Charging control method, microgrid power system, device, and storage medium

Abstract

The present application discloses a charging control method, a microgrid power system, a device, and a storage medium. The charging control method is applied to a microgrid power system, and comprises: acquiring predicted power generation environment data of a next control period, determining, from among a plurality of historical control periods, a historical control period in which actual power generation environment data has the highest degree of matching with the predicted power generation environment data, and recording the historical control period as a historical matching period; and on the basis of pre-charging demand information corresponding to the historical matching period, completing charging an energy storage assembly in a microgrid power system by a power grid in a valley period comprised in the next control period.

Description

Charging control method, microgrid power system, device and storage medium

[0001] This application claims priority to the Chinese patent application filed on February 7, 2024, with application number 202410174204.0 and invention name “A Charging Control Method, Microgrid Power System, Device and Storage Medium”, the contents of which should be understood as incorporated into this application by reference. Technical Field

[0002] The present application relates to the field of microgrid power system control, and in particular to a charging control method, a microgrid power system, a device and a storage medium. Background Art

[0003] In the context of peak-valley electricity prices, the operating modes of microgrid power systems often have limitations, making it difficult to simultaneously maximize energy self-sufficiency and minimize electricity costs. Therefore, further optimizing the operation and control schemes of microgrid power systems to achieve both maximization of energy self-sufficiency and minimization of electricity costs is a key area of ​​technical exploration in this field. Summary of the Invention

[0004] The embodiments of the present application provide a charging control method, a microgrid power system, a device, and a storage medium, which can reduce the cost of purchasing electricity from the power grid as much as possible while ensuring energy self-sufficiency.

[0005] In a first aspect, an embodiment of the present application provides a charging control method, which is applied to a microgrid power system, comprising:

[0006] Obtaining power generation environment prediction data for the next control cycle, and determining, from multiple historical control cycles, a historical control cycle in which the actual power generation environment data has the highest matching degree with the power generation environment prediction data, and recording the historical matching cycle;

[0007] According to the pre-charging demand information corresponding to the historical matching cycle, the grid completes charging of the energy storage components in the microgrid power system during the valley period included in the next control cycle;

[0008] The microgrid power system includes a power generation component, and the power generation of the power generation component is affected by the power generation environment.

[0009] In a second aspect, an embodiment of the present application provides a microgrid power system, comprising:

[0010] Control modules, power generation components, and energy storage components;

[0011] The control module is configured to execute the charging control method described in any embodiment of the present application to charge the energy storage component;

[0012] The power generation capacity of the power generation component is affected by the power generation environment.

[0013] In a third aspect, an embodiment of the present application provides an electronic device, including:

[0014] one or more processors;

[0015] a storage device configured to store one or more programs,

[0016] When the one or more programs are executed by the one or more processors, the one or more processors implement the charging control method as described in any embodiment of the present application.

[0017] In a fourth aspect, an embodiment of the present application provides a computer storage medium, wherein the storage medium stores a computer program, wherein the computer program is configured to execute the charging control method as described in any embodiment of the present application when running.

[0018] In an embodiment of the present application, based on the pre-charging demand information corresponding to similar power generation environment data in the historical control cycle, the charging of the energy storage components in the microgrid power system is controlled to be completed during the electricity price valley period in the next control cycle to meet the power supply demand of the control cycle, further reducing or avoiding the use of grid power to supply power to the power load and / or charge the energy storage components during the non-valley period, and being able to reduce the grid power purchase cost as much as possible under the premise of ensuring energy self-sufficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, a brief introduction will be given below to the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0020] FIG1 is a flow chart of a charging control method provided in an embodiment of the present application;

[0021] FIG2 is a flow chart of another charging control method provided in an embodiment of the present application;

[0022] FIG3 is a flow chart of a method for determining pre-charging requirement information provided by an embodiment of the present application;

[0023] FIG4 is a flow chart of another charging control method provided in an embodiment of the present application;

[0024] FIG5 is a schematic diagram of a microgrid power system structure provided by an embodiment of the present application;

[0025] FIG6 is a schematic diagram of another microgrid power system structure provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] This application describes multiple embodiments, but this description is exemplary rather than restrictive, and it will be apparent to those skilled in the art that more embodiments and implementations may be included within the scope of the embodiments described herein. Although many possible feature combinations are shown in the drawings and discussed in the detailed description, many other combinations of the disclosed features are possible. Unless specifically limited, any feature or element of any embodiment may be used in combination with any other feature or element in any other embodiment, or may replace any other feature or element in any other embodiment.

