CN107834580B - Method for reducing load peak-valley difference of power grid based on battery energy storage - Google Patents
Method for reducing load peak-valley difference of power grid based on battery energy storage Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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Abstract
The invention discloses a method for reducing load peak-valley difference of a power grid based on battery energy storage, and belongs to the field of power system automation. The method for finding the optimal reduction of the peak-valley difference of the power grid by using the battery energy storage is used for monitoring and recording data of a possible influence factor layer and a direct influence factor layer so as to obtain a peak regulation coefficient, then a credible factor is introduced, a direct influence factor target layer is constructed, and the battery charging and discharging power is obtained. The invention gives full play to the technical advantages of electric energy storage, plays the role of reducing the peak-valley difference of the electric energy storage in the operation of a power system, can promote the establishment of a long-acting mechanism for promoting the consumption of renewable energy, and plays the role of reducing the peak-valley difference of battery energy storage by scientifically scheduling and operating the electric energy storage facility.
Description
Technical Field
The invention belongs to the field of power system automation, and relates to a method for reducing load peak-valley difference of a power grid based on battery energy storage.
Background
The battery energy storage has the advantages of high response speed, no geographic condition limitation and the like, and is more and more widely applied to power systems. At present, the load peak-valley difference of a power grid in some areas is large, which can cause frequent start and stop of a unit and reduction of operation efficiency, the power supply reliability is also reduced, and the power failure risk of a user is increased. The method for carrying out peak clipping and valley filling by using a battery energy storage technology is an effective way for solving the phenomenon.
In China, energy storage modes such as lead-carbon batteries, lithium ion batteries and all-vanadium redox flow batteries exist, however, various short plates exist in various energy storage modes in the aspects of environmental protection, cost, economy, sustainability and the like, and most of the energy storage modes do not enter a large-scale application stage, so thermal power is still a main peak regulation mode of new energy; and almost all AGC frequency modulation power supplies in the power grid are thermal power generating units. At present, in a large number of new energy power generation projects all over the country, the phenomenon of electricity abandonment of the power grid is severe day by day, wherein the construction loss of energy storage equipment is one of important factors influencing the incapability of successfully surfing the Internet in the new energy power generation.
For the operation control problem of reducing the peak-valley difference of the energy storage battery system in the power system, research and discussion are carried out in the prior art, and the prior art 1: a dynamic programming-based real-time optimization method for peak clipping and valley filling of a battery energy storage system (2012, 36 th volume 12 of an electric power system) provides a dynamic programming-based real-time correction optimization control strategy, discontinuous constraint conditions such as charging and discharging frequency limitation and discharging depth limitation can be introduced into an optimization model, and the peak clipping and valley filling real-time control method is provided by combining the influences of the charging and discharging frequency and the discharging depth of the battery on the service life of the battery based on the dynamic programming. Prior art 2: in the research on constant-power peak clipping and valley filling optimization strategies of battery energy storage systems (vol. 36, No. 9 of the power grid technology), a constant-power peak clipping and valley filling optimization model of the battery energy storage system and a practical simplified algorithm for solving the model are provided with the background of MW-level battery energy storage demonstration engineering of a southern power grid and the aim of solving the peak clipping and valley filling strategies of the battery energy storage system which operates by adopting a constant-power charge-discharge strategy. And a constant-power peak clipping and valley filling optimization model is established for 2 groups of actual load data of a certain station, and the effectiveness of the algorithm is verified.
However, these techniques do not take distributed new energy power generation systems into consideration, and are not applicable to power generation systems containing distributed new energy. The battery energy storage system is an essential part of a distributed new energy power generation system, a great deal of scientific research and practical demonstration has been made on the related technology of the battery energy storage system, the power generation system containing the distributed new energy has greater and greater proportion in the current power system along with the development of the times, however, the prior art has not appeared in the control technology of carrying out peak clipping and valley filling on the power generation system of the distributed new energy in the energy storage battery.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, seeks an optimal method for reducing the peak-valley difference of a power grid by using battery energy storage, and is a control technology for performing peak clipping and valley filling on an energy storage battery in a distributed new energy power generation system. And monitoring and recording data of the possible influence factor layer and the direct influence factor layer to obtain a peak regulation coefficient, introducing a credible factor, and further constructing a direct influence factor target layer to obtain the battery charge and discharge power.
