the optimal capacity configuration of the hybrid energy storage. The method specifically comprises the following steps:

(S1) establishing an electric vehicle charging load model and predicting the electric vehicle charging load;

the charging load of the electric vehicle is affected by various factors, including the condition of the electric vehicle itself, charging facilities, user habits, and the like. In order to simplify the research, main influence factors such as initial charging time, daily driving mileage, charging power and charging time are selected, and an electric vehicle charging probability model is established.

The last trip time of the electric automobile is used as the initial charging time, the ending time of the last trip of the electric automobile meets normal distribution, and the formula is as follows:

wherein t is the initial charging time; mu.s_sIs the standard deviation of the initial charge time; sigma_sStandard deviation of initial charging time, mu_sAnd σ_sVarying from vehicle type to vehicle type.

The daily mileage of the electric automobile meets the lognormal distribution, and the formula is as follows:

wherein s is the daily mileage and the unit is km; mu.s_DIs a desire for lns; sigma_DIs the standard deviation of lns.

The common charging modes of the electric automobile comprise slow charging, conventional charging and quick charging, and different charging modes are selected according to different types of electric automobiles.

The charging time of the electric automobile is calculated from the daily driving mileage, and accords with the lognormal distribution, and the formula is as follows:

wherein, mu_tc＝ln[W₁₀₀/(100ηP_c)]+μ_DFor expected charging duration, W₁₀₀The unit of the power consumption for 100km of automobile running is (kW.h)/hundred kilometers, sigma_tc＝σ_DThe standard deviation of the charging time length is shown as eta, and the charging efficiency is shown as eta.

The initial charging time, the daily mileage and the charging power of various electric automobiles are independent random variables, the electric automobiles are fully charged every time, and the charging load at the ith moment is as follows:

wherein N is the total number of the electric vehicles charged at the moment i, P_n，iAnd charging power of the nth electric automobile at the moment i.

Fig. 2 shows a flowchart of electric vehicle charging load prediction, in which the number of electric vehicles is set to be fixed, the total number of three types of electric vehicles, i.e., taxies, private cars and business cars, is 200, the proportions of the three types of electric vehicles are 0.16, 0.58 and 0.26, respectively, and the obtained prediction result is shown in fig. 3.

(S2) considering the limit of time-of-use electricity price, establishing a mathematical model of a smart park power distribution network containing the electric automobile and renewable energy, and determining the power of the hybrid energy storage system to be stabilized through the electric automobile charging load data and the smart park photovoltaic power generation data predicted in the step (S1);

the wisdom garden distribution network framework is seen in fig. 4, and the power fluctuation of photovoltaic power generation and electric automobile charging load in the distribution network is stabilized to the mixed energy storage, guarantees the supply and demand balance of electric wire netting, makes the distribution network can the steady operation. The power of the hybrid energy storage is as follows:

P_HESS＝P_sc+P_b

P_HESS＝P_EV-P_PV-P_grid

in the formula: p_sc、P_bThe charging and discharging power of the super capacitor and the lithium battery respectively; p_EVCharging power for the electric vehicle; p_PVFor photovoltaic generated power, P_gridAnd transmitting power for the power grid.

Considering the limit of time-of-use electricity price, the power P is transmitted to the power grid at low electricity price_gridSet to maximum, transmit power P to the network at the price of electricity_gridThe setting is low, and the power P is transmitted to the power grid when the price of electricity is high_gridSet to minimum, charging load P of electric vehicle_EVLess than the photovoltaic power generation power P_PVTransmitting power P to the power grid_gridIs set to 0.

The charging load prediction data of the electric automobile and the photovoltaic power generation data of the intelligent park are used as the input of the model, the power required to be stabilized by the hybrid energy storage system is obtained through calculation, and the obtained result is shown in fig. 5.

(S3) according to the power required to be stabilized by the hybrid energy storage determined in the step (S2), distributing the power of the hybrid energy storage system through a high-pass filtering algorithm (as shown in FIG. 6), and determining the power required to be stabilized by the super capacitor and the lithium battery respectively;

using high pass filter to mix energy storagePower P of the system_HESSFiltering to obtain high-frequency fluctuation component as active instruction P of energy storage of super capacitor_scAnd then taking the residual power instruction after the high-pass filtering as an active instruction P of the energy storage of the lithium battery_bThe following relationship exists for hybrid energy storage system power allocation:

wherein s is a differential operator; t is_fThe filtering time constant is determined according to the power fluctuation frequency band required to be stabilized by the super capacitor energy storage system and is usually from the second level to the minute level.

Since the energy density of the super capacitor is low and the energy density of the lithium battery is high, the time constant is set to be small when the total power of the hybrid energy storage is less than 0, and most of the energy is stored in the lithium battery.

The power required to be stabilized by the hybrid stored energy obtained in the step (S2) is used as an input in the step (S3), and the power required to be stabilized by each of the two stored energies is calculated, and the result is shown in fig. 7.

(S4) considering the limit of the time-of-use electricity price, and establishing a hybrid energy storage capacity configuration model;

the hybrid energy storage is charged at a low price and discharged at a low price and a high price. The two energy storage devices should satisfy the limit of the state of charge when charging or discharging, so they should satisfy the limit of discharging from the maximum state of charge to the minimum state of charge when discharging at high price and low price, and the rated capacity calculation formulas of the two energy storage devices are as follows:

wherein the content of the first and second substances,rated capacity of super capacitor, t is initial discharge time of stored energy, delta t_pFor the time period of electricity price average, Δ t_hA time period of high price of electricity; SOC_sc,maxAnd SOC_sc,minMaximum and minimum values of the state of charge of the supercapacitor;

wherein the content of the first and second substances,is the rated capacity, SOC, of the lithium battery_b,maxAnd an SOCb, and a non-volatile memory,_minthe maximum and minimum values of the state of charge of the lithium battery.