[0027] This application includes and contemplates combinations of features and elements known to those of ordinary skill in the art. The embodiments, features, and elements disclosed in this application may also be combined with any conventional features or elements to form a unique inventive solution defined by the claims. Any features or elements of any embodiment may also be combined with features or elements from other inventive solutions to form another unique inventive solution defined by the claims. Therefore, it should be understood that any feature shown and / or discussed in this application may be implemented individually or in any appropriate combination. Therefore, the embodiments are not subject to other limitations except for the limitations set forth in the appended claims and their equivalents. In addition, various modifications and changes may be made within the scope of protection of the appended claims.

[0028] In addition, when describing representative embodiments, the specification may have presented the method and / or process as a specific sequence of steps. However, to the extent that the method or process does not rely on the specific order of the steps described herein, the method or process should not be limited to the steps in the specific order described. As will be understood by those skilled in the art, other orders of steps are also possible. Therefore, the specific order of the steps set forth in the specification should not be interpreted as a limitation on the claims. In addition, the claims for the method and / or process should not be limited to the steps performed in the order described, and those skilled in the art can readily understand that these orders can be changed and still remain within the spirit and scope of the embodiments of the present application.

[0029] With the development of new energy technologies, the integration and usability of various new energy generation components have continued to increase, leading to the rapid development of microgrid power systems. A microgrid power system, also known as a microgrid, is a small-scale power generation and distribution system primarily composed of distributed power sources (generation components), energy storage components, energy conversion devices, related power loads, and control modules. It is an autonomous system capable of self-control and management, capable of operating either in parallel with the external power grid or in isolation, and is a key component of the smart grid. Microgrid power systems that include new energy generation components, such as photovoltaic and wind power generation components, are also known as new energy microgrid systems / clean energy microgrid systems. In particular, microgrid power systems that include photovoltaic components are increasingly being adopted by businesses and households due to their outstanding overall electricity cost advantages and environmental benefits. Furthermore, due to the essential energy storage components within these systems, they are also known as household photovoltaic energy storage systems.

[0030] Taking microgrid power systems, which use photovoltaic panels as distributed power sources, as an example, their power generation capacity is directly affected by the power generation environment, particularly the sunlight conditions: the intensity and duration of sunlight directly affect the power generation of the photovoltaic panels. Wind turbines, for example, are also significantly affected by the power generation environment, with wind speed and direction directly affecting their power generation. Therefore, microgrid power systems are equipped with energy storage components to store energy in advance, so that they can supply power to the load when the direct power supply capacity of the photovoltaic panels is insufficient.

[0031] In some exemplary technologies, microgrid power systems generally operate in three modes: maximum self-consumption mode, energy security mode, and full grid-connected mode. Each mode is based on a different control method. In maximum self-consumption mode, photovoltaic power is prioritized for electrical loads. When there is excess photovoltaic power, batteries are charged, and the excess power is sold to the grid. When photovoltaic power is insufficient, batteries are prioritized to meet electrical loads. If this is still insufficient, power is purchased from the grid. This mode maximizes the system's self-consumption rate and energy self-sufficiency. However, since the battery charging energy comes solely from surplus photovoltaic power, it cannot utilize low-priced nighttime electricity under peak and off-peak electricity pricing policies, thus not necessarily minimizing electricity costs. In energy security mode, batteries are charged during daytime periods of surplus photovoltaic power and during nighttime periods of low electricity prices. During daytime periods of high electricity prices, batteries are prioritized for indoor power supply. This mode may have lower electricity costs than the maximum self-consumption mode, but the self-consumption rate and energy self-sufficiency rates are higher. In the full grid-connected mode, all photovoltaic power generation is sold directly to the power grid, so the energy self-sufficiency rate and self-consumption rate are both 0.

