Hybrid energy storage charge state control method and device
By predicting the probability and duration of extreme weather and calculating the stored power based on meteorological data, the problem of equipment damage caused by unstable grid current under extreme weather conditions has been solved, achieving power stability and cost reduction.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- STATE GRID ZHEJIANG ELECTRIC POWER CO LTD NINGBO POWER SUPPLY CO
- Filing Date
- 2025-12-23
- Publication Date
- 2026-05-01
AI Technical Summary
In extreme weather conditions, unstable power grid current can lead to equipment damage. Existing technologies are unable to effectively predict and store sufficient electricity to cope with extreme weather, resulting in equipment damage and high electricity costs.
By predicting current power generation and the probability of extreme weather using historical databases, and combining this with meteorological data, the duration and power generation of extreme weather can be predicted, the target power storage capacity can be calculated, and power can be stored in advance to cope with extreme weather.
It improves power stability under extreme weather conditions, avoids equipment damage, and reduces electricity costs.
Smart Images
Figure CN121965660A_ABST
Abstract
Description
A method and apparatus for controlling hybrid energy storage state of charge Technical Field
[0001] This invention relates to the field of energy storage technology, and more specifically, to a method and apparatus for controlling the state of charge of hybrid energy storage. Background Technology
[0002] To promote the transformation of energy production and consumption towards clean and low-carbon practices and achieve sustainable development, renewable energy sources such as wind power and photovoltaics have developed rapidly. The company primarily relies on the power grid and renewable energy for power generation. However, during extreme weather events, such as thunderstorms, typhoons, and severe cold weather leading to freezing rain, abnormal weather conditions can easily cause transformer overload, line short circuits, or line breaks, resulting in significant voltage fluctuations and instability. This can affect the factory's operation. Furthermore, during extreme weather, the unstable current in the power grid can easily damage equipment connected to the grid. Summary of the Invention
[0003] In view of the above-mentioned problems existing in the prior art, the purpose of the present invention is to provide a hybrid energy storage state of charge control method and device. By predicting the power generation of the photovoltaic power station and the duration of the extreme weather before the extreme weather arrives, and by judging the extreme weather conditions, a portion of the power of the photovoltaic power station is stored before the extreme weather, so that it can be used for emergency response when the extreme weather arrives, avoiding the situation where the current is unstable during the extreme weather and causing equipment damage, and further reducing electricity costs.
[0004] The technical solution adopted in this invention is to provide a control method for hybrid energy storage state of charge. The control method includes: predicting the current power generation P1 based on a historical database, where the current power generation P1 is the daily power generation of the photovoltaic power station; determining the probability of extreme weather occurring within a target time period based on the historical database; when the probability of occurrence is high, determining the severity of the extreme weather occurring within the target time period; predicting the duration D1 of the extreme weather based on the severity; predicting the power generation P2 of the extreme weather based on the severity, where the extreme weather power generation P2 is the daily power generation of the photovoltaic power station during the extreme weather; obtaining the daily power consumption B during the extreme weather according to the work plan; and obtaining the target storage capacity A based on the extreme weather duration D1, the extreme weather power generation P2, and the power consumption B, where the target storage capacity A is the minimum storage capacity required during the extreme weather.
[0005] Compared with existing technologies, the technical effects achieved by adopting this technical solution are as follows: by judging the probability and severity of extreme weather occurrence within a target time period, the minimum amount of electricity required to store electricity when extreme weather occurs can be predicted. A portion of the electricity of the photovoltaic power station can be stored before extreme weather occurs so that it can be used for emergency response when extreme weather occurs, avoid equipment damage caused by unstable current during extreme weather, and also reduce electricity costs.
[0006] In an optional implementation, the step of predicting the current power generation P1 based on a historical database, wherein the current power generation P1 is the daily power generation of the photovoltaic power station, includes: obtaining the average power generation P of the same period in previous years from the historical database. 平均 ; Obtain the average ambient temperature T1 and average effective sunshine duration G1 of the same period in previous years from the historical database; Collect the current ambient temperature T2 and current effective sunshine duration G2 of the photovoltaic power station; Obtain the ambient temperature coefficient f1 based on the average ambient temperature T1 and the current ambient temperature T2; Obtain the effective sunshine duration coefficient f2 based on the average effective sunshine duration G1 and the current effective sunshine duration G2; Based on the average power generation P 平均 The ambient temperature coefficient f1 and the effective sunshine duration coefficient f2 are used to predict the current power generation P1, where P1 = P 平均 ×f1×f2, where 0.9≤f1≤1.1, f2>0.
