Energy storage optimization method and electronic equipment for distributed photovoltaic and energy storage system

Abstract

The present invention provides a method and electronic device for optimizing the energy storage of a distributed photovoltaic and energy storage system, and relates to the field of electric power technology. The daily output power curve of the distributed photovoltaic and the daily load demand curve are calculated, and the energy storage margin model is constructed in combination with the life and capacity relationship of the energy storage system to obtain the optimized characteristic curve of the daily energy storage margin and the output power and load demand relationship, and then the output power curve of the distributed photovoltaic and the load demand curve of the next day are combined to determine the energy storage margin of the energy storage system for the next day; with the minimum cost as the goal, the current day's charging margin of the energy storage system is optimized; 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 the output power stable value. The present invention obtains more accurate daily energy storage and grid-connected power generation, so that the total output power meets the stability index, with less calculation and simple calculation.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to an energy storage optimization method and electronic equipment for a distributed photovoltaic and energy storage system. Background Art

[0002] With the development and maturity of photovoltaic power generation technology, distributed photovoltaics, as an important means of future photovoltaic power generation, has been vigorously promoted and developed. Distributed photovoltaics alone do not have a certain peak-shaving capacity and need to be equipped with an energy storage system to participate in peak-shaving. Therefore, the development of distributed photovoltaics and energy storage system architectures such as distributed photovoltaics equipped with energy storage systems is becoming increasingly important.

[0003] However, the output power and voltage of distributed photovoltaics are greatly affected by factors such as light time, diurnal and seasonal factors, which lead to large fluctuations in output power and voltage, affecting the safe operation of equipment and power grid. Generally, when distributed photovoltaics start to output power after receiving light, their output power value is very small and their voltage value is very low, which cannot meet the load demand value. However, as the light intensity increases and the time becomes longer, their output power value gradually increases and the voltage value gradually rises, which may also exceed the load demand value.

[0004] The deployment of energy storage systems can effectively reduce the fluctuations in the output power and voltage of distributed photovoltaics. However, the current technical solution is only to deploy a large-capacity energy storage system, and there is no more sophisticated technical solution that combines the output power characteristics of distributed photovoltaics, the life-capacity characteristics of the energy storage system, cost factors and other aspects to optimize the storage capacity of the energy storage system and the grid-connected power generation, so that the total output power of distributed photovoltaics and energy storage systems is stable while controlling the overall cost to the minimum. Summary of the invention

[0005] In view of the above problems, the present invention is proposed to provide a method and electronic device for optimizing energy storage of a distributed photovoltaic and energy storage system that solves the above problems or partially solves the above problems.

[0006] A first aspect of an embodiment of the present invention provides an energy storage optimization method for a distributed photovoltaic and energy storage system, the energy storage optimization method comprising:

[0007] According to the historical load data and distributed photovoltaic output power historical data, the daily distributed photovoltaic output power curve and the daily load demand curve are calculated. The distributed photovoltaic output power curve and load demand curve on different days correspond to different illumination time and illumination intensity.

[0008] 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 margin model is constructed, and the energy storage margin model is calculated to obtain an optimized characteristic curve of the relationship between the daily energy storage margin and the output power and load demand;

[0009] According to the illumination time and illumination intensity of two adjacent days, combined with the daily distributed photovoltaic output power curve and the daily load demand curve, determine the next day's distributed photovoltaic output power curve and the next day's load demand curve;

[0010] Determine the next day's energy storage margin of the energy storage system in the distributed photovoltaic and energy storage system according to the next day's distributed photovoltaic output power curve, the next day's load demand curve and the characteristic curve;

[0011] According to the energy storage margin of the next day and the output power curve of the distributed photovoltaic on the current day, the current day charging margin of the energy storage system is optimized 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, and the value of the current day charging margin is greater than the value of the energy storage margin of the next day;

[0012] 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 systems on the current day reaches a steady output power value, and the steady output power value meets the stability index, wherein, after the energy storage system stops outputting power on the current day, the charging method of the energy storage system is determined to charge the energy storage system until the energy storage system reaches the energy storage margin of the next day or the charging margin of the current day, and the energy storage system stops charging.

[0013] Optionally, 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 margin model is constructed, and the energy storage margin model is optimized by calculation to obtain a characteristic curve of the relationship between the daily energy storage margin and the output power and load demand, including:

[0014] According to the daily distributed photovoltaic output power curve and the daily load demand curve, a daily dual output power curve is obtained by statistical calculation, wherein the daily dual output power curve represents a power change curve from the beginning of the distributed photovoltaic output power to the output power meeting the daily load demand;

[0015] A model is constructed based on the Adaboost algorithm. Combined with the relationship between the life and capacity of the energy storage system, the energy storage margin corresponding to the daily dual output power curve is modeled. Through model training, cross-validation and parameter optimization operations, the optimized daily energy storage margin and output power, load demand relationship curve is obtained, wherein the daily energy storage margin is used to output power at the same time as the distributed photovoltaic starts to output power on that day, until the distributed photovoltaic output power on that day meets the load demand of that day and stops outputting power.

[0016] 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 systems on the current day reaches a stable output power value, wherein after the energy storage system stops outputting power on the current day, a charging method of the energy storage system is determined to charge the energy storage system until the energy storage system reaches the energy storage margin of the next day or the charging margin of the current day, and the energy storage system stops charging, including:

[0017] When the distributed photovoltaic system starts to output power on the current day, the output power of the distributed photovoltaic system does not meet the current day's load demand, and the energy storage system is controlled to output power using the current day's energy storage margin, so that the total output power of the distributed photovoltaic system and the energy storage system on the current day reaches a stable output power value;

[0018] If there is power obtained from the outside, when the sum of the distributed photovoltaic output power and the power obtained from the outside 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, and the power obtained from the outside is the power provided by the V2G electric vehicle that discharges to the distributed photovoltaic and energy storage system;

[0019] If there is no external power, when the distributed photovoltaic output power meets the current daily load demand, the energy storage system stops outputting power, and the current daily energy storage margin is close to 0;

[0020] After the energy storage system stops outputting power, if the sum of the real-time output power of the distributed photovoltaic and the power obtained from the outside is greater than the current daily load demand, the distributed photovoltaic and the power obtained from the outside jointly charge the energy storage system until the energy storage system reaches the energy storage margin for the next day or the charging margin for the current day, then charging of the energy storage system is stopped;

[0021] 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 power grid to generate power in parallel with the grid;

[0022] After the energy storage system stops outputting power, if the sum of the real-time output power of the distributed photovoltaic system and the power obtained from the outside is not greater than the current daily load demand, the distributed photovoltaic system and the power obtained from the outside will not charge the energy storage system. Thereafter, the distributed photovoltaic system and the energy storage system purchase electricity from the power grid during the off-peak period to charge the energy storage system until the energy storage system reaches the energy storage margin for the next day.