[0032] As can be seen, in the context of peak and valley electricity prices, the three exemplary methods for operating household photovoltaic energy storage systems struggle to simultaneously maximize energy self-sufficiency and minimize electricity costs. Further optimizing their operational control schemes to achieve both maximization of energy self-sufficiency and minimization of electricity costs is a future direction for technological exploration in this field.

[0033] The present disclosure provides a charging control method for a microgrid power system, which is used to maximize energy self-sufficiency and minimize electricity costs. As shown in FIG1 , the method includes:

[0034] Step 110, obtaining power generation environment prediction data for the next control cycle, and determining a historical control cycle in which the actual power generation environment data has the highest matching degree with the power generation environment prediction data from multiple historical control cycles, and recording it as a historical matching cycle;

[0035] Step 120: completing the charging of the energy storage components in the microgrid power system by the power grid during the valley period included in the next control cycle according to the pre-charging demand information corresponding to the historical matching cycle;

[0036] The microgrid power system includes a power generation component, and the power generation of the power generation component is affected by the power generation environment.

[0037] In some exemplary embodiments, the power generation component includes any one of the following types: a photovoltaic power generation component, a wind power generation component, and a hydropower generation component.

[0038] As can be seen, the actual power generation of these power generation components is significantly affected by environmental factors. For example, weather data significantly affects photovoltaic and wind power generation components, while seasonal climate data significantly affects hydropower generation components. More specific influencing factors can be determined based on the power generation component and will not be discussed in detail in this application.

[0039] In some exemplary embodiments, as shown in FIG2 , the method further includes:

[0040] Step 101: After the current control cycle ends, the power generation, power consumption and actual power generation environment data of the microgrid power system in the current control cycle are obtained;

[0041] Step 102: determining the pre-charging requirement information of the current control cycle according to the power generation and the power consumption and a set optimization principle;

[0042] Step 103: Save the pre-charging requirement information and the actual power generation environment data as power generation and consumption record data corresponding to the current control cycle.

[0043] In some exemplary embodiments, the optimization principle is to maximize energy self-sufficiency and minimize pre-charging demand during off-peak hours;

[0044] Alternatively, the optimization principle is the principle of maximum energy self-sufficiency;

[0045] Alternatively, the optimization principle is the principle of minimizing the pre-charging demand during the valley period;

[0046] Alternatively, the optimization principle is to maximize the self-consumption rate.

[0047] The off-peak pre-charging demand represents the amount of electricity required to charge the grid in advance during the off-peak period. It can be understood that the lowest off-peak pre-charging demand means the lowest grid charging cost. Based on different optimization principles and actual system operation data from completed control cycles, the corresponding pre-charging demand information can be optimized and calculated. The corresponding optimization principle can then be determined based on the control needs of each microgrid power system.

[0048] In some exemplary embodiments, step 102 includes: determining the pre-charging requirement information of the current control cycle according to the power generation, the power consumption and the actual power generation environment data and according to a set optimization principle.

[0049] That is, the power generation and consumption record data corresponding to each control cycle records the power generation and consumption of the microgrid power system during this cycle, including: pre-charging demand information and actual power generation environment data.

[0050] In some exemplary embodiments, the power generation and consumption record data further includes: power generation and power consumption.

[0051] In some exemplary embodiments, the pre-charging requirement information includes one or more of the following:

[0052] Charging time, charging level, total battery percentage and charging level percentage.

[0053] In some exemplary embodiments, the control cycle is one day, from midnight to midnight, i.e., overall charging control and electricity usage data collection and recording are performed on a daily basis. The off-peak period is from 1:00 AM to 5:00 AM daily. The length of the control cycle and its start and end times can be flexibly set as needed and are not limited to the aspects of the disclosed examples.

[0054] Accordingly, the power generation environment forecast data for the next control cycle is the next day's power generation environment forecast data, and the actual power generation environment data for the current control cycle is the actual power generation environment data for the current day. Taking photovoltaic power generation as an example, the power generation environment forecast data and actual power generation environment data include weather data, including one or more of the following: sunshine intensity, sunshine duration, temperature, cloud cover, wind speed, rainfall, humidity, snow depth, air cleanliness, etc.

