CN115693741A - Energy storage optimization method for distributed photovoltaic and energy storage system and electronic equipment - Google Patents
Energy storage optimization method for distributed photovoltaic and energy storage system and electronic equipment Download PDFInfo
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Abstract
The invention provides an energy storage optimization method of a distributed photovoltaic and energy storage system and electronic equipment, and relates to the technical field of electric power. Calculating to obtain a daily distributed photovoltaic output power curve and a daily load demand curve, constructing an energy storage margin model by combining the service life and capacity relation of the energy storage system to obtain an optimized daily energy storage margin and a characteristic curve of the output power and load demand relation, and determining the next-day energy storage margin of the energy storage system by combining the next-day distributed photovoltaic output power curve and the next-day load demand curve; optimizing to obtain the current daily charging allowance of the energy storage system by taking the minimum cost as a target; and when the distributed photovoltaic system starts to output power on the current day, controlling the energy storage system 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. According to the invention, more accurate energy storage in the same day and grid-connected power generation capacity are obtained, so that the total output power meets the stability index, the calculation amount is less, and the operation is simple.
Description
Technical Field
The invention relates to the technical field of electric power, in particular to a method for optimizing energy storage of a distributed photovoltaic and energy storage system and electronic equipment.
Background
With the development of the photovoltaic power generation technology, distributed photovoltaic is taken as an important means of future photovoltaic power generation, and is greatly popularized and developed at present. The single distributed photovoltaic does not have certain peak regulation capacity, and an energy storage system is required to participate in peak regulation. The development of such distributed photovoltaic and energy storage system architectures for distributed photovoltaic equipped energy storage systems is therefore of increasing importance.
However, the output power, voltage and the like of the distributed photovoltaic system are greatly influenced by illumination time, day and night and seasonal factors, so that the output power, voltage and the like of the distributed photovoltaic system fluctuate greatly, and the safe operation of equipment and a power grid is influenced. Generally, when the distributed photovoltaic system starts to output power after receiving illumination, the output power value is very small, the voltage value is very low, and the load requirement value cannot be met, and as the illumination intensity is increased and the time is prolonged, the output power value is gradually increased, the voltage value is gradually increased, and the condition that the output power value exceeds the load requirement value also occurs.
The energy storage system can well reduce the fluctuation of the distributed photovoltaic output power and voltage. However, in the current technical scheme, only one energy storage system with large capacity is provided, and a technical scheme for optimizing the energy storage and grid-connected power generation of the energy storage system by combining the distributed photovoltaic output power characteristics, the life-capacity characteristics of the energy storage system, cost factors and other aspects more finely, so that the total output power of the distributed photovoltaic system and the energy storage system is stable and the overall cost is controlled to be the minimum is not provided.
Disclosure of Invention
In view of the above, the present invention has been made to provide a method for optimizing stored energy of a distributed photovoltaic and energy storage system and an electronic device that solve the above problems, or partially solve the above problems.
A first aspect of an embodiment of the present invention provides a method for optimizing energy storage of a distributed photovoltaic and energy storage system, where the method for optimizing energy storage includes:
according to historical load data and historical distributed photovoltaic output power data, calculating to obtain a daily distributed photovoltaic output power curve and a daily load demand curve, wherein the daily distributed photovoltaic output power curve and the daily load demand curve correspond to different illumination time and illumination intensity;
constructing an energy storage margin model based on the output power curve of the daily distributed photovoltaic, the daily load demand curve and the relation between the service life and the capacity of the energy storage system, and calculating the energy storage margin model to obtain the optimized daily energy storage margin and the characteristic curve of the relation between the output power and the load demand;
according to the illumination time and the illumination intensity of two adjacent days, combining the output power curve of the daily distributed photovoltaic and the daily load demand curve, and determining the output power curve of the next-day distributed photovoltaic and the next-day load demand curve;
determining the next-day energy storage allowance of an energy storage system in the distributed photovoltaic and energy storage system according to the output power curve of the next-day distributed photovoltaic, the next-day load demand curve and the characteristic curve;
according to the energy storage allowance of the next day and the output power curve of the distributed photovoltaic of the current day, optimizing to obtain the current day charging allowance of the energy storage system by taking the minimum cost of the V2G electric vehicle under the condition of participating in the charging and discharging of the distributed photovoltaic and energy storage system as a target, wherein the value of the current day charging allowance is larger than that of the energy storage allowance of the next day;
at the current day when distributed photovoltaic begins output, control energy storage system output power simultaneously for the current day distributed photovoltaic and energy storage system's total output reaches the steady value of output, the steady value of output satisfies the stability index, wherein, at the current day energy storage system stops behind the output, confirms energy storage system's the mode of charging is in order to right energy storage system charges, until energy storage system reaches the next day energy storage allowance or when the current day charging allowance, energy storage system stops charging.
Optionally, an energy storage margin model is constructed based on the output power curve of the daily distributed photovoltaic, the daily load demand curve and the relationship between the service life and the capacity of the energy storage system, and the energy storage margin model is subjected to operation optimization to obtain a characteristic curve of the relationship between the daily energy storage margin and the output power and the load demand, including:
according to the output power curve of the daily distributed photovoltaic and the daily load demand curve, performing statistical operation to obtain a daily dual-output power curve, wherein the daily dual-output power curve represents a power change curve in the period from the start of output power of the daily distributed photovoltaic to the time when the output power meets the daily load demand;
building a model based on an Adaboost algorithm, modeling an energy storage margin corresponding to the daily dual-output power curve by combining the relation between the service life and the capacity of the energy storage system, and obtaining an optimized daily energy storage margin and an optimized output power and load demand relation curve through model training, cross validation and parameter optimization, wherein the daily energy storage margin is used for outputting power when the distributed photovoltaic starts to output power on the day, and stopping outputting power until the distributed photovoltaic output power on the day meets the load demand on the day.
Optionally, when the distributed photovoltaic starts to output power on the current day, controlling the energy storage system to output power simultaneously, so that the total output power of the distributed photovoltaic and the energy storage system on the current day reaches an output power steady value, wherein after the energy storage system stops outputting power on the current day, determining a charging mode of the energy storage system to charge the energy storage system, and when the energy storage system reaches the energy storage margin on the next day or the charging margin on the current day, the energy storage system stops charging, including:
when the distributed photovoltaic starts to output power on the current day, the output power of the distributed photovoltaic does not meet the load requirement on the current day, and the energy storage system is controlled to output power by using the energy storage margin on the current day, so that the total output power of the distributed photovoltaic and the energy storage system on the current day reaches an output power steady value;
if the external power is obtained, when the sum of the distributed photovoltaic output power and the external power obtained meets the current daily load requirement, the energy storage system stops outputting the power, the current daily energy storage allowance is larger than 0, and the external power obtained is the power provided by the V2G electric vehicle which discharges to the distributed photovoltaic and energy storage system;
if the external power is not obtained, when the distributed photovoltaic output power meets the current daily load requirement, the energy storage system stops outputting the power, and at the moment, the current daily energy storage allowance 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 externally obtained power is larger than the current daily load requirement, the distributed photovoltaic and the externally obtained power charge the energy storage system together, and the energy storage system stops charging until the energy storage system reaches the next day energy storage allowance or the current day charging allowance;
the distributed photovoltaic system stops charging the energy storage system, and after the energy storage system reaches the current day charging allowance, the distributed photovoltaic system outputs power to a power grid to be connected to the power grid for power generation;
after the energy storage system stops outputting power, if the sum of the real-time output power of the distributed photovoltaic and the externally obtained power is not larger than the current daily load demand, the distributed photovoltaic and the externally obtained power do not charge the energy storage system, and then the distributed photovoltaic and the energy storage system charge the energy storage system from the power grid power purchase in the valley period until the energy storage system reaches the next day energy storage allowance.