[0023] Optionally, the distributed photovoltaic power and the external power jointly charge the energy storage system until the energy storage system reaches the energy storage margin of the next day or the charging margin of the current day, and then stops charging the energy storage system, including:

[0024] During the process of the distributed photovoltaic and the external power jointly charging the energy storage system, if the output power of the distributed photovoltaic fails to meet the current daily load demand and the energy storage system has not yet reached the next day's energy storage margin, the distributed photovoltaic and the external power stop charging the energy storage system. Thereafter, the distributed photovoltaic and energy storage system purchase electricity from the power grid during the off-peak period to charge the energy storage system until the energy storage system reaches the next day's energy storage margin;

[0025] During the process of the distributed photovoltaic and the external power jointly charging the energy storage system, if the sum of the output power of the distributed photovoltaic and the external power does not meet the current daily load demand, and the energy storage system reaches the next day's energy storage margin, the distributed photovoltaic and the external power stop charging the energy storage system, and the energy storage system no longer receives charging;

[0026] During the process of the distributed photovoltaic and the external power jointly charging the energy storage system, if the sum of the output power of the distributed photovoltaic and the external power always meets the current daily load demand, the distributed photovoltaic and the external 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;

[0027] During the process of the distributed photovoltaic and the external power jointly charging the energy storage system, if the output power of the distributed photovoltaic always meets the current daily load demand, the external power is used to 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.

[0028] Optionally, when the distributed photovoltaic system starts to output power on the current day, the energy storage system is controlled to output power simultaneously, so that the total output power of the distributed photovoltaic system and the energy storage system on the current day reaches a stable output power value, including:

[0029] After the energy storage system stops charging on the current day and is charged to a level equal to the energy storage margin for the next day, if the sum of the output power of the distributed photovoltaic system and the power obtained from the outside fails to meet the load demand of the current day, the distributed photovoltaic system and energy storage system immediately purchases electricity from the power grid, so that the total output power of the distributed photovoltaic system and energy storage system on the current day reaches a stable output power value;

[0030] After the energy storage system stops charging on the current day and is charged to a level greater than the energy storage margin for the next day and less than the charging margin for the current day, if the sum of the output power of the distributed photovoltaic system and the power obtained from the outside fails to meet the load demand for the current day, 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 energy storage margin for the next day, and the energy storage system stops outputting power, after which the distributed photovoltaic system and energy storage system immediately purchase electricity from the power grid;

[0031] After the energy storage system stops charging on the current day and is charged to a level equal to the current day's charging margin, if the sum of the output power of the distributed photovoltaic system and the power obtained from the outside fails to meet the current day's load demand, the energy storage system is controlled to output power simultaneously using the current day's charging margin until the real-time storage capacity of the energy storage system is reduced to the next day's energy storage margin, and the energy storage system stops outputting power. Thereafter, the distributed photovoltaic and energy storage system immediately purchases electricity from the power grid.

[0032] Optionally, the stability index S is expressed as follows:

[0033]

[0034] In the above formula, N represents the time period between sunrise and sunset every day, w1(t) represents the output power of the distributed photovoltaic system in the t period on the output power curve of the daily distributed photovoltaic system, p represents the current daily energy storage margin, w2 represents the average output power of the distributed photovoltaic system obtained by calculating the historical data of the distributed photovoltaic output power, and w3 represents the average output power of the distributed photovoltaic system from sunrise to sunset every day.

[0035] Optionally, the function f of the cost minimization objective is:

[0036]

[0037] In the above formula, C1(t) represents the depreciation cost of distributed photovoltaic equipment and energy storage system equipment, C2(t) represents the maintenance cost of distributed photovoltaic equipment and energy storage system equipment, and C3(t) represents the cost of distributed photovoltaic and energy storage systems purchasing electricity from the power grid, wherein the cost of distributed photovoltaic and energy storage systems purchasing electricity from the power grid refers to: the cost of the distributed photovoltaic and energy storage system purchasing electricity from the power grid and the V2G electric vehicle, and the difference between the income earned by the distributed photovoltaic and energy storage system from connecting to the power grid and generating electricity for the V2G electric vehicle. The cost of the distributed photovoltaic and energy storage system purchasing electricity from the power grid is a positive value, indicating that the cost of purchasing electricity from the power grid and the V2G electric vehicle is greater than the income earned from power generation, and a negative value indicates that the cost of purchasing electricity from the power grid and the V2G electric vehicle is less than the income earned from power generation;

[0038] The expression of the cost C3(t) of the distributed photovoltaic and energy storage system purchasing electricity from the grid is:

[0039] C3(t)=(k i1 (t)w i1 (t)+k i2 (t)w i2 (t)-k j1 (t)w j1 (t)-k j2 (t)

[0040] w j2 (t))△t

[0041] In the above formula, k i1 (t) represents the electricity price purchased from the power grid during period t, w i1 (t) represents the power purchased by the distributed photovoltaic and energy storage system from the grid during period t, k i2 (t) represents the electricity price purchased from the V2G electric vehicle during period t, w i2 (t) represents the power purchased by the distributed photovoltaic and energy storage system from the V2G electric vehicle during period t, k j1 (t) represents the electricity price for power grid during period t, w j1 (t) represents the output power of the distributed photovoltaic and energy storage system connected to the grid during period t, k j2 (t) represents the electricity price for the V2G electric vehicle during period t, w j2 (t) represents the output power of the distributed photovoltaic and energy storage system to the V2G electric vehicle during period t, and Δt represents the time period;

[0042] The power purchase power w of the distributed photovoltaic and energy storage system in the period t i(t) According to the gap of energy storage margin corresponding to time period t, the power w purchased by the distributed photovoltaic and energy storage system from the V2G electric vehicle during time period t i2 (t) and the gap of the load demand value, the gap of the energy storage margin corresponding to the t period is determined according to the difference between the next day's energy storage margin and the current charging storage energy of the energy storage system, and the gap of the load demand is determined according to the output power of the distributed photovoltaic corresponding to the t period and the power w purchased by the distributed photovoltaic and energy storage system from the V2G electric vehicle in the t period i2 (t), and the difference between the load demand value corresponding to the time period t;

[0043] The grid-connected output power w of the distributed photovoltaic and energy storage system to the grid during the period t j (t) and the output power generated for the V2G electric vehicle, which is determined by the current daily charging margin of the energy storage system obtained by optimization and the output power of the distributed photovoltaic corresponding to the time period t.

[0044] Optionally, according to the energy storage margin of the next day and the output power curve of the distributed photovoltaic system of the current day, with the goal of minimizing the cost of the V2G electric vehicle participating in the charging and discharging of the distributed photovoltaic system and the energy storage system, the current day charging margin of the energy storage system is optimized, and the value of the current day charging margin is greater than the value of the energy storage margin of the next day, including:

[0045] Step S1: Select the latter curve of the output power curve of the distributed photovoltaic on the current day, and divide it into multiple t time periods, wherein the latter curve refers to the sum of the output power of the distributed photovoltaic on the current day and the power obtained from the outside, and corresponds to the time period from meeting the current daily load demand value to the distributed photovoltaic no longer outputting power;

[0046] Step S2: According to the energy storage margin of the next day, the latter curve and the full charge of the energy storage system, the cost of purchasing electricity from the power grid for the distributed photovoltaic and energy storage system C3(t) is expressed to calculate the cost of purchasing electricity for multiple t periods. At the same time, the depreciation cost C1(t) of the distributed photovoltaic equipment and energy storage system equipment and the maintenance cost C2(t) of the distributed photovoltaic equipment and energy storage system equipment are expressed to calculate the depreciation cost and maintenance cost for multiple t periods respectively.