[0055] In some exemplary embodiments, after each control cycle, pre-charging requirement information is determined based on the power generation and power consumption data of the current cycle according to a predetermined optimization principle. This information is then associated with the actual power generation environment data of the current cycle and saved as historical data to be used as candidate reference data in step 110. It will be understood that the pre-charging requirement information thus determined is not the actual charging information for the energy storage component during the (completed) control cycle, but rather recommended or optimal pre-charging requirement information determined based on the actual power consumption and power generation data, which will be referenced for charging control in subsequent control cycles.

[0056] Because the power generation of the power generation components in a microgrid power system is significantly affected by the power generation environment, using the pre-charging requirement information corresponding to a historical control cycle with similar power generation environment data in the historical data as a reference provides a more accurate reference. It can be understood that steps 110-120 execute the corresponding charging control in the new control cycle based on the pre-charging requirement information corresponding to the selected historical matching cycle before or at the beginning of the new control cycle.

[0057] In some exemplary embodiments, step 102 includes:

[0058] According to the power generation and the power consumption, and in accordance with the principle of maximizing energy self-sufficiency and minimizing pre-charging demand during off-peak hours, the pre-charging demand information of the current control cycle is determined.

[0059] In some exemplary embodiments, the current control period is divided into a plurality of sub-periods, the power generation of the current control period includes the power generation of the plurality of sub-periods, and the power consumption of the current control period includes the power consumption of the plurality of sub-periods;

[0060] The determining, based on the power generation and the power consumption and in accordance with a set optimization principle, the pre-charging requirement information of the current control cycle includes:

[0061] Determining the required charging amount for each sub-cycle based on the power generation amount and the power consumption amount of the sub-cycles and the optimization principle and using a preset optimization algorithm;

[0062] Determining pre-charging requirement information for the current control cycle based on the required charging amounts of all sub-cycles;

[0063] The optimization algorithm includes: genetic algorithm, gradient descent method, simulated annealing algorithm, dynamic programming algorithm or linear programming algorithm.

[0064] For example, a one-day control cycle is divided into 24 sub-cycles, each sub-cycle is 1 hour. Accordingly, the power generation (data) includes the power generation (data) of the 24 sub-cycles, the power consumption includes the power consumption (data) of the 24 sub-cycles, and the actual weather power generation environment data includes the actual power generation environment data of the 24 sub-cycles.

[0065] In some exemplary embodiments, the optimization principle is: the principle of minimizing pre-charging demand during off-peak hours, that is, minimizing grid charging costs. As shown in FIG3 , step 102 includes:

[0066] Step 1021, determining an initial value of the required charge capacity for each sub-cycle within the maximum pre-charge capacity range;

[0067] Step 1022: Determine the required charging capacity for each sub-cycle using the optimization algorithm based on the initial value according to the power generation and power consumption of the multiple sub-cycles.

[0068] Step 1023: Determine whether the pre-charging demand during the valley period has reached the minimum based on the required charging amount of all sub-cycles. If so, execute step 1024; if not, iterate step 1022.

[0069] Step 1024 : Determine the pre-charging requirement information for the current control cycle based on the required charging amounts of all sub-cycles.

[0070] The total charging amount corresponding to the pre-charging requirement information is less than or equal to the maximum pre-charging amount.

[0071] In some exemplary embodiments, the preset optimization algorithm includes: a genetic algorithm, a gradient descent algorithm, a simulated annealing algorithm, a dynamic programming algorithm, or a linear programming algorithm.

[0072] In some exemplary embodiments, step 102 includes:

[0073] constructing an objective function based on the power generation of the multiple sub-cycles, the power consumption of the multiple sub-cycles, and the required charging amount of the multiple sub-cycles, solving the objective function, and determining the required charging amount of the sub-cycle;

[0074] The pre-charging requirement information of the current control cycle is determined according to the required charging amounts of all sub-cycles.