Optionally, the step of charging the energy storage system by the distributed photovoltaic and the externally obtained power together until the energy storage system reaches the energy storage margin of the next day or the charging margin of the current day includes:
in the process that the distributed photovoltaic and the externally obtained power charge the energy storage system together, if the output power of the distributed photovoltaic does not meet the current daily load requirement and the energy storage system does not reach the next day energy storage margin yet, the distributed photovoltaic and the externally obtained power stop charging the energy storage system, and then the distributed photovoltaic and the energy storage system purchase power from the power grid to charge the energy storage system in the valley period until the energy storage system reaches the next day energy storage margin;
in the process that the distributed photovoltaic and the external acquired power jointly charge the energy storage system, if the sum of the output power of the distributed photovoltaic and the external acquired power does not meet the current daily load requirement and the energy storage system reaches the next day energy storage allowance, the distributed photovoltaic and the external acquired power stop charging the energy storage system, and the energy storage system does not receive charging any more;
in the process that the distributed photovoltaic and the external acquired power jointly charge the energy storage system, if the sum of the output power of the distributed photovoltaic and the external acquired power always meets the load requirement of the current day, the distributed photovoltaic and the external acquired power jointly charge the energy storage system until the energy storage system reaches the charging allowance of the current day, and the energy storage system does not receive charging any more;
in the process that the distributed photovoltaic and the external acquired power charge the energy storage system together, if the output power of the distributed photovoltaic meets the load requirement of the current day all the time, the external acquired power charges the energy storage system until the energy storage system reaches the charging allowance of the current day, and the energy storage system does not receive charging any more.
Optionally, when the distributed photovoltaic starts to output power on the current day, controlling the energy storage system to output power simultaneously, so that the total output power of the distributed photovoltaic and the energy storage system on the current day reaches an output power steady value, including:
after the energy storage system is charged to be equal to the energy storage allowance of the next day after the energy storage system stops charging on the current day, if the sum of the output power of the distributed photovoltaic and the externally obtained power does not meet the load requirement of 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 an output power steady value;
after the energy storage system is charged to a value larger than the next-day energy storage allowance and smaller than the current-day charging allowance after the energy storage system stops charging on the current day, if the sum of the output power of the distributed photovoltaic and the externally obtained power does not meet the current-day load requirement, controlling the energy storage system to simultaneously output power by using the stored energy when the charging is stopped until the real-time stored energy of the energy storage system is reduced to the next-day energy storage allowance, stopping the output power of the energy storage system, and then immediately purchasing power from the power grid by the distributed photovoltaic and the energy storage system;
at present day energy storage system is charged to being equal to after stopping charging current day charging allowance, if the output power of distributed photovoltaic and the circumstances that the load demand of present day does not appear unsatisfied in the sum of the external power that obtains, then control energy storage system utilizes current day charging allowance is output power simultaneously, until energy storage system's real-time storage energy reduces to next day energy storage allowance stops energy storage system output power, afterwards, distributed photovoltaic and energy storage system follow immediately the electric wire netting is purchased electricity.
Optionally, the stability indicator S is expressed as follows:
in the above formula, N represents the time period from the daily rise time to the daily fall time, w 1 (t) represents the output power over the output power curve of the daily distributed photovoltaic over a period of t, p represents the current daily energy storage margin, w 2 Represents the average output power, w, of the distributed photovoltaic calculated from the historical data of the distributed photovoltaic output power 3 Represents the average output power of the distributed photovoltaic from the time of day up to the time of sunset per day.
Optionally, the function f of the cost minimization objective is;
in the above formula, C 1 (t) represents depreciation costs of distributed photovoltaic equipment, energy storage system equipment, C 2 (t) represents maintenance costs of distributed photovoltaic equipment, energy storage system equipment, C 3 (t) represents the cost of electricity purchase from the distributed photovoltaic and energy storage system to the power grid, wherein the cost of electricity purchase from the distributed photovoltaic and energy storage system to the power grid refers to: the cost for purchasing electricity from the distributed photovoltaic and energy storage system to the power grid and the V2G electric vehicle is combined with the cost for purchasing electricity from the distributed photovoltaic and energy storage system to the power gridThe cost of purchasing electricity from the distributed photovoltaic and energy storage system to the power grid is a positive value, which means that the cost of purchasing electricity from the power grid and the V2G electric vehicle is greater than the profit of earning electricity generation, and the cost of purchasing electricity from the power grid and the V2G electric vehicle is a negative value, which means that the cost of purchasing electricity from the power grid and the V2G electric vehicle is less than the profit of earning electricity generation;
wherein the distributed photovoltaic and energy storage system purchases electricity to the grid at a cost C 3 The expression of (t) is:
C 3 (t)＝(k i1 (t)w i1 (t)+k i2 (t)w i2 (t)-k j1 (t)w j1 (t)-k j2 (t)
w j2 (t))△t
in the above formula, k i1 (t) represents the price of electricity purchased from the grid during the period t, w i1 (t) represents the power purchased by the distributed photovoltaic and energy storage system from the power grid during the period t, k i2 (t) represents the price of electricity purchased from the V2G electric automobile in a period of t, w i2 (t) power, k, of the distributed photovoltaic and energy storage system purchasing electricity to the V2G electric vehicle in a time period of t j1 (t) represents the price of electricity generated to the grid during the time period t, w j1 (t) represents the output power of the distributed photovoltaic and energy storage system to grid connection in a period t, k j2 (t) represents the price of electricity generated by the V2G electric vehicle during the period t, w j2 (t) represents the output power generated by the distributed photovoltaic and energy storage system to the V2G electric vehicle in a time period t, and Δ t represents a time period;
and the t time period is the purchasing electric power w of the distributed photovoltaic and energy storage system i (t) according to the gap amount of the energy storage allowance corresponding to the t time period, and the power w of the distributed photovoltaic and energy storage system for purchasing electricity to the V2G electric automobile in the t time period i2 (t) determining the gap amount of a load demand value, wherein the gap amount of the energy storage margin corresponding to the t time period is determined according to the difference between the energy storage margin of the next day and the current charging and energy storage capacity of the energy storage system, and the gap amount of the load demand is determined according to the output power of the distributed photovoltaic power corresponding to the t time period and the power w of the distributed photovoltaic power and energy storage system for purchasing electricity to the V2G electric automobile at the t time period i2 (t) the sum, the difference between the load demand values corresponding to the t period;
and outputting power w to the grid of the power grid by the distributed photovoltaic and energy storage system in the period t j (t) determining the sum of the output power generated by the V2G electric automobile and the current daily charging allowance of the energy storage system according to optimization and the output power of the distributed photovoltaic corresponding to the t time period.
Optionally, according to the energy storage margin of the next day and the output power curve of the distributed photovoltaic of the current day, with the goal of minimum cost when the V2G electric vehicle participates in the charging and discharging of the distributed photovoltaic and energy storage system, optimizing to obtain the charging margin of the current day of the energy storage system, where a value of the charging margin of the current day is greater than a value of the energy storage margin of the next day, including:
step S1: selecting a back-end curve in the output power curve of the distributed photovoltaic at the current day, and dividing the back-end curve into a plurality of t periods, wherein the back-end curve is a curve corresponding to a time period from the meeting of the current daily load demand value to the no output power of the distributed photovoltaic;
step S2: according to the next day energy storage allowance, the back-end curve and the full charge amount of the energy storage system, the cost C of purchasing electricity from the power grid through the distributed photovoltaic and energy storage system 3 (t) calculating to obtain the cost of purchasing electricity in a plurality of t periods by using the expression of (t), and meanwhile, obtaining the depreciation cost C of the distributed photovoltaic equipment and the energy storage system equipment 1 (t) expression, maintenance costs C of said distributed photovoltaic devices, energy storage system devices 2 Respectively calculating the depreciation cost and the maintenance cost of a plurality of t periods by using the expression of (t);
step S2: the cost, depreciation cost and maintenance cost of purchasing electricity in a plurality of t periods are used as initial solutions of the genetic algorithm;
and step S3: on the basis of the initial solution, calculating to obtain a generation optimal value of the cost, depreciation cost and maintenance cost of electricity purchase in a plurality of t periods and a generation minimum value of the cost through a function formula of the cost minimum target;
and step S4: calculating the first-generation optimal value of the cost, depreciation cost and maintenance cost of electricity purchasing in the t periods and the first-generation minimum value of the cost to obtain the corresponding mass center;
step S5: propagating according to the centroid to produce a new population;
step S6: generating two offspring through crossing according to each pair of sets in the new population generated by propagation;
step S7: if q is the probability of mutation, the operation of mutation is completed by randomly replacing a certain element on the set;
step S8: generating a new solution after the operations of crossing and mutation, wherein the new solution represents the current day charging allowance of the energy storage system obtained after the full charging amount of the energy storage system is optimized;
step S9: taking the new solution as an initial solution of the genetic algorithm, sampling the iterative algorithm, repeating the steps S3-S9, and detecting whether an end condition is met after each iterative operation, wherein the end condition is that the upper limit of the iteration times reaches the standard, or the function value with the minimum cost is smaller than a preset function value;
the specific method of the iterative algorithm is as follows:
for solutions based on any one set of initial solutions XJ i Generated new solution XJ i ', if f (XJ) i )≥f(XJ i ') directly receive the new solution XJ i ′；
If f (XJ) i )＜f(XJ i ') then the new solution XJ i The acceptance probability E of' is given by the following equation:
in the above formula, R w For "heat" after iteration of the iterative algorithm n times, and to the late stage of iteration of the iterative algorithm, with R w Is continuously reduced when f (XJ) i )-f(XJ i ') the value of E will gradually decrease with a constant value, making the iterative algorithm tend to fallStabilizing;
will be paired with R after each iteration is completed w And performing 'heat reduction' operation, wherein a formula corresponding to the heat reduction operation is as follows:
wherein MT is the upper limit of the number of iterations of the iterative algorithm.