[0007] Compared with existing technologies, the technical effects achieved by adopting this technical solution are as follows: by acquiring the average power generation, average ambient temperature, and average effective sunshine duration of the same period in previous years from the historical database, and collecting the current ambient temperature T2 and current effective sunshine duration G2 of the photovoltaic power station, the power generation of the current photovoltaic power station can be predicted based on the changes in ambient temperature and effective sunshine duration, thereby improving the accuracy of the prediction.
[0008] In an optional embodiment, the step of obtaining the ambient temperature coefficient f1 based on the average ambient temperature T1 and the current ambient temperature T2 includes: obtaining the difference between the current ambient temperature T2 and the average ambient temperature T1 as ΔT based on the average ambient temperature T1 and the current ambient temperature T2; when ΔT is greater than or equal to -a and less than or equal to a, f1 is equal to k1, where k1 = 1; when ΔT is greater than a and less than or equal to b, f1 is equal to k2, where 1 < k2 < 1.1; when ΔT is greater than b, f1 is equal to k3, where k3 = 1.1; when ΔT is greater than or equal to -b and less than -a, f1 is equal to k4, where 0.9 < k4 < 1; when ΔT is less than -b, f1 is equal to k5, where k5 = 0.9; where b > a > 0.
[0009] Compared with the existing technology, the technical effect achieved by adopting this technical solution is as follows: Based on the average ambient temperature T1 and the current ambient temperature T2, the difference between the average ambient temperature T1 and the current ambient temperature T2 is obtained as ΔT. Based on the magnitude of ΔT, the value of the ambient temperature coefficient f1 is determined, which includes presets for multiple cases.
[0010] In an optional implementation, the step of obtaining the effective illumination duration coefficient f2 based on the average effective illumination duration G1 and the current effective illumination duration G2 includes: the effective illumination duration coefficient f2 = G2 ÷ G1 based on the average effective illumination duration G1 and the current effective illumination duration G2, where G1 > 0 and G2 > 0.
[0011] Compared with existing technologies, the technical effects achieved by adopting this technical solution are as follows: Based on the average effective sunshine duration G1 and the current effective sunshine duration G2, the value of the effective sunshine duration coefficient f2 is determined as an influencing factor on the current power generation, thereby improving the feasibility of predicting the current power generation.
[0012] In an optional implementation, the step of determining the probability of extreme weather occurrence within a target time period based on the historical database includes: acquiring meteorological information data from the historical database for the same time period in previous years where extreme weather occurred, to obtain a standard meteorological information data curve; collecting meteorological information data within the target time period in real time to obtain a current meteorological information data curve; matching the standard meteorological information data curve with the current meteorological information data curve; if the matching degree is greater than d, then the probability of extreme weather occurrence within the target time period is high, where d > 0.
[0013] Compared with existing technologies, the technical effects achieved by this solution are as follows: By acquiring meteorological information data from the same time period of extreme weather events in previous years from a historical database, a standard meteorological information data curve is obtained; meteorological information data within the target time period is collected in real time to obtain the current meteorological information data curve; the standard meteorological information data curve is matched with the current meteorological information data curve to obtain the matching degree; the probability of extreme weather occurrence is determined by the magnitude of the matching degree; if the matching degree is high, the probability of occurrence is high, and if the matching degree is low, the probability of occurrence is low.
[0014] In an optional implementation, the step of determining the severity of the extreme weather occurring within the target time period when the probability of occurrence is high includes: obtaining the value M1 of the highest point of the standard meteorological information data curve; obtaining the value M2 of the highest point of the current meteorological information data curve; determining the magnitude of the absolute value of the difference between M1 and M2; if the absolute value of the difference between M2 and M1 is greater than or equal to c, then the severity is high; if the absolute value of the difference between M2 and M1 is less than c, then the severity is low; wherein, M1 > 0, M2 > 0, and c > 0.
