CN115693741A - Energy storage optimization method for distributed photovoltaic and energy storage system and electronic equipment - Google Patents

Abstract

The invention provides an energy storage optimization method and electronic equipment for a distributed photovoltaic and energy storage system, and relates to the technical field of electric power. Calculate the daily distributed photovoltaic output power curve and daily load demand curve, and construct an energy storage margin model based on the relationship between the life and capacity of the energy storage system, and obtain the optimized daily energy storage margin and the relationship between output power and load demand Combined with the output power curve of the next day's distributed photovoltaic and the next day's load demand curve, the next day's energy storage margin of the energy storage system is determined; with the goal of minimizing the cost, the current daily charging margin of the energy storage system is optimized When the current daily distributed photovoltaic starts to output power, control the energy storage system to output power at the same time, so that the total output power of the current daily distributed photovoltaic and energy storage system reaches the stable output power value. The invention obtains more accurate daily energy storage and grid-connected power generation, so that the total output power meets the stability index, and the calculation amount is small and the operation is simple.

Description

Energy storage optimization methods and electronic devices for distributed photovoltaic and energy storage systems

technical field

The invention relates to the field of electric power technology, in particular to an energy storage optimization method and electronic equipment for a distributed photovoltaic and energy storage system.

Background technique

With the development and maturity of photovoltaic power generation technology, distributed photovoltaic, as an important means of photovoltaic power generation in the future, has been vigorously promoted and developed. Distributed photovoltaics alone do not have a certain peak-shaving capability, and need to be equipped with an energy storage system to participate in peak-shaving. Therefore, the development of such distributed photovoltaic and energy storage system architectures with distributed photovoltaics equipped with energy storage systems is becoming more and more important.

However, the output power and voltage of distributed photovoltaics are greatly affected by sunlight time, day and night, and seasonal factors, resulting in large fluctuations in output power and voltage, which affect the safe operation of equipment and power grids. Under normal circumstances, when distributed photovoltaics receive light and start to output power, their output power value is very small, the voltage value is very low, and cannot meet the load demand value, and as the light intensity increases and the time becomes longer, its output power value Gradually increase, the voltage value gradually increases, and the situation of exceeding the load demand value will also appear.

The equipment of the energy storage system can better reduce the fluctuation of distributed photovoltaic output power and voltage. However, the current technical solution is only equipped with a large-capacity energy storage system, and there is no more detailed combination of distributed photovoltaic output power characteristics, energy storage system life-capacity characteristics, cost factors and other aspects to optimize the storage system. The energy storage of the energy system and the grid-connected power generation make the total output power of the distributed photovoltaic and energy storage system stable and at the same time control the technical solution with the smallest overall cost.

Contents of the invention

In view of the above problems, the present invention is proposed to provide an energy storage optimization method and electronic equipment for a distributed photovoltaic and energy storage system that solve the above problems or partially solve the above problems.

The first aspect of the embodiments of the present invention provides an energy storage optimization method for a distributed photovoltaic and energy storage system. The energy storage optimization method includes:

According to the historical load data and the historical data of distributed photovoltaic output power, the daily distributed photovoltaic output power curve and daily load demand curve are calculated, and the output power curve and load demand curve of distributed photovoltaic on different days correspond to different light hours and light intensity;

Based on the daily distributed photovoltaic output power curve, the daily load demand curve, and the relationship between the life and capacity of the energy storage system, construct an energy storage margin model, and perform calculations on the energy storage margin model to obtain optimization The characteristic curve of the relationship between daily energy storage margin, output power and load demand;

According to the illumination time and illumination intensity of two adjacent days, combined with the output power curve of the daily distributed photovoltaic and the daily load demand curve, determine the output power curve and the load demand curve of the next day distributed photovoltaic;

According to the output power curve of the next-day distributed photovoltaic, the next-day load demand curve and the characteristic curve, determine the next-day energy storage margin of the energy storage system in the distributed photovoltaic and energy storage system;

According to the next day's energy storage margin and the current daily distributed photovoltaic output power curve, the cost of the V2G electric vehicle participating in the charging and discharging of the distributed photovoltaic and energy storage system is the minimum, and the energy storage is optimized. The current daily charging margin of the system, the value of the current daily charging margin is greater than the value of the next day's energy storage margin;

When the distributed photovoltaic on the current day starts to output power, control the energy storage system to output power at the same time, so that the total output power of the distributed photovoltaic and energy storage system on the current day reaches a stable value of output power, and the output power is stable The value satisfies the stability index, wherein, after the energy storage system stops outputting power on the current day, determine the charging mode of the energy storage system to charge the energy storage system until the energy storage system reaches the When the daily energy storage margin or the current daily charging margin is exceeded, the energy storage system stops charging.

Optionally, an energy storage margin model is constructed based on the daily distributed photovoltaic output power curve, the daily load demand curve, and the relationship between the life and capacity of the energy storage system, and the energy storage margin model is Calculation optimization to obtain the characteristic curve of the relationship between daily energy storage margin, output power and load demand, including:

According to the daily distributed photovoltaic output power curve and the daily load demand curve, statistical calculations can be used to obtain a daily double output power curve, and the daily double output power curve represents the distribution from the beginning of the output power to the output of the distributed photovoltaic on that day. The power change curve during the period when the power meets the daily load demand;

Build a model based on the Adaboost algorithm, combine the life and capacity relationship of the energy storage system, model the energy storage margin corresponding to the daily dual output power curve, and through model training, cross-validation and parameter optimization operations, The optimized daily energy storage margin, output power, and load demand relationship curve are obtained, wherein the daily energy storage margin is used to output power at the same time when the distributed photovoltaic starts to output power on that day, until the distributed photovoltaic output on that day Stop output power when the power meets the daily load demand.

Optionally, when the distributed photovoltaic starts to output power on the current day, the energy storage system is controlled to output power at the same time, so that the total output power of the distributed photovoltaic and energy storage system on the current day reaches a stable value of output power, wherein , after the energy storage system stops output power on the current day, determine the charging mode of the energy storage system to charge the energy storage system until the energy storage system reaches the energy storage margin of the next day or the When the charging margin of the current day is mentioned above, the energy storage system stops charging, including:

When the distributed photovoltaic starts to output power on the current day, the output power of the distributed photovoltaic does not meet the current daily load demand, and the energy storage system is controlled to use the current daily energy storage margin to output power, so that the current day's distributed The total output power of the photovoltaic and energy storage system reaches a stable value of output power;

If there is external power, when the sum of the distributed photovoltaic output power and the external power meets the current daily load demand, the energy storage system stops outputting power. At this time, the current daily energy storage margin is greater than 0, the power obtained from the outside world is the power provided by the V2G electric vehicle that discharges to the distributed photovoltaic and energy storage system;

If there is no power obtained from the outside world, when the distributed photovoltaic output power meets the current daily load demand, the energy storage system stops outputting power, and at this time, the current daily energy storage margin is close to 0;

After the energy storage system stops outputting power, if the sum of the real-time output power of the distributed photovoltaic and the external power is greater than the current daily load demand, the distributed photovoltaic and the external Charging the energy storage system until the energy storage system reaches the next day's energy storage margin or the current day's charging margin, stop charging the energy storage system;

The distributed photovoltaic stops charging the energy storage system, and after the energy storage system reaches the current daily charging margin, the distributed photovoltaic outputs power to the grid for grid-connected power generation;

After the energy storage system stops outputting power, if the sum of the real-time output power of the distributed photovoltaic and the external power is not greater than the current daily load demand, the distributed photovoltaic and the external power The energy storage system is not charged. After that, the distributed photovoltaic and energy storage system purchases electricity from the grid to charge the energy storage system during the off-peak period until the energy storage system reaches the next day's energy storage capacity. Margin.