Optionally, the expression of the relationship between the life and the 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 The method comprises the steps of representing initial capacity when the energy storage system is not used, k represents a proportionality coefficient, soc represents an average charge state value of a battery in the energy storage system in a single cycle, D represents charge-discharge depth of the battery in the energy storage system, and M represents charge-discharge cycle times of the battery in the energy storage system.
A second aspect of an embodiment of the present invention provides an electronic device, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a method of energy storage optimization for a distributed photovoltaic and energy storage system according to any one of the first aspect.
The energy storage optimization method of the distributed photovoltaic and energy storage system comprises the steps of firstly constructing an energy storage margin model, and calculating the energy storage margin model to obtain a characteristic curve of the relationship between the optimized daily energy storage margin and output power and load requirements. And secondly, determining an output power curve and a next-day load demand curve of the next-day distributed photovoltaic and a next-day energy storage allowance of the energy storage system.
Optimizing to obtain the current day charging margin of the energy storage system by taking the minimum cost as a target according to the energy storage margin of the next day and the output power curve of the distributed photovoltaic of the current day; and finally, controlling the energy storage system to output power simultaneously when the distributed photovoltaic system starts to output power on the current day, so that the total output power of the distributed photovoltaic system and the energy storage system on the current day reaches the stable output power value and meets the stability index.
The invention finely combines the distributed photovoltaic output power characteristics, the energy storage system service life and capacity characteristics, cost factors and other contents, aims at minimizing the cost, optimizes the relationship between the daily energy storage allowance and the output power and load demand relationship curve, optimizes the current daily charging allowance, and obtains more accurate daily energy storage and grid-connected power generation of the energy storage system, so that the total output power of the distributed photovoltaic and energy storage system meets the stability index, and the invention has the advantages of less calculation amount in the whole process, simple operation, less calculation time, simple and convenient control logic and higher practical value.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the description of the embodiments of the present invention will be briefly introduced below, and it is obvious that the drawings in the description below are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive labor.
Fig. 1 is 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;
fig. 2 is a block diagram of an energy storage optimization apparatus of a distributed photovoltaic and energy storage system according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, a flow chart of a method for optimizing stored energy of a distributed photovoltaic and energy storage system according to an embodiment of the present invention is shown, where the method for optimizing stored energy of a distributed photovoltaic and energy storage system includes the following steps:
step 101: according to the historical load data and the historical data of the output power of the distributed photovoltaic, an output power curve and a daily load demand curve of the daily distributed photovoltaic are obtained through calculation, and the output power curve and the load demand curve of the distributed photovoltaic on different days correspond to different illumination time and illumination intensity.
The output power, voltage and the like of the distributed photovoltaic are greatly influenced by illumination time, day and night and seasonal factors, so that the daily output power curves of the distributed photovoltaic are different. The load demand is relatively stable, and the load demand is less steep or less steep on similar days, such as: the daily load demands on weekdays are substantially similar, and the load demands on holidays are substantially similar. In order to control the cost, the total voltage output power of the distributed photovoltaic and energy storage system, control the daily storage capacity of the energy storage system, reduce the cost and prolong the service life of the energy storage system more accurately, an output power curve and a daily load demand curve of the daily distributed photovoltaic need to be obtained, and the output power curve and the load demand curve of the distributed photovoltaic on different days correspond to different illumination time and illumination intensity. The two curves can be obtained by calculation according to historical load data and historical distributed photovoltaic output power data, and certainly can also be obtained by other modes, and the invention is not limited in particular.
Step 102: and constructing an energy storage margin model based on the output power curve of the daily distributed photovoltaic, the daily load demand curve and the relation between the service life and the capacity of the energy storage system, and calculating the energy storage margin model to obtain the optimized daily energy storage margin and the characteristic curve of the relation between the output power and the load demand.
And after obtaining the two curves, constructing an energy storage margin model by combining the relation between the service life and the capacity of the energy storage system, and calculating the energy storage margin model to obtain a more accurate relation curve between the daily energy storage margin of the energy storage system and the relation between the distributed photovoltaic output power and the load demand, namely obtaining the optimized daily energy storage margin and the relation curve between the output power and the load demand.
Step 103: and determining an output power curve and a next-day load demand curve of the next-day distributed photovoltaic system according to the illumination time and the illumination intensity of two adjacent days and by combining the output power curve and the daily load demand curve of the daily distributed photovoltaic system.
As illustrated in step 101, the output power of the distributed photovoltaic may vary from day to day, depending on the daily illumination time and illumination intensity. Daily load demands are also different like weekdays and holidays, and also are different according to different illumination time and illumination intensity. Therefore, the illumination time and the illumination intensity need to be determined according to the illumination time and the illumination intensity of the current day, and then the output power curve of the distributed photovoltaic of the next day and the load demand curve of the next day are determined based on the illumination time and the illumination intensity of the next day, so that a foundation is laid for the energy storage allowance of the next day of the follow-up precise operation.
Step 104: and determining the next-day energy storage allowance of the energy storage system in the distributed photovoltaic and energy storage system according to the output power curve, the next-day load demand curve and the characteristic curve of the next-day distributed photovoltaic.
After the output power curve and the next-day load demand curve of the next-day distributed photovoltaic are obtained, the next-day energy storage allowance of the energy storage system can be accurately determined by combining the daily energy storage allowance obtained in the step 102 and the characteristic curve of the output power and load demand relation. The next-day energy storage allowance has the function of ensuring that the total output power of the next-day distributed photovoltaic and energy storage system reaches an output power stable value meeting the stability index, so that the next-day energy storage allowance is equivalent to the minimum energy storage required by the energy storage system during charging on the current day, but is used in the next day. It can be naturally understood that the energy storage of the energy storage system used when the distributed photovoltaic starts to output power on the current day is the previous day energy storage margin.
Step 105: and optimizing to obtain the current day charging allowance of the energy storage system according to the energy storage allowance of the next day and the output power curve of the distributed photovoltaic of the current day, with the aim of minimizing the cost of the V2G electric vehicle under the condition of participating in the charging and discharging of the distributed photovoltaic and energy storage system, wherein the value of the current day charging allowance is larger than that of the current day energy storage allowance.
The cost is minimum, the cost is minimum by not only considering grid connection of more generated energy to earn income, but also comprehensively considering the service life of the energy storage system and the daily output characteristics of the distributed photovoltaic, and an optimal current daily charging allowance of the energy storage system is obtained.
