Charging pile load control method based on light storage system

Abstract

The invention relates to the technical field of power load control, in particular to a charging pile load control method based on a light storage system. The deposition and removal process of dust on the surfaces of the photovoltaic panel and the charging pile is dynamically simulated through wind speed, humidity and cleaning events, sludge which is formed by bonding of dust and rainwater and is difficult to naturally wash is considered, and the removal efficiency of rainwater flowing away along the surface is estimated according to the difference between the inclination angle and the shell material; according to the dust coverage, the shielding intensity and the ultraviolet aging effect, an exponential attenuation model is fused to obtain an overall inhibition coefficient for the power generation of the photovoltaic panel and the output power of the charging pile, so that the accuracy of load calculation is improved under real changeable weather and environment conditions; based on the time sequence model, the time sequence characteristics of the load, the periodic fluctuation characteristics and the driving effect of the exogenous variables are fused, the loads of the charging pile and the photovoltaic system in the future time are accurately predicted, and it is ensured that the subsequent power loads among the charging pile, the photovoltaic system and the energy storage system can be reasonably planned and distributed.

Description

Technical Field

[0001] The present invention relates to the technical field of power load control, and particularly to a charging pile load control method based on a photovoltaic energy storage system. Background Art

[0002] With the large-scale deployment of electric vehicle charging piles and distributed photovoltaic power generation, the power system is becoming increasingly sensitive to the spatio-temporal dynamic changes in the charging load demand and the output of renewable energy. Accurately perceiving and predicting load characteristics has become the key to achieving efficient scheduling and energy optimization management.

[0003] Traditional load monitoring methods usually estimate the active power based on voltage and current, ignoring physical processes such as changes in power factor, drift errors generated by sensors over time, heat dissipation and measurement deviations caused by dust deposition on the surface of charging piles, abnormal light or signals caused by equipment occlusion, and material aging effects caused by long-term environmental ultraviolet radiation. These physical processes gradually accumulate on different time scales, resulting in systematic deviations in load monitoring data. Secondly, in terms of load prediction, currently, short-term autocorrelation features and the driving effects of some exogenous variables are captured, but the ability to respond to seasonal changes, multi-scale fluctuation characteristics, and complex environmental dynamics is limited, making it difficult to support the load prediction requirements of high precision and long cycle. Therefore, when it is difficult to implement relatively accurate load calculation and prediction as described above, that is, the prediction results cannot be fully utilized to realize the load distribution planning of charging piles and the power system, resulting in inefficient operation such as power surplus waste or peak-valley mismatch. Summary of the Invention

[0004] Aiming at the above-mentioned shortcomings of the prior art, the present invention provides a charging pile load control method based on a photovoltaic energy storage system, which can effectively solve the problem that the subsequent load prediction accuracy is not precise enough due to the lack of consideration of the actual application environment of charging piles and photovoltaic systems in the prior art, affecting the load balance control of charging piles and the power system.

[0005] To achieve the above object, the present invention is realized through the following technical solutions: The present invention provides a charging pile load control method based on a photovoltaic energy storage system, including the following steps: Based on the application environment of charging piles and photovoltaic systems, introduce data on dust deposition status, occlusion status, and mud and dirt status; Construct an environmental attenuation coefficient according to the introduced data; In response to the environmental attenuation coefficient, combine the electrical parameters of charging piles and photovoltaic systems to output corresponding load data; Receive the corresponding load data, and based on the influence of seasonality and exogenous variables, a time series model predicts the load data of the charging piles and photovoltaic systems at future times; Determine the energy balance difference in response to the load data of the charging pile and the photovoltaic system, construct a scheduling optimization objective function, and solve the optimal scheduling strategy to implement the charging pile power load control.

[0006] Furthermore, determine the dust deposition rate based on the dust deposition state, and the steps are as follows: Calculate the dust deposition rate ; Represents the basic cumulative rate, , Respectively represent the wind speed and humidity as the correction coefficients for the dust deposition rate; Calculate the dust deposition rate ; Represents the minimum deposition rate of dust residue after cleaning the photovoltaic panel or charging pile, Represents the cleaning or rainfall event, Represents the sampling time interval.

