CN112290568A - Hybrid energy storage configuration method of 'light-storage' power generation system - Google Patents
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Abstract
The invention provides a hybrid energy storage configuration method of a light-storage power generation system, which can be used for carrying out hybrid energy storage planning on a photovoltaic power station and comprises the following steps: s1, sorting the output data of the photovoltaic power station and meteorological data of the location of the power station, and counting the time ratio of each typical weather day; s2, decomposing photovoltaic output on each typical weather day to obtain a target output curve and high and low frequency fluctuation components corresponding to each typical weather day; s3, drawing high and low frequency fluctuation component histograms and fitting a probability density curve by combining the daily time ratio of each typical weather; s4, selecting a proper hybrid energy storage combination according to the operation period of the photovoltaic power station, the energy storage cycle times and the hybrid energy storage power determined in the S3; s5, constructing a hybrid energy storage capacity optimization model with the minimum total life cycle cost and the maximum target output satisfaction degree; and S6, solving by adopting a multi-objective optimization algorithm to obtain an optimal hybrid energy storage capacity configuration scheme of the light-storage power generation system.
Description
Technical Field
The invention relates to the field of configuration of mixed energy storage capacity of a new energy power generation system, in particular to a hybrid energy storage configuration method of a light-storage power generation system.
Background
With the deepened adjustment of energy structures and the continuously improved energy and emission reduction requirements in China, clean low-carbon energy plays an increasingly important and irreplaceable role on the energy supply side. In recent years, the photovoltaic industry is rapidly developed, and the development scale is increased day by day, so that the influence of characteristics such as photovoltaic output fluctuation and intermittence on the output stability is increased day by day, the grid-surfing difficulty of photovoltaic power generation is increased, and a large amount of resource waste is caused. Therefore, in order to improve the output stability of the photovoltaic power station and fully utilize resources, a proper amount of energy storage devices need to be equipped for the photovoltaic power station. However, the photovoltaic power station output fluctuation is large, the requirement on energy storage is high, and the single energy storage type is difficult to meet the requirements of the photovoltaic power station in various aspects, so that a mixed energy storage mode is provided, the advantages of various energy storages are fully played, and the requirements of the photovoltaic power station in various aspects are better met.
According to the energy storage and power generation characteristics, the energy storage device can be divided into two categories of power type energy storage and energy type energy storage. The power type energy storage has high power density, long cycle life and quick charge-discharge response, and is suitable for stabilizing the high-frequency fluctuation of photovoltaic output; the energy type energy storage has high energy density and large energy storage capacity, and is suitable for stabilizing low-frequency fluctuation of photovoltaic output. Energy storage characteristics such as system cost, cycle life and the like of different energy storage modes are different. Therefore, there is a need for research on a reasonable configuration method of the hybrid energy storage capacity of the light-storage power generation system.
Disclosure of Invention
The invention aims to solve the technical problem of providing a hybrid energy storage configuration method of a 'light-storage' power generation system, which can determine the optimal configuration scheme of the hybrid energy storage capacity of a photovoltaic power station based on the photovoltaic output data of each typical weather day under the condition of determining the installed capacity of the photovoltaic power station, provide technical support for energy storage planning of the photovoltaic power station, and is suitable for popularization and application in the hybrid energy storage capacity configuration of the 'light-storage' power generation system in China.
The purpose of the invention is realized by the following technical scheme:
a hybrid energy storage configuration method of an optical-storage power generation system comprises the following steps:
(S1) obtaining 1-minute output processes of different typical weather days (sunny days, cloudy days, rainy days and snowy days) according to the historical output data of the photovoltaic power station; according to the meteorological data of the research area, counting to obtain the time ratio of each typical weather day (sunny day, cloudy day, rainy day and snowy day);
(S2) decomposing 1 minute-level output fluctuation data of each typical weather day of the photovoltaic power station by adopting a wavelet decomposition signal processing method so as to obtain a target output curve, a high-frequency fluctuation component and a low-frequency fluctuation component;
(S3) according to the typical weather time-of-day proportion, the data quantity of the high-frequency component and the low-frequency component is amplified in a multiplying mode, the data of the high-frequency fluctuation component and the data of the low-frequency fluctuation component are counted, a frequency histogram is drawn, and probability density curve fitting is conducted. And then according to the probability density curve obtained by fitting, obtaining confidence intervals corresponding to different confidence levels, wherein the maximum value of the absolute value of the interval boundary is the energy storage power. The energy storage power corresponding to the high-frequency fluctuation component is power type energy storage power, and the energy storage power corresponding to the low-frequency fluctuation component is energy type energy storage power;
(S4) selecting a specific power type energy storage technology and an energy type energy storage technology according to the operation period of the photovoltaic power station, the energy storage cycle number and the hybrid energy storage power determined in the step 3;
(S5) constructing a hybrid energy storage capacity optimization model with a full life cycle cost minimum and a target output satisfaction maximum;
(S6) taking the ratio of the typical weather days and the time obtained in the step (1), the target output curve, the high-frequency power curve and the low-frequency power curve of each typical weather day obtained in the step (2), the power type energy storage power and the energy type energy storage power determined in the step (3), and the basic parameters of each energy storage technology as the input of the step (5), and adopting a multi-objective optimization algorithm to solve, thereby obtaining the optimal hybrid energy storage capacity configuration.