[0047] Step S2: The cost of purchasing electricity, depreciation cost, and maintenance cost in multiple t periods are used as the initial solution of the genetic algorithm;

[0048] Step S3: Based on the initial solution, the optimal value of the cost of purchasing electricity, the depreciation cost, and the maintenance cost in multiple time periods t and the minimum value of the minimum cost are calculated through the function formula of the cost minimization target;

[0049] Step S4: Calculate the optimal value of the generation of costs, depreciation costs, and maintenance costs of the multiple t periods and the minimum value of the generation of costs with the minimum cost to obtain the corresponding centroids;

[0050] Step S5: breeding to generate new groups according to the centroid;

[0051] Step S6: Generate two offspring by crossover according to each pair of sets in the new population generated by reproduction;

[0052] Step S7: Let q be the probability of mutation, then the mutation operation is completed by randomly replacing an element in the set;

[0053] Step S8: After the "crossover" and "mutation" operations, a new solution is generated, wherein the new solution represents the current daily charging margin of the energy storage system obtained after optimizing the full charge capacity of the energy storage system;

[0054] Step S9: using the new solution as the initial solution of the genetic algorithm, the sampling iterative algorithm repeats steps S3 to S9, and after each iterative operation, it is detected whether the end condition is met, and the end condition is that the upper limit of the number of iterations is reached, or the function value with the minimum cost is less than the preset function value;

[0055] Among them, the specific method of the iterative algorithm is:

[0056] For any set of initial solutions XJ i The new solution XJ generated i ′, if f(XJ i )≥f(XJ i ′), then directly accept this new solution XJ i ′;

[0057] If f(XJ i )<f(XJ i ′), then the new solution XJ i The acceptance probability E of ′ is obtained by the following formula:

[0058]

[0059] 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 The continuous decrease of f(XJ i )-f(XJ i ′) is constant, the value of E will gradually decrease, making the iterative algorithm stable;

[0060] After each iteration, R wPerform the "heat reduction" operation. The formula corresponding to the heat reduction operation is:

[0061]

[0062] Where MT is the upper limit of the number of iterations of the iterative algorithm.

[0063] Optionally, the expression for the relationship between the life and capacity of the energy storage system is as follows:

[0064] E n (t) = E b -∑k*e -0.02Soc *M 0.5 *D 0.7 .

[0065] 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 proportionality coefficient, Soc represents the average state of charge value of a single cycle of the battery in the energy storage system, 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.

[0066] A second aspect of an embodiment of the present invention provides an electronic device, including:

[0067] at least one processor; and,

[0068] a memory communicatively connected to the at least one processor; wherein,

[0069] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the energy storage optimization method of the distributed photovoltaic and energy storage system as described in any one of the first aspects.

[0070] 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 operates the energy storage margin model to obtain the optimized characteristic curve of the relationship between the daily energy storage margin and the output power and load demand. Secondly, the output power curve of the distributed photovoltaic and the load demand curve of the next day and the energy storage margin of the energy storage system of the next day are determined.

[0071] Then, based on the energy storage margin of the next day and the output power curve of the distributed photovoltaic system on the current day, the current day's charging margin of the energy storage system is optimized with the goal of minimizing cost; finally, when the distributed photovoltaic system 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 system and the energy storage system on the current day reaches a stable output power value and meets the stability index.

[0072] The present invention carefully combines the characteristics of distributed photovoltaic output power, the life and capacity characteristics of the energy storage system, cost factors and other aspects, takes cost minimization as the goal, optimizes the relationship between the daily energy storage margin and the output power and load demand relationship curve, optimizes the current day's charging margin, and obtains a more accurate daily energy storage capacity 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 whole process has a small amount of calculation, simple operation, less calculation time, simple control logic, and high practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0074] Figure 1 is a flow chart of a method for optimizing energy storage of a distributed photovoltaic and energy storage system in an embodiment of the present invention;

[0075] Figure 2 It 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 DESCRIPTION

[0076] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0077] A method for optimizing the energy storage capacity of a distributed photovoltaic and energy storage system is provided in an embodiment of the present invention. Figure 1 , shows a flow chart of a method for optimizing energy storage of a distributed photovoltaic and energy storage system according to an embodiment of the present invention, the method for optimizing energy storage comprises the following steps:

[0078] Step 101: Based on historical load data and distributed photovoltaic output power historical data, daily distributed photovoltaic output power curve and daily load demand curve are calculated. The distributed photovoltaic output power curve and load demand curve on different days correspond to different illumination time and illumination intensity.

[0079] Since the output power and voltage of distributed photovoltaics are greatly affected by factors such as illumination time, diurnality, and seasonality, the daily output power curve of distributed photovoltaics may be different. The load demand is relatively stable, and there are fewer cases of sudden increases or decreases in load demand on similar days. For example, the daily load demand on weekdays is basically similar, and the load demand on weekends is basically similar. In order to more accurately control the cost, the total volt output power of distributed photovoltaics and energy storage systems, and control the daily storage capacity of the energy storage system, reduce costs, and extend the service life of the energy storage system, it is necessary to obtain the daily distributed photovoltaic output power curve and the daily load demand curve. The distributed photovoltaic output power curve and load demand curve on different days correspond to different illumination times and light intensities. These two curves can be calculated based on historical load data and distributed photovoltaic output power historical data. Of course, they can also be obtained by other means, and the present invention does not make specific limitations.

[0080] Step 102: Based on the daily output power curve of distributed photovoltaics, the daily load demand curve, and 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 characteristic curve of the optimized daily energy storage margin and the relationship between the output power and the load demand.

[0081] After obtaining the two curves, we then build an energy storage margin model based on the relationship between the life and capacity of the energy storage system. We then calculate this energy storage margin model to obtain a more accurate relationship curve between the daily energy storage margin of the energy storage system and the output power and load demand of distributed photovoltaics, that is, to obtain the optimized daily energy storage margin and output power, load demand relationship curve.

[0082] Step 103: Determine the next day's distributed photovoltaic output power curve and the next day's load demand curve according to the illumination time and illumination intensity of two adjacent days and in combination with the daily distributed photovoltaic output power curve and the daily load demand curve.

[0083] As described in step 101, the output power of distributed photovoltaics may be different every day, determined by the daily illumination time and light intensity. The daily load demand also has differences similar to working days and rest days, and will also differ due to differences in illumination time and light intensity. Therefore, it is necessary to determine the illumination time and light intensity based on the illumination time and light intensity of the current day, and then determine the output power curve of the distributed photovoltaics and the load demand curve of the next day based on the illumination time and light intensity of the next day, so as to lay a good foundation for the subsequent accurate calculation of the energy storage margin of the next day.

[0084] Step 104: Determine the next day's energy storage margin of the distributed photovoltaic and energy storage systems according to the next day's distributed photovoltaic output power curve, the next day's load demand curve and the characteristic curve.

[0085] After obtaining the output power curve of the distributed photovoltaic system for the next day and the load demand curve for the next day, combined with the characteristic curve of the relationship between the daily energy storage margin and the output power and load demand obtained in step 102, the energy storage margin of the energy storage system for the next day can be accurately determined. The purpose of this energy storage margin for the next day is to ensure that the total output power of the distributed photovoltaic system and the energy storage system for the next day reaches a stable output power value that meets the stability index. Therefore, the energy storage margin for the next day is equivalent to the minimum energy storage capacity that the energy storage system needs to achieve when charging on the current day, but it is used tomorrow. It is naturally understandable that the energy storage capacity of the energy storage system used when the distributed photovoltaic system starts to output power on the current day is the energy storage margin of the previous day.