[0015] Compared with existing technologies, the technical effects achieved by adopting this technical solution are as follows: obtain the value M1 of the highest point of the standard meteorological information data curve and the value M2 of the highest point of the current meteorological information data curve; determine the severity of extreme weather by judging the absolute value of the difference between M1 and M2; when the severity of extreme weather is high, the power generation of the photovoltaic power station is less than the power consumption, and it is necessary to store electricity so that the stored electricity can be used to supply the normal operation of the equipment during extreme weather.
[0016] In an optional implementation, the step of predicting the duration D1 of the extreme weather based on the severity includes: when the severity of the extreme weather is high, calculating the severity factor S of the extreme weather based on M1 and M2, S = 2 - M2 ÷ M1; obtaining the duration D2 of extreme weather that occurred in the same period of previous years from the historical database; and obtaining the current duration D1 of the extreme weather based on D2 and S, D1 = D2 ÷ S, where D1 > 0 and D2 > 0.
[0017] Compared with existing technologies, the technical effects achieved by adopting this technical solution are as follows: based on the severity of extreme weather, a severity factor is calculated, and by obtaining the duration of extreme weather in previous years, the duration of the current extreme weather is predicted. The higher the severity of the extreme weather, the longer the duration of the extreme weather. This is used to calculate the amount of electricity needed when extreme weather occurs, and the reliability is high.
[0018] In an optional implementation, the step of obtaining the target energy storage A based on the extreme weather duration D1, the extreme weather power generation P2, and the power consumption B, wherein the target energy storage A is the minimum energy storage required during the extreme weather, includes: obtaining the extreme weather power generation P2 based on the severity factor S, where P2 = S × P1; obtaining the daily power consumption B during the occurrence of the extreme weather according to the work plan; and obtaining the target energy storage A based on the extreme weather duration D1, the extreme weather power generation P2, and the power consumption B, where A = D1(B - P2) = (D2 × B) ÷ S - D2 × P1, and A > 0.
[0019] Compared with existing technologies, the technical effects achieved by adopting this technical solution are as follows: predict the power generation of the photovoltaic power station during extreme weather based on the severity of extreme weather. The higher the severity of extreme weather, the lower the power generation of the photovoltaic power station. Obtain the daily power consumption of the equipment during extreme weather. Since the power generation of the photovoltaic power station during extreme weather is less than the power consumption of the equipment, the minimum amount of stored power is the total power consumption of the equipment during extreme weather minus the total power generation of the photovoltaic power station during extreme weather.
[0020] In an optional implementation, the meteorological information data includes at least one of the following: average temperature, maximum temperature, minimum temperature, precipitation, maximum wind speed, and average wind speed.
[0021] Compared with existing technologies, the technical effects achieved by adopting this technical solution are as follows: Meteorological information data mainly refers to meteorological elements or changing conditions, including but not limited to average temperature, maximum temperature, minimum temperature, precipitation, maximum wind speed, and average wind speed. Using the above-mentioned meteorological information data can comprehensively reflect the meteorological environment, which is beneficial for subsequent judgment of the probability and severity of extreme weather events.
[0022] The present invention also provides a control device for hybrid energy storage state of charge, the control device being used to implement the control method as described in any of the above embodiments, the control device comprising: a first prediction module, the first prediction module being used to predict the daily power generation of the photovoltaic power station, i.e., the current power generation P1; a second prediction module, the second prediction module being used to predict the duration D1 of the extreme weather; a third prediction module, the third prediction module being used to predict the daily power generation of the photovoltaic power station during the extreme weather, i.e., the extreme weather power generation P2; a first judgment module, the first judgment module being used to judge the probability of the occurrence of extreme weather within a target time period; a second judgment module, the second judgment module being used to judge the severity of the occurrence of the extreme weather within the target time period; an acquisition module, the acquisition module being used to acquire the daily power consumption B when the extreme weather occurs; and a fourth prediction module, the fourth prediction module being used to predict the minimum amount of power required to store during the extreme weather, i.e., the target amount of power to store A.