In addition, considering that the target existing V2G electric automobile has the function of outputting electric energy to a power grid or related energy storage equipment, the V2G electric automobile is taken into consideration when participating in distributed photovoltaic and energy storage system charging and discharging. For example: for a certain distributed photovoltaic and energy storage system, according to a map trip plan of a 2G electric vehicle, according to future charging and discharging conditions of the same destination (such as the location of the distributed photovoltaic and energy storage system), historical conditions of participation of the V2G electric vehicle in charging and discharging are determined, a database is formed, and therefore the charging and discharging conditions of the V2G electric vehicle in the distributed photovoltaic and energy storage system every day are accurately predicted, the energy storage margin of the next day is adaptively adjusted by taking the minimum cost as a target, or the charging margin of the current day is adjusted at the minimum cost, meanwhile, the enthusiasm of the V2G electric vehicle in participation of the distributed photovoltaic and energy storage system in charging and discharging is fully mobilized, and a better basis is provided for the future sustainable development of the electric vehicle.
Step 106: 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 an output power steady value, and the output power steady value meets a stability index, wherein after the energy storage system on the current day stops outputting power, the charging mode of the energy storage system is determined to charge the energy storage system, and the energy storage system stops charging until the energy storage system reaches the energy storage allowance of the next day or the charging allowance of the current day.
When the distributed photovoltaic system starts to output power on the current day, the real-time power of the distributed photovoltaic system certainly does not meet the load requirement on the current day, and therefore the energy storage system needs to be controlled to output power at the same time, namely the energy storage system is controlled to output power by utilizing the energy storage allowance on the previous day, so that when the total output power of the distributed photovoltaic system and the energy storage system on the current day meets the load requirement on the current day, the stable output power value is reached, the stable output power value meets the stability index, and electricity purchasing from a power grid is not needed.
When the energy storage allowance of the current day in the previous day of the energy storage system is exhausted, the energy storage system stops outputting power, and the real-time output power of the distributed photovoltaic system rises to meet the load requirement. And then determining the charging mode of the energy storage system so as to charge the energy storage system in different modes, and stopping charging until the energy storage system reaches the energy storage allowance of the next day or the charging allowance of the current day. The specific charging condition is described below and will not be described in detail.
By the method, the characteristics of distributed photovoltaic output power, the service life and capacity characteristics of the energy storage system, cost factors and other contents are combined elaborately, the relation between the daily energy storage allowance and the relation curve of output power and load demand is optimized by taking the minimum cost as a target, the current daily charging allowance is optimized, and more accurate daily energy storage and grid-connected power generation of the energy storage system are obtained, so that the total output power of the distributed photovoltaic and energy storage system meets the stability index, the calculation amount in the whole process is small, the operation is simple, the calculation time is short, and the control logic is simple and convenient.
In one possible embodiment, the specific method of step 102 may include:
step T1: and according to the output power curve of the daily distributed photovoltaic and the daily load demand curve, carrying out statistical operation to obtain a daily dual-output power curve, wherein the daily dual-output power curve represents a power change curve in the period from the start of outputting power to the output power meeting the daily load demand of the daily distributed photovoltaic.
As described above, the distributed photovoltaic system starts to output power after receiving light intensity irradiation during the daytime, the real-time power at this time is small and generally cannot meet the load demand in the same time period, and then the output power of the distributed photovoltaic system naturally starts to rise along with the rotation of the earth and the change of the solar altitude, and the illumination intensity starts to increase until the maximum operation output power of the rising distributed photovoltaic system, which generally exceeds the load demand.
For the above reasons, in order to ensure that the total output power of the distributed photovoltaic system and the energy storage system meets the stability index, a power change curve from the start of output power of the distributed photovoltaic system to the time when the output power meets the daily load demand needs to be obtained.
And step T2: building a model based on an Adaboost algorithm, modeling an energy storage margin corresponding to a daily dual-output power curve by combining the relation between the service life and the capacity of an energy storage system, and obtaining an optimized daily energy storage margin and a characteristic curve of the relation between output power and load demand through model training, cross validation and parameter optimization, wherein the daily energy storage margin is used for outputting power when distributed photovoltaic starts to output power on the day, and stopping outputting power until the daily distributed photovoltaic output power meets the daily load demand.
After a power change curve from the start of output power of the distributed photovoltaic to the time when the output power meets the daily load demand is obtained, a model is built based on an Adaboost algorithm by combining the relation between the service life and the capacity of the energy storage system, the energy storage margin corresponding to the obtained power change curve is modeled, and then a characteristic curve of the relation between the optimized daily energy storage margin and the output power and the load demand can be obtained through model training, cross validation and parameter optimization.
In one possible embodiment, the energy storage system life-capacity relationship is expressed 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 during the time period t, E b The method comprises the steps of representing the initial capacity when the energy storage system is not used, k represents a proportionality coefficient, soc represents the average charge state value of a battery in the energy storage system in a single cycle, D represents the charge-discharge depth of the battery in the energy storage system, and M represents the charge-discharge cycle number of the battery in the energy storage system.
In one possible embodiment, the specific method of step 106 may include: the stage of the energy storage system outputting power in the early stage and the stage of the energy storage system charging in the later stage.
1) For the phase of output power: 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 load requirement of the current day in the period, and the energy storage system needs to be controlled to output power by utilizing the previous day energy storage allowance, so that the total output power of the distributed photovoltaic system and the energy storage system on the current day reaches the stable output power value; along with the increase of the distributed photovoltaic output power, when the distributed photovoltaic output power meets the current daily load requirement, the energy storage system stops outputting the power, and at the moment, the previous-day energy storage allowance is close to 0, namely, the previous-day energy storage allowance is consumed.
In the embodiment of the invention, the expression of the stability index S is as follows:
in the above formula, N represents the time period from the daily rise time to the daily fall time, w 1 (t) represents the output power of the daily distributed photovoltaic on the output power curve during the period t, p represents the current daily energy storage margin, w 2 Represents the average output power, w, of the distributed photovoltaic calculated from the historical data of the distributed photovoltaic output power 3 Represents the average output power of the distributed photovoltaic from the time of day up to the time of sunset per day.
It should be noted here that, due to weather or the like, there may be a case where the distributed photovoltaic is not outputting on the current day, or the distributed photovoltaic starts outputting power when the weather turns clear after a certain period of time, for example, 12 pm. When the situation occurs, in order to prolong the service life of the energy storage system, the previous day energy storage allowance is not used, namely the current day energy storage system does not output power. After the energy storage allowance of the next day is obtained by the subsequent operation, if the energy storage allowance of the next day is not more than the energy storage allowance of the previous day, waiting for charging to the charging allowance of the next day or not charging according to a charging mode; and if the energy storage allowance of the next day is larger than that of the previous day, waiting for charging to the energy storage allowance of the next day or the charging allowance of the current day according to the charging mode.
In the output stage of the distributed photovoltaic system, besides the period from the beginning of outputting power to the meeting of the load demand, along with the decrease of the sun emphasis in the afternoon, the output power of the distributed photovoltaic system may have a time period which does not meet the load demand and has a larger difference with the load demand, and when the condition occurs, according to the current energy storage of the energy storage system, the following conditions are further divided:
a) When the energy storage system on the current day stops charging, the energy storage system is charged to be equal to the energy storage allowance on the current day, namely, the energy storage system is already charged to the energy storage allowance on the current day, if the sum of the output power of the distributed photovoltaic and the external acquired power (the external acquired power is the power provided by the V2G electric vehicle discharging to the distributed photovoltaic and the energy storage system) does not meet the load requirement on the current day, the energy storage allowance on the next day cannot be consumed on the current day, so that the distributed photovoltaic and the energy storage system purchase power from the power grid at once, and the total output power of the distributed photovoltaic and the energy storage system on the current day reaches the stable output power value.
B) And when the energy storage system on the current day stops charging, the energy storage system is charged to a value which is larger than the energy storage allowance on the next day and smaller than the charging allowance on the current day, namely, the energy storage system is already charged to the energy storage allowance on the next day, but the energy storage system does not reach the charging allowance on the current day. If the sum of the output power of the distributed photovoltaic and the externally obtained power does not meet the current daily load requirement, the energy storage system is controlled to output power by using the energy storage of the energy storage system when the charging is stopped because the energy storage higher than the next day energy storage allowance can be consumed, until the real-time energy storage of the energy storage system is reduced to the next day energy storage allowance, the energy storage system stops outputting the power, and then the distributed photovoltaic and the energy storage system purchase power from the power grid at once.