Optionally, the distributed photovoltaic and the power obtained from the outside jointly charge the energy storage system until the energy storage system reaches the next day's energy storage margin or the current day's charging margin, then stop Charging the energy storage system includes:

During the process of charging the energy storage system with the distributed photovoltaic and the power obtained from the outside, if the output power of the distributed photovoltaic does not meet the current daily load demand, and the energy storage system has not yet When the next day’s energy storage margin is reached, the distributed photovoltaic and the external power will stop charging the energy storage system. After that, the distributed photovoltaic and energy storage system will start from the The grid purchases electricity to charge the energy storage system until the energy storage system reaches the energy storage margin for the next day;

During the process of charging the energy storage system with the distributed photovoltaic and the external power, if the sum of the output power of the distributed photovoltaic and the external power does not meet the current daily load demand, And when the energy storage system reaches the energy storage margin of the next day, the distributed photovoltaic and the external power will stop charging the energy storage system, and the energy storage system will no longer receive charging;

During the process of charging the energy storage system by the distributed photovoltaic and the power obtained from the outside, if the sum of the output power of the distributed photovoltaic and the power obtained from the outside always meets the current daily load demand, then The distributed photovoltaic and the externally obtained power jointly charge the energy storage system until the energy storage system reaches the current daily charging margin, and the energy storage system no longer receives charging;

During the process of charging the energy storage system with the distributed photovoltaic and the power obtained from the outside, if the output power of the distributed photovoltaic always meets the current daily load demand, the power obtained from the outside will be supplied to the The energy storage system is charged until the energy storage system reaches the current daily charging margin, and the energy storage system no longer receives charging.

Optionally, when the distributed photovoltaic starts to output power on the current day, the energy storage system is controlled to output power at the same time, so that the total output power of the distributed photovoltaic and energy storage system on the current day reaches a stable value of output power, including :

After the energy storage system stops charging on the current day and is charged to be equal to the energy storage margin of the next day, if the sum of the output power of the distributed photovoltaic and the power obtained from the outside does not meet the current daily load demand, the distributed photovoltaic and energy storage system immediately purchases electricity from the grid, so that the total output power of the distributed photovoltaic and energy storage system reaches a stable output power value on the current day;

After the energy storage system stops charging on the current day and is charged to be greater than the next day’s energy storage margin and less than the current day’s charging margin, if the output power of the distributed photovoltaic and the power obtained from the outside And if the current daily load demand is not met, the energy storage system is controlled to output power simultaneously using the stored energy when charging is stopped until the real-time stored energy of the energy storage system is reduced to the next day’s energy storage margin. amount, stop the output power of the energy storage system, after that, the distributed photovoltaic and energy storage system immediately purchase electricity from the grid;

After the energy storage system stops charging on the current day and is charged to be equal to the current daily charging margin, if the sum of the distributed photovoltaic output power and the external power does not meet the current daily load demand In the case of an emergency, the energy storage system is controlled to output power at the same time using the current daily charging margin until the real-time energy storage of the energy storage system is reduced to the next day’s energy storage margin, and the output of the energy storage system is stopped. After that, the distributed photovoltaic and energy storage system immediately purchases electricity from the grid.

Optionally, the expression of the stability index S is as follows:

In the above formula, N represents the time period between the daily sunrise time and sunset time, w ₁ (t) represents the output power of the daily distributed photovoltaic output power curve in the period t, and p represents the The current daily energy storage margin, w ₂ represents the average output power of distributed photovoltaics calculated according to the historical data of distributed photovoltaic output power, w ₃ represents the average output power of distributed photovoltaics from sunrise time to sunset time every day .

Optionally, the function f of the cost minimum objective is;

In the above formula, C ₁ (t) represents the depreciation cost of distributed photovoltaic equipment and energy storage system equipment, C ₂ (t) represents the maintenance cost of distributed photovoltaic equipment and energy storage system equipment, and C ₃ (t) represents the distributed The cost of electricity purchased by the photovoltaic and energy storage system from the grid, wherein the cost of the distributed photovoltaic and energy storage system to purchase electricity from the grid refers to: the cost of electricity purchased by the distributed photovoltaic and energy storage system from the grid and the V2G electric vehicle The difference between the cost of the distributed photovoltaic and energy storage system and the grid-connected power generation of the distributed photovoltaic and energy storage system and the income earned by the V2G electric vehicle. The cost of the distributed photovoltaic and energy storage system to purchase electricity from the grid is A positive value means that the cost of purchasing electricity from the grid and the V2G electric vehicle is greater than the income earned by power generation, and a negative value indicates that the cost of purchasing electricity from the grid and the V2G electric vehicle is less than the income earned by power generation;

Wherein, the expression of the cost C ₃ (t) of the distributed photovoltaic and energy storage system to purchase electricity from the grid is:

C ₃ (t)＝(k _i1 (t)w _i1 (t)+k _i2 (t)w _i2 (t)-k _j1 (t)w _j1 (t)-k _j2 (t)

w _j2 (t))△t

In the above formula, k _i1 (t) represents the price of electricity purchased from the grid during the t period, w _i1 (t) represents the power purchased by the distributed photovoltaic and energy storage system from the grid during the t period, and k _i2 (t) represents the power purchased from the grid during the t period The price of electricity purchased from the V2G electric vehicle, w _i2 (t) represents the power purchased by the distributed photovoltaic and energy storage system from the V2G electric vehicle during the period t, and k _j1 (t) represents the price of electricity generated to the grid during the period t , w _j1 (t) represents the output power of the distributed photovoltaic and energy storage system connected to the grid during the t period, k _j2 (t) represents the price of electricity generated by the V2G electric vehicle during the t period, and w _j2 (t) represents t The output power generated by the distributed photovoltaic and energy storage system to the V2G electric vehicle during the time period, Δt represents the time period;

The purchased power w _i (t) of the distributed photovoltaic and energy storage system during the t period is based on the gap of the energy storage margin corresponding to the t period, and the distributed photovoltaic and energy storage system supplies the V2G electric power to the V2G during the t period. The power w _i2 (t) of the vehicle power purchase and the gap of the load demand value are determined. The gap of the energy storage margin corresponding to the t period is based on the energy storage margin of the next day and the current charging of the energy storage system The difference between the stored energy is determined, and the gap of the load demand is based on the output power of the distributed photovoltaic corresponding to the period t and the power w purchased by the distributed photovoltaic and energy storage system from the V2G electric vehicle during the period t The sum of _i2 (t) is determined by the difference between the load demand value corresponding to the period t;

The sum of the grid-connected output power w _j (t) of the distributed photovoltaic and energy storage system to the grid during the period t and the output power generated by the V2G electric vehicle is obtained according to optimization to obtain the current daily output power of the energy storage system The charging margin is determined by the output power of the distributed photovoltaic corresponding to the period t.

Optionally, according to the next day's energy storage margin and the current day's output power curve of distributed photovoltaics, with the goal of minimizing the cost of V2G electric vehicles participating in the charging and discharging of the distributed photovoltaic and energy storage system, the optimization is obtained The current daily charging margin of the energy storage system, the value of the current daily charging margin is greater than the value of the next day's energy storage margin, including:

Step S1: Select the latter curve of the current daily distributed photovoltaic output power curve, and divide it into multiple t periods. The latter curve refers to the sum of the current daily distributed photovoltaic output power and the external power obtained , the curve corresponding to the time period from meeting the current daily load demand value to the time period when the distributed photovoltaic no longer outputs power;

Step S2: According to the next day's energy storage margin, the subsequent curve, and the full charge capacity of the energy storage system, the cost C ₃ (t ) to calculate the cost of power purchase for multiple t periods, and at the same time, through the expression of the depreciation cost C ₁ (t) of the distributed photovoltaic equipment and energy storage system equipment, the distributed photovoltaic equipment, energy storage The expression of the maintenance cost C ₂ (t) of the system equipment is calculated separately to obtain the depreciation cost and maintenance cost for multiple t periods;

Step S2: The initial solution of the genetic algorithm is the cost of electricity purchase, depreciation cost, and maintenance cost for multiple t periods;

Step S3: Based on the initial solution, through the function formula of the cost minimum objective, calculate the optimal value of each generation of cost of power purchase, depreciation cost, and maintenance cost for multiple t periods and the minimum cost of the generation with the smallest cost. value;

Step S4: Calculate the first generation optimal value and the lowest cost generation minimum value of the cost of power purchase, depreciation cost, and maintenance cost of the multiple t-periods to obtain the corresponding centroid;

Step S5: Propagate according to the centroid to generate a new population;

Step S6: According to each pair of sets in the new population generated by breeding, generate two offspring by crossover;

Step S7: Let q be the probability of mutation, then the operation of mutation is completed by randomly replacing an element on the set;

Step S8: After the "crossover" and "mutation" operations, a new solution is generated, and the new solution represents the current daily charge of the energy storage system obtained after optimizing the full charge of the energy storage system margin;

Step S9: Using the new solution as the initial solution of the genetic algorithm, the sampling iterative algorithm repeats steps S3-S9, and checks whether the end condition is met after each iterative operation, and the end condition is that the upper limit of the number of iterations reaches the standard, or The function value with the smallest cost is less than a preset function value;

Among them, the specific method of the iterative algorithm is:

For a new solution XJ _i ′ generated based on any set of initial solutions XJ _i , if f(XJ _i )≥f(XJ _i ′), then accept the new solution XJ _i ′ directly;

If f(XJ _i )<f(XJ _i ′), then the acceptance probability E of the new solution XJ _i ′ is obtained by the following formula:

In the above formula, R _w is the "heat" after the iterative algorithm iterates n times, and in the later stage of the iterative algorithm iteration, as R _w decreases, when the value of f(XJ _i )-f(XJ _i ′) At a certain time, the value of E will gradually decrease, so that the iterative algorithm tends to be stable;

After each iteration is completed, the "heat drop" operation will be performed on R _w . The formula corresponding to the heat drop operation is:

Where MT is the upper limit of iterations of the iterative algorithm.