The mathematical model established in step (S5) is composed of an objective function, decision variables and constraint conditions, as follows:
a. objective function
The mathematical model established in the step (S5) is a multi-objective mathematical model with the minimum cost of the hybrid energy storage full life cycle and the maximum satisfaction degree of the target output curve as the target.
Objective function F1: the hybrid energy storage full life cycle cost is minimal. The specific form is as follows:
objective function F2: the target output curve satisfaction degree is maximum. The specific form is as follows:
in the formula:
Pi sthe system cost of the ith energy storage device is Yuan/MWh;
Cithe capacity of the i-th energy storage device, MWh;
Nithe number of times of replacement of the ith energy storage device is counted;
Sijthe total electric energy storage capacity, MWh, of the ith energy storage device on the jth typical weather day;
ajtime to day ratio for jth typical weather day;
Opthe operation age and the year of the photovoltaic power station are shown;
dithe discharge depth of the ith energy storage device;
cyithe cycle number of the ith energy storage device;
δjka determination coefficient for whether the "light-stored" output value at the kth minute on the jth typical weather day satisfies the target output value;
pjkthe "light-stored" output value, MW, at the kth minute of typical weather day j;
b. Decision variables
Decision variable is power type energy storage capacity C1Energy type energy storage capacity C2，MWh。
c. Constraint conditions
Electric quantity balance constraint: eijk＝ηi·Sijk；
Maximum charge-discharge power constraint:
③ non-negative constraint: the variables referred to above are all non-negative values.
In the formula:
Eijkthe power generation amount of the ith energy storage device at the kth minute on the jth typical weather day, MWh;
Sijkthe power consumption of the ith energy storage device at the kth minute on the jth typical weather day, MWh;
ηiis the ithThe conversion efficiency of the seed energy storage device;
Pp,maxthe maximum charge/discharge power, MW, for power type energy storage;
Pe,maxthe energy storage maximum charge/discharge power, MW.
The signal processing method in the step (S2) includes wavelet decomposition, wavelet packet decomposition, EMD, EEMD.
Step (S5) proposes a configuration model that takes into account the hybrid energy storage full life cycle cost, the ripple stabilization effect.
The optimization algorithm in the step (S6) adopts dynamic programming and its improved algorithm or heuristic algorithm.
The dynamic programming and the improved algorithm thereof comprise discrete differential dynamic programming, gradual progressive dynamic programming and a gradual optimization method.
The heuristic algorithm comprises a genetic algorithm, an artificial neural network algorithm, a particle swarm algorithm and an ant colony algorithm.
Advantageous effects
The invention provides a hybrid energy storage configuration method of a 'light-storage' power generation system, which adopts an optimal capacity configuration decision method, obtains a target output curve, a high-frequency fluctuation component and a low-frequency fluctuation component by decomposing solar output fluctuation of photovoltaic typical weather, determines hybrid energy storage power by analyzing each typical high-frequency fluctuation component and each typical low-frequency fluctuation component, and then adopts a multi-objective optimization algorithm to obtain a hybrid energy storage optimal capacity configuration scheme. The invention has the advantages that:
1. the typical weather daily time ratio is determined according to historical meteorological data of the area where the photovoltaic power station is located, analysis is carried out according to typical weather daily output data of the photovoltaic power station, and the method is suitable for any photovoltaic power station with certain historical data (meteorological data and photovoltaic measured data) and has good portability.