[0086] Step 105: Based on the energy storage margin of the next day and the output power curve of the distributed photovoltaic system of the current day, the current day charging margin of the energy storage system is optimized with the goal of minimizing the cost when the V2G electric vehicle participates in the charging and discharging of the distributed photovoltaic system and the energy storage system. The value of the current day charging margin is greater than the value of the current day energy storage margin.

[0087] The so-called minimum cost should not only consider connecting more power generation to the grid to earn profits 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 current daily charging margin for the 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, purchase as little electricity from the grid as possible, and at the same time enable distributed photovoltaics to connect a certain amount of electricity to the grid to earn profits, while taking into account the life factor of the energy storage system.

[0088] In addition, considering that the existing V2G electric vehicles have the function of outputting electric energy to the power grid or related energy storage equipment, the charging and discharging of V2G electric vehicles in the distributed photovoltaic and energy storage system are taken into account. For example: for a distributed photovoltaic and energy storage system, the historical situation of V2G electric vehicles participating in charging and discharging can be determined based on the travel plan of the 2G electric vehicle map and the future charging and discharging situation of the same destination (such as the location of the distributed photovoltaic and energy storage system), forming a database, so as to accurately predict the daily charging and discharging situation of V2G electric vehicles participating in the distributed photovoltaic and energy storage system, so as to minimize the cost and adaptively adjust the energy storage margin of the next day, or the charging margin of the current day, while minimizing the 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 good foundation for the future sustainable development of electric vehicles.

[0089] Step 106: When the distributed photovoltaic system 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 system and the energy storage system on the current day reaches a stable output power value, and the stable output power value meets the stability index. After the energy storage system stops outputting power on the current day, the charging method of the energy storage system is determined to charge the energy storage system until the energy storage system reaches the energy storage margin for the next day or the charging margin for the current day, and the energy storage system stops charging.

[0090] When the distributed photovoltaic system starts to output power on the current day, its real-time power will definitely not meet the current day's load demand. Therefore, 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 total output power of the distributed photovoltaic system and the energy storage system on the current day can meet the current day's load demand while reaching a stable output power value and the stable output power value meets the stability index, and there is no need to purchase electricity from the power grid.

[0091] When the energy storage margin of the previous day is exhausted, the energy storage system stops outputting power. At this time, the real-time output power of distributed photovoltaics rises to meet the load demand. Then the charging method of the energy storage system is determined to charge the energy storage system in different ways until the energy storage system reaches the energy storage margin of the next day or the charging margin of the current day. The specific charging situation is described below and will not be repeated here.

[0092] Through the above method, the distributed photovoltaic output power characteristics, energy storage system life and capacity characteristics, cost factors and other aspects are carefully combined, with the goal of minimizing cost, optimizing the relationship between the daily energy storage margin and the output power and load demand curve, optimizing the current day's charging margin, and obtaining a more accurate daily energy storage capacity 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 whole process has less calculation, simple operation, less calculation time, and simple control logic.

[0093] In a possible embodiment, the specific method of step 102 may include:

[0094] Step T1: According to the daily distributed photovoltaic output power curve and the daily load demand curve, statistical calculation is performed to obtain the daily dual output power curve, which represents the power change curve from the time when the distributed photovoltaic starts to output power to the time when the output power meets the daily load demand.

[0095] After obtaining the daily distributed photovoltaic output power curve and the daily load demand curve in step 101, statistical operations are performed on the two curves to obtain a daily dual output power curve. The daily dual output power curve represents the power change curve from the time when the distributed photovoltaic starts to output power to the time when the output power meets the daily load demand.

[0096] As mentioned earlier, distributed photovoltaics begin to output power after receiving strong light at sunrise. The real-time power at this time is relatively small and generally cannot meet the load demand in the same period. Later, as the earth rotates and the solar altitude angle changes, the light intensity begins to increase, and the output power of distributed photovoltaics naturally begins to rise until the maximum operating output power of distributed photovoltaics is reached. This maximum operating output power generally exceeds the load demand.

[0097] 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 from the time when the distributed photovoltaic starts to output power to the time when the output power meets the daily load demand.

[0098] Step T2: 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 obtain the optimized characteristic curve of the relationship between daily energy storage margin, output power and load demand through model training, cross-validation and parameter optimization operations. The daily energy storage margin is used to output power at the same time as the distributed photovoltaic starts to output power on that day, until the distributed photovoltaic output power on that day meets the load demand of that day and stops outputting power.

[0099] After obtaining the power change curve from the time when the distributed photovoltaic starts to output power to the time when the output power meets the daily load demand, a model is built based on the Adaboost algorithm in combination with the relationship between the life and capacity of the energy storage system, and the energy storage margin corresponding to the obtained power change curve is modeled. Then, through model training, cross-validation and parameter optimization operations, the optimized characteristic curve of the relationship between daily energy storage margin, output power and load demand can be obtained. This characteristic curve accurately reflects the power relationship between the daily energy storage margin of the energy storage system, the time period when the output power of the distributed photovoltaics has not yet met the load demand, and the load demand during this period.

[0100] In a possible embodiment, the expression of the energy storage system life-capacity relationship is as follows:

[0101] E n (t) = E b -∑k*e -0.02Soc *M 0.5 *D 0.7 .

[0102] In the above formula, E n (t) represents the capacity of the energy storage system in period t, E bIt represents the initial capacity of the energy storage system before it is put into use, k represents the proportionality coefficient, Soc represents the average state of charge value of the battery in a single cycle in the energy storage system, 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.

[0103] In a possible embodiment, the specific method of step 106 may include: an early stage in which the energy storage system outputs power and a later stage in which the energy storage system charges.

[0104] 1) For the output power stage: when the distributed photovoltaic starts to output power on the current day, the output power of the distributed photovoltaic does not meet the load demand of the current period. 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 distributed photovoltaic and energy storage system on the current day reaches the output power stable value; as the distributed photovoltaic output power increases, when the distributed photovoltaic output power meets the current day's load demand, the energy storage system stops outputting power. At this time, the energy storage margin of the previous day is close to 0, that is, the energy storage margin of the previous day is consumed.

[0105] In the embodiment of the present invention, the expression of the stability index S is as follows:

[0106]

[0107] In the above formula, N represents the time period from sunrise to sunset every day, w1(t) represents the output power of distributed photovoltaics in the t period on the output power curve of daily distributed photovoltaics, p represents the current daily energy storage margin, w2 represents the average output power of distributed photovoltaics calculated based on the historical data of distributed photovoltaic output power, and w3 represents the average output power of distributed photovoltaics from sunrise to sunset every day.

[0108] It should be noted here that due to weather and other reasons, the distributed photovoltaic system may not output power on the current day, or the weather may clear up after 12 noon, and the distributed photovoltaic system may start to output power. When this happens, in order to increase 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 subsequent calculation to obtain the energy storage margin of the next day, if the energy storage margin of the next day is not greater than the energy storage margin of the previous day, the charging method is followed to wait for charging to the next day's charging margin or not charging; if the energy storage margin of the next day is greater than the energy storage margin of the previous day, the charging method is followed to wait for charging to the next day's energy storage margin or the current day's charging margin.