Optionally, the expression of the relationship between the life and capacity of the energy storage system is as follows:

E _n (t) = E _b -∑k*e ^-0.02Soc *M ^0.5 *D ^0.7 .

In the above formula, E _n (t) represents the capacity of the energy storage system at time t, E _b represents the initial capacity of the energy storage system before it is put into use, k represents the proportional coefficient, and Soc represents the energy storage system’s capacity The average charge state value of the battery in a single cycle, D represents the charge and discharge depth of the battery in the energy storage system, and M represents the number of charge and discharge cycles of the battery in the energy storage system.

The second aspect of the embodiment of the present invention provides an electronic device, including:

at least one processor; and,

a memory communicatively coupled to the at least one processor; wherein,

The memory stores instructions executable by the at least one processor, the instructions are executed by the at least one processor, so that the at least one processor can perform the method described in any one of the first aspect Energy storage optimization methods for distributed photovoltaic and energy storage systems.

The energy storage optimization method of the distributed photovoltaic and energy storage system provided by the present invention first constructs an energy storage margin model, and performs calculations on the energy storage margin model to obtain the optimized daily energy storage margin and output power, The characteristic curve of the load-demand relationship. Secondly, determine the output power curve of the next day's distributed photovoltaic, the next day's load demand curve, and the next day's energy storage margin of the energy storage system.

Then, according to the next day’s energy storage margin and the current daily output power curve of distributed photovoltaics, with the goal of minimizing the cost, optimize the current daily charging margin of the energy storage system; The energy system can output power at the same time, so that the total output power of the current daily distributed photovoltaic and energy storage system reaches the output power stable value and meets the stability index.

The present invention finely combines the characteristics of distributed photovoltaic output power, energy storage system life and capacity characteristics, cost factors and other aspects, with the goal of minimizing the cost, and optimizes the relationship between the daily energy storage margin and the output power and load demand curve. relationship, optimize the current daily charging margin, and obtain more accurate daily energy storage and grid-connected power generation of the energy storage system, so that the total output power of the distributed photovoltaic and energy storage system meets the stability index, and the calculation of the whole process is less , the calculation is simple, the calculation time is less, the control logic is simple, and it has high practical value.

Description of drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the accompanying drawings that need to be used in the description of the embodiments of the present invention. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention , for those skilled in the art, other drawings can also be obtained according to these drawings without paying creative labor.

Fig. 1 is a flowchart of an energy storage optimization method for a distributed photovoltaic and energy storage system in an embodiment of the present invention;

Fig. 2 is a block diagram of an energy storage optimization device for a distributed photovoltaic and energy storage system in an embodiment of the present invention.

Detailed ways

The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

An energy storage optimization method for a distributed photovoltaic and energy storage system proposed in an embodiment of the present invention. Referring to FIG. 1 , it shows a flow chart of a method for optimizing energy storage in a distributed photovoltaic and energy storage system according to an embodiment of the present invention. The energy storage optimization method includes the following steps:

Step 101: According to the historical load data and the historical data of distributed photovoltaic output power, the daily output power curve and daily load demand curve of distributed photovoltaic are obtained by calculation. The output power curve and load demand curve of distributed photovoltaic on different days correspond to different lighting time and light intensity.

Since the output power and voltage of distributed photovoltaics are greatly affected by sunlight time, day and night, and seasonal factors, the daily output power curves of distributed photovoltaics may be different. The load demand is relatively stable, and it is rare for the load demand to increase or decrease sharply on similar days. For example, the daily load demand on working days is basically similar, and the load demand on rest days is basically similar. In order to more accurately control the cost, the total volt output power of the distributed photovoltaic and energy storage system, and control the daily storage capacity of the energy storage system, reduce the cost and prolong the service life of the energy storage system, it is necessary to obtain the daily output power curve and The daily load demand curve, the output power curve and load demand curve of distributed photovoltaics on different days correspond to different light hours and light intensities. These two curves can be calculated according to historical load data and distributed photovoltaic output power historical data, and of course can also be obtained by other methods, which are not specifically limited in the present invention.

Step 102: Based on the daily distributed photovoltaic output power curve, daily load demand curve, and the relationship between the life and capacity of the energy storage system, construct an energy storage margin model, and perform calculations on the energy storage margin model to obtain the optimized The characteristic curve of the relationship between daily energy storage margin, output power and load demand.

After obtaining the two curves, combined with the relationship between the life and capacity of the energy storage system, an energy storage margin model is constructed, and the energy storage margin model is calculated to obtain a more accurate daily energy storage margin of the energy storage system The relationship curve between distributed photovoltaic output power and load demand relationship is the optimized daily energy storage margin and output power and load demand relationship curve.

Step 103: According to the light time and light intensity of two adjacent days, combined with the output power curve of the daily distributed photovoltaic and the daily load demand curve, determine the output power curve and load of the next day distributed photovoltaic demand curve.

As explained in step 101, the output power of distributed photovoltaics may be different every day, which is determined by the daily light time and light intensity. The daily load demand is also similar to the difference between working days and rest days, and it will also be different due to the difference in light time and light intensity. Therefore, it is necessary to determine the light time and light intensity according to the current day's light time and light intensity, and then determine the next day's output power curve and the next day's load demand curve of distributed photovoltaics based on the next day's light time and light intensity. Lay a solid foundation for the subsequent accurate calculation of the energy storage margin for the next day.

Step 104: According to the next day's distributed photovoltaic output power curve, next day's load demand curve and characteristic curve, determine the next day's energy storage margin of the energy storage system in the distributed photovoltaic and energy storage system.

After obtaining the output power curve of the next day's distributed photovoltaics and the next day's load demand curve, combined with the characteristic curve of the relationship between the daily energy storage margin and output power and load demand obtained in step 102, the secondary load demand curve of the energy storage system can be accurately determined. Daily energy storage margin. The function of the next day’s energy storage margin is to ensure that the total output power of the next day’s distributed photovoltaic and energy storage system reaches a stable output power value that meets the stability index, so the next day’s energy storage margin is equivalent to the energy storage system at the current The minimum storage energy that needs to be reached during daily charging, but it will be used tomorrow. Naturally, it is understandable that the energy storage capacity of the energy storage system used when the distributed photovoltaic starts to output power on the current day is the energy storage margin of the previous day.

Step 105: According to the next day's energy storage margin and the current output power curve of distributed photovoltaics, with the goal of minimizing the cost of V2G electric vehicles participating in the charging and discharging of distributed photovoltaics and energy storage systems, optimize the current energy storage system. Daily charging margin, the value of the current daily charging margin is greater than the value of the current daily energy storage margin.

The so-called minimum cost should not only consider connecting more power generation to the grid to earn income to achieve the minimum cost, but should comprehensively consider the service life of the energy storage system and the daily output characteristics of distributed photovoltaics to obtain an optimal cost. The current daily charging margin of an excellent energy storage system, this current daily charging margin can enable distributed photovoltaics to provide output power when the output power does not meet the load demand, as little as possible to purchase electricity from the grid, and at the same time make distributed photovoltaics A certain amount of electricity can be connected to the grid to earn income, and the life factor of the energy storage system is taken into account.