2. The hybrid energy storage capacity allocation scheme obtained by the method can effectively reduce the investment cost on the premise of maximally stabilizing the photovoltaic output fluctuation, and has good economical efficiency and practicability.
3. The method considers the complementary performance between the power type energy storage and the energy type energy storage, and can effectively improve the fluctuation stabilizing effect.
Drawings
FIG. 1 is a simplified flow diagram of an embodiment of the present invention.
FIG. 2 is a graph of typical weather sunrise of a photovoltaic power plant in an embodiment of the invention.
Fig. 3 is an exploded view of photovoltaic output in an embodiment of the invention (using a cloudy typical weather day as an example).
FIG. 4 is a histogram and probability density curve of high and low frequency fluctuation components in an embodiment of the present invention.
Fig. 5 shows the power type and energy type stored energy power corresponding to different confidence levels in the embodiment of the invention.
Fig. 6 is a graph of the photovoltaic output fluctuation optimization effect of the embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the following will discuss the present invention in detail with reference to the accompanying drawings and examples:
in this example, taking a certain photovoltaic power station (total installed power of 850MW, 25 years in operation period) as an example, according to the simplified flowchart of fig. 1, the specific implementation steps are as follows:
101, (S1) obtaining 1-minute output processes (shown in figure 2) of different typical weather days (sunny days, cloudy days, rainy days and snowy days) according to the historical output data of the photovoltaic power station; and (4) according to the meteorological data of the research area, counting to obtain the time ratio of each typical weather day (sunny day, cloudy day, rainy day and snowy day).
TABLE 1 typical weather day-time ratios
| Typical weather day | In sunny days | Cloudy | Rainy day | Snow sky |
| Ratio of time to volume | 39％ | 36％ | 22％ | 3％ |
102, (S2) decomposing the 1-minute-level output fluctuation data of each typical weather day of the photovoltaic power plant by using a wavelet decomposition method, so as to obtain a target output curve, a high-frequency fluctuation component and a low-frequency fluctuation component (taking a cloudy typical weather day as an example, see fig. 3). The signal processing method in the step (S2) includes wavelet decomposition, wavelet packet decomposition, EMD, EEMD.
103, (S3) according to the typical weather time-of-day ratio, the data amount of the high-frequency and low-frequency components is amplified by multiple, and then the data of the high-frequency fluctuation component and the data of the low-frequency fluctuation component are counted, a frequency histogram is drawn, and probability density curve fitting is performed (see fig. 4). Then, according to the probability density curve obtained by fitting, confidence intervals corresponding to different confidence levels can be obtained, and the maximum value of the absolute value of the boundary of the interval is the energy storage power (see fig. 5). The energy storage power corresponding to the high-frequency fluctuation component is power type energy storage power, and the energy storage power corresponding to the low-frequency fluctuation component is energy type energy storage power. The hybrid energy storage power is optimal with a confidence level of 95%, and a higher confidence level can be met without excessive power. Therefore, the power type energy storage power is 3.54MW and the energy type energy storage power is 77.66MW in the example.
104, (S4) selecting a power type energy storage technology (super capacitor) and an energy type energy storage technology (all-vanadium redox flow battery) according to the operation period (25 years) of the photovoltaic power station, the energy storage cycle times and the mixed energy storage power determined in the step 3.
105, (S5) establishing a "hybrid energy storage capacity optimization configuration model of the optical-storage" power generation system with the minimum full life cycle cost and the maximum target output satisfaction degree.
The established 'light-storage' power generation system hybrid energy storage capacity optimization configuration model considering photovoltaic output characteristics is composed of an objective function, decision variables and constraint conditions, and is specifically as follows:
the mathematical model established in the step (S5) is a multi-objective mathematical model with the minimum cost of the hybrid energy storage full life cycle and the maximum satisfaction degree of the target output curve as the target. Step (S5) proposes a configuration model that takes into account the hybrid energy storage full life cycle cost, the ripple stabilization effect.