[0075] In some exemplary embodiments, the objective function includes: Max(F1(x1, x2, ...xn)), and Min(F(x1, x2, ...xn)), wherein F1(x1, x2, ...xn) is a self-consumption rate calculation function, F(x1, x2, ...xn) is a demanded total charging amount calculation function for the current control cycle, and x1, x2, ...xn is a pre-charging demand amount for n sub-cycles. It can be seen that the optimal solution obtained according to the objective function is the pre-charging demand amount with the highest self-consumption rate and the smallest total charging amount determined based on the power generation of the multiple sub-cycles and the power consumption of the multiple sub-cycles. The specific forms of the self-consumption rate calculation function F1(x1, x2, ...xn) and the demanded total charging amount calculation function F(x1, x2, ...xn) for the current control cycle are not limited to specific aspects.

[0076] In some exemplary embodiments, the objective function includes: Max(F2(x1, x2, ...xn)), and Min(F(x1, x2, ...xn)), wherein F2(x1, x2, ...xn) is an energy self-sufficiency rate calculation function, F(x1, x2, ...xn) is a function for calculating the total amount of required charging for the current control cycle, and x1, x2, ...xn is the pre-charging demand for n sub-cycles. It can be seen that the optimal solution obtained according to the objective function is the pre-charging demand with the highest energy self-sufficiency rate and the smallest total charging amount determined based on the power generation of the multiple sub-cycles and the power consumption of the multiple sub-cycles. The specific forms of the energy self-sufficiency rate calculation function F2(x1, x2, ...xn) and the total amount of required charging for the current control cycle F(x1, x2, ...xn) are not limited to specific aspects.

[0077] In some exemplary embodiments, the objective function includes: Max(aF1(x1, x2, ...xn)+bF2(x1, x2, ...xn)), and Min(F(x1, x2, ...xn)), wherein F1(x1, x2, ...xn) is a function for calculating the self-consumption rate, F2(x1, x2, ...xn) is a function for calculating the energy self-sufficiency rate, a and b are weight coefficients, F(x1, x2, ...xn) is a function for calculating the total required charging amount of the current control cycle, and x1, x2, ...xn is the pre-charging demand of n sub-cycles. It can be seen that the optimal solution obtained according to the objective function is the pre-charging demand with the highest weighted sum of the self-consumption rate and the energy self-sufficiency rate and the minimum total charging amount, as determined based on the power generation of the multiple sub-cycles and the power consumption of the multiple sub-cycles.

[0078] Where n corresponds to the total number of sub-periods. The specific solution for solving the above multi-objective function will not be discussed in detail in this application.

[0079] In some exemplary embodiments, determining the pre-charging requirement information of the current control cycle according to the required charging amounts of all sub-cycles includes:

[0080] According to the required charging amount of all sub-cycles, the required charging amount calculation function F(x1, x2, ... xn) is used to calculate the required charging amount of the current control cycle, and determine the corresponding pre-charging requirement information.

[0081] In some exemplary embodiments, each of the control cycles is divided into a plurality of sub-cycles, the power generation environment prediction data includes power generation environment prediction data corresponding to the plurality of sub-cycles, and the actual power generation environment data of each control cycle includes actual power generation environment data corresponding to the plurality of sub-cycles;

[0082] Determining, from a plurality of historical control periods, a historical control period in which the actual power generation environment data has the highest matching degree with the power generation environment prediction data includes:

[0083] Matching the predicted curve corresponding to the power generation environment prediction data with the actual curves corresponding to the actual power generation environment data of multiple historical control periods to determine a historical control period with the highest curve similarity;

[0084] The prediction curve is determined based on the power generation environment prediction data of multiple sub-cycles included in the power generation environment prediction data; each actual curve is determined based on the actual power generation environment prediction data of multiple sub-cycles included in the actual power generation environment data of each historical control period.

[0085] For example, a control cycle of one day is divided into 24 sub-cycles, and the power generation environment prediction data also includes power generation environment prediction data of the 24 sub-cycles, and the actual power generation environment data also includes power generation environment prediction data of the 24 sub-cycles.

[0086] It can be understood that the specific parameter items included in the power generation environment prediction data and the actual power generation environment data are consistent, and may include one or more specific parameters.