[0023] Compared with the prior art, the technical effects achieved by adopting this technical solution are as follows: the control device of the technical solution of the present invention implements the steps of the control method of any technical solution of the present invention, and therefore has all the beneficial effects of the control method of any technical solution of the present invention, which will not be repeated here.
[0024] The beneficial effects of this invention are as follows: by predicting the current power generation of the photovoltaic power station, the probability and severity of extreme weather events within the target time period are determined, and the duration of extreme weather events and the power generation of the photovoltaic power station during extreme weather events are further predicted. Finally, the minimum amount of electricity that needs to be stored when extreme weather occurs is obtained. Storing a portion of the photovoltaic power station's electricity before extreme weather events is possible to provide emergency power when extreme weather occurs, avoids equipment damage caused by unstable current during extreme weather events, and can also reduce electricity costs. Attached Figure Description
[0025] Figure 1 shows a flowchart of a hybrid energy storage state of charge control method provided by an embodiment of the present invention; Figure 2 shows a flowchart of step S100 in Figure 1; Figure 3 shows a flowchart of step S200 in Figure 1; Figure 4 shows a flowchart of step S300 in Figure 3; Figure 5 shows a flowchart of step S400 in Figure 3; Figure 6 is a structural schematic diagram of the control device provided by an embodiment of the present invention.
[0026] Explanation of reference numerals in the attached drawings: 100 - Control device; 110 - First prediction module; 120 - Second prediction module; 130 - Third prediction module; 140 - First judgment module; 150 - Second judgment module; 160 - Acquisition module; 170 - Fourth prediction module. Detailed Implementation
[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.
[0028] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.
[0029] It should be noted that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
[0030] It should be noted that, where there is no conflict, the features in the embodiments of the present invention can be combined with each other.
[0031] Referring to Figure 1, the technical solution adopted by the present invention is: providing a control method for hybrid energy storage state of charge, including: S100, predicting the current power generation P1 based on a historical database, where the current power generation P1 is the daily power generation of the photovoltaic power station; S200, determining the probability of extreme weather occurring within a target time period based on a historical database; S300, when the probability of occurrence is high, determining the severity of the extreme weather occurring within the target time period; S400, predicting the duration D1 of the extreme weather based on the severity; S500, predicting the power generation P2 of the extreme weather based on the severity, where the extreme weather power generation P2 is the daily power generation of the photovoltaic power station during extreme weather; S600, obtaining the daily power consumption B during extreme weather based on a work plan; S700, obtaining the target storage capacity A based on the extreme weather duration D1, the extreme weather power generation P2, and the power consumption B, where the target storage capacity A is the minimum storage capacity required during extreme weather.
[0032] Specifically, by judging the probability and severity of extreme weather events within a target time period, it is predicted that at least some electricity will need to be stored when extreme weather occurs. By storing a portion of the photovoltaic power station's electricity before extreme weather, it can be used for emergency response when extreme weather arrives, avoid equipment damage caused by unstable current during extreme weather, and also reduce electricity costs.
[0033] Referring to Figure 2, in step S100, in a specific embodiment, the step of predicting the current power generation P1 based on the historical database, where the current power generation P1 is the daily power generation of the photovoltaic power station, includes: S110, obtaining the average power generation P of the same period in previous years from the historical database. 平均S120. Obtain the average ambient temperature T1 and average effective sunshine duration G1 from the historical database for the same period in previous years; S130. Collect the current ambient temperature T2 and current effective sunshine duration G2 of the photovoltaic power station; S140. Obtain the ambient temperature coefficient f1 based on the average ambient temperature T1 and the current ambient temperature T2; S150. Obtain the effective sunshine duration coefficient f2 based on the average effective sunshine duration G1 and the current effective sunshine duration G2; S160. Based on the average power generation P... 平均 Using the ambient temperature coefficient f1 and the effective sunshine duration coefficient f2, predict the current power generation P1, where P1 = P 平均 ×f1×f2, where 0.9≤f1≤1.1, f2>0.