In order to achieve the purpose, the invention adopts the following technical scheme that firstly, data of possible influence factors and data of direct influence factors are collected and processed to respectively obtain a peak regulation coefficient A and a peak regulation coefficient B; and credibility factors of the two peak regulation coefficients are introduced through fusion with historical data, so that the credibility of the peak regulation coefficients is increased. Finally, substituting the current charge-discharge power into a mathematical model of the current battery charge-discharge power to obtain the current battery charge-discharge power PB。
Modeling relevant factors for reducing peak-valley difference of battery energy storage, wherein the relevant factors are separated in the design and divided into a possible influence factor layer, a direct influence factor constraint layer and a direct influence factor target layer; the possible influencing factor layer mainly comprises the temperature, the air relative humidity, the altitude and the weather state of a power supply area, the direct influencing factor constraint layer is the capacity and the battery charging/discharging speed of the battery, and the direct influencing factor target layer reduces the expected value of the peak-valley difference, and the specific method comprises the following steps:
the method comprises the following steps: the data of the possible influence factor layer is effectively processed to obtain a peak regulation coefficient A and is expressed in a mathematical modeling mode, and the expression formula is as follows:
in the formula: n: the number of the regions for dividing the region of the battery in charge of peak shaving;
cj: a temperature value for each divided region;
Hj: each divided zone air relative humidity value;
h: altitude value of the battery energy storage area;
kr、kc: a weather condition related coefficient value;
Sj: the trusted function is a function of the network,
m: an untrustworthy number.
The credible function: when calculating the peak regulation coefficient A, data fusion is carried out on the temperature and the humidity of each area, and the data obtained by fusion is substituted into a test function
If phi (x) is more than or equal to 0.0148 and less than or equal to 0.1768, marking as a credible interval, otherwise, the credible interval is the incredible interval.
The power load curve shows a constantly changing trend along with the time, and the adjustment of the first two time periods is carried out in the designPeak coefficient Ai-1、Ai-2Deriving a confidence factor lambda that may influence the current time period of the factor layer1iThe expression is as follows:
confidence factor lambda1iUnder normal conditions a value approaching 1 and less than 1.
Step two: performing mathematical modeling on the peak regulation coefficient B of the direct influence factor constraint layer, wherein the expression is as follows:
in the formula: t isB: the battery energy storage temperature;
ηIi: battery charging efficiency;
VBIi: a battery charge rate;
ηOi: the efficiency of battery discharge;
VBOi: a rate of battery discharge;
Cr: a battery capacity;
εr: thermal conductivity of the battery material.
Similarly, a credible factor lambda directly influencing the current time interval of the factor constraint layer is led out2iThe expression is as follows:
step three: establishing a mathematical model of the current battery charge and discharge power according to the expected value of the peak-valley difference required to be reduced and the peak regulation coefficients obtained in the previous two steps, wherein the expression is as follows:
in the formula:
e: the percentage of peak-to-valley difference is expected to decrease;
PLi: current load usage.
Calculating the current battery charging and discharging power PBI.e. energy storage battery with power PBCharging and discharging, if PB>0, discharging the battery; when P is presentB<And 0, charging the battery.
Compared with the prior art, the invention has the following beneficial effects:
the method can not only meet the control effect of reducing the peak-valley difference by using the battery energy storage in the prior art, but also better inhibit the load fluctuation brought by the peak regulation process by fusing the historical data; by adding analysis and fusion of data of factors such as weather with large influence on distributed new energy power generation, the method is very suitable for a control method for reducing peak-valley difference by battery energy storage of a power system comprising a distributed new energy system. Nowadays, national policies strongly support the development of new energy power generation to replace traditional thermal power generation, the proportion of the new energy power generation is more and more, the new energy power generation is considered to be necessary in a control technology for reducing peak-valley difference through battery energy storage in an analysis power system, and the invention brings huge economic benefits.
Drawings
Fig. 1 is a flowchart of a method for reducing a peak-to-valley difference of a power system based on battery energy storage according to the present invention.
Fig. 2 is a comparison graph of load curves after the battery energy storage provided by the present invention reduces the peak-to-valley difference.