[0109] In addition to the period from the start of power output to meeting the load demand, the output power of distributed photovoltaics may fail to meet the load demand as the afternoon sunshine decreases, and the difference with the load demand becomes larger and larger. When this happens, the following situations can be classified according to the current storage capacity of the energy storage system:

[0110] A) When the energy storage system stops charging on the current day and is charged to the current day's energy storage margin, that is, the energy storage system has been charged to the current day's energy storage margin, if the sum of the output power of the distributed photovoltaic and the external power (the external power is the power provided by the V2G electric vehicle that discharges to the distributed photovoltaic and energy storage system) does not meet the current day's load demand, since the energy storage margin of the next day cannot be consumed on the current day, the distributed photovoltaic and energy storage system immediately purchases electricity from the power grid, so that the total output power of the distributed photovoltaic and energy storage system on the current day reaches the output power stable value.

[0111] B) When the energy storage system stops charging on the current day, it is charged to a level 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 day's charging margin. If the output power of the distributed photovoltaic and the power obtained from the outside do not meet the current day's load demand, since the storage energy that is higher than the next day's energy storage margin can be consumed, the energy storage system is controlled to use the storage energy of the energy storage system when charging is stopped to output power at the same time, until the real-time storage energy of the energy storage system is reduced to the next day's energy storage margin, and the energy storage system stops outputting power. After that, the distributed photovoltaic and energy storage systems immediately purchase electricity from the power grid.

[0112] C) When the energy storage system stops charging on the current day and is charged to the current day's charging margin, that is, the energy storage system has been charged to the current day's charging margin. If the output power of the distributed photovoltaic power generation and the power obtained from the outside do not meet the current day's load demand, since the storage energy that is higher than the next day's energy storage margin can be consumed, the energy storage system is controlled to output power at the same time using the current day's charging margin until the real-time storage capacity of the energy storage system is reduced to the next day's energy storage margin, and the energy storage system stops outputting power. After that, the distributed photovoltaic power generation and energy storage system immediately purchase electricity from the power grid.

[0113] 2) For the charging stage: after 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 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 is greater and exceeds the load demand, then the distributed photovoltaics and the power obtained from the outside will start to charge the energy storage system together, and will not be connected 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, and then stop charging the energy storage system. The determining factor here is whether the sum of the real-time power of distributed photovoltaics and the power obtained from the outside continues to be greater than the current daily load demand. If the sum of the real-time power of distributed photovoltaics and the power obtained from the outside continues to be greater than the current daily load demand for a long time, when the energy storage system is charged to the next day's energy storage margin, 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, then it will continue to be charged to the current daily charging margin, at which time charging will stop, and the distributed photovoltaics will start to output power to the grid to earn revenue by connecting to the grid for power generation.

[0114] 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 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 just meets the load demand and there is no excess output power, then neither the distributed photovoltaics nor the power obtained from the outside will charge the energy storage system, and the entire system will not purchase electricity from the grid, because the electricity price of the grid is at the peak period, and its price is relatively high, and the energy storage system does not need to output power thereafter, and does not need to store energy temporarily. Therefore, the distributed photovoltaic and energy storage systems can choose to purchase electricity from the grid to charge the energy storage system during the valley period when the electricity price is lower, until the energy storage system reaches the energy storage margin for the next day, without having to charge to the current daily charging margin.

[0115] In the specific process of distributed photovoltaic charging to the energy storage system, there are several situations:

[0116] 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 next day's 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, but due to various factors, such as sudden changes in weather, the real-time power of distributed photovoltaics has dropped and no longer meets the load demand, or it just meets the load demand and there is no excess output power to charge the energy storage system. Then the distributed photovoltaics and the power obtained from the outside immediately stop charging the energy storage system. After that, the distributed photovoltaics and the energy storage system purchase electricity from the power grid during the valley period to charge the energy storage system, or the sum of the subsequent real-time power of distributed photovoltaics and the power obtained from the outside is greater than the load demand, then continue to charge the energy storage system, instead of the distributed photovoltaics and the energy storage system purchasing electricity from the power grid during the valley period to charge the energy storage system. In either case, it will continue until the energy storage system reaches the next day's energy storage margin.

[0117] F) During the process of distributed photovoltaic charging the energy storage system, if the sum of the output power of distributed photovoltaic 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 photovoltaic and the power obtained from the outside is greater than the load demand, the energy storage system is being charged and the energy storage system reaches the next day's energy storage margin, but due to various factors, such as sudden changes in weather, the real-time power of distributed photovoltaic has dropped and 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 power obtained from the outside stop charging the energy storage system, and the energy storage system no longer receives charging, even if the subsequent real-time power of distributed photovoltaic and the power obtained from the outside are greater than the load demand, it will not continue to charge the energy storage system.

[0118] G) During the process of distributed photovoltaics charging the energy storage system, if the sum of the output power of distributed photovoltaics and the power obtained from the outside always meets the current daily load demand, that is, the sum of the real-time power of distributed photovoltaics and the power obtained from the outside is always higher than the load demand, then the distributed photovoltaics 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.

[0119] H) In the process of charging the energy storage system together with distributed photovoltaics and external power, if the output power of distributed photovoltaics always meets the current daily load demand, that is, the output power of distributed photovoltaics alone has met the current daily load demand, then the external power is directly used to 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.

[0120] In a possible embodiment, the function f of the cost minimization objective may be defined as;

[0121]

[0122] In the above formula, C1(t) represents the depreciation cost of distributed photovoltaic equipment and energy storage system equipment, C2(t) represents the maintenance cost of distributed photovoltaic equipment and energy storage system equipment, and C3(t) represents the cost of distributed photovoltaic and energy storage systems purchasing electricity from the grid. Among them, the cost of distributed photovoltaic and energy storage systems purchasing electricity from the grid refers to the difference between the cost of distributed photovoltaic and energy storage systems purchasing electricity from the grid and V2G electric vehicles and the income earned by distributed photovoltaic and energy storage systems from connecting to the grid and generating electricity for V2G electric vehicles. The cost of distributed photovoltaic and energy storage systems purchasing electricity from the grid is a positive value, indicating that the cost of purchasing electricity from the grid and V2G electric vehicles is greater than the income earned from power generation, and a negative value indicates that the cost of purchasing electricity from the grid and V2G electric vehicles is less than the income earned from power generation.

[0123] Among them, the expression of the cost C3(t) of distributed photovoltaic and energy storage systems purchasing electricity from the grid is:

[0124] C3(t)=(k i1 (t)w i1 (t)+k i2 (t)w i2 (t)-k j1 (t)w j1 (t)-k j2 (t)

[0125] w j2 (t))△t

[0126] In the above formula, k i1 (t) represents the electricity price purchased from the power grid during period t, w i1 (t) represents the power purchased by the distributed photovoltaic and energy storage systems from the grid during period t, k i2 (t) represents the electricity price purchased from V2G electric vehicles during period t, w i2 (t) represents the power purchased by the distributed photovoltaic and energy storage system from the V2G electric vehicle during period t, k j1 (t) represents the electricity price for power grid during period t, w j1 (t) represents the output power of the distributed photovoltaic and energy storage systems connected to the grid during period t, k j2 (t) represents the electricity price for V2G electric vehicles during period t, w j2 (t) represents the output power of the distributed photovoltaic and energy storage system to the V2G electric vehicle during period t, and △t represents the time period.