In addition, considering that the target existing V2G electric vehicles have the function of outputting electric energy to the grid or related energy storage equipment, the participation of V2G electric vehicles in the charging and discharging of distributed photovoltaic and energy storage systems is taken into account. For example: for a distributed photovoltaic and energy storage system, the V2G electric vehicle can be determined according to the map travel plan of the 2G electric vehicle and the future charging and discharging conditions of the same destination (such as the location of the distributed photovoltaic and energy storage system). The historical situation of vehicles participating in charging and discharging forms a database, so as to accurately predict the daily charging and discharging of V2G electric vehicles participating in distributed photovoltaic and energy storage systems, so as to aim at the minimum cost and adaptively adjust the energy storage margin for the next day. Or the current daily charging margin and the minimum cost, fully mobilize the enthusiasm of V2G electric vehicles to participate in the charging and discharging of distributed photovoltaic and energy storage systems, and provide a better foundation for the sustainable development of electric vehicles in the future.

Step 106: When the current daily distributed photovoltaic starts to output power, control the energy storage system to output power at the same time, so that the total output power of the current daily distributed photovoltaic and energy storage system reaches the output power stable value, and the output power stable value meets the stability index , where, after the energy storage system stops outputting power on the current day, determine the charging mode of the energy storage system to charge the energy storage system until the energy storage system reaches the next day’s energy storage margin or the current day’s charging margin, the energy storage The system stops charging.

Since when the distributed photovoltaic starts to output power in the current day, its real-time power will definitely not meet the load demand of the current day, so it is necessary to control the energy storage system to output power at the same time, that is, to control the energy storage system to use the energy storage margin of the previous day to output power so that the current While the total output power of the daily distributed photovoltaic and energy storage system meets the current daily load demand, it reaches a stable value of output power and the stable value of output power meets the stability index, and there is no need to purchase electricity from the grid.

When the previous day's energy storage margin of the previous day's energy storage system is exhausted, the energy storage system stops output power, and the real-time output power of distributed photovoltaics rises to meet the load demand. Then determine the charging method of the energy storage system to charge the energy storage system in different ways until the energy storage system reaches the next day's energy storage margin or the current day's charging margin and stops charging. The specific charging situation is described below, so I won’t repeat it here.

Through the above method, finely combine the characteristics of distributed photovoltaic output power, energy storage system life and capacity characteristics, cost factors and other aspects, with the goal of minimizing cost, optimize the relationship curve between daily energy storage margin and output power, load demand The relationship between them optimizes the current daily charging margin, and obtains a more accurate daily energy storage and grid-connected power generation of the energy storage system, so that the total output power of the distributed photovoltaic and energy storage system meets the stability index, and the calculation of the entire process Small amount, simple calculation, less calculation time, and simple control logic.

In a possible embodiment, the specific method of step 102 may include:

Step T1: According to the daily output power curve of distributed photovoltaics and the daily load demand curve, the daily dual output power curve is obtained through statistical calculations. The power change curve of the daily load demand during this period.

Step 101 After obtaining the daily distributed photovoltaic output power curve and the daily load demand curve, perform statistical calculations on these two curves to obtain a daily double output power curve, which represents the daily distributed photovoltaic output power curve. The power change curve during the period from the start of output power to the output power meeting the daily load demand.

As mentioned above, distributed photovoltaics start to output power after receiving light intensity at sunrise time. At this time, the real-time power is small and generally cannot meet the load demand at the same time period. Afterwards, with the rotation of the earth, the height of the sun Angle changes, the light intensity begins to increase, and the output power of distributed photovoltaics naturally begins to rise until the rising maximum operating output power of distributed photovoltaics, which generally exceeds the load demand.

Based on the above reasons, in order to ensure that the total output power of the distributed photovoltaic and energy storage system meets the stability index, it is necessary to obtain the power change curve during the period from the beginning of the distributed photovoltaic output power to the output power meeting the daily load demand.

Step T2: Construct a model based on the Adaboost algorithm, combine the relationship between the life and capacity of the energy storage system, model the energy storage margin corresponding to the daily dual output power curve, and obtain The optimized daily energy storage margin and the characteristic curve of the relationship between output power and load demand, wherein the daily energy storage margin is used to output power at the same time when the distributed photovoltaic starts to output power on that day, until the distributed photovoltaic on that day Stop output power when the output power meets the daily load demand.

After obtaining the power change curve from the beginning of the distributed photovoltaic output power to the output power meeting the daily load demand, combined with the relationship between the life and capacity of the energy storage system, the model is constructed based on the Adaboost algorithm, and the energy storage corresponding to the obtained power change curve The margin is modeled, and then through model training, cross-validation, and parameter optimization operations, the optimized daily energy storage margin and the characteristic curve of the relationship between output power and load demand can be obtained, which accurately reflects the The relationship between the daily energy storage margin of the energy storage system, the time period when the output power of distributed photovoltaics has not met the load demand, and the load demand during this period.

In a possible embodiment, the expression of the life-capacity relationship of the energy storage system is as follows:

E _n (t) = E _b -∑k*e ^-0.02Soc *M ^0.5 *D ^0.7 .

In the above formula, E _n (t) represents the capacity of the energy storage system in the period t, E _b represents the initial capacity of the energy storage system before it is put into use, k represents the proportional coefficient, and Soc represents the average value of a single cycle of the battery in the energy storage system The state of charge value, D represents the charge and discharge depth of the battery in the energy storage system, and M represents the number of charge and discharge cycles of the battery in the energy storage system.

In a possible embodiment, the specific method of step 106 may include: a stage of outputting power of the energy storage system in the early stage and a stage of charging the energy storage system in the later stage.

1) For the stage of output power: when the distributed photovoltaic starts to output power in the current day, the output power of the distributed photovoltaic does not meet the load demand of the time period of the current day, and it is necessary to control the energy storage system to use the energy storage margin of the previous day to output power at the same time , so that the total output power of the current daily distributed photovoltaic and energy storage system reaches a stable value of output power; as the distributed photovoltaic output power rises, when the distributed photovoltaic output power meets the current daily load demand, the energy storage system stops output Power, at this time the previous day's energy storage margin is close to 0, that is, the previous day's energy storage margin will be consumed.

In the embodiment of the present invention, the expression of the stability index S is as follows:

In the above formula, N represents the time period between the daily sunrise time and sunset time, w ₁ (t) represents the output power of the daily distributed photovoltaic output power curve in the time period t, and p represents the current daily energy storage Margin, w ₂ represents the average output power of distributed photovoltaics calculated according to the historical data of distributed photovoltaic output power, w ₃ represents the average output power of distributed photovoltaics from sunrise time to sunset time every day.

What needs to be explained here is that due to weather and other reasons, there may be situations where distributed photovoltaics have no current daily output, or when the weather clears up after a certain period of time, such as after 12:00 noon, distributed photovoltaics may start to output power. When this happens, in order to improve the service life of the energy storage system, the energy storage margin of the previous day is not used, that is, the energy storage system does not output power on the current day. After waiting for the next day's energy storage margin to be obtained through subsequent calculations, if the next day's energy storage margin is not greater than the previous day's energy storage margin, wait for charging until the next day's charging margin or do not charge according to the charging method; if the next day's energy storage margin If it is greater than the previous day's energy storage margin, wait for charging to the next day's energy storage margin or the current day's charging margin according to the charging method.

In the output stage of distributed photovoltaics, in addition to the period from the beginning of output power to meeting the load demand, as the afternoon sunshine intensity decreases, the output power of distributed photovoltaics may not meet the load demand, and the difference between the load demand and the load demand is getting worse. The larger the time period, when such a situation occurs, according to the current storage energy of the energy storage system, it can be divided into the following situations:

A) After the energy storage system is charged to the current daily energy storage margin when the energy storage system stops charging, that is, the energy storage system has been charged to the current daily energy storage margin. (The power obtained from the outside world is the power provided by the V2G electric vehicle that discharges the distributed photovoltaic and energy storage system) and the sum does not meet the current daily load demand. Since the next day's energy storage margin cannot be consumed in the current day, so The distributed photovoltaic and energy storage system immediately purchases electricity from the grid, so that the total output power of the distributed photovoltaic and energy storage system reaches a stable output power value.