[0007] Furthermore, determine the mud amount based on the mud state, and the steps are as follows: Determine the mud generation rate ; Represents the mud generation coefficient, Represents the instantaneous rainfall intensity, Represents the scouring threshold, Represents the indicator function; Introduce the surface inclination angle and surface morphology of the photovoltaic panel and the charging pile to determine the rainwater scouring removal rate :

[0008] Among them, Represents the scouring efficiency coefficient, Represents the surface inclination angle, Represents the surface roughness or adhesion factor; Based on the rainwater scouring removal rate and the mud generation rate Construct the mud amount :

[0009] Represents the current time.

[0010] Furthermore, determine the occlusion degree based on the occlusion state, and the steps are as follows: Suppose there are types of occlusions, introduce the mud state, and give the probabilities of each type based on the image segmentation model : Among them, represents the degree of occlusion, represents the weight of the th type of occlusion, represents the mud stain extra magnification factor.

[0011] Furthermore, the environmental attenuation coefficient is specifically: represents the environmental attenuation coefficient, , , respectively represent the attenuation sensitivity coefficients, , respectively represent the adjustment coefficients of the degree of occlusion and the dust deposition rate in the logarithmic buffer function, represents the ultraviolet attenuation sensitivity coefficient, represents the ultraviolet index.

[0012] Furthermore, the method for predicting the load data of the charging pile and the photovoltaic system at a future time is as follows: Define exogenous variables, including temperature data , holidays , electricity price , rainfall and seasonal indicators ; Align the exogenous variables with the time series data of the charging pile load and perform load prediction based on the time series model: Among them, represents the intercept term, represents the non-seasonal autoregressive coefficient, and the lag order is , , , respectively represent the corresponding exogenous variable coefficients, represents the seasonal period, represents the seasonal autoregressive coefficient, represents the seasonal lag order, represents the error term, represents the current time; Predict the charging pile load at a future moment by obtaining the future moment and substituting it into the time series model ; Similarly, collect the exogenous variables of the photovoltaic power generation load, including rainfall , seasonal indicators , temperature , predicting the photovoltaic power generation load according to the time series model .

[0013] Furthermore, the specific scheduling optimization objective function is as follows: ; Wherein, represents the energy supplemented by the energy storage system or the power grid, , respectively represent the start point and the end point of the time window, represents the marginal electricity price for purchasing or selling electricity from the power grid per unit time, represents the cost coefficient of the energy storage system for charging and discharging, and the optimal scheduling strategy is obtained by solving the scheduling optimization objective function through dynamic programming, linear programming or heuristic optimization algorithm .

[0014] Furthermore, the electrical parameters include: Current, voltage and power factor.

[0015] A computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the method described in any one of the above are implemented.

[0016] A computer-readable storage medium stores a computer program thereon, and when the computer program is executed by a processor, the steps of the method described in any one of the above are implemented.

[0017] The technical solution provided by the present invention has the following beneficial effects compared with the known prior art: By dynamically simulating the deposition and removal processes of dust on the surfaces of photovoltaic panels and charging piles through wind speed, humidity and cleaning (rainfall) events, and considering the formation of mud and dirt that is difficult to wash away naturally due to the adhesion of dust and rainwater under short-term light rain conditions, and then estimating the removal efficiency of rainwater flowing away along the surface according to the differences in the tilt angle of the equipment and the shell material; Combining the conventional occlusion and the cumulative amount of mud and dirt into the overall occlusion intensity, integrating the dust coverage, occlusion intensity and ultraviolet aging effect into the exponential decay model, obtaining the overall inhibition coefficient for the power generation of photovoltaic panels and the output power of charging piles, and then applying it to the calculation of voltage, current and power factor, so as to improve the accuracy of load calculation under real and variable meteorological and environmental conditions; Based on the time series model, integrating the time series characteristics, periodic fluctuation characteristics of the load itself and the driving effect of exogenous variables, and considering the actual application environment of the charging pile and the photovoltaic system, realizing the accurate prediction of the load of the charging pile and the photovoltaic system in the future time, so as to ensure the reasonable planning and distribution of the electric power load among the subsequent charging piles, photovoltaic and energy storage systems. Description of the Drawings

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0019] Figure 1 It is a schematic diagram of the overall method of the present invention. Specific embodiments

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0021] The following further describes the present invention with reference to embodiments.