[0127] Among them, the power purchase power w of the distributed photovoltaic and energy storage system in period t i1 (t) According to the gap of energy storage margin corresponding to time period t, the power w purchased by distributed photovoltaic and energy storage systems from V2G electric vehicles in time period t i2 (t) and the gap in load demand, and the gap in energy storage margin corresponding to time period t is determined by the difference between the energy storage margin of the next day and the current charging storage capacity of the energy storage system. The gap in load demand is determined by the output power of distributed photovoltaics corresponding to time period t and the power w purchased by distributed photovoltaics and energy storage systems from V2G electric vehicles during time period t. i2 (t) and the difference between the load demands corresponding to time period t.

[0128] The grid-connected output power w of the distributed photovoltaic and energy storage systems to the grid during period t j1 (t) is determined by the sum of the output power of the energy storage system obtained by optimization and the output power of the distributed photovoltaic power generation corresponding to the period t.

[0129] Based on the above-mentioned minimum cost calculation method, in a possible embodiment, the specific method of step 105 may include:

[0130] Step S1: Select the latter curve of the output power curve of the distributed photovoltaic power generation on the current day, and divide it into multiple t time periods. The latter curve refers to the sum of the output power of the distributed photovoltaic power generation on the current day and the power obtained from the outside, and corresponds to the time period from meeting the current daily load demand value to the distributed photovoltaic power generation no longer outputting power;

[0131] Step S2: According to the current daily energy storage margin, the back-end curve and the full charge of the energy storage system, the cost of purchasing electricity from the power grid for the distributed photovoltaic and energy storage systems C3(t) is expressed to calculate the cost of purchasing electricity for multiple t periods. At the same time, the depreciation cost C1(t) of the distributed photovoltaic equipment and energy storage system equipment and the maintenance cost C2(t) of the distributed photovoltaic equipment and energy storage system equipment are expressed to calculate the depreciation cost and maintenance cost for multiple t periods respectively.

[0132] Step S2: The cost of purchasing electricity, depreciation cost, and maintenance cost in multiple t periods are used as the initial solution of the genetic algorithm;

[0133] Step S3: Based on the initial solution, the optimal value of the cost of purchasing electricity, the depreciation cost, and the maintenance cost in multiple t periods and the minimum value of the minimum cost are calculated through the function formula of the cost minimization objective;

[0134] Step S4: Calculate the optimal value of the generation of cost, depreciation cost, and maintenance cost of each of the multiple t periods and the minimum value of the generation of cost, and obtain the corresponding centroid;

[0135] Step S5: breeding to generate new groups according to the centroid;

[0136] Step S6: Generate two offspring by crossover according to each pair of sets in the new population generated by reproduction;

[0137] Step S7: Let q be the probability of mutation, then the mutation operation is completed by randomly replacing an element in the set;

[0138] 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 charging capacity of the energy storage system;

[0139] Step S9: Using the new solution as the initial solution of the genetic algorithm, the sampling iterative algorithm repeats steps S3 to S9, and checks whether the end condition is met after each iterative operation. The end condition is that the upper limit of the number of iterations is reached, or the function value with the minimum cost is less than the preset function value.

[0140] After obtaining a new solution, the new solution is used as the initial solution of the genetic algorithm. Instead of repeating the operation according to the evolutionary algebra and the maximization algebra, the iterative algorithm is used to repeat steps S3 to S9, and after each iteration, it is checked 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 minimum cost is less than the preset function value. For example, if the preset function value is 10, then when the function value with the minimum cost is less than 10 after a certain iteration, the genetic algorithm ends.

[0141] 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. After the "crossover" and "mutation" operations generate a new solution, it no longer repeats the operation according to the evolution algebra and the maximization algebra. Instead, it uses an iterative algorithm to repeat steps S3 to S9, and detects whether the end condition of the iterative algorithm is met. When the end condition is met, it ends directly, thereby reducing the amount of computation of the genetic algorithm and improving the efficiency, speed and accuracy of optimizing the current daily charging margin and grid-connected power generation.

[0142] Based on the above algorithm, in a possible embodiment, the specific method of the iterative algorithm is:

[0143] For any set of initial solutions XJ i The new solution XJ generated i ′, if f(XJ i )≥f(XJ i ′), then directly accept this new solution XJ i ′;

[0144] If f(XJ i )<f(XJ i ′), then the new solution XJ i The acceptance probability E of ′ is obtained by the following formula:

[0145]

[0146] 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 The continuous decrease of f(XJ i )-f(XJ i ′) is constant, the value of E will gradually decrease, making the iterative algorithm stable;

[0147] After each iteration, R w Perform the "heat reduction" operation. The formula corresponding to the heat reduction operation is:

[0148]

[0149] Where MT is the upper limit of the number of iterations of the iterative algorithm. Adjust the energy storage system according to the travel plan, etc.

[0150] Based on the above-mentioned distributed photovoltaic and energy storage system energy storage optimization method, the embodiment of the present invention also proposes a distributed photovoltaic and energy storage system energy storage optimization device, referring to Figure 2 , the energy storage optimization device includes:

[0151] The calculation module 210 is used to calculate and obtain the daily distributed photovoltaic output power curve and the daily load demand curve according to the historical load data and the distributed photovoltaic output power historical data. The distributed photovoltaic output power curve and the load demand curve on different days correspond to different illumination time and illumination intensity.

[0152] 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 to calculate the energy storage margin model to obtain an optimized characteristic curve of the relationship between the daily energy storage margin and the output power and load demand;

[0153] The next day curve determination module 230 is used to determine the next day's distributed photovoltaic output power curve and the next day's load demand curve according to the illumination time and illumination intensity of two adjacent days, combined with the daily distributed photovoltaic output power curve and the daily load demand curve;

[0154] The energy storage margin determination module 240 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;

[0155] The optimization module 250 is used to optimize the current day charging margin of the energy storage system according to the energy storage margin of the next day and the output power curve of the distributed photovoltaic system of the current day, with the goal of minimizing the cost of the V2G electric vehicle participating in the charging and discharging of the distributed photovoltaic system and the energy storage system, and the value of the current day charging margin is greater than the value of the current day energy storage margin;

[0156] The control module 260 is used to control the energy storage system to output power at the same time when the distributed photovoltaic starts to output power on the current day, so that the total output power of the distributed photovoltaic and energy storage systems on the current day reaches the output power steady value, and the output power steady value meets the stability index. After the energy storage system stops outputting power on the current day, the charging method of the energy storage system is determined to charge the energy storage system until the energy storage system reaches the energy storage margin for the next day or the charging margin for the current day, and the energy storage system stops charging.

[0157] Based on the above-mentioned energy storage optimization method of the distributed photovoltaic and energy storage system, an embodiment of the present invention further proposes an electronic device, including:

[0158] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the energy storage optimization method for a distributed photovoltaic and energy storage system as described in any one of steps 101 to 106.

[0159] Based on the above-mentioned distributed photovoltaic and energy storage system energy storage optimization method, an embodiment of the present invention further proposes a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the distributed photovoltaic and energy storage system energy storage optimization method as described in any one of steps 101 to 106.

[0160] Through the above examples, 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 operates the energy storage margin model to obtain the optimized characteristic curve of the relationship between the daily energy storage margin and the output power and load demand. Secondly, the output power curve of the distributed photovoltaic system and the load demand curve of the next day and the energy storage margin of the energy storage system for the next day are determined.