[0034] In step S140, in a specific embodiment, the step of obtaining the ambient temperature coefficient f1 based on the average ambient temperature T1 and the current ambient temperature T2 includes: obtaining the difference between the current ambient temperature T2 and the average ambient temperature T1 as ΔT based on the average ambient temperature T1 and the current ambient temperature T2; when ΔT is greater than or equal to -a and less than or equal to a, f1 is equal to k1, where k1=1; when ΔT is greater than a and less than or equal to b, f1 is equal to k2, where 1<k2<1.1; when ΔT is greater than b, f1 is equal to k3, where k3=1.1; when ΔT is greater than or equal to -b and less than -a, f1 is equal to k4, where 0.9<k4<1; when ΔT is less than -b, f1 is equal to k5, where k5=0.9; where b>a>0.
[0035] For example, let a be 1 and b be 3; when -1≤ΔT≤1, f1=1; when 1<ΔT≤3, 1<f1<1.1, for example, f1=1.05; when ΔT>3, f1=1.1; when -3≤ΔT<-1, 0.9<f1<1, for example, f1=0.95; when ΔT<-3, f1=0.9.
[0036] For example, when the average ambient temperature T1 is 20℃ and the current ambient temperature T2 is 24℃, we get ΔT = 4. Since ΔT > 3, f1 = 1.1. For example, when the average ambient temperature T1 is 20℃ and the current ambient temperature T2 is 22℃, we get ΔT = 2. Since 1 < ΔT ≤ 3, f1 = 1.05. For example, when the average ambient temperature T1 is 20℃ and the current ambient temperature T2 is 20.5℃, we get ΔT = 0.5. Since -1 ≤ ΔT ≤ 1, f1 = 1. For example, when the average ambient temperature T1 is 20℃ and the current ambient temperature T2 is 18℃, we get ΔT = -2. Since -3 ≤ ΔT ≤ -1, f1 = 0.95. For example, when the average ambient temperature T1 is 20℃ and the current ambient temperature T2 is 16℃, we get ΔT = -4. Since ΔT ≤ 3, f1 = 1.05. -3, therefore f1=0.9; In step S150, in a specific embodiment, the step of obtaining the effective illumination duration coefficient f2 based on the average effective illumination duration G1 and the current effective illumination duration G2 includes: based on the average effective illumination duration G1 and the current effective illumination duration G2, the effective illumination duration coefficient f2=G2÷G1, where G1>0, G2>0.
[0037] For example, the average effective illumination duration G1 is 4 hours, and the current effective illumination duration G2 is 6 hours. Therefore, the effective illumination duration coefficient f2 = G2 ÷ G1 = 6 ÷ 4 = 1.5.
[0038] In step S160, for example, the average power generation P during the same period in previous years 平均 Set the voltage to 500V; when the average ambient temperature T1 is 20℃ and the current ambient temperature T2 is 22℃, we get ΔT = 2. Since 1 < ΔT ≤ 3, the ambient temperature coefficient f1 = 1.05; the average effective sunshine duration G1 is 4h and the current effective sunshine duration G2 is 6h, therefore the effective sunshine duration coefficient f2 = G2 ÷ G1 = 6 ÷ 4 = 1.5; predict the current power generation P1, P1 = P 平均 ×f1×f2=500×1.05×1.5=787.5V.
[0039] Referring to Figure 3, in step S200, in a specific embodiment, the step of determining the probability of extreme weather occurrence within a target time period based on a historical database includes: S210, acquiring meteorological information data from the historical database for the same time period in previous years to obtain a standard meteorological information data curve; S220, collecting meteorological information data within the target time period in real time to obtain a current meteorological information data curve; S230, matching the standard meteorological information data curve with the current meteorological information data curve; S240, if the matching degree is greater than d, the probability of extreme weather occurrence within the target time period is high, where d > 0.
[0040] Specifically, by acquiring meteorological information data from historical databases showing extreme weather events occurring during the same time period in previous years, a standard meteorological information data curve is obtained. Meteorological information data within the target time period is collected in real time to obtain the current meteorological information data curve. The standard meteorological information data curve is then matched with the current meteorological information data curve to obtain the matching degree. For example, d is set to 60%. When the matching degree is greater than 60%, the probability of extreme weather is high, and when the matching degree is less than 60%, the probability of extreme weather is low.