B) When the energy storage system stops charging on the current day, it is charged to be greater than the next day's energy storage margin and less than the current day's charging margin, that is, the energy storage system has been charged to the next day's energy storage margin, but has not yet reached The current charging margin. If the sum of the output power of distributed photovoltaics and the power obtained from the outside does not meet the current daily load demand, since the stored energy higher than the energy storage margin of the next day can be consumed, the energy storage system is controlled to use when charging is stopped. The stored energy of the energy storage system outputs power at the same time until the real-time stored energy of the energy storage system is reduced to the energy storage margin of the next day, and the output power of the energy storage system is stopped. After that, the distributed photovoltaic and energy storage system immediately purchases electricity from the grid.

C) After the current daily energy storage system stops charging and is charged to be equal to the current daily charging margin, that is, the energy storage system has been charged to the current daily charging margin, if the sum of the output power of the distributed photovoltaic and the power obtained from the outside If the current daily load demand is not met, since the stored energy higher than the next day’s energy storage margin can be consumed, the energy storage system is controlled to use the current daily charging margin to output power at the same time until the real-time storage of the energy storage system The energy storage capacity is reduced to the next day's energy storage margin, and the output power of the energy storage system is stopped. After that, the distributed photovoltaic and energy storage systems immediately purchase electricity from the grid.

2) For the charging phase: when the energy storage system stops outputting power, if the sum of the real-time output power of distributed photovoltaics and the power obtained from the outside world is greater than the current daily load demand, that is, the sum of the real-time power of distributed photovoltaics and the power obtained from the outside world will be higher. If it exceeds the load demand, the distributed PV and external power will start to charge the energy storage system together, instead of connecting to the grid for power generation, until the energy storage system reaches the next day's energy storage margin or the current day's charging margin, it will stop. Charge the energy storage system. The decisive factor is whether the sum of the real-time power of distributed photovoltaics and the power obtained from the outside world is continuously greater than the current daily load demand. The sum of the real-time power of distributed photovoltaics and the power obtained from the outside continues to exceed the current daily load demand for a long time. When the energy storage system is charged to the energy storage margin of the next day, the sum of the real-time power of distributed photovoltaics and the power obtained from the outside is still greater than The current daily load demand will continue to be charged to the current daily charging margin. At this time, the charging will be stopped, and the distributed photovoltaic will begin to output power to the grid to earn income through grid-connected power generation.

In another case, when the energy storage system stops outputting power, if the sum of the real-time output power of distributed photovoltaics and the power obtained from the outside world is not greater than the current daily load demand, that is, the sum of the real-time output power of distributed photovoltaics and the power obtained from the outside world is exactly To meet the load demand, there is no redundant output power, then neither the distributed photovoltaic nor the power obtained from the outside will charge the energy storage system, and the whole system will not purchase electricity from the grid, because the electricity price of the grid is at the peak time electricity price at this time, and its price is relatively low. High, and the energy storage system does not need to output power anymore, and energy storage is not needed for the time being, so the distributed photovoltaic and energy storage system can choose to purchase electricity from the grid to charge the energy storage system during the off-peak period when the electricity price is low, until the energy storage The system only needs to reach the energy storage margin of the next day, and does not need to be charged to the current day's charging margin.

In the specific process of charging the energy storage system from distributed photovoltaics, there are several situations as follows:

E) If the sum of the output power of distributed photovoltaics and the power obtained from the outside does not meet the current daily load demand, and the energy storage system has not yet reached the energy storage margin for the next day, that is, the real-time power of distributed photovoltaics and the power obtained from the outside The sum of the power is greater than the load demand, and the energy storage system is being charged, but due to various factors, such as sudden changes in the weather, the real-time power of distributed photovoltaics has dropped, and the load demand is no longer met, or it just meets the load demand, and there is no excess The output power charges the energy storage system. Then the distributed photovoltaic and the outside world will immediately stop charging the energy storage system. After that, the distributed photovoltaic and energy storage system will purchase electricity from the grid to charge the energy storage system during off-peak hours, or the subsequent real-time power of the distributed photovoltaic and the external If the sum of the power is greater than the load demand, then continue to charge the energy storage system instead of charging the energy storage system by purchasing electricity from the grid during off-peak hours by the distributed photovoltaic and energy storage system. Either way, until the energy storage system reaches the energy storage margin of the next day.

F) During the process of charging the energy storage system from distributed photovoltaics, if the sum of the output power of distributed photovoltaics and the power obtained from the outside does not meet the current daily load demand, and the energy storage system reaches the current daily energy storage margin, That is, the sum of the real-time power of distributed photovoltaics and the power obtained from the outside is greater than the load demand, and the energy storage system is being charged and the energy storage system has reached the energy storage margin for the next day. However, due to various factors, such as sudden changes in weather, the distributed photovoltaic The real-time power has dropped, and it no longer meets the load demand, or just meets the load demand, and there is no excess output power to charge the energy storage system. Then the distributed photovoltaic and the external power will stop charging the energy storage system, and the energy storage system will no longer receive charging. Charge.

G) In the process of charging the energy storage system by distributed photovoltaics, if the sum of the output power of distributed photovoltaics and the external power always meets the current daily load demand, that is, the sum of the real-time power of distributed photovoltaics and the external power is always high To meet the load demand, the distributed photovoltaic will charge the energy storage system until the energy storage system reaches the current daily charging margin, and the energy storage system will no longer receive charging.

H), during the process of charging the energy storage system with the power obtained from distributed photovoltaics and the outside world, if the output power of distributed photovoltaics always meets the current daily load demand, that is, the output power of distributed photovoltaics alone has already met the current daily load demand. Then, the external power is directly charged to the energy storage system until the energy storage system reaches the current daily charging margin, and the energy storage system no longer receives charging.

In a possible embodiment, the function f of the minimum cost objective can be defined as:

In the above formula, C ₁ (t) represents the depreciation cost of distributed photovoltaic equipment and energy storage system equipment, C ₂ (t) represents the maintenance cost of distributed photovoltaic equipment and energy storage system equipment, and C ₃ (t) represents the distributed The cost of purchasing electricity from the grid for photovoltaic and energy storage systems. Among them, the cost of distributed photovoltaic and energy storage system to purchase electricity from the grid refers to: the cost of distributed photovoltaic and energy storage system to purchase electricity from the grid and V2G electric vehicles, and the cost of distributed photovoltaic and energy storage The difference between earning income from power generation for V2G electric vehicles, the cost of purchasing electricity from the grid for distributed photovoltaic and energy storage systems is positive, indicating that the cost of purchasing electricity from the grid and V2G electric vehicles is greater than the income earned by power generation, which is negative The value indicates that the cost of purchasing electricity from the grid and V2G electric vehicles is less than generating income.

Among them, the expression of the cost C ₃ (t) of the distributed photovoltaic and energy storage system to purchase electricity from the grid is:

C ₃ (t)＝(k _i1 (t)w _i1 (t)+k _i2 (t)w _i2 (t)-k _j1 (t)w _j1 (t)-k _j2 (t)

w _j2 (t))△t

In the above formula, k _i1 (t) represents the price of electricity purchased from the grid during the t period, w _i1 (t) represents the power purchased by the distributed photovoltaic and energy storage system from the grid during the t period, and k _i2 (t) represents the power purchased from the V2G The power purchase price of electric vehicles, w _i2 (t) represents the power purchased by the distributed photovoltaic and energy storage system from V2G electric vehicles during the t period, k _j1 (t) represents the power generation price to the grid during the t period, and w _j1 (t) represents the t The output power of the distributed photovoltaic and energy storage system to the grid during the time period, k _j2 (t) represents the price of electricity generated by the V2G electric vehicle during the t period, and w _j2 (t) represents the power generated by the distributed photovoltaic and energy storage system for the V2G electric vehicle during the t period The output power of power generation, Δt represents the time period.

Among them, the purchased power w _i1 (t) of the distributed photovoltaic and energy storage system during the t period is based on the gap of the energy storage margin corresponding to the t period, and the power purchased by the distributed photovoltaic and energy storage system from the V2G electric vehicle during the t period w _i2 (t) and the gap of the load demand value are determined, and the value of the gap of the energy storage margin corresponding to the t period is based on the difference between the energy storage margin of the next day and the current charging storage capacity of the energy storage system Value decides. The value of the load demand gap is based on the sum of the output power of distributed photovoltaics corresponding to period t and the power w _i2 (t) purchased by distributed photovoltaics and energy storage systems from V2G electric vehicles during period t, corresponding to period t determined by the difference between the load demands.