C) When the energy storage system on the current day stops charging, the energy storage system is charged to be equal to the charging allowance on the current day, namely, the energy storage system is already charged to the charging allowance on the current day, if the sum of the output power of the distributed photovoltaic system and the external acquired power does not meet the load requirement on the current day, the energy storage system is controlled to output power by using the charging allowance on the current day at the same time because the energy storage amount higher than the energy storage allowance on the next day can be consumed, until the real-time storage amount of the energy storage system is reduced to the energy storage allowance on the next day, the output power of the energy storage system is stopped, and then the distributed photovoltaic system and the energy storage system purchase power from a power grid at once.
2) For the charging phase: when the energy storage system stops outputting power, if the sum of the real-time output power of the distributed photovoltaic and the external acquired power is larger than the current daily load demand, namely the sum of the real-time power of the distributed photovoltaic and the external acquired power is larger than the load demand, the distributed photovoltaic and the external acquired power start to charge the energy storage system together without grid-connected power generation, and the energy storage system stops charging the energy storage system until the energy storage system reaches the next-day energy storage allowance or the current-day charging allowance. The determining factor is whether the sum of the real-time power of the distributed photovoltaic system and the externally obtained power is continuously larger than the current daily load demand. The sum of the real-time power of the distributed photovoltaic and the external acquired power is continuously longer than the current daily load demand, when the energy storage system is charged to the next day energy storage allowance, the sum of the real-time power of the distributed photovoltaic and the external acquired power is still larger than the current daily load demand, the energy storage system is continuously charged to the current daily charging allowance, at the moment, the charging is stopped, and the distributed photovoltaic starts to output power to the power grid so as to earn benefits through grid-connected power generation.
In another situation, after the energy storage system stops outputting power, if the sum of the real-time output power of the distributed photovoltaic and the externally obtained power is not larger than the current daily load demand, namely the sum of the real-time output power of the distributed photovoltaic and the externally obtained power just meets the load demand and does not have redundant output power, the distributed photovoltaic and the externally obtained power do not charge the energy storage system, the whole system does not purchase electricity for the power grid, the price of the power grid is higher in the peak period of time, the energy storage system does not need to output power any more thereafter and does not need to store energy temporarily, so the distributed photovoltaic and the energy storage system can select the valley period of time, the electricity is purchased from the power grid to charge the energy storage system when the electricity price is lower until the energy storage system reaches the next day energy storage allowance, and the charging to the current day is not needed.
In a specific process of charging the energy storage system by the distributed photovoltaic system, the following conditions are also adopted:
e) If the sum of the output power of the distributed photovoltaic and the external acquired power does not meet the current daily load requirement and the energy storage system does not reach the next-day energy storage allowance, namely the sum of the real-time power of the distributed photovoltaic and the external acquired power is greater than the load requirement, the energy storage system is being charged, but because of factors in various aspects such as sudden change of weather, the real-time power of the distributed photovoltaic is reduced and the load requirement is not met any more, or the load requirement is just met, and no redundant output power is used for charging the energy storage system. And immediately stopping charging the energy storage system by the distributed photovoltaic and the external power, and then continuously charging the energy storage system in the valley period instead of charging the energy storage system from the power grid by the distributed photovoltaic and the energy storage system in the valley period, wherein the distributed photovoltaic and the energy storage system purchase power from the power grid to the energy storage system in the valley period, or the sum of the subsequent real-time power of the distributed photovoltaic and the external power is larger than the load requirement. In either way, the energy storage system is kept the next day energy storage margin.
F) In the process that the distributed photovoltaic is used for charging the energy storage system, if the sum of the output power of the distributed photovoltaic and the external acquired power does not meet the current daily load requirement and the energy storage system reaches the current daily energy storage allowance, namely the sum of the real-time power of the distributed photovoltaic and the external acquired power is greater than the load requirement, the distributed photovoltaic is charging the energy storage system and the energy storage system reaches the next-day energy storage allowance, but due to factors in various aspects, such as sudden change of weather, the real-time power of the distributed photovoltaic is reduced and does not meet the load requirement any more, or the load requirement is just met, and no redundant output power is used for charging the energy storage system. And stopping charging the energy storage system by the distributed photovoltaic and the external acquired power, and stopping charging the energy storage system by the energy storage system, so that the energy storage system is not charged continuously even if the sum of the subsequent real-time power of the subsequent distributed photovoltaic and the external acquired power is greater than the load requirement.
G) In the process that the distributed photovoltaic is charged to the energy storage system, if the sum of the output power of the distributed photovoltaic and the power obtained from the outside always meets the current daily load requirement, namely the sum of the real-time power of the distributed photovoltaic and the power obtained from the outside is always higher than the load requirement, the distributed photovoltaic charges the energy storage system until the energy storage system reaches the current daily charging allowance, and the energy storage system does not receive charging any more.
H) In the process that the distributed photovoltaic and the external acquired power charge the energy storage system together, if the output power of the distributed photovoltaic always meets the current daily load requirement, namely the independent output power of the distributed photovoltaic meets the current daily load requirement, the external acquired power directly charges the energy storage system until the energy storage system reaches the current daily charging allowance, and the energy storage system does not receive charging any more.
In one possible embodiment, the function f that may define the cost minimum objective is;
in the above formula, C 1 (t) represents depreciation costs of distributed photovoltaic equipment, energy storage system equipment, C 2 (t) represents maintenance costs of the distributed photovoltaic equipment, energy storage system equipment, C 3 (t) represents the cost of the distributed photovoltaic and energy storage system to purchase electricity into the grid. The cost of electricity purchase from the distributed photovoltaic and energy storage system to the power grid refers to: the cost of the distributed photovoltaic and energy storage system for purchasing electricity to the power grid and the V2G electric automobile is different from the difference between the cost of the distributed photovoltaic and energy storage system for generating electricity to the power grid in a grid-connected mode and the cost of earning revenue for the electricity generated by the distributed photovoltaic and energy storage system for generating electricity to the V2G electric automobile, the cost of the distributed photovoltaic and energy storage system for purchasing electricity to the power grid is a positive value, the cost of purchasing electricity to the power grid and the V2G electric automobile is larger than the revenue for generating electricity, and the cost of purchasing electricity to the power grid and the V2G electric automobile is smaller than the revenue for generating electricity.
Wherein, the cost C of electricity purchase from the distributed photovoltaic and energy storage system to the power grid 3 The expression of (t) is:
C 3 (t)＝(k i1 (t)w i1 (t)+k i2 (t)w i2 (t)-k j1 (t)w j1 (t)-k j2 (t)
w j2 (t))△t
in the above formula, k i1 (t) represents the price of electricity purchased from the grid during the period t, w i1 (t) represents the power purchased by the distributed photovoltaic and energy storage system from the power grid during the period t, k i2 (t) represents the price of electricity purchased from the V2G electric vehicle in the period t, w i2 (t) represents the power purchased by the distributed photovoltaic and energy storage system to the V2G electric automobile in the period of t, k j1 (t) represents the price of electricity generated to the grid during the period t, w j1 (t) represents the output power of the distributed photovoltaic and energy storage system to the grid connection in the period of t, k j2 (t) represents the price of electricity generated by the V2G electric vehicle in the period t, w j2 And (t) represents the output power generated by the distributed photovoltaic and energy storage system to the V2G electric automobile in a time period t, and Δ t represents a time period.
Wherein t period of time is the purchased electric power w of the distributed photovoltaic and energy storage system i1 (t) a gap amount of an energy storage allowance corresponding to a time period t, time tPower w for purchasing electricity from section-distributed photovoltaic and energy storage system to V2G electric automobile i2 And (t) determining the gap amount of the load demand value, wherein the value of the gap amount of the energy storage allowance corresponding to the t time period is determined according to the difference between the energy storage allowance of the next day and the current charging storage amount of the energy storage system. The value of the gap amount of the load demand is obtained according to the output power of the distributed photovoltaic corresponding to the t time period and the power w of the distributed photovoltaic and the energy storage system purchasing electricity to the V2G electric automobile in the t time period i2 (t), the difference between the load demands corresponding to the time period t.