[0161] Then, based on the energy storage margin of the next day and the output power curve of the distributed photovoltaic system on the current day, the current day's charging margin of the energy storage system is optimized with the goal of minimizing cost; finally, when the distributed photovoltaic system 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 system and the energy storage system on the current day reaches a stable output power value and meets the stability index.

[0162] The present invention carefully combines the characteristics of distributed photovoltaic output power, the life and capacity characteristics of the energy storage system, cost factors and other aspects, takes cost minimization as the goal, optimizes the relationship between the daily energy storage margin and the output power and load demand relationship curve, optimizes the current day's charging margin, and obtains a more accurate daily energy storage capacity 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 whole process has a small amount of calculation, simple operation, less calculation time, simple control logic, and high practical value.

[0163] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0164] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are within the protection of the present invention.

Claims

1. A method for optimizing energy storage of a distributed photovoltaic and energy storage system, characterized in that: The energy storage optimization method comprises: According to the historical load data and distributed photovoltaic output power historical data, the daily distributed photovoltaic output power curve and the daily load demand curve are calculated. The distributed photovoltaic output power curve and load demand curve on different days correspond to different illumination time and illumination 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, an energy storage margin model is constructed, and the energy storage margin model is calculated to obtain an optimized characteristic curve of the relationship between the daily energy storage margin and the output power and load demand; According to the illumination time and illumination intensity of two adjacent days, combined with the daily distributed photovoltaic output power curve and the daily load demand curve, determine the next day's distributed photovoltaic output power curve and the next day's load demand curve; Determine the next day's energy storage margin of the energy storage system in the distributed photovoltaic and energy storage system according to the next day's distributed photovoltaic output power curve, the next day's load demand curve and the characteristic curve; According to the energy storage margin of the next day and the output power curve of the distributed photovoltaic on the current day, the current day charging margin of the energy storage system is optimized 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, and the value of the current day charging margin is greater than the value of the energy storage margin of the next day; 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 systems on the current day reaches a steady output power value, and the steady output power value meets the stability index, wherein, after the energy storage system stops outputting power on the current day, the charging method of the energy storage system is determined to charge the energy storage system until the energy storage system reaches the energy storage margin of the next day or the charging margin of the current day, and 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 margin model is constructed, and the energy storage margin model is optimized by calculation to obtain a characteristic curve of the relationship between the daily energy storage margin and the output power and load demand, including: According to the daily distributed photovoltaic output power curve and the daily load demand curve, a daily dual output power curve is obtained by statistical calculation, wherein the daily dual output power curve represents a power change curve from the beginning of the distributed photovoltaic output power to the output power meeting the daily load demand; A model is constructed based on the Adaboost algorithm. Combined with the relationship between the life and capacity of the energy storage system, the energy storage margin corresponding to the daily dual output power curve is modeled. Through model training, cross-validation and parameter optimization operations, the optimized daily energy storage margin and output power, load demand relationship curve is obtained, wherein the daily energy storage margin is used to output power at the same time as the distributed photovoltaic starts to output power on that day, until the distributed photovoltaic output power on that day meets the load demand of that day and stops outputting power.

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 total output power of the distributed photovoltaic and energy storage systems on the current day reaches the output power steady value, wherein after the energy storage system stops outputting power on the current day, the charging mode of the energy storage system is determined to charge the energy storage system until the energy storage system reaches the energy storage margin of the next day or the charging margin of the current day, and the energy storage system stops charging, including: When the distributed photovoltaic system starts to output power on the current day, the output power of the distributed photovoltaic system does not meet the current day's load demand, and the energy storage system is controlled to output power using the current day's energy storage margin, so that the total output power of the distributed photovoltaic system and the energy storage system on the current day reaches a stable output power value; If there is power obtained from the outside, when the sum of the distributed photovoltaic output power and the power obtained from the outside 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, and the power obtained from the outside is the power provided by the V2G electric vehicle that discharges to the distributed photovoltaic and energy storage system; If there is no external power, when the distributed photovoltaic output power meets the current daily load demand, the energy storage system stops outputting power, and 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 power obtained from the outside is greater than the current daily load demand, the distributed photovoltaic and the power obtained from the outside jointly charge the energy storage system until the energy storage system reaches the energy storage margin for the next day or the charging margin for the current day, then charging of the energy storage system is stopped; 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 power grid to generate power in parallel with the grid; After the energy storage system stops outputting power, if the sum of the real-time output power of the distributed photovoltaic system and the power obtained from the outside is not greater than the current daily load demand, the distributed photovoltaic system and the power obtained from the outside will not charge the energy storage system. Thereafter, the distributed photovoltaic system and the energy storage system purchase electricity from the power grid during the off-peak period to charge the energy storage system until the energy storage system reaches the energy storage margin for the next day.

4. The energy storage optimization method according to claim 3, characterized in that: The distributed photovoltaic and the external power jointly charge the energy storage system until the energy storage system reaches the energy storage margin of the next day or the charging margin of the current day, and then stops charging the energy storage system, including: During the process of the distributed photovoltaic and the external power jointly charging the energy storage system, if the output power of the distributed photovoltaic fails to meet the current daily load demand and the energy storage system has not yet reached the next day's energy storage margin, the distributed photovoltaic and the external power stop charging the energy storage system. Thereafter, the distributed photovoltaic and energy storage system purchase electricity from the power grid during the off-peak period to charge the energy storage system until the energy storage system reaches the next day's energy storage margin; During the process of the distributed photovoltaic and the external power jointly charging the energy storage system, if the sum of the output power of the distributed photovoltaic and the external power does not meet the current daily load demand, and the energy storage system reaches the next day's energy storage margin, the distributed photovoltaic and the external power stop charging the energy storage system, and the energy storage system no longer receives charging; During the process of the distributed photovoltaic and the external power jointly charging the energy storage system, if the sum of the output power of the distributed photovoltaic and the external power always meets the current daily load demand, the distributed photovoltaic and the external 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 the distributed photovoltaic and the external power jointly charging the energy storage system, if the output power of the distributed photovoltaic always meets the current daily load demand, the external power is used to 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.

5. The energy storage optimization method according to claim 3, characterized in that: When the distributed photovoltaic system starts to output power on the current day, the energy storage system is controlled to output power simultaneously, so that the total output power of the distributed photovoltaic system and the energy storage system on the current day reaches a stable output power value, including: After the energy storage system stops charging on the current day and is charged to a level equal to the energy storage margin for the next day, if the sum of the output power of the distributed photovoltaic system and the power obtained from the outside fails to meet the load demand of the current day, the distributed photovoltaic system and energy storage system immediately purchases electricity from the power grid, so that the total output power of the distributed photovoltaic system and energy storage system on the current day reaches a stable output power value; After the energy storage system stops charging on the current day and is charged to a level greater than the energy storage margin for the next day and less than the charging margin for the current day, if the sum of the output power of the distributed photovoltaic system and the power obtained from the outside fails to meet the load demand for the current day, 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 energy storage margin for the next day, and the energy storage system stops outputting power, after which the distributed photovoltaic system and energy storage system immediately purchase electricity from the power grid; After the energy storage system stops charging on the current day and is charged to a level equal to the current day's charging margin, if the sum of the output power of the distributed photovoltaic system and the power obtained from the outside fails to meet the current day's load demand, the energy storage system is controlled to output power simultaneously using the current day's charging margin until the real-time storage capacity of the energy storage system is reduced to the next day's energy storage margin, and the energy storage system stops outputting power. Thereafter, the distributed photovoltaic and energy storage system immediately purchases electricity from the power grid.