The sum of the grid-connected output power w _j1 (t) of the distributed photovoltaic and energy storage system to the grid and the output power generated by the V2G electric vehicle during the t period is optimized to obtain the current daily charging margin of the energy storage system, which corresponds to the t period The output power of distributed photovoltaic is determined.

Based on the above-mentioned minimum cost calculation method, in a possible embodiment, the specific method of step 105 may include:

Step S1: Select the latter curve of the output power curve of the current daily distributed photovoltaic, and divide it into multiple t periods. The latter curve refers to the sum of the output power of the current daily distributed photovoltaic and the power obtained from the outside world, from meeting the current daily load The curve corresponding to the time period from the demand value to the time period when the distributed photovoltaic no longer outputs power;

Step S2: According to the current daily energy storage margin, the subsequent curve and the full charge capacity of the energy storage system, through the expression of the cost C ₃ (t) of the distributed photovoltaic and energy storage system to purchase electricity from the grid, calculate how much The cost of electricity purchase in a period of time t, at the same time through the expression of the depreciation cost C ₁ (t) of the distributed photovoltaic equipment and energy storage system equipment, the maintenance cost C ₂ ( The expression of t) is calculated respectively to obtain the depreciation cost and maintenance cost of multiple t periods;

Step S2: The initial solution of the genetic algorithm is the cost of electricity purchase, depreciation cost, and maintenance cost for multiple t periods;

Step S3: Based on the initial solution, through the function formula of the minimum cost objective, calculate the optimal value of the first generation of the cost of power purchase, depreciation cost, and maintenance cost for multiple t periods, and the minimum value of the generation with the smallest cost;

Step S4: Calculate the first-generation optimal value and the smallest cost generation minimum value of the cost of power purchase, depreciation cost, and maintenance cost for multiple t-periods to obtain the corresponding centroid;

Step S5: Propagate according to the centroid to generate a new population;

Step S6: According to each pair of sets in the new population generated by breeding, generate two offspring by crossover;

Step S7: Let q be the probability of mutation, then the operation of mutation is completed by randomly replacing an element on the set;

Step S8: After the "crossover" and "mutation" operations, a new solution is generated, and the new solution represents the current daily charging margin of the energy storage system obtained after optimizing the full charge of the energy storage system;

Step S9: Taking the new solution as the initial solution of the genetic algorithm, the sampling iterative algorithm repeats steps S3-S9, and checks whether the end condition is met after each iterative operation. The value is less than the preset function value.

After obtaining the new solution, use the new solution as the initial solution of the genetic algorithm, do not repeat the operation according to the evolution algebra and the maximization algebra, but use an iterative algorithm to repeat steps S3-S9, and after each iteration operation Detect whether the end condition is met, the end condition is that the upper limit of the number of iterations is reached, or the function value with the smallest cost is less than the preset function value. For example: the preset function value is 10, then when the function value with the smallest cost is less than 10 after a certain iterative operation, the genetic algorithm ends.

The genetic algorithm mentioned in this embodiment is different from the existing genetic algorithm. It does not need to set the evolution algebra and the maximization algebra. Instead of performing repeated operations using algebra, the iterative algorithm is used to repeat steps S3 to S9, and it is checked whether the end condition of the iterative algorithm is met. When the end condition is met, it is directly ended, thereby reducing the amount of calculation of the genetic algorithm and improving the current daily charging rate obtained by optimization. Efficiency, rapidity, and accuracy of margin and grid-connected power generation.

Based on the above algorithm, in a possible embodiment, the specific method of the iterative algorithm is:

For a new solution XJ _i ′ generated based on any set of initial solutions XJ _i , if f(XJ _i )≥f(XJ _i ′), then accept the new solution XJ _i ′ directly;

If f(XJ _i )<f(XJ _i ′), then the acceptance probability E of the new solution XJ _i ′ is obtained by the following formula:

In the above formula, R _w is the "heat" after the iterative algorithm iterates n times, and in the later stage of the iterative algorithm iteration, as R _w decreases, when the value of f(XJ _i )-f(XJ _i ′) At a certain time, the value of E will gradually decrease, so that the iterative algorithm tends to be stable;

After each iteration is completed, the "heat drop" operation will be performed on R _w . The formula corresponding to the heat drop operation is:

Where MT is the upper limit of iterations of the iterative algorithm. According to the travel plan, adjust the energy storage system, etc.

Based on the energy storage optimization method of the above-mentioned distributed photovoltaic and energy storage system, an embodiment of the present invention also proposes an energy storage optimization device for a distributed photovoltaic and energy storage system. Referring to Figure 2, the energy storage optimization device includes:

The calculation module 210 is used to calculate and obtain the daily output power curve and daily load demand curve of distributed photovoltaic, and the output power curve and load demand curve of distributed photovoltaic on different days according to historical load data and distributed photovoltaic output power historical data The curves correspond to different lighting time and light intensity;

The model curve module 220 is used to construct an energy storage margin model based on the daily distributed photovoltaic output power curve, the daily load demand curve, and the relationship between the life and capacity of the energy storage system, and perform calculations on the energy storage margin model to obtain The optimized daily energy storage margin and the characteristic curve of the relationship between output power and load demand;

Determine the next day's curve module 230, which is used to determine the output of the next day's distributed photovoltaic according to the illumination time and intensity of the adjacent two days, combined with the output power curve of the daily distributed photovoltaic and the daily load demand curve Power curve and next day load demand curve;

Determine the energy storage margin module 240, which is used to determine the next day's energy storage margin of the energy storage system in the distributed photovoltaic and energy storage system according to the output power curve of the next day's distributed photovoltaic, the next day's load demand curve and the characteristic curve;

The optimization module 250 is used to optimize the energy storage according to the next day's energy storage margin and the current output power curve of distributed photovoltaics, with the goal of minimizing the cost of V2G electric vehicles participating in the charging and discharging of distributed photovoltaics and energy storage systems. The current daily charging margin of the system, the value of the current daily charging margin is greater than the value of the current daily energy storage margin;

The control module 260 is used to control the output power of the energy storage system at the same time when the distributed photovoltaic starts to output power in the current day, so that the total output power of the distributed photovoltaic and energy storage system in the current day reaches a stable value of output power, and the stable value of output power satisfies Stability index, wherein, after the energy storage system stops output power on the current day, determine the charging method of the energy storage system to charge the energy storage system until the energy storage system reaches the next day's energy storage margin or the current day's charging margin , the energy storage system stops charging.

Based on the energy storage optimization method of the above-mentioned distributed photovoltaic and energy storage system, an embodiment of the present invention also proposes an electronic device, including:

at least one processor; and, a memory connected in communication with the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions are executed by the at least one processor, To enable the at least one processor to execute the energy storage optimization method for a distributed photovoltaic and energy storage system as described in any one of steps 101 to 106.

Based on the energy storage optimization method of the above-mentioned distributed photovoltaic and energy storage system, an embodiment of the present invention also proposes a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, steps 101 to 106 are implemented. The energy storage optimization method of any one of the distributed photovoltaic and energy storage systems.

Through the above example, the energy storage optimization method of the distributed photovoltaic and energy storage system provided by the present invention first constructs an energy storage margin model, and performs calculations on the energy storage margin model to obtain the optimized daily energy storage margin The characteristic curve of the relationship between output power and load demand. Secondly, determine the output power curve of the next day's distributed photovoltaic, the next day's load demand curve, and the next day's energy storage margin of the energy storage system.

Then, according to the next day’s energy storage margin and the current daily output power curve of distributed photovoltaics, with the goal of minimizing the cost, optimize the current daily charging margin of the energy storage system; The energy system can output power at the same time, so that the total output power of the current daily distributed photovoltaic and energy storage system reaches the output power stable value and meets the stability index.

The present invention finely combines the characteristics of distributed photovoltaic output power, energy storage system life and capacity characteristics, cost factors and other aspects, with the goal of minimizing the cost, and optimizes the relationship between the daily energy storage margin and the output power and load demand curve. relationship, optimize the current daily charging margin, and obtain more accurate daily energy storage and grid-connected power generation of the energy storage system, so that the total output power of the distributed photovoltaic and energy storage system meets the stability index, and the calculation of the whole process is less , the calculation is simple, the calculation time is less, the control logic is simple, and it has high practical value.