Grid-connected output power w from t-period distributed photovoltaic and energy storage system to power grid j1 And (t) determining the sum of the output power generated by the V2G electric automobile and the current daily charging allowance of the energy storage system according to optimization and the output power of the distributed photovoltaic corresponding to the t time period.
Based on the above calculation with the minimum cost, in a possible embodiment, the specific method of step 105 may include:
step S1: selecting a back-end curve in an output power curve of the distributed photovoltaic on the current day, and dividing a plurality of t periods, wherein the back-end curve is a curve corresponding to a time period from the meeting of a current daily load demand value to the time period from the distributed photovoltaic no longer outputting power, and the back-end curve is the sum of the output power of the distributed photovoltaic on the current day and the power obtained from the outside;
step S2: according to the current day energy storage allowance, the back-end curve and the full charge amount of the energy storage system, the cost C of purchasing electricity from the power grid through the distributed photovoltaic and energy storage system 3 (t) calculating to obtain the cost of purchasing electricity in a plurality of t periods, and meanwhile, according to depreciation cost C of the distributed photovoltaic equipment and the energy storage system equipment 1 (t) expression, maintenance costs C of said distributed photovoltaic devices, energy storage system devices 2 Respectively calculating the depreciation cost and the maintenance cost of a plurality of t periods by the expression of (t);
step S2: the cost, depreciation cost and maintenance cost of purchasing electricity in a plurality of t periods are used as initial solutions of the genetic algorithm;
and step S3: calculating to obtain a generation optimal value of respective costs of electricity purchasing cost, depreciation cost and maintenance cost and a generation minimum value of the minimum cost in a plurality of t periods by using a function formula of a minimum cost target on the basis of the initial solution;
and step S4: calculating the first-generation optimal value of the cost, depreciation cost and maintenance cost of electricity purchase in a plurality of t periods and the first-generation minimum value with the minimum cost to obtain a corresponding mass center;
step S5: propagating according to the centroid to produce a new population;
step S6: generating two offspring through crossing according to each pair of sets in the new population generated by propagation;
step S7: if q is the probability of mutation, the mutation operation is completed by randomly replacing a certain element on the set;
step S8: after the operations of crossing and mutation, a new solution is generated, and the new solution represents the current daily charge allowance of the energy storage system obtained after the full charge capacity of the energy storage system is optimized;
step S9: and (3) taking the new solution as the initial solution of the genetic algorithm, repeating the steps S3-S9 by using a sampling iterative algorithm, and detecting whether an ending condition is met after each iterative operation, wherein the ending condition is that the upper limit of the iterative times reaches the standard or the function value with the minimum cost is smaller than a preset function value.
And after obtaining a new solution, taking the new solution as an initial solution of the genetic algorithm, repeating the steps S3-S9 by adopting an iterative algorithm without taking into consideration a chemometric algebra and a maximized algebra, and detecting whether an end condition is met after each iterative operation, wherein the end condition is that the upper limit of the iteration times reaches the standard, or the function value with the minimum cost is smaller than a preset function value. For example: and presetting a function value to be 10, and ending the genetic algorithm when the minimum cost function value after a certain iterative operation is less than 10.
The genetic algorithm mentioned in the embodiment is different from the existing genetic algorithm, the evolution algebra and the maximization algebra do not need to be set, after a new solution is generated after the operation of 'crossing' and 'mutation', the repeated operation is not performed according to the evolution algebra and the maximization algebra, the iterative algorithm is adopted to repeat the steps S3-S9, whether the ending condition of the iterative algorithm is met or not is detected, and the ending condition is met, so that the computation of the genetic algorithm is reduced, and the efficiency, the rapidity and the accuracy of obtaining the charging margin of the current day and the grid-connected generating capacity through optimization are improved.
Based on the above algorithm, in a possible embodiment, a specific method of the iterative algorithm is as follows:
for solutions based on any one set of initial solutions XJ i Generated new solution XJ i ', if f (XJ) i )≥f(XJ i ') directly receive the new solution XJ i ′；
If f (XJ) i )＜f(XJ i ') then the new solution XJ i The acceptance probability E of' is given by the following equation:
in the above formula, R w For "heat" after iteration of the iterative algorithm n times, and to the late stage of iteration of the iterative algorithm, with R w Is continuously reduced when f (XJ) i )-f(XJ i ') the value of E will gradually decrease when the value of E is constant, so that the iterative algorithm tends to be stable;
will be paired with R after each iteration is completed w And performing 'heat reduction' operation, wherein a formula corresponding to the heat reduction operation is as follows:
wherein MT is the upper limit of the number of iterations of the iterative algorithm. Adjusting energy storage systems, etc. according to travel plans
Based on the foregoing method for optimizing stored energy of a distributed photovoltaic and energy storage system, an embodiment of the present invention further provides an apparatus for optimizing stored energy of a distributed photovoltaic and energy storage system, and referring to fig. 2, the apparatus for optimizing stored energy includes:
the operation module 210 is configured to calculate an output power curve of the daily distributed photovoltaic and a daily load demand curve according to the historical load data and the historical data of the distributed photovoltaic output power, where the output power curve and the load demand curve of the distributed photovoltaic on different days correspond to different illumination times and illumination intensities;
the model curve module 220 is configured to construct an energy storage margin model based on an output power curve of the daily distributed photovoltaic, a daily load demand curve and a relation between the service life and the capacity of the energy storage system, and calculate the energy storage margin model to obtain an optimized daily energy storage margin and a characteristic curve of the relation between the output power and the load demand;
a determining next day curve module 230, configured to determine, according to the illumination time and the illumination intensity of two adjacent days, an output power curve of the daily distributed photovoltaic and the daily load demand curve in combination, an output power curve of the next day distributed photovoltaic and a next day load demand curve;
an energy storage margin determining module 240, configured to determine a next-day energy storage margin of an energy storage system in the distributed photovoltaic and energy storage system according to the output power curve of the next-day distributed photovoltaic, the next-day load demand curve, and the characteristic curve;
the optimizing module 250 is used for optimizing to obtain 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, by taking the minimum cost of the V2G electric vehicle under the condition of participating in the charge and discharge of the distributed photovoltaic system and the energy storage system as a target, wherein the value of the current day charging margin is greater than that of the current day energy storage margin;
and the control module 260 is configured to control the energy storage system to output power simultaneously when the distributed photovoltaic system starts to output power on the current day, so that the total output power of the distributed photovoltaic system and the energy storage system on the current day reaches an output power steady value, and the output power steady value meets a stability index, wherein after the energy storage system on the current day stops outputting power, a charging mode of the energy storage system is determined to charge the energy storage system, and the energy storage system stops charging until the energy storage system reaches an energy storage margin on the next day or a charging margin on the current day.
Based on the energy storage optimization method of the distributed photovoltaic and energy storage system, an embodiment of the present invention further provides an electronic device, including:
at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a method of energy storage optimization for a distributed photovoltaic and energy storage system as described in any one of steps 101-106.
Based on the foregoing method for optimizing stored energy of a distributed photovoltaic and energy storage system, an embodiment of the present invention further provides a computer-readable storage medium, in which a computer program is stored, and when the computer program is executed by a processor, the method for optimizing stored energy of a distributed photovoltaic and energy storage system according to any one of steps 101 to 106 is implemented.
According to the energy storage optimization method of the distributed photovoltaic and energy storage system, an energy storage margin model is constructed firstly, and the energy storage margin model is operated to obtain a characteristic curve of the relationship between the optimized daily energy storage margin and output power and load requirements. And secondly, determining an output power curve and a next-day load demand curve of the next-day distributed photovoltaic and a next-day energy storage allowance of the energy storage system.
Then, according to the energy storage allowance of the next day and the output power curve of the distributed photovoltaic of the current day, optimizing to obtain the charging allowance of the current day of the energy storage system by taking the minimum cost as a target; and finally, controlling the energy storage system to output power simultaneously when the distributed photovoltaic system starts to output power on the current day, so that the total output power of the distributed photovoltaic system and the energy storage system on the current day reaches the stable output power value and meets the stability index.