6. The energy storage optimization method according to claim 3, characterized in that: The expression of the stability index S is as follows: In the above formula, N represents the time period between sunrise and sunset every day, w1(t) represents the output power of the distributed photovoltaic system in the t period on the output power curve of the daily distributed photovoltaic system, p represents the current daily energy storage margin, w2 represents the average output power of the distributed photovoltaic system obtained by calculating the historical data of the distributed photovoltaic output power, and w3 represents the average output power of the distributed photovoltaic system from sunrise to sunset every day.

7. The energy storage optimization method according to claim 1, characterized in that: The function f with the minimum cost as the objective is: In the above formula, C1(t) represents the depreciation cost of distributed photovoltaic equipment and energy storage system equipment, C2(t) represents the maintenance cost of distributed photovoltaic equipment and energy storage system equipment, and C3(t) represents the cost of distributed photovoltaic and energy storage systems purchasing electricity from the power grid, wherein the cost of distributed photovoltaic and energy storage systems purchasing electricity from the power grid refers to: the cost of the distributed photovoltaic and energy storage system purchasing electricity from the power grid and the V2G electric vehicle, and the difference between the income earned by the distributed photovoltaic and energy storage system from connecting to the power grid and generating electricity for the V2G electric vehicle. The cost of the distributed photovoltaic and energy storage system purchasing electricity from the power grid is a positive value, indicating that the cost of purchasing electricity from the power grid and the V2G electric vehicle is greater than the income earned from power generation, and a negative value indicates that the cost of purchasing electricity from the power grid and the V2G electric vehicle is less than the income earned from power generation; The expression of the cost C3(t) of the distributed photovoltaic and energy storage system purchasing electricity from the grid is: C3(t)=(k i1 (t)in i1 (t)+k i2 (t)in i2 (t)-k j1 (t)in j1 (t)-k j2 (t) w j2 (t))△t In the above formula, k i1 (t) represents the electricity price purchased from the power grid during period t, w i1 (t) represents the power purchased from the grid by the distributed photovoltaic and energy storage system during period t, k i2 (t) represents the electricity price purchased from the V2G electric vehicle during period t, w i2 (t) represents the power purchased by the distributed photovoltaic and energy storage system from the V2G electric vehicle during period t, k j1 (t) represents the electricity price for power grid during period t, w j1 (t) represents the output power of the distributed photovoltaic and energy storage system connected to the grid during period t, k j2 (t) represents the electricity price for the V2G electric vehicle during period t, w j2 (t) represents the output power of the distributed photovoltaic and energy storage system to the V2G electric vehicle during period t, and Δt represents the time period; The power purchase power w of the distributed photovoltaic and energy storage system in the period t i (t) According to the gap of energy storage margin corresponding to time period t, the power w purchased by the distributed photovoltaic and energy storage system from the V2G electric vehicle during time period t i2 (t) and the gap of the load demand value, the gap of the energy storage margin corresponding to the t period is determined according to the difference between the next day's energy storage margin and the current charging storage energy of the energy storage system, and the gap of the load demand is determined according to the output power of the distributed photovoltaic corresponding to the t period and the power w purchased by the distributed photovoltaic and energy storage system from the V2G electric vehicle in the t period i2 (t), and the difference between the load demand value corresponding to the time period t; The grid-connected output power w of the distributed photovoltaic and energy storage system to the grid during the period t j (t) and the output power generated for the V2G electric vehicle, which is determined by the current daily charging margin of the energy storage system obtained by optimization and the output power of the distributed photovoltaic corresponding to the time period t.

8. The energy storage optimization method according to claim 7, characterized in that: According to the energy storage margin of the next day and the output power curve of the distributed photovoltaic on the current day, the current day charging margin of the energy storage system is optimized with the goal of minimizing the cost of the V2G electric vehicle participating in the charging and discharging of the distributed photovoltaic and energy storage system. The value of the current day charging margin is greater than the value of the energy storage margin of the next day, including: Step S1: Select the latter curve of the output power curve of the distributed photovoltaic on the current day, and divide it into multiple t time periods, wherein the latter curve refers to the sum of the output power of the distributed photovoltaic on the current day and the power obtained from the outside, and corresponds to the time period from meeting the current daily load demand value to the distributed photovoltaic no longer outputting power; Step S2: According to the energy storage margin of the next day, the latter curve and the full charge of the energy storage system, the cost of purchasing electricity from the power grid for the distributed photovoltaic and energy storage system C3(t) is expressed to calculate the cost of purchasing electricity for multiple t periods. At the same time, the depreciation cost C1(t) of the distributed photovoltaic equipment and energy storage system equipment and the maintenance cost C2(t) of the distributed photovoltaic equipment and energy storage system equipment are expressed to calculate the depreciation cost and maintenance cost for multiple t periods respectively. Step S3: using the cost of purchasing electricity, depreciation cost, and maintenance cost in multiple t periods as the initial solution of the genetic algorithm; Step S4: Based on the initial solution, the optimal value of the cost of purchasing electricity, the depreciation cost, and the maintenance cost in multiple time periods t and the minimum value of the minimum cost are calculated through the function formula of the cost minimization target; Step S5: Calculate the optimal value of each generation and the minimum value of each generation with the minimum cost for the electricity purchase cost, depreciation cost, and maintenance cost in the multiple t time periods to obtain the corresponding centroid; Step S6: breeding to generate a new group according to the centroid; Step S7: generating two offspring by crossover according to each pair of sets in the new population generated by reproduction; Step S8: Let q be the probability of mutation, then the mutation operation is completed by randomly replacing an element in the set; Step S9: after the "crossover" and "mutation" operations, a new solution is generated, wherein the new solution represents the current daily charging margin of the energy storage system obtained after optimizing the full charge capacity of the energy storage system; Step S10: using the new solution as the initial solution of the genetic algorithm, the sampling iterative algorithm repeats steps S4 to S10, and after each iterative operation, it is detected whether the end condition is met, and the end condition is that the upper limit of the number of iterations is reached, or the function value with the minimum cost is less than the preset function value; Among them, the specific method of the iterative algorithm is: For any set of initial solutions XJ i The new solution XJ generated i ′, if f(XJ i )≥f(XJ i ′), then directly accept this new solution XJ i ′; If f(XJ i )<f(XJ i ′), then the new solution XJ i The acceptance probability E of ′ is obtained by the following formula: In the above formula, R w is the "heat" after the iterative algorithm iterates W times, and in the later stage of the iterative algorithm iteration, as R w The continuous decrease of f(XJ i )-f(XJ i ′) is constant, the value of E will gradually decrease, making the iterative algorithm stable; After each iteration, R w Perform the "heat reduction" operation. The formula corresponding to the heat reduction operation is: Where MT is the upper limit of the number of iterations of the iterative algorithm, and W represents the number of iterations of the iterative algorithm.

9. The energy storage optimization method according to claim 5, characterized in that: 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 proportionality coefficient, Soc represents the average state of charge value of a single cycle of the battery in the energy storage system, 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: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the energy storage optimization method for the distributed photovoltaic and energy storage system as described in any one of claims 1 to 9.

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