Objective function F1: the hybrid energy storage full life cycle cost is minimal. The specific form is as follows:
objective function F2: the target output curve satisfaction degree is maximum. The specific form is as follows:
in the formula:
Pi sthe system cost of the ith energy storage device is Yuan/MWh;
Cithe capacity of the i-th energy storage device, MWh;
Nithe number of times of replacement of the ith energy storage device is counted;
Sijthe total electric energy storage capacity, MWh, of the ith energy storage device on the jth typical weather day;
ajtime to day ratio for jth typical weather day;
Opthe operating age, year, in this example 25 years, of the photovoltaic power plant;
dithe discharge depth of the ith energy storage device;
cyithe cycle number of the ith energy storage device;
δjka determination coefficient for whether the "light-stored" output value at the kth minute on the jth typical weather day satisfies the target output value;
pjkthe "light-stored" output value, MW, at the kth minute of typical weather day j;
b. Decision variables
The decision variable being of power typeEnergy storage capacity C1Energy type energy storage capacity C2，MWh。
c. Constraint conditions
Electric quantity balance constraint: eijk＝ηi·Sijk；
Maximum charge-discharge power constraint:
③ non-negative constraint: the variables referred to above are all non-negative values.
In the formula:
Eijkthe power generation amount of the ith energy storage device at the kth minute on the jth typical weather day, MWh;
Sijkthe power consumption of the ith energy storage device at the kth minute on the jth typical weather day, MWh;
ηithe conversion efficiency of the ith energy storage device;
106, (S6) a multi-objective optimization algorithm is adopted to solve, and an optimal hybrid energy storage capacity configuration scheme of the light-storage power generation system is obtained. The optimization algorithm in the step (6) adopts dynamic programming and an improved algorithm or a heuristic algorithm thereof. The dynamic programming and the improved algorithm thereof comprise discrete differential dynamic programming, gradual progressive dynamic programming and a gradual optimization method. The heuristic algorithm comprises a genetic algorithm, an artificial neural network algorithm, a particle swarm algorithm and an ant colony algorithm. Namely:
and (3) taking the ratio of the typical weather days and the time obtained in the step (1), the target output curve, the high-frequency power curve and the low-frequency power curve of each typical weather day obtained in the step (2), the power type energy storage power and the energy type energy storage power determined in the step (3), the basic parameters of each energy storage technology (see table 2) and other data as the input of the step (5), and solving by adopting a multi-objective optimization algorithm to obtain the optimal hybrid energy storage capacity configuration (see table 3) and the optimal photovoltaic output fluctuation optimization result (see table 6).
TABLE 2 energy storage parameters
| Energy storage parameter | Super capacitor | All-vanadium redox flow battery |
| Cost of system (Yuan/kWh) | 12000 | 3500 |
| Number of cycles | 100000 | 16000 |
| |
95％ | 80％ |
| Depth of |
100％ | 90％ |
TABLE 3 optimal capacity allocation scheme for hybrid energy storage of photovoltaic power station
It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (5)
1. A hybrid energy storage configuration method of an optical-storage power generation system is characterized by comprising the following steps:
s1, sorting the output data of the photovoltaic power station and meteorological data of the location of the power station to obtain 1-minute output data of each typical weather day, and counting the time ratio of each typical weather day;
s2, decomposing photovoltaic output on each typical weather day by adopting signal processing methods such as wavelet decomposition and the like to obtain a target output curve and high and low frequency fluctuation components corresponding to each typical weather day;
s3, drawing high-frequency and low-frequency fluctuation component histograms and fitting a probability density curve by combining the daily time ratio of each typical weather, and further determining power type and energy type energy storage power;
s4, selecting a proper hybrid energy storage combination according to the operation period of the photovoltaic power station, the energy storage cycle times and the hybrid energy storage power determined in the S3;
s5, constructing a hybrid energy storage capacity optimization model with the minimum total life cycle cost and the maximum target output satisfaction degree;
and S6, solving by adopting a multi-objective optimization algorithm to obtain an optimal hybrid energy storage capacity configuration scheme of the light-storage power generation system.
2. The hybrid energy storage configuration method of the optical-storage power generation system of claim 1, wherein the signal processing method in step S2 comprises wavelet decomposition, wavelet packet decomposition, EMD, EEMD.
3. The hybrid energy storage configuration method of the optical-storage power generation system according to claim 1, wherein the mathematical model established in step S5 is composed of objective functions, decision variables and constraint conditions, and is as follows:
a. objective function
The mathematical model established in step S5 is a multi-objective mathematical model with the minimum cost of the hybrid energy storage full life cycle and the maximum satisfaction degree of the target output curve as the target.