The method provided by the invention finely combines the characteristics of distributed photovoltaic output power, the characteristics of the service life and the capacity of the energy storage system, cost factors and other contents, optimizes the relation between the daily energy storage allowance and the relation curve of output power and load demand by taking the minimum cost as a target, optimizes the current daily charging allowance, and obtains more accurate daily energy storage and grid-connected power generation of the energy storage system, so that the total output power of the distributed photovoltaic and energy storage system meets the stability index, and the method has the advantages of less calculation amount in the whole process, simplicity in operation, less calculation time, simplicity and convenience in control logic and higher practical value.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one of 8230, and" comprising 8230does not exclude the presence of additional like elements in a process, method, article, or apparatus comprising the element.
While the present invention has been described with reference to the particular illustrative embodiments, it is to be understood that the invention is not limited to the disclosed embodiments, but is intended to cover various modifications, equivalent arrangements, and equivalents thereof, which may be made by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A method for optimizing stored energy in a distributed photovoltaic and energy storage system, the method comprising:
according to the historical load data and the historical data of the output power of the distributed photovoltaic, calculating to obtain an output power curve of the daily distributed photovoltaic and a daily load demand curve, wherein the output power curve and the load demand curve of the distributed photovoltaic on different days correspond to different illumination time and illumination intensity;
constructing an energy storage margin model based on the output power curve of the daily distributed photovoltaic, the daily load demand curve and the relation between the service life and the capacity of the energy storage system, and calculating the energy storage margin model to obtain an optimized characteristic curve of the daily energy storage margin and the relation between the output power and the load demand;
according to the illumination time and the illumination intensity of two adjacent days, combining the output power curve of the daily distributed photovoltaic and the daily load demand curve, and determining the output power curve of the next-day distributed photovoltaic and the next-day load demand curve;
determining the next-day energy storage allowance of an energy storage system in the distributed photovoltaic and energy storage system according to the output power curve of the next-day distributed photovoltaic, the next-day load demand curve and the characteristic curve;
according to the energy storage allowance of the next day and the output power curve of the distributed photovoltaic of the current day, optimizing to obtain the charging allowance of the current day of the energy storage system by taking the minimum cost of the V2G electric vehicle under the condition of participating in charging and discharging of the distributed photovoltaic and energy storage system as a target, wherein the value of the charging allowance of the current day is larger than that of the energy storage allowance of the next day;
at the current day when distributed photovoltaic begins output, control energy storage system output power simultaneously for the current day distributed photovoltaic and energy storage system's total output reaches the steady value of output, the steady value of output satisfies the stability index, wherein, at the current day energy storage system stops behind the output, confirms energy storage system's the mode of charging is in order to right energy storage system charges, until energy storage system reaches the next day energy storage allowance or when the current day charging allowance, energy storage system stops charging.
2. The energy storage optimization method according to claim 1, wherein an energy storage margin model is constructed based on an output power curve of the daily distributed photovoltaic, a daily load demand curve and a relation between the service life and the capacity of the energy storage system, and the energy storage margin model is subjected to operation optimization to obtain a characteristic curve of the relation between the daily energy storage margin and the output power and the load demand, and the method comprises the following steps:
according to the output power curve of the daily distributed photovoltaic and the daily load demand curve, performing statistical operation to obtain a daily dual-output power curve, wherein the daily dual-output power curve represents a power change curve in the period from the start of output power of the daily distributed photovoltaic to the time when the output power meets the daily load demand;
building a model based on an Adaboost algorithm, modeling an energy storage margin corresponding to the daily dual-output power curve by combining the relation between the service life and the capacity of the energy storage system, and obtaining an optimized daily energy storage margin and an optimized output power and load demand relation curve through model training, cross validation and parameter optimization, wherein the daily energy storage margin is used for outputting power when the distributed photovoltaic starts to output power on the day, and stopping outputting power until the distributed photovoltaic output power on the day meets the load demand on the day.
3. The energy storage optimization method according to claim 1, wherein when the distributed photovoltaic starts to output power on a 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 the energy storage system on the current day reaches an output power steady value, wherein after the energy storage system stops outputting power on the current day, a charging mode of the energy storage system is determined to charge the energy storage system, and when the energy storage system reaches the next day energy storage margin or the current day charging margin, the energy storage system stops charging, including:
when the distributed photovoltaic starts to output power on the current day, the output power of the distributed photovoltaic does not meet the load requirement on the current day, and the energy storage system is controlled to output power by using the energy storage margin on the current day, so that the total output power of the distributed photovoltaic and the energy storage system on the current day reaches an output power steady value;
if the external power is obtained, when the sum of the distributed photovoltaic output power and the external power obtained meets the current daily load requirement, the energy storage system stops outputting the power, the current daily energy storage allowance is larger than 0, and the external power obtained is the power provided by the V2G electric vehicle which discharges to the distributed photovoltaic and energy storage system;
if the external power is not obtained, when the distributed photovoltaic output power meets the load requirement of the current day, the energy storage system stops outputting the power, and the energy storage allowance of the current day 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 externally obtained power is larger than the current daily load requirement, the distributed photovoltaic and the externally obtained power charge the energy storage system together, and the energy storage system stops charging until the energy storage system reaches the next day energy storage allowance or the current day charging allowance;
the distributed photovoltaic system stops charging the energy storage system, and after the energy storage system reaches the current day charging allowance, the distributed photovoltaic system outputs power to a power grid to be connected to the power grid for power generation;
after the energy storage system stops outputting power, if the sum of the real-time output power of the distributed photovoltaic and the externally obtained power is not larger than the current daily load demand, the distributed photovoltaic and the externally obtained power do not charge the energy storage system, and then the distributed photovoltaic and the energy storage system charge the energy storage system from the power grid power purchase in the valley period until the energy storage system reaches the next day energy storage allowance.
4. The energy storage optimization method according to claim 3, wherein the distributed photovoltaic and the externally obtained power charge the energy storage system together until the energy storage system reaches the energy storage margin of the next day or the charging margin of the current day, and the method includes:
in the process that the distributed photovoltaic and the external acquired power jointly charge the energy storage system, if the output power of the distributed photovoltaic does not meet the current daily load requirement and the energy storage system does not reach the next-day energy storage margin, the distributed photovoltaic and the external acquired power stop charging the energy storage system, and then the distributed photovoltaic and the energy storage system purchase power from the power grid to charge the energy storage system in a valley period until the energy storage system reaches the next-day energy storage margin;
in the process that the distributed photovoltaic and the external acquired power jointly charge the energy storage system, if the sum of the output power of the distributed photovoltaic and the external acquired power does not meet the current daily load requirement and the energy storage system reaches the next day energy storage allowance, the distributed photovoltaic and the external acquired power stop charging the energy storage system, and the energy storage system does not receive charging any more;
in the process that the distributed photovoltaic and the external acquired power jointly charge the energy storage system, if the sum of the output power of the distributed photovoltaic and the external acquired power always meets the load requirement of the current day, the distributed photovoltaic and the external acquired power jointly charge the energy storage system until the energy storage system reaches the charging allowance of the current day, and the energy storage system does not receive charging any more;
in the process that the distributed photovoltaic and the externally obtained power charge the energy storage system together, if the output power of the distributed photovoltaic always meets the current daily load requirement, the externally obtained power charges the energy storage system until the energy storage system reaches the current daily charging allowance, and the energy storage system does not receive charging any more.
5. The method for optimizing stored energy according to claim 3, wherein when the distributed photovoltaic starts to output power on the current day, controlling the energy storage system to output power simultaneously so that the total output power of the distributed photovoltaic and the energy storage system on the current day reaches an output power steady value comprises:
after the energy storage system is charged to be equal to the energy storage allowance of the next day after the energy storage system stops charging on the current day, if the sum of the output power of the distributed photovoltaic and the externally obtained power does not meet the load requirement of 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 an output power steady value;
after the energy storage system is charged to a value larger than the next-day energy storage allowance and smaller than the current-day charging allowance after the energy storage system stops charging on the current day, if the sum of the output power of the distributed photovoltaic and the externally obtained power does not meet the current-day load requirement, controlling the energy storage system to simultaneously output power by using the stored energy when the charging is stopped until the real-time stored energy of the energy storage system is reduced to the next-day energy storage allowance, stopping the output power of the energy storage system, and then immediately purchasing power from the power grid by the distributed photovoltaic and the energy storage system;
at present day energy storage system is charged to being equal to after stopping charging current day charging allowance, if the output power of distributed photovoltaic and the circumstances that the load demand of present day does not appear unsatisfied in the sum of the external power that obtains, then control energy storage system utilizes current day charging allowance is output power simultaneously, until energy storage system's real-time storage energy reduces to next day energy storage allowance stops energy storage system output power, afterwards, distributed photovoltaic and energy storage system follow immediately the electric wire netting is purchased electricity.