It should be noted that, in this document, the term "comprising", "comprising" or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article or apparatus comprising a set of elements includes not only those elements, It also includes other elements not expressly listed, or elements inherent in the process, method, article, or device. Without further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus comprising that element.

Embodiments of the present invention have been described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementations, and the above-mentioned specific implementations are only illustrative, rather than restrictive, and those of ordinary skill in the art will Under the enlightenment of the present invention, many forms can also be made without departing from the gist of the present invention and the protection scope of the claims, and these all belong to the protection of the present invention.

Claims (10)

1. An energy storage optimization method for a distributed photovoltaic and energy storage system, characterized in that the energy storage optimization method comprises: According to the historical load data and the historical data of distributed photovoltaic output power, the daily distributed photovoltaic output power curve and daily load demand curve are calculated, and the output power curve and load demand curve of distributed photovoltaic on different days correspond to different light hours and light intensity; Based on the daily distributed photovoltaic output power curve, the daily load demand curve, and the relationship between the life and capacity of the energy storage system, construct an energy storage margin model, and perform calculations on the energy storage margin model to obtain optimization The characteristic curve of the relationship between daily energy storage margin, output power and load demand; According to the illumination time and illumination intensity of two adjacent days, combined with the output power curve of the daily distributed photovoltaic and the daily load demand curve, determine the output power curve and the load demand curve of the next day distributed photovoltaic; According to the output power curve of the next-day distributed photovoltaic, the next-day load demand curve and the characteristic curve, determine the next-day energy storage margin of the energy storage system in the distributed photovoltaic and energy storage system; According to the next day's energy storage margin and the current daily distributed photovoltaic output power curve, the cost of the V2G electric vehicle participating in the charging and discharging of the distributed photovoltaic and energy storage system is the minimum, and the energy storage is optimized. The current daily charging margin of the system, the value of the current daily charging margin is greater than the value of the next day's energy storage margin; When the distributed photovoltaic on the current day starts to output power, control the energy storage system to output power at the same time, so that the total output power of the distributed photovoltaic and energy storage system on the current day reaches a stable value of output power, and the output power is stable The value satisfies the stability index, wherein, after the energy storage system stops outputting power on the current day, determine the charging mode of the energy storage system to charge the energy storage system until the energy storage system reaches the When the daily energy storage margin or the current daily charging margin is exceeded, the energy storage system stops charging. 2. The energy storage optimization method according to claim 1, characterized in that, based on the daily distributed photovoltaic output power curve, the daily load demand curve, and the relationship between the life and capacity of the energy storage system, an energy storage system is constructed. margin model, and optimize the calculation of the energy storage margin model to obtain the daily energy storage margin and the characteristic curve of the relationship between output power and load demand, including: According to the daily distributed photovoltaic output power curve and the daily load demand curve, statistical calculations can be used to obtain a daily double output power curve, and the daily double output power curve represents the distribution from the beginning of the output power to the output of the distributed photovoltaic on that day. The power change curve during the period when the power meets the daily load demand; Build a model based on the Adaboost algorithm, combine the life and capacity relationship of the energy storage system, model the energy storage margin corresponding to the daily dual output power curve, and through model training, cross-validation and parameter optimization operations, The optimized daily energy storage margin, output power, and load demand relationship curve are obtained, wherein the daily energy storage margin is used to output power at the same time when the distributed photovoltaic starts to output power on that day, until the distributed photovoltaic output on that day Stop output power when the power meets the daily load demand. 3. The energy storage optimization method according to claim 1, characterized in that, when the distributed photovoltaic starts to output power on the current day, the energy storage system is controlled to output power at the same time, so that the distributed photovoltaic and the distributed photovoltaic on the current day The total output power of the energy storage system reaches a stable value of output power, wherein, after the energy storage system stops output power on the current day, the charging mode of the energy storage system is determined to charge the energy storage system until the When the energy storage system reaches the next day's energy storage margin or the current day's charging margin, the energy storage system stops charging, including: When the distributed photovoltaic starts to output power on the current day, the output power of the distributed photovoltaic does not meet the current daily load demand, and the energy storage system is controlled to use the current daily energy storage margin to output power, so that the current day's distributed The total output power of the photovoltaic and energy storage system reaches a stable value of output power; If there is external power, when the sum of the distributed photovoltaic output power and the external power meets the current daily load demand, the energy storage system stops outputting power. At this time, the current daily energy storage margin is greater than 0, the power obtained from the outside world is the power provided by the V2G electric vehicle that discharges to the distributed photovoltaic and energy storage system; If there is no power obtained from the outside world, when the distributed photovoltaic output power meets the current daily load demand, the energy storage system stops outputting power, and at this time, the current daily energy storage margin is close to 0; After the energy storage system stops outputting power, if the sum of the real-time output power of the distributed photovoltaic and the external power is greater than the current daily load demand, the distributed photovoltaic and the external Charging the energy storage system until the energy storage system reaches the next day's energy storage margin or the current day's charging margin, stop charging the energy storage system; The distributed photovoltaic stops charging the energy storage system, and after the energy storage system reaches the current daily charging margin, the distributed photovoltaic outputs power to the grid for grid-connected power generation; After the energy storage system stops outputting power, if the sum of the real-time output power of the distributed photovoltaic and the external power is not greater than the current daily load demand, the distributed photovoltaic and the external power The energy storage system is not charged. After that, the distributed photovoltaic and energy storage system purchases electricity from the grid to charge the energy storage system during the off-peak period until the energy storage system reaches the next day's energy storage capacity. Margin. 4. The energy storage optimization method according to claim 3, characterized in that, the distributed photovoltaic and the externally obtained power jointly charge the energy storage system until the energy storage system reaches the next day storage capacity. When the energy margin or the current daily charging margin, stop charging the energy storage system, including: During the process of charging the energy storage system with the distributed photovoltaic and the power obtained from the outside, if the output power of the distributed photovoltaic does not meet the current daily load demand, and the energy storage system has not yet When the next day’s energy storage margin is reached, the distributed photovoltaic and the external power will stop charging the energy storage system. After that, the distributed photovoltaic and energy storage system will start from the The grid purchases electricity to charge the energy storage system until the energy storage system reaches the energy storage margin for the next day; During the process of charging the energy storage system with the distributed photovoltaic and the external power, if the sum of the output power of the distributed photovoltaic and the external power does not meet the current daily load demand, And when the energy storage system reaches the energy storage margin of the next day, the distributed photovoltaic and the external power will stop charging the energy storage system, and the energy storage system will no longer receive charging; During the process of charging the energy storage system by the distributed photovoltaic and the power obtained from the outside, if the sum of the output power of the distributed photovoltaic and the power obtained from the outside always meets the current daily load demand, then The distributed photovoltaic and the externally obtained power jointly charge the energy storage system until the energy storage system reaches the current daily charging margin, and the energy storage system no longer receives charging; During the process of charging the energy storage system with the distributed photovoltaic and the power obtained from the outside, if the output power of the distributed photovoltaic always meets the current daily load demand, the power obtained from the outside will be supplied to the The energy storage system is charged until the energy storage system reaches the current daily charging margin, and the energy storage system no longer receives charging. 5. The energy storage optimization method according to claim 3, characterized in that, when the distributed photovoltaic starts to output power on the current day, the energy storage system is controlled to output power at the same time, so that the distributed photovoltaic and the distributed photovoltaic on the current day The total output power of the energy storage system reaches a stable output power value, including: After the energy storage system stops charging on the current day and is charged to be equal to the energy storage margin of the next day, if the sum of the output power of the distributed photovoltaic and the power obtained from the outside does not meet the current daily load demand, the distributed photovoltaic and energy storage system immediately purchases electricity from the grid, so that the total output power of the distributed photovoltaic and energy storage system reaches a stable output power value on the current day; After the energy storage system stops charging on the current day and is charged to be greater than the next day’s energy storage margin and less than the current day’s charging margin, if the output power of the distributed photovoltaic and the power obtained from the outside And if the current daily load demand is not met, the energy storage system is controlled to output power simultaneously using the stored energy when charging is stopped until the real-time stored energy of the energy storage system is reduced to the next day’s energy storage margin. amount, stop the output power of the energy storage system, after that, the distributed photovoltaic and energy storage system immediately purchase electricity from the grid; After the energy storage system stops charging on the current day and is charged to be equal to the current daily charging margin, if the sum of the distributed photovoltaic output power and the external power does not meet the current daily load demand In the case of an emergency, the energy storage system is controlled to output power at the same time using the current daily charging margin until the real-time energy storage of the energy storage system is reduced to the next day’s energy storage margin, and the output of the energy storage system is stopped. After that, the distributed photovoltaic and energy storage system immediately purchases electricity from the grid. 6. The energy storage optimization method according to claim 3, wherein the expression of the stability index S is as follows:

In the above formula, N represents the time period between the daily sunrise time and sunset time, w ₁ (t) represents the output power of the daily distributed photovoltaic output power curve in the period t, and p represents the The current daily energy storage margin, w ₂ represents the average output power of distributed photovoltaics calculated according to the historical data of distributed photovoltaic output power, w ₃ represents the average output power of distributed photovoltaics from sunrise time to sunset time every day . 7. The energy storage optimization method according to claim 1, characterized in that, the function f of the cost minimum objective is;

In the above formula, C ₁ (t) represents the depreciation cost of distributed photovoltaic equipment and energy storage system equipment, C ₂ (t) represents the maintenance cost of distributed photovoltaic equipment and energy storage system equipment, and C ₃ (t) represents the distributed The cost of electricity purchased by the photovoltaic and energy storage system from the grid, wherein the cost of the distributed photovoltaic and energy storage system to purchase electricity from the grid refers to: the cost of electricity purchased by the distributed photovoltaic and energy storage system from the grid and the V2G electric vehicle The difference between the cost of the distributed photovoltaic and energy storage system and the grid-connected power generation of the distributed photovoltaic and energy storage system and the income earned by the V2G electric vehicle. The cost of the distributed photovoltaic and energy storage system to purchase electricity from the grid is A positive value means that the cost of purchasing electricity from the grid and the V2G electric vehicle is greater than the income earned by power generation, and a negative value indicates that the cost of purchasing electricity from the grid and the V2G electric vehicle is less than the income earned by power generation; Wherein, the expression of the cost C ₃ (t) of the distributed photovoltaic and energy storage system to purchase electricity from the grid is: C ₃ (t)＝(k _i1 (t)w _i1 (t)+k _i2 (t)w _i2 (t)-k _j1 (t)w _j1 (t)-k _j2 (t) w _j2 (t))△t In the above formula, k _i1 (t) represents the price of electricity purchased from the grid during the t period, w _i1 (t) represents the power purchased by the distributed photovoltaic and energy storage system from the grid during the t period, and k _i2 (t) represents the power purchased from the grid during the t period The price of electricity purchased from the V2G electric vehicle, w _i2 (t) represents the power purchased by the distributed photovoltaic and energy storage system from the V2G electric vehicle during the period t, and k _j1 (t) represents the price of electricity generated to the grid during the period t , w _j1 (t) represents the output power of the distributed photovoltaic and energy storage system connected to the grid during the t period, k _j2 (t) represents the price of electricity generated by the V2G electric vehicle during the t period, and w _j2 (t) represents t The output power generated by the distributed photovoltaic and energy storage system to the V2G electric vehicle during the time period, Δt represents the time period; The purchased power w _i (t) of the distributed photovoltaic and energy storage system during the t period is based on the gap of the energy storage margin corresponding to the t period, and the distributed photovoltaic and energy storage system supplies the V2G electric power to the V2G during the t period. The power w _i2 (t) of the vehicle power purchase and the gap of the load demand value are determined. The gap of the energy storage margin corresponding to the t period is based on the energy storage margin of the next day and the current charging of the energy storage system The difference between the stored energy is determined, and the gap of the load demand is based on the output power of the distributed photovoltaic corresponding to the period t and the power w purchased by the distributed photovoltaic and energy storage system from the V2G electric vehicle during the period t The sum of _i2 (t) is determined by the difference between the load demand value corresponding to the period t; The sum of the grid-connected output power w _j (t) of the distributed photovoltaic and energy storage system to the grid during the period t and the output power generated by the V2G electric vehicle is obtained according to optimization to obtain the current daily output power of the energy storage system The charging margin is determined by the output power of the distributed photovoltaic corresponding to the period t. 8. The energy storage optimization method according to claim 7, characterized in that, according to the next day's energy storage margin and the output power curve of the current day's distributed photovoltaic, V2G electric vehicles participate in the distributed photovoltaic and storage The goal is to minimize the cost in the case of charging and discharging the energy system, and optimize the current daily charging margin of the energy storage system. The value of the current daily charging margin is greater than the value of the next day's energy storage margin, including: Step S1: Select the latter curve of the current daily distributed photovoltaic output power curve, and divide it into multiple t periods. The latter curve refers to the sum of the current daily distributed photovoltaic output power and the external power obtained , the curve corresponding to the time period from meeting the current daily load demand value to the time period when the distributed photovoltaic no longer outputs power; Step S2: According to the next day's energy storage margin, the subsequent curve, and the full charge capacity of the energy storage system, the cost C ₃ (t ) to calculate the cost of power purchase for multiple t periods, and at the same time, through the expression of the depreciation cost C ₁ (t) of the distributed photovoltaic equipment and energy storage system equipment, the distributed photovoltaic equipment, energy storage The expression of the maintenance cost C ₂ (t) of the system equipment is calculated separately to obtain the depreciation cost and maintenance cost for multiple t periods; Step S2: The initial solution of the genetic algorithm is the cost of electricity purchase, depreciation cost, and maintenance cost for multiple t periods; Step S3: Based on the initial solution, through the function formula of the cost minimum objective, calculate the optimal value of each generation of cost of power purchase, depreciation cost, and maintenance cost for multiple t periods and the minimum cost of the generation with the smallest cost. value; Step S4: Calculate the first generation optimal value and the lowest cost generation minimum value of the cost of power purchase, depreciation cost, and maintenance cost of the multiple t-periods to obtain the corresponding centroid; Step S5: Propagate according to the centroid to generate a new population; Step S6: According to each pair of sets in the new population generated by breeding, generate two offspring by crossover; Step S7: Let q be the probability of mutation, then the operation of mutation is completed by randomly replacing an element on the set; Step S8: After the "crossover" and "mutation" operations, a new solution is generated, and the new solution represents the current daily charge of the energy storage system obtained after optimizing the full charge of the energy storage system margin; Step S9: Using the new solution as the initial solution of the genetic algorithm, the sampling iterative algorithm repeats steps S3-S9, and checks whether the end condition is met after each iterative operation, and the end condition is that the upper limit of the number of iterations reaches the standard, or The function value with the smallest cost is less than a preset function value; Among them, the specific method of the iterative algorithm is: For a new solution XJ _i ′ generated based on any set of initial solutions XJ _i , if f(XJ _i )≥f(XJ _i ′), then accept the new solution XJ _i ′ directly; If f(XJ _i )<f(XJ _i ′), then the acceptance probability E of the new solution XJ _i ′ is obtained by the following formula:

In the above formula, R _w is the "heat" after the iterative algorithm iterates n times, and in the later stage of the iterative algorithm iteration, as R _w decreases, when the value of f(XJ _i )-f(XJ _i ′) At a certain time, the value of E will gradually decrease, so that the iterative algorithm tends to be stable; After each iteration is completed, the "heat drop" operation will be performed on R _w . The formula corresponding to the heat drop operation is:

Where MT is the upper limit of iterations of the iterative algorithm. 9. The energy storage optimization method according to claim 5, wherein the expression of the relationship between the life of the energy storage system and the capacity is as follows: E _n (t) = E _b -∑k*e ^-0.02Soc *M ^0.5 *D ^0.7 . In the above formula, E _n (t) represents the capacity of the energy storage system at time t, E _b represents the initial capacity of the energy storage system before it is put into use, k represents the proportional coefficient, and Soc represents the energy storage system’s capacity The average charge state value of the battery in a single cycle, D represents the charge and discharge depth of the battery in the energy storage system, and M represents the number of charge and discharge cycles of the battery in the energy storage system. 10. An electronic device, characterized in that it comprises: at least one processor; and, a memory communicatively coupled to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, the instructions are executed by the at least one processor, so that the at least one processor can perform the operation described in any one of claims 1 to 9 Energy storage optimization method for distributed photovoltaic and energy storage systems described above.

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