[0022] Embodiment 1 (refer to Figure 1 ): A charging pile load control method based on a photovoltaic energy storage system includes the following steps: Deploy voltage and current sensors in the charging pile and the photovoltaic system, and set a fixed acquisition time interval to collect the voltage and current at each time point, and calculate the charging pile load and the power generation load of the photovoltaic system. Among them, considering that both the charging pile and the photovoltaic system (photovoltaic panels) are applied in an outdoor environment, in actual applications, the following situations will occur: Dust deposition state: Sand and dust, industrial dust will adhere to the photovoltaic panels or the charging pile housing, reducing the light transmittance or heat dissipation efficiency, that is, the dust deposition rate; Occlusion state: Slight occlusion (leaf shadow) and severe occlusion (thick layer of bird droppings) adhere to the photovoltaic panels or the charging pile housing, and the impact on light attenuation is very different; Therefore, introduce the above state factors (dust deposition, occlusion) and their dynamic relationships (accumulation - cleaning, slight - severe) to reflect the real environmental attenuation process (in practice, dust deposition and occlusion respectively have the state switching characteristics of accumulation - cleaning, slight - severe), and assist in the subsequent calculation of the power generation load and the charging pile load , improve the calculation accuracy, then there is: For the dust deposition state, calculate the dust deposition rate, and the steps are as follows: Calculate the dust deposition rate ; represents the basic accumulation rate, and represent the wind speed , humidity respectively, which are the correction coefficients of the dust deposition rate with respect to the wind speed and humidity; Calculate the dust deposition rate ; represents the minimum deposition rate of dust remaining after cleaning the photovoltaic panel or charging pile, represents the cleaning or rainfall event, which is 1 if it is rainfall or cleaning, and 0 otherwise, represents the sampling time interval; The above dust deposition state can truly reflect the process of dynamic accumulation and cleaning of dust due to meteorological conditions.

[0023] Furthermore, considering that when dust deposition occurs on the photovoltaic panel or charging pile, if there is rainy weather, although the short-term light rain in the rainy weather can moisten the dust, it is not sufficient to form enough runoff. Instead, it will adhere the dust into mud and then quickly dry, resulting in more serious shading; Among them, the photovoltaic panel is usually installed inclined according to the pitched roof or the optimal power generation angle (such as 20° - 40°), and the rainwater flows down naturally along the inclined plane, which can take away part of the dust and mud; the outer shell of the charging pile is a nearly vertical surface, and the rainwater is more likely to flow away quickly along the vertical surface, and it is more difficult for mud to accumulate. Therefore: Differences in different inclination angles and surface characteristics: The flushing effect of low-inclination photovoltaic panels (<15°) is weak, and the formed mud may stay longer; High-inclination photovoltaic panels (>45°) and vertical charging piles are flushed more thoroughly; Furthermore, based on the above mud state, calculate the mud amount, then there is: Generate mud only under the condition of "effective light rain", and determine the mud generation rate : ; Among them, represents the mud generation coefficient, represents the instantaneous rainfall intensity, represents the flushing threshold, represents the indicator function; If the rainfall intensity is between 0 and is established, then is 1, that is, mud is generated according to the dust amount, otherwise no mud is generated (either it is dry and will not stick into mud, or the rain is heavy enough to wash away the dust or mud); Introduce the surface inclination angle and surface morphology of the photovoltaic panel and charging pile to determine the rainwater flushing removal rate :