6. The stored energy optimization method according to claim 3, characterized in that the stability index S is expressed as follows:
in the above formula, N represents the time period from the daily rise time to the daily fall time, w 1 (t) represents the output power over the output power curve of the daily distributed photovoltaic over a period of t, p represents the current daily energy storage margin, w 2 Represents the average output power, w, of the distributed photovoltaic calculated from the historical data of the distributed photovoltaic output power 3 Represents the average output power of the distributed photovoltaic from the rise time to the fall time of the day.
7. The energy storage optimization method of claim 1, wherein the function f of the cost minimization objective is;
in the above formula, C 1 (t) represents depreciation costs of distributed photovoltaic equipment, energy storage system equipment, C 2 (t) represents maintenance costs of the distributed photovoltaic equipment, energy storage system equipment, C 3 (t) represents the cost of purchasing electricity from the distributed photovoltaic and energy storage system to the power grid, wherein the cost of purchasing electricity from the distributed photovoltaic and energy storage system to the power grid refers to: the cost of electricity purchase to the power grid and the V2G electric vehicle by the distributed photovoltaic and energy storage system is different from the profit earned by the grid-connected electricity generation to the power grid and the electricity generation to the V2G electric vehicle by the distributed photovoltaic and energy storage system, the cost of electricity purchase to the power grid by the distributed photovoltaic and energy storage system is a positive value, the cost of electricity purchase to the power grid and the V2G electric vehicle is more than the profit earned by the electricity generation, and the cost of electricity purchase to the power grid and the V2G electric vehicle is less than the profit earned by the electricity generation;
wherein the cost C of purchasing electricity from the distributed photovoltaic and energy storage system to the power grid 3 The expression of (t) is:
C 3 (t)＝(k i1 (t)w i1 (t)+k i2 (t)w i2 (t)-k j1 (t)w j1 (t)-k j2 (t)
w j2 (t))△t
in the above formula, k i1 (t) represents the price of electricity purchased from the grid during the period t, w i1 (t) represents the power purchased by the distributed photovoltaic and energy storage system from the power grid during the period t, k i2 (t) represents the price of electricity purchased from the V2G electric vehicle in the period of t, w i2 (t) represents the power purchased by the distributed photovoltaic and energy storage system to the V2G electric automobile in the period of t, k j1 (t) represents the price of electricity generated to the grid during the period t, w j1 (t) represents the output power of the distributed photovoltaic and energy storage system to grid connection of a power grid at the time t, k j2 (t) represents the time period t toThe electricity price, w, of the V2G electric vehicle j2 (t) represents the output power generated by the distributed photovoltaic and energy storage system to the V2G electric vehicle in a time period t, and Δ t represents a time period;
and the t period of time is the electricity purchasing power w of the distributed photovoltaic and energy storage system i (t) according to the gap amount of the energy storage allowance corresponding to the t time period, and the power w of the distributed photovoltaic and energy storage system for purchasing electricity to the V2G electric automobile in the t time period i2 (t) determining the gap amount of the load demand value, wherein the gap amount of the energy storage allowance corresponding to the t time period is determined according to the difference value between the energy storage allowance of the next day and the current charging and energy storage energy of the energy storage system, and the gap amount of the load demand is determined according to the output power of the distributed photovoltaic corresponding to the t time period and the power w purchased from the distributed photovoltaic and energy storage system to the V2G electric automobile in the t time period i2 (t) the sum, determined as the difference between the load demand values corresponding to the t periods;
and outputting power w to the grid of the power grid by the distributed photovoltaic and energy storage system in the period t j (t) the sum of the output power generated by the V2G electric automobile and the current daily charging allowance of the energy storage system is obtained according to optimization, and the output power of the distributed photovoltaic corresponding to the t time period is determined.
8. The energy storage optimization method according to claim 7, wherein the optimizing the current day charging margin of the energy storage system according to the next day energy storage margin and the output power curve of the distributed photovoltaic system on the current day, with the aim of minimizing the cost of the V2G electric vehicle when the electric vehicle participates in the charging and discharging of the distributed photovoltaic system and the energy storage system, wherein the current day charging margin is greater than the next day energy storage margin, comprises:
step S1: selecting a back-end curve in the output power curve of the distributed photovoltaic at the current day, and dividing the back-end curve into a plurality of t periods, wherein the back-end curve is a curve corresponding to a time period from the meeting of the current daily load demand value to the no output power of the distributed photovoltaic;
step S2: root of herbaceous plantsAnd C, according to the next-day energy storage allowance, the back-end curve and the full charge amount of the energy storage system, the cost C of purchasing electricity from the power grid through the distributed photovoltaic and energy storage system 3 (t) calculating to obtain the cost of purchasing electricity in a plurality of t periods by using the expression of (t), and meanwhile, obtaining the depreciation cost C of the distributed photovoltaic equipment and the energy storage system equipment 1 (t) expression, maintenance costs C of said distributed photovoltaic devices, energy storage system devices 2 Respectively calculating the depreciation cost and the maintenance cost of a plurality of t periods by using the expression of (t);
step S2: the cost, depreciation cost and maintenance cost of purchasing electricity in a plurality of t periods are used as initial solutions of the genetic algorithm;
and step S3: on the basis of the initial solution, calculating to obtain a generation optimal value of the cost, depreciation cost and maintenance cost of electricity purchase in a plurality of t periods and a generation minimum value of the cost through a function formula of the cost minimum target;
and step S4: calculating the first-generation optimal value of the cost, depreciation cost and maintenance cost of electricity purchasing in the t periods and the first-generation minimum value of the cost to obtain the corresponding mass center;
step S5: propagating according to the centroid to produce a new population;
step S6: generating two offspring through crossing according to each pair of sets in the new population generated by propagation;
step S7: if q is the probability of mutation, the operation of mutation is completed by randomly replacing a certain element on the set;
step S8: generating a new solution after the operations of crossing and mutation, wherein the new solution represents the current day charging allowance of the energy storage system obtained after the full charging amount of the energy storage system is optimized;
step S9: taking the new solution as an initial solution of the genetic algorithm, repeating the steps S3-S9 by using a sampling iterative algorithm, and detecting whether an ending condition is met after each iterative operation, wherein the ending condition is that the upper limit of the iteration times reaches the standard, or the function value with the minimum cost is smaller than a preset function value;
the specific method of the iterative algorithm is as follows:
for solutions based on any one set of initial solutions XJ i Generated new solution XJ i ', if f (XJ) i )≥f(XJ i ') directly receive the new solution XJ i ′；
If f (XJ) i )＜f(XJ i ') then the new solution XJ i The acceptance probability E of' is given by the following equation:
in the above formula, R w For "heat" after iteration of the iterative algorithm n times, and to the late stage of iteration of the iterative algorithm, with R w Is continuously reduced when f (XJ) i )-f(XJ i ') the value of E will gradually decrease when the value of E is constant, so that the iterative algorithm tends to be stable;
will be paired with R after each iteration is completed w And performing 'heat reduction' operation, wherein a formula corresponding to the heat reduction operation is as follows:
wherein MT is the upper limit of the iteration times of the iterative algorithm.
9. The stored energy optimization method of claim 5, wherein 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 Representing the initial capacity of the energy storage system when the energy storage system is not used, k representing a proportionality coefficient, soc representing the average state of charge value of a battery in the energy storage system in a single cycle, D representing the charging and discharging depth of the battery in the energy storage system, and M representing the charge and discharge depth of the battery in the energy storage systemNumber of charge and discharge cycles of the battery.
10. An electronic device, comprising:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a method of energy storage optimization of a distributed photovoltaic and energy storage system according to any one of claims 1 to 9.
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