[0041] Referring to Figure 4, in step S300, in a specific embodiment, when the probability of occurrence is high, the step of determining the severity of extreme weather occurring within the target time period includes: S310, obtaining the value M1 of the highest point of the standard meteorological information data curve; S320, obtaining the value M2 of the highest point of the current meteorological information data curve; S330, determining the magnitude of the absolute value of the difference between M1 and M2; S340, if the absolute value of the difference between M2 and M1 is greater than or equal to c, then the severity is high; S350, if the absolute value of the difference between M2 and M1 is less than c, then the severity is low; wherein, M1 > 0, M2 > 0, c > 0.
[0042] Specifically, the value of the highest point of the standard meteorological information data curve, M1, and the value of the highest point of the current meteorological information data curve, M2, are obtained. By judging the absolute value of the difference between M1 and M2, the severity of extreme weather is determined. When the severity of extreme weather is high, the power generation of the photovoltaic power station is less than the power consumption, and it is necessary to store electricity so that the stored electricity can be used to supply the normal operation of the equipment during extreme weather.
[0043] For example, c can be set to 10. Then, when the absolute value of the difference between M2 and M1 is greater than or equal to 10, the severity of the extreme weather is high; when the absolute value of the difference between M2 and M1 is less than 10, the severity of the extreme weather is low.
[0044] Referring to Figure 5, in step S400, in a specific embodiment, the step of predicting the duration D1 of extreme weather based on severity includes: S410, when the severity of extreme weather is high, calculating the severity factor S of extreme weather based on M1 and M2, S=2-M2÷M1; S420, obtaining the duration D2 of extreme weather that occurred in the same period of previous years from the historical database; S430, obtaining the current duration D1 of extreme weather based on D2 and S, D1=D2÷S, where D1>0, D2>0.
[0045] For example, the highest point value M1 of the standard meteorological information data curve is set to 120, and the highest point value M2 of the current meteorological information data curve is set to 150; the severity coefficient S of the extreme weather is obtained, S = 2 - 150 ÷ 120 = 0.75; the duration D2 of extreme weather that occurred in the same period of previous years is obtained from the historical database and set to 3 days, then the current extreme weather duration D1 is predicted, D1 = 3 ÷ 0.75 = 4 days.
[0046] In step S500, in a specific embodiment, the step of predicting the extreme weather power generation P2 based on the severity of the weather, where the extreme weather power generation P2 is the daily power generation of the photovoltaic power station during extreme weather, includes: obtaining the extreme weather power generation P2 based on the severity factor S, where P2 = S × P1.
[0047] In step S700, in a specific embodiment, the target storage capacity A is obtained based on the extreme weather duration D1, the extreme weather power generation P2, and the power consumption B. The step of obtaining the target storage capacity A as the minimum storage capacity required during extreme weather includes: obtaining the target storage capacity A based on the extreme weather duration D1, the extreme weather power generation P2, and the power consumption B, A = D1(B-P2) = (D2×B)÷S-D2×P1, where A > 0.
[0048] Specifically, the power generation of the photovoltaic power station during extreme weather is predicted based on the severity of the extreme weather. The higher the severity of the extreme weather, the lower the power generation of the photovoltaic power station. The daily power consumption of the equipment during extreme weather is obtained. The power generation of the photovoltaic power station during extreme weather is less than the power consumption of the equipment. Therefore, the minimum amount of electricity that needs to be stored is the total power consumption of the equipment during extreme weather minus the total power generation of the photovoltaic power station during extreme weather.
[0049] For example, the value M1 of the highest point of the standard meteorological information data curve is set to 120, and the value M2 of the highest point of the current meteorological information data curve is set to 150; the severity coefficient S of the extreme weather is obtained, S = 2 - 150 ÷ 120 = 0.75; in step S160, P1 = P 平均 ×f1×f2=500×1.05×1.5=787.5V; Based on the severity coefficient S, the power generation during extreme weather is P2, P2=S×P1=0.75×787.5=590.625V; According to the work plan, the daily power consumption B when the extreme weather occurs is obtained, and B is set to 700V; Therefore, the target power storage A can be obtained, A=D1(B-P2)=(D2×B)÷S-D2×P1=(4×700)÷0.75-4×590.625=437.5V.