[0024] Among them, represents the flushing efficiency coefficient, represents the surface inclination angle, for the photovoltaic panel is 20° - 40°, for the charging pile is 90°, represents the surface roughness or adhesion factor (smooth glass ≈ 0.2, rough metal ≈ 0.6); Based on the rain flushing removal rate and the mud and dirt generation rate construct the mud and dirt quantity :

[0025] represents the current moment, represents the generation term, describing the mud and dirt brought by light rain, represents the removal term, describing the mud and dirt carried away by the rain flowing along the inclination angle and the surface, and is proportional to the existing mud and dirt quantity in proportion;

[0026] For the occlusion state, calculate the occlusion degree, then there is: Install a camera at the photovoltaic panel or the charging pile housing, and take one or more front-view images regularly; There are types of occlusion (including mild: thin layer of dust adhesion, foliage projection or sporadic bird droppings, this kind of situation has little impact on light transmission and heat dissipation, and the attenuation of the load is also the lightest; moderate: relatively thick dust, visible increase in the tree shadow coverage area, certain concentration of bird droppings, at this time there is both light blocking and local thermal resistance, and the load will decrease significantly; severe: large area of dust, thick tree shade or serious bird droppings coverage, at this time the light blocking is the most serious, and it is easy to cause hot spot effect, and the load decays greatly), and at the same time introduce the mud and dirt involved above alone to enhance the manifestation of the stronger occlusion of mud and dirt than ordinary dust, and thus the image segmentation model gives various probabilities : Among them, represents the occlusion degree, represents the weight of the type of occlusion, represents the mud and dirt extra amplification factor, and is greater than 1;

[0027] Furthermore, to prevent the exponential growth from being too fast at high dust deposition rates and high occlusion degrees, introduce a logarithmic buffer function: represents the environmental attenuation coefficient, and and respectively represent the attenuation sensitivity coefficients, and respectively represent the adjustment coefficients of the occlusion degree and dust deposition rate in the logarithmic buffer function, represents the ultraviolet attenuation sensitivity coefficient, represents the ultraviolet index (ultraviolet aging); Therefore, combining the environmental attenuation coefficient with the power factor (in an AC system, especially in industrial / photovoltaic / charging systems, the current and voltage are not necessarily in phase) to define the power generation load and the charging pile load , then there is: , represents the voltage, represents the current, represents the phase angle between the voltage and the current, and the power generation load of the photovoltaic system is calculated similarly according to this formula , and the time series data of the charging pile load and the time series data of the power generation load are constructed respectively therefrom ; In summary, by dynamically simulating the deposition and removal processes of dust on the surfaces of photovoltaic panels and charging piles through wind speed, humidity, and cleaning (rainfall) events, and further considering the formation of mud and dirt that is difficult to wash away naturally due to the adhesion of dust and rainwater under short-term light rain conditions, and then estimating the removal efficiency of rainwater flowing away along the surface based on the differences in the tilt angle of the equipment and the shell material; At the same time, combining the conventional occlusion and the cumulative amount of mud and dirt into the overall occlusion intensity, integrating the dust coverage, occlusion intensity, and ultraviolet aging effect into an environmental attenuation model, obtaining the overall suppression coefficient for the power generation of photovoltaic panels and the output power of charging piles, and then applying it to the correction of voltage, current, and power factor, so as to significantly improve the accuracy of load calculation under real and variable meteorological and environmental conditions.