The state of charge is used for measuring the residual capacity of the energy storage system, and the calculation formula of the state of charge of the energy storage system is as follows:

SOC (t) and SOC (t-1) respectively represent the state of charge of the battery at the time t and the time t-1; p (t) represents the charge/discharge power at time t (greater than 0 represents discharge, and less than 0 represents charge); e_NRepresenting a rated capacity of stored energy; eta_c,η_dRespectively show charge and discharge efficiency; δ represents the self-discharge efficiency; Δ t is the step size.

And (4) taking the power required to be stabilized by the two types of stored energy obtained in the step (S3) as the input of the step (S4), and preliminarily calculating the upper and lower limits of the capacity of the two types of stored energy.

(S5) constructing constraint conditions of the hybrid energy storage system according to the maximum annual income as an objective function of the hybrid energy storage system;

the profit of energy storage system is that energy storage system buys electric power in the time of low price of electricity, sells electric power in the time of high price of electricity, and the direct profit of hybrid energy storage system is as follows:

wherein, Δ t₁Represents the charging and discharging time of the super capacitor at the ith moment, delta t₂The charging and discharging time of the lithium battery at the ith moment is represented, the energy storage at the same moment can only maintain the charging or discharging state, and the charging and discharging cannot be carried out simultaneously; r_iThe price of electricity at the ith time is shown, and k is the number of days.

The construction cost of the hybrid energy storage system is mainly related to the capacity of the hybrid energy storage system, and the calculation formula is as follows:

C₁＝α_scP_sc+α_bP_b+β_scQ_sc+β_bQ_b

wherein, C₁The construction cost of the hybrid energy storage system; alpha is alpha_scUnit power unit price of the super capacitor; p_scCharging and discharging power for the super capacitor; alpha is alpha_bThe unit power unit price of the lithium battery; p_bThe charge and discharge power of the lithium battery; beta is a_scUnit price per unit capacity of the stage capacitor; q_scThe capacity of the super capacitor; beta is a_bUnit price per unit capacity of the lithium battery; q_bThe capacity of a lithium battery.

The hybrid energy storage system needs maintenance cost from the beginning to the whole process of scrapping, and the calculation formula is as follows:

wherein epsilon_scMaintaining a cost factor for the operation of the supercapacitor; epsilon_bThe cost coefficient of the operation and maintenance of the lithium battery is obtained.

From the above, the objective function of the hybrid energy storage system can be derived as follows:

wherein r is the current rate and l is the energy storage service life.

To ensure that the energy storage system can operate properly, the following constraints should be satisfied:

(1) energy conservation constraint, and the power needs to meet the supply and demand balance at any time;

(2) output power constraint, wherein the photovoltaic power, the super capacitor and the lithium battery are reasonably set according to respective limitations;

(3) the state of charge (SOC) constraint is to set the upper limit and the lower limit of the SOC reasonably according to the characteristics of energy storage, so as to avoid the influence of overcharge and overdischarge on the service life of the battery.

From the above, the constraint conditions are:

wherein, P_sc，cmax,P_b,cmax,P_sc,dmax，P_b，dmaxThe maximum charging and discharging power of the super capacitor and the storage battery respectively; p_PV,maxThe maximum output of the photovoltaic system; SOC_max，SOC_minIs the maximum minimum state of charge of the battery.

And (S6) according to the power required to be stabilized by the super capacitor and the lithium battery determined in the step (S3) and the constraint conditions of the hybrid energy storage system constructed in the step (S5), performing model solution on the hybrid energy storage capacity configuration model established in the step (S4) by combining an optimization algorithm to obtain the optimal capacity configuration of the hybrid energy storage system.

The optimization algorithm used is an APSO (adaptive Particle Swarm optimization) adaptive weight Particle Swarm algorithm, the algorithm flow is shown in FIG. 8, and the fitness algorithm flow is shown in FIG. 9.

In order to enable the capacity configuration of the hybrid energy storage system to have the characteristics of high precision and stability, the selected population scale is 80, the number of times of particle iteration is 80, the maximum value and the minimum value of the inertia weight coefficient are 0.9 and 0.4 respectively, the learning factors are all selected to be 2, and the simulation step length is 5 min. The results of the hybrid energy storage capacity optimization configuration obtained by the adaptive weight particle swarm optimization through parameter setting (see table 1) and limiting conditions are shown in table 2, fig. 10 and fig. 11.

TABLE 1

TABLE 2

Rated power/kW of lithium battery	112
		Rated capacity/kW.h of super capacitor	910
Hybrid energy storage annual profit/yuan	346040
		Reduced annual cost/dollar	323030
Annual income/yuan	23015

As can be seen from fig. 10, as the number of iterations increases, the profit of the hybrid energy storage gradually increases, and when the number of iterations reaches 9, the annual profit of the hybrid energy storage reaches a maximum of 23015 yuan.

As can also be seen from fig. 11, the super capacitor and the lithium battery are charged to the upper limit of their states of charge from 0 to 12; after 12 to 15 and 19 points, the super capacitor and the lithium battery start to discharge, and the state of charge starts to drop sharply; at 15 to 19, the supercapacitor and lithium battery are charged. In the whole process, the charge states of the super capacitor and the lithium battery can be changed within the charge state range, and the energy conservation constraint is met. By adopting the configuration method, the charging and discharging times of the hybrid energy storage can be reduced, and the service life of the hybrid energy storage is prolonged to a certain extent.

The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.