[0050] In one specific embodiment, the meteorological information data includes at least one of the following: average temperature, maximum temperature, minimum temperature, precipitation, maximum wind speed, and average wind speed.
[0051] Specifically, meteorological information data mainly refers to meteorological elements or changing conditions, including but not limited to average temperature, maximum temperature, minimum temperature, precipitation, maximum wind speed, and average wind speed. Using the above-mentioned meteorological information data can comprehensively reflect the meteorological environment, which is helpful for subsequent judgments on the probability and severity of extreme weather events.
[0052] Referring to Figure 6, the present invention also provides a control device for hybrid energy storage state of charge. The control device 100 is used to implement the control method described in any of the above embodiments. The control device 100 includes: a first prediction module 110, which is used to predict the daily power generation of the photovoltaic power station, i.e., the current power generation P1; a second prediction module 120, which is used to predict the duration D1 of extreme weather; a third prediction module 130, which is used to predict the daily power generation of the photovoltaic power station during extreme weather, i.e., the extreme weather power generation P2; a first judgment module 140, which is used to judge the probability of extreme weather occurring within a target time period; a second judgment module 150, which is used to judge the severity of extreme weather occurring within a target time period; an acquisition module 160, which is used to acquire the daily power consumption B when extreme weather occurs; and a fourth prediction module 170, which is used to predict the minimum amount of electricity that needs to be stored during extreme weather, i.e., the target amount of electricity stored A.
[0053] Specifically, the control device 100 of the technical solution of the present invention implements the steps of the control method of any technical solution of the present invention, and thus has all the beneficial effects of the control method of any technical solution of the present invention, which will not be repeated here.
[0054] The above description is intended to be illustrative and not restrictive. Those skilled in the art can make variations, modifications, substitutions, and alterations to the above embodiments within the scope of this disclosure. Moreover, the above examples (or one or more of them) can be used in combination with each other, and these embodiments can be combined with each other in various combinations or arrangements.
Claims
1. A method for controlling hybrid energy storage state of charge, characterized in that, The control method includes: predicting the current power generation P1 based on a historical database, where P1 is the daily power generation of the photovoltaic power station; determining the probability of extreme weather occurring within a target time period based on the historical database; when the probability of occurrence is high, determining the severity of the extreme weather occurring within the target time period; predicting the duration D1 of the extreme weather based on the severity; predicting the extreme weather power generation P2 based on the severity, where P2 is the daily power generation of the photovoltaic power station during the extreme weather; obtaining the daily power consumption B during the extreme weather according to the work plan; and obtaining the target storage capacity A based on the extreme weather duration D1, the extreme weather power generation P2, and the power consumption B, where the target storage capacity A is the minimum storage capacity required during the extreme weather.
2. The method for controlling the state of charge of hybrid energy storage according to claim 1, characterized in that, The step of predicting the current power generation P1 based on historical database, where the current power generation P1 is the daily power generation of the photovoltaic power station, includes: obtaining the average power generation P of the same period in previous years from the historical database. 平均 ; Obtain the average ambient temperature T1 and average effective sunshine duration G1 of the same period in previous years from the historical database; Collect the current ambient temperature T2 and current effective sunshine duration G2 of the photovoltaic power station; Obtain the ambient temperature coefficient f1 based on the average ambient temperature T1 and the current ambient temperature T2; Obtain the effective sunshine duration coefficient f2 based on the average effective sunshine duration G1 and the current effective sunshine duration G2; Based on the average power generation P 平均 The ambient temperature coefficient f1 and the effective sunshine duration coefficient f2 are used to predict the current power generation P1, where P1 = P 平均 ×f1×f2, where 0.9≤f1≤1.1, f2>0.
3. The method for controlling the state of charge of hybrid energy storage according to claim 2, characterized in that, The step of obtaining the ambient temperature coefficient f1 based on the average ambient temperature T1 and the current ambient temperature T2 includes: obtaining the difference between the current ambient temperature T2 and the average ambient temperature T1 as ΔT based on the average ambient temperature T1 and the current ambient temperature T2; when ΔT is greater than or equal to -a and less than or equal to a, f1 is equal to k1, where k1 = 1; when ΔT is greater than a and less than or equal to b, f1 is equal to k2, where 1 < k2 < 1.1; when ΔT is greater than b, f1 is equal to k3, where k3 = 1.1; when ΔT is greater than or equal to -b and less than -a, f1 is equal to k4, where 0.9 < k4 < 1; when ΔT is less than -b, f1 is equal to k5, where k5 = 0.9; where b > a > 0.