[0028] Define exogenous variables, including temperature (for the charging pile load , the frequency of users using electric vehicles decreases at high temperatures, or the power consumption of electric vehicles increases rapidly in cold weather and requires more frequent charging; for the power generation load : the efficiency of photovoltaic panels actually decreases at high temperatures), holidays (The holiday information is represented by a binary variable, which is 1 during holidays and 0 otherwise. The travel habits of users are significantly different during holidays: travel is intensive during long holidays, and the charging demand increases. During weekdays, charging is biased towards the peak commuting hours; during holidays, it fluctuates irregularly), electricity price (The large difference in peak-valley electricity prices affects the distribution of load periods), rainfall (During rainy days, the sunlight is weakened, and the clouds seriously block the irradiance, resulting in a significant decrease in PV output. It may also cause the dust to be washed clean and recover slightly after a short period) and seasonal indicators (Indicators such as months or solar radiation describe seasonal changes. There is an obvious power generation peak in summer, with long sunshine hours and high output. In winter, the sunshine is short and the irradiation angle is low, resulting in low output). Align the time stamps of the exogenous variables with the time series data of the charging pile load to ensure that each moment has a complete set of input features. Thus, based on the time series model combined with exogenous variables, the load prediction of the charging pile is realized: Among them, represents the intercept term, represents the non-seasonal autoregressive coefficient, and the lag order is , , , represent the corresponding exogenous variable coefficients respectively, represents the seasonal period, represents the seasonal autoregressive coefficient, represents the seasonal lag order, represents the error term, represents the current time; Therefore, by obtaining the future moment and substituting it into the above formula, the charging pile load at the future moment can be predicted ; Based on the above formula, similarly, the exogenous variables affecting the PV power generation load (rainfall , seasonal indicators , temperature ) can be substituted and combined with the time series data of the power generation load to construct a time series model, and the PV power generation load at the future moment can be predicted ; In the present invention, by considering these exogenous variables, the time series model can more accurately predict the charging pile load demand and the power generation load demand, and help the power grid dispatching and charging pile operators to optimize the allocation of power resources.

[0029] Furthermore, based on the PV power generation load and the charging pile load at the future moment, a reasonable allocation of energy demand is realized, including: ​Calculate energy balance difference : ; like If it is greater than 0, it means there is excess energy that can be used for energy storage or feeding back to the grid; On the contrary, it means that the load exceeds the power generation and needs to be supplemented from energy storage or the external grid; Therefore, by constructing the dispatch optimization objective function, considering the operating cost, energy storage limitation and system balance requirements, the optimal dispatch strategy is solved. , where the scheduling optimization objective function is specifically: ; in, It indicates the energy added by the energy storage system (discharging at positive value, charging at negative value) or the grid (purchasing electricity at positive value). , They represent the starting point and end point of the time window respectively. It represents the marginal price of electricity purchased or sold from the power grid per unit time. Represents the cost coefficient of charging and discharging of the energy storage system (calculated based on the depreciation cost coefficient, energy loss cost coefficient and maintenance cost conversion. The depreciation cost coefficient is as follows: for example, the energy storage battery can be cycled 6000 times, and the initial cost is 1200 yuan / kWh, then the depreciation cost of each charge and discharge cycle is 1200 / 6000≈0.2; the energy loss cost coefficient is such as the charge and discharge efficiency of 90%, which means that for every 1kWh charged, only 0.9kWh can be lost in the end, and 0.1kWh is lost. If the purchase price of electricity is 0.8 yuan / kWh, then the loss cost is about 0.1×0.8=0.08 yuan / kWh. The maintenance cost conversion coefficient is such as 500 yuan maintenance fee per year, and the energy storage battery charges and discharges 10000kWh a year, which is amortized to 500 / 10000≈0.05). The optimal scheduling strategy is obtained by solving the optimization objective kernel function through dynamic programming, linear programming or other heuristic optimization algorithms. , which will not be elaborated here.

[0030] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above methods when executing the computer program.

[0031] A computer-readable storage medium stores a computer program thereon. When the computer program is executed by a processor, the steps of any one of the methods described above are implemented. For details, please refer to the above method and will not be repeated in this embodiment.

[0032] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the protection scope of the technical solutions of the embodiments of the present invention.