Objective function F1: the hybrid energy storage full life cycle cost is minimal. The specific form is as follows:
objective function F2: the target output curve satisfaction degree is maximum. The specific form is as follows:
in the formula:
Pi sthe system cost of the ith energy storage device is Yuan/MWh;
Cithe capacity of the i-th energy storage device, MWh;
Nithe number of times of replacement of the ith energy storage device is counted;
Sijthe total electric energy storage capacity, MWh, of the ith energy storage device on the jth typical weather day;
ajtime to day ratio for jth typical weather day;
Opthe operation age and the year of the photovoltaic power station are shown;
dithe discharge depth of the ith energy storage device;
cyithe cycle number of the ith energy storage device;
δjka determination coefficient for whether the "light-stored" output value at the kth minute on the jth typical weather day satisfies the target output value;
pjkthe "light-stored" output value, MW, at the kth minute of typical weather day j;
b. Decision variables
Decision variable is power type energy storage capacity C1Energy type energy storage capacity C2，MWh。
c. Constraint conditions
Electric quantity balance constraint: eijk＝ηi·Sijk；
Maximum charge-discharge power constraint:
③ non-negative constraint: the variables referred to above are all non-negative values.
In the formula:
Eijkfor the ith kind of energy storage deviceThe power generation amount, MWh, is set at the kth minute of the jth typical weather day;
Sijkthe power consumption of the ith energy storage device at the kth minute on the jth typical weather day, MWh;
ηithe conversion efficiency of the ith energy storage device;
Pp,maxthe maximum charge/discharge power, MW, for power type energy storage;
Pe,maxthe energy storage maximum charge/discharge power, MW.
4. The hybrid energy storage configuration method of the optical-storage power generation system according to claim 1, wherein the optimization algorithm in step S6 is a dynamic programming and its improvement algorithm or a heuristic algorithm.
5. The hybrid energy storage configuration method of the optical-storage power generation system according to claim 4, wherein the dynamic planning and the improved algorithm thereof comprise discrete differential dynamic planning, gradual progressive dynamic planning and gradual optimization method.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113113927A (en) * | 2021-05-13 | 2021-07-13 | 北方工业大学 | Energy storage system capacity configuration method for comprehensive multi-typical weather scene |
| CN113489429A (en) * | 2021-05-31 | 2021-10-08 | 上海航天电源技术有限责任公司 | Power control method and system of photovoltaic energy storage system |
| CN116345507A (en) * | 2023-04-17 | 2023-06-27 | 长沙学院 | Multi-objective optimal configuration method and system for capacity of adaptive energy storage periodic energy storage power station |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108711878A (en) * | 2018-05-03 | 2018-10-26 | 天津大学 | Consider the photovoltaic plant polymorphic type component capacity collocation method of power producing characteristics |
| CN109038655A (en) * | 2018-07-18 | 2018-12-18 | 天津大学 | It rations the power supply and requires the mating stored energy capacitance calculation method in lower large-sized photovoltaic power station |
| CN111146793A (en) * | 2019-12-18 | 2020-05-12 | 济南大学 | Photovoltaic-energy storage system capacity optimization design method and system based on power feature extraction |
-
2020
- 2020-09-27 CN CN202011029758.XA patent/CN112290568A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108711878A (en) * | 2018-05-03 | 2018-10-26 | 天津大学 | Consider the photovoltaic plant polymorphic type component capacity collocation method of power producing characteristics |
| CN109038655A (en) * | 2018-07-18 | 2018-12-18 | 天津大学 | It rations the power supply and requires the mating stored energy capacitance calculation method in lower large-sized photovoltaic power station |
| CN111146793A (en) * | 2019-12-18 | 2020-05-12 | 济南大学 | Photovoltaic-energy storage system capacity optimization design method and system based on power feature extraction |
Non-Patent Citations (1)
| Title |
|---|
| CHAO MA等: "Multi-Objective Sizing of Hybrid Energy Storage System for Large-Scale Photovoltaic Power Generation System", 《SUSTAINABILITY》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113113927A (en) * | 2021-05-13 | 2021-07-13 | 北方工业大学 | Energy storage system capacity configuration method for comprehensive multi-typical weather scene |
| CN113489429A (en) * | 2021-05-31 | 2021-10-08 | 上海航天电源技术有限责任公司 | Power control method and system of photovoltaic energy storage system |
| CN116345507A (en) * | 2023-04-17 | 2023-06-27 | 长沙学院 | Multi-objective optimal configuration method and system for capacity of adaptive energy storage periodic energy storage power station |
| CN116345507B (en) * | 2023-04-17 | 2023-09-05 | 长沙学院 | Multi-objective optimal configuration method and system for capacity of adaptive energy storage periodic energy storage power station |
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