4. The method for controlling the state of charge of hybrid energy storage according to claim 2, characterized in that, The step of obtaining the effective illumination duration coefficient f2 based on the average effective illumination duration G1 and the current effective illumination duration G2 includes: the effective illumination duration coefficient f2 = G2 ÷ G1 based on the average effective illumination duration G1 and the current effective illumination duration G2, where G1 > 0 and G2 > 0.
5. The method for controlling the state of charge of hybrid energy storage according to claim 1, characterized in that, The step of determining the probability of extreme weather occurrence within a target time period based on the historical database includes: acquiring meteorological information data from the historical database for the same time period in previous years to obtain a standard meteorological information data curve; collecting meteorological information data within the target time period in real time to obtain a current meteorological information data curve; matching the standard meteorological information data curve with the current meteorological information data curve; if the matching degree is greater than d, the probability of extreme weather occurrence within the target time period is high, where d > 0.
6. The method for controlling the state of charge of hybrid energy storage according to claim 5, characterized in that, The step of determining the severity of the extreme weather occurring within the target time period when the probability of occurrence is high includes: obtaining the value M1 of the highest point of the standard meteorological information data curve; obtaining the value M2 of the highest point of the current meteorological information data curve; determining the absolute value of the difference between M1 and M2; if the absolute value of the difference between M2 and M1 is greater than or equal to c, the severity is high; if the absolute value of the difference between M2 and M1 is less than c, the severity is low; wherein, M1 > 0, M2 > 0, and c > 0.
7. The method for controlling the state of charge of hybrid energy storage according to claim 6, characterized in that, The step of predicting the duration D1 of the extreme weather based on the severity includes: when the severity of the extreme weather is high, calculating the severity factor S of the extreme weather based on M1 and M2, S=2-M2÷M1; obtaining the duration D2 of extreme weather that occurred in the same period of previous years from the historical database; and obtaining the current duration D1 of the extreme weather based on D2 and S, D1=D2÷S, where D1>0 and D2>0.
8. The method for controlling the state of charge of hybrid energy storage according to claim 7, characterized in that, The step of obtaining the target energy storage A based on the extreme weather duration D1, the extreme weather power generation P2, and the power consumption B, wherein the target energy storage A is the minimum energy storage required during the extreme weather, includes: obtaining the extreme weather power generation P2 based on the severity factor S, where P2 = S × P1; obtaining the daily power consumption B during the occurrence of the extreme weather according to the work plan; and obtaining the target energy storage A based on the extreme weather duration D1, the extreme weather power generation P2, and the power consumption B, where A = D1(B - P2) = (D2 × B) ÷ S - D2 × P1, and A > 0.
9. The method for controlling the state of charge of hybrid energy storage according to claim 5, characterized in that, Meteorological information data includes at least one of the following: average temperature, maximum temperature, minimum temperature, precipitation, maximum wind speed, and average wind speed.
10. A control device for hybrid energy storage state of charge, characterized in that, The control device is used to implement the control method as described in any one of claims 1 to 9, the control device comprising: a first prediction module, the first prediction module being used to predict the daily power generation of the photovoltaic power station, i.e., the current power generation P1; a second prediction module, the second prediction module being used to predict the duration D1 of the extreme weather; a third prediction module, the third prediction module being used to predict the daily power generation of the photovoltaic power station during the extreme weather, i.e., the extreme weather power generation P2; a first judgment module, the first judgment module being used to judge the probability of the occurrence of extreme weather within a target time period; a second judgment module, the second judgment module being used to judge the severity of the occurrence of extreme weather within the target time period; an acquisition module, the acquisition module being used to acquire the daily power consumption B when the extreme weather occurs; and a fourth prediction module, the fourth prediction module being used to predict the minimum amount of electricity that needs to be stored during the extreme weather, i.e., the target amount of electricity stored A.