[0087] In some exemplary embodiments, when the power generation component includes a photovoltaic power generation component or a wind power generation component, the power generation environment data includes weather data.

[0088] The weather data includes one or more of the following parameters: sunshine intensity, sunshine duration, temperature, cloud cover, wind speed, rainfall, humidity, snow thickness, air cleanliness, and season.

[0089] In some exemplary embodiments, when a specific parameter is included, the data of this parameter in multiple sub-periods constitute a weather data curve (plane curve) that changes with time, which are a prediction curve and an actual curve. According to the curve similarity algorithm, the curve similarity between each actual curve and the prediction curve is calculated respectively, and the historical period corresponding to the actual curve with the highest curve similarity is selected as the historical matching period.

[0090] In some exemplary embodiments, when multiple specific parameters are included, data of multiple sub-periods of multiple parameters constitute a weather data curve (spatial curve) that changes with time, which are respectively a prediction curve and an actual curve. According to the curve similarity algorithm, the curve similarity between each actual curve and the prediction curve is calculated respectively, and the historical period corresponding to the actual curve with the highest curve similarity is selected as the historical matching period.

[0091] The specific curve construction method and curve similarity algorithm will not be discussed in detail here.

[0092] In some exemplary embodiments, the weather data includes: (outdoor) temperature; that is, the actual power generation environment data includes: actual (outdoor) temperature, and the power generation environment prediction data includes: predicted (outdoor) temperature; each includes the actual (outdoor) temperature and the predicted (outdoor) temperature of multiple sub-periods;

[0093] Accordingly, determining, from a plurality of historical control periods, a historical control period in which the actual power generation environment data has the highest matching degree with the power generation environment prediction data includes:

[0094] For each historical control period, the temperature mean square error is determined according to the following method:

[0095] Calculating a mean square error based on the predicted (outdoor) temperatures of the multiple sub-periods and the actual (outdoor) temperatures of the multiple sub-periods included in the historical control period;

[0096] The historical control period with the smallest mean square error is selected as the historical control period with the highest matching degree.

[0097] In some exemplary embodiments, when the pre-charging requirement information includes a charging duration, completing the charging of the energy storage component in the microgrid power system by the power grid during the valley period included in the next control cycle according to the pre-charging requirement information corresponding to the historical matching period includes:

[0098] During the valley period included in the next control cycle, charging the energy storage component in the microgrid power system for the charging duration;

[0099] The charging time is less than or equal to the valley period; or the charging time is less than or equal to the sum of all valley period durations.

[0100] In some exemplary embodiments, when the pre-charging requirement information includes a charging amount, completing the charging of the energy storage component in the microgrid power system by the power grid during the valley period included in the next control cycle according to the pre-charging requirement information corresponding to the historical matching cycle includes:

[0101] During the valley period included in the next control cycle, the energy storage components in the microgrid power system are charged to reach the charging amount.

[0102] In some exemplary embodiments, when the pre-charging requirement information includes a percentage of total power, completing the charging of the energy storage component in the microgrid power system by the power grid during the valley period included in the next control cycle according to the pre-charging requirement information corresponding to the historical matching cycle includes:

[0103] During the valley period included in the next control cycle, the energy storage component in the microgrid power system is charged so that the power level of the energy storage component reaches the percentage of the total power level.

[0104] For example, the total power percentage is 80%, that is, the charging during the valley period enables the charged capacity of the energy storage component to reach 80% of the total power.

[0105] In some exemplary embodiments, when the pre-charging requirement information includes a charging capacity percentage, completing the charging of the energy storage component in the microgrid power system by the power grid during the valley period included in the next control cycle according to the pre-charging requirement information corresponding to the historical matching cycle includes:

[0106] During the valley period included in the next control cycle, the energy storage component in the microgrid power system is charged so that the power of the energy storage component is increased by the charging percentage.

[0107] For example, the charging percentage is 40%, which means that charging during the valley period increases the power of the energy storage component by 40% before and after charging.

[0108] It will be appreciated that, depending on the specifications of the energy storage components, the above pre-charge requirement information can be converted to each other through relevant calculations. Any one or more of these information can be selected based on the needs of charging control. During valley charging, actual charging is performed within the maximum capacity of the energy storage components, based on the remaining power in the energy storage components.