Detailed Description
According to the technical scheme, as shown in figure 1, firstly, data of possible influence factors and data of direct influence factors are collected and processed to respectively obtain a peak regulation coefficient A and a peak regulation coefficient B; and credibility factors of the two peak regulation coefficients are introduced through fusion with historical data, so that the credibility of the peak regulation coefficients is increased. Finally, substituting the current charge-discharge power into a mathematical model of the current battery charge-discharge power to obtain the current battery charge-discharge power PBThe specific scheme is as follows:
the method comprises the following steps: the data of the possible influence factor layer is effectively processed to obtain a peak regulation coefficient A and is expressed in a mathematical modeling mode, and the expression formula is as follows:
in the formula: n: the number of the regions for dividing the region of the battery in charge of peak shaving;
cj: a temperature value for each divided region;
Hj: each divided zone air relative humidity value;
h: altitude value of the battery energy storage area;
kr、kc: a weather condition related coefficient value;
Sj: the trusted function is a function of the network,
m: an untrustworthy number.
The credible function: when calculating the peak regulation coefficient A, data fusion is carried out on the temperature and the humidity of each area, and the data obtained by fusion is substituted into a test function
If phi (x) is more than or equal to 0.0148 and less than or equal to 0.1768, marking as a credible interval, otherwise, the credible interval is the incredible interval.
The power load curve shows a constantly changing trend along with the time, and the peak regulation coefficient A of the previous two time periods is used in the designi-1、Ai-2Deriving a confidence factor lambda that may influence the current time period of the factor layer1iThe expression is as follows:
confidence factor lambda1iUnder normal conditions a value approaching 1 and less than 1.
Step two: performing mathematical modeling on the peak regulation coefficient B of the direct influence factor constraint layer, wherein the expression is as follows:
in the formula: t isB: the battery energy storage temperature;
ηIi: battery charging efficiency;
VBIi: a battery charge rate;
ηOi: the efficiency of battery discharge;
VBOi: a rate of battery discharge;
Cr: a battery capacity;
εr: thermal conductivity of the battery material.
Similarly, a credible factor lambda directly influencing the current time interval of the factor constraint layer is led out2iThe expression is as follows:
step three: establishing a mathematical model of the current battery charge and discharge power according to the expected value of the peak-valley difference required to be reduced and the peak regulation coefficients obtained in the previous two steps, wherein the expression is as follows:
in the formula:
e: the percentage of peak-to-valley difference is expected to decrease;
PLi: current load usage.
Calculating the current battery charging and discharging power PBI.e. energy storage battery with power PBCharging and discharging, if PB>0, discharging the battery; when P is presentB<And 0, charging the battery.
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
For example, a set of complete battery energy storage peak shaving equipment is established in a certain area containing a distributed new energy power generation system grid connection, the area is divided into 10 areas, and a current temperature value c is obtained through detectionj-27.2, 26.7,30.1, 28.2,27.1, 29.7,28.8,30.2,28.3,29.9} in degrees celsius; each regional humidity value Hj ═ 33,56,45,87,65,32,78,55,57,61, in%; the altitude value h of the battery energy storage area is 659 meters; the value of the weather condition correlation coefficient is empirically kr＝1.0147，kc0.9915; calculating the peak regulation coefficient A at the momentiThe value:
and according to the peak regulation coefficients A of the first two timesi-1＝1.0117、Ai-2Calculation of the confidence factor λ 0.99911i：
The material thermal conductivity coefficient epsilon of the energy storage battery is also knownr4.172W; battery energy storage temperature TBThe temperature is 27 ℃; capacity C of batteryr10 MW; battery charging efficiency etaIi97.77%; battery discharge efficiency ηOi98.79%; battery charging rate VBIi2.3C/h; battery charging rate VBOiThe peak shaver coefficient value B at that time was calculated as 2.1C/hi：
And according to the peak regulation coefficients B of the first two timesi-1＝0.1997、Bi-2Calculation of the confidence factor λ 0.20152i：
If the desired percentage reduction in peak-to-valley difference is 15%, the current load usage PLiAnd when the power is 120MW, the charge and discharge power of the battery at the moment is as follows:
the above is an introduction to the use of the example designed by the present invention, and we can compare the results of the monitoring experiment of one day: FIG. 2 shows, in the upper diagram, a dotted line portion is an original loader curve, which is implemented as a load curve after peak clipping and valley filling; the lower graph is the real-time charging power of the energy storage battery, the charging power is larger than 0 to represent that the battery is charged, and when the charging power is smaller than 0 to represent that the battery is discharged; comparing the two load curves, the control method for reducing the peak-valley difference by using the battery energy storage of the invention can well play the roles of peak clipping and valley filling, well inhibit the problem of load fluctuation in the peak clipping process, and lead the load curve after peak clipping and valley filling to be stable, thereby providing great help for the economic operation of a power system and the absorption of a new energy power generation system.