Claims

1. A charging pile load control method based on a photovoltaic storage system, characterized in that: The steps include: Based on the application environment of charging piles and photovoltaic systems, data on dust deposition status, shielding status, and mud pollution status are introduced; Based on the introduced data, the environmental attenuation coefficient is constructed; In response to the environmental attenuation coefficient, combined with the electrical parameters of the charging pile and the photovoltaic system, the corresponding load data is output; The corresponding load data is received, and the time series model predicts the load data of the charging pile and the photovoltaic system in the future based on the influence of seasonality and exogenous variables; In response to the load data of the charging pile and the photovoltaic system, the energy balance difference is determined, and a scheduling optimization objective function is constructed to solve the optimal scheduling strategy to perform charging pile power load control.

2. The charging pile load control method based on the photovoltaic storage system according to claim 1 is characterized in that: The dust deposition rate is determined according to the dust deposition state, and the steps are as follows: Calculating dust deposition rate ; represents the basic accumulation rate, , Respectively represent wind speed ,humidity Correction factor for dust deposition rate; Calculating dust deposition rate ; Indicates the minimum deposition rate of dust residue after cleaning the photovoltaic panel or charging pile. Indicates a cleansing or rainfall event, Indicates the sampling time interval.

3. The charging pile load control method based on the photovoltaic storage system according to claim 2 is characterized in that: Determine the amount of sludge according to the sludge state, the steps are as follows: Determine the sludge generation rate ; represents the sludge generation coefficient, Indicates instantaneous rainfall intensity. represents the flushing threshold, represents the indicator function; Introducing the surface inclination and surface morphology of photovoltaic panels and charging piles to determine the rainwater removal rate : in, represents the flushing efficiency coefficient, represents the surface inclination, Indicates the surface roughness or adhesion factor; Based on rainwater removal rate Sludge generation rate Build sludge volume : Indicates the current moment.

4. The charging pile load control method based on the photovoltaic storage system according to claim 3 is characterized in that: Determine the degree of occlusion based on the occlusion state. The steps are as follows: Features Class occlusion, introduce muddy state, and give various probabilities based on image segmentation model : in, Indicates the degree of occlusion. Indicates The weight of the class occlusion, It represents the additional amplification factor of sludge.

5. The charging pile load control method based on the photovoltaic storage system according to claim 4 is characterized in that: The environmental attenuation coefficient is specifically: represents the environmental attenuation coefficient, , , They represent the attenuation sensitivity coefficients, , They represent the adjustment coefficients of the occlusion degree and dust deposition rate in the logarithmic buffer function respectively. represents the ultraviolet attenuation sensitivity coefficient, Indicates the UV index.

6. The charging pile load control method based on the photovoltaic storage system according to claim 1 is characterized in that: The method for predicting the load data of the charging pile and the photovoltaic system at a future time is: Define exogenous variables, including temperature data , Holidays , electricity price , rainfall With seasonal indicators ; The exogenous variables are compared with the time series data of charging pile load. Alignment, load forecasting based on time series model: in, represents the intercept term, represents the non-seasonal autoregressive coefficient, and the lag order is , , , They represent the corresponding exogenous variable coefficients, Represents the seasonal cycle, represents the seasonal autoregressive coefficient, represents the seasonal lag order, represents the error term, Indicates the current time; By obtaining the future time and substituting it into the time series model, the charging pile load at the future time is predicted ; Similarly, exogenous variables of photovoltaic power generation load, including rainfall, are collected. , Seasonal indicators ,temperature , predicting photovoltaic power generation load based on time series model .

7. The charging pile load control method based on the photovoltaic storage system according to claim 6 is characterized in that: The scheduling optimization objective function is specifically: ； in, Indicates the energy added by the energy storage system or the grid. , They represent the starting point and end point of the time window respectively. It represents the marginal price of electricity purchased or sold from the power grid per unit time. Represents the cost coefficient of charging and discharging the energy storage system. The optimal scheduling strategy is obtained by solving the scheduling optimization objective function through dynamic programming, linear programming or heuristic optimization algorithm. .

8. The charging pile load control method based on the photovoltaic storage system according to claim 1 is characterized in that: The electrical parameters include: Current, voltage and power factor.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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