Table 1 below shows the typical time node raw load during a day compared to the load after the peak-to-valley difference is reduced by using the battery energy storage of the present invention, and the real-time charging power of the battery.
TABLE 1 load curves and Battery charging Power for typical times of day
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (1)
1. A method for reducing load peak-valley difference of a power grid based on battery energy storage is characterized in that the method acquires and processes data of possible influence factors and direct influence factors to respectively obtain a peak regulation coefficient A and a peak regulation coefficient B; credibility factors of the two peak regulation coefficients are introduced through fusion with historical data, and the credibility of the peak regulation coefficients is increased; finally substituting the peak regulation coefficient and the credible factor into the mathematical model of the current battery charge and discharge power to obtain the current battery charge and discharge power PB(ii) a The method comprises the following steps:
the method comprises the following steps: the data of the possible influence factor layer is effectively processed to obtain a peak regulation coefficient A and is expressed in a mathematical modeling mode, and the expression formula is as follows:
in the formula: n: dividing the regions of the battery in charge of peak shaving into regions;
cj: a temperature value for each divided region;
Hj: each divided zone air relative humidity value;
h: altitude value of the battery energy storage area;
kr、kc: a weather condition related coefficient value;
Sj: the trusted function is a function of the network,
m: an untrusted number;
the credible function: when calculating the peak regulation coefficient A, data fusion is carried out on the temperature and the humidity of each area, and the data obtained by fusion is substituted into a test function
If 0.0Phi (x) is more than or equal to 148 and less than or equal to 0.1768, and the confidence interval is recorded, otherwise, the confidence interval is an unreliable interval;
the power load curve shows a trend of changing continuously along with time, so the peak regulation coefficient A passing through the first two periods in the designi-1、Ai-2Deriving a confidence factor lambda that may influence the current time period of the factor layer1iThe expression is as follows:
confidence factor lambda1iNormally a value approaching 1 and less than 1;
step two: performing mathematical modeling on the peak regulation coefficient B of the direct influence factor constraint layer, wherein the expression is as follows:
in the formula: t isB: the battery energy storage temperature;
ηIi: battery charging efficiency;
VBIi: a battery charge rate;
ηOi: the efficiency of battery discharge;
VBOi: a rate of battery discharge;
Cr: a battery capacity;
εr: the thermal conductivity of the battery material;
similarly, a credible factor lambda directly influencing the current time interval of the factor constraint layer is led out2iThe expression is as follows:
step three: establishing a mathematical model of the current battery charge and discharge power according to the expected value of the peak-valley difference required to be reduced and the peak regulation coefficients obtained in the previous two steps, wherein the expression is as follows:
in the formula:
e: the percentage of peak-to-valley difference is expected to decrease;
PLi: current load usage;
calculating the current battery charging and discharging power PBI.e. energy storage battery with power PBCharging and discharging, if PB>0, discharging the battery; when P is presentB<And 0, charging the battery.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| EP2190097A1 (en) * | 2008-11-25 | 2010-05-26 | ABB Research Ltd. | Method for operating an energy storage system |
| CN103746397A (en) * | 2014-01-22 | 2014-04-23 | 广东电网公司电力科学研究院 | Correction method and system for thermoelectricity mixed energy storing system |
| CN105990842A (en) * | 2015-02-10 | 2016-10-05 | 华为技术有限公司 | Electric peak regulation method and apparatus thereof |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2190097A1 (en) * | 2008-11-25 | 2010-05-26 | ABB Research Ltd. | Method for operating an energy storage system |
| CN103746397A (en) * | 2014-01-22 | 2014-04-23 | 广东电网公司电力科学研究院 | Correction method and system for thermoelectricity mixed energy storing system |
| CN105990842A (en) * | 2015-02-10 | 2016-10-05 | 华为技术有限公司 | Electric peak regulation method and apparatus thereof |
Non-Patent Citations (2)
| Title |
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| "兼顾技术性和经济性的储能辅助调峰组合方案优化";黎静华等;《电力系统自动化》;20170510;第41卷(第9期);第44-50、150页 * |
| "基于动态规划的电池储能系统削峰填谷实时优化";鲍冠南等;《电力系统自动化》;20120625;第36卷(第12期);第11-16页 * |
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