[0109] In some exemplary embodiments, according to the pre-charging demand information corresponding to the historical matching period, completing the charging of the energy storage component in the microgrid power system by the power grid during the valley period included in the next control period includes:

[0110] According to the pre-charging demand information corresponding to the historical matching cycle and the preset energy storage loss redundancy, the grid completes charging of the energy storage components in the microgrid power system during the valley period included in the next control cycle.

[0111] In some exemplary embodiments, energy storage loss redundancy includes:

[0112] Charging time redundancy, charging power redundancy, total power percentage redundancy and charging power percentage redundancy.

[0113] That is, in order to cope with the charging and discharging losses of the energy storage components, some redundant charging capacity is added based on the pre-charging demand information.

[0114] For example, the charging percentage is 40%, and the charging percentage redundancy is 5%, that is, charging during the valley period increases the power of the energy storage component by 45% before and after charging.

[0115] The minimum energy storage loss redundancy is 0.

[0116] In some exemplary embodiments, the charging of the energy storage component in the microgrid power system by the power grid during the valley period included in the next control cycle according to the pre-charging demand information corresponding to the historical matching cycle includes:

[0117] Determining the pre-charge amount for the next control cycle according to the pre-charge required power, the remaining power of the energy storage component, and the preset energy storage redundancy power;

[0118] According to the pre-charge amount, the grid completes charging of the energy storage component in the microgrid power system during the valley period included in the next control cycle;

[0119] Among them, Q=Y-X+P, Q is the pre-charge amount of the next control cycle, Y is the pre-charge demand power determined according to the pre-charge demand information corresponding to the historical matching cycle, X is the remaining power of the energy storage component, and P>=0 is the preset charging power redundancy.

[0120] For example, Y=2000Ah (ampere-hour), X=500Ah, P=200Ah, and the calculated value is Q=2000-500+200=1700Ah. Then, in the next control cycle, 1700Ah is actually charged.

[0121] Among them, the preset charging power redundancy is used to cope with the charging and discharging losses of the energy storage components.

[0122] In the embodiments of the present application, the current control cycle and the next control cycle are relative concepts. The next control cycle refers to the cycle in which the operation and control of the microgrid power system will be executed. The current control cycle and the next control cycle can be continuous in time or separated by a time interval. They are not limited to specific aspects and can be determined according to the operation and management needs of the microgrid system.

[0123] The present application also provides a charging control method, wherein the control cycle is one day, from 0 to 24 o'clock, divided into 24 sub-cycles, and the valley period is from 1:00 to 5:00. The power generation component is a photovoltaic power generation component, and the energy storage component is a battery, as shown in FIG4 , including:

[0124] Step 410 , in the Nth control cycle, collecting 24 sub-cycles of photovoltaic power generation, system power consumption, and actual weather data;

[0125] Step 420: At the end of the Nth control cycle, determine the pre-charging requirement information corresponding to the current cycle;

[0126] Step 430, storing the power generation and consumption record data of the Nth control cycle;

[0127] Step 440 , obtaining weather forecast data for the N+1th control period, and determining a historical matching period, the i-th control period, from the N historical periods;

[0128] Step 450 : Complete charging during the valley period of the (N+1)th control cycle according to the pre-charging requirement information of the i-th control cycle.

[0129] Among them, in step 420, the pre-charging demand information of the current control cycle is determined based on the photovoltaic power generation and system power consumption of the 24 sub-cycles, according to the principle of maximizing the energy self-sufficiency rate and minimizing the pre-charging demand during the valley period.

[0130] In step 440 , the weather forecast data of the N+1th control period is matched with the actual weather data in the power generation and consumption record data of N historical periods, and the one with the highest matching degree is selected.

[0131] For example, from 0:00 to 24:00 on July 1, the system's hourly photovoltaic power generation, electricity consumption and actual weather data were continuously recorded.

[0132] Just after midnight on July 2, the pre-charging demand information for July 1 is calculated and stored in the data storage module together with the actual weather data for July 1.

[0133] Just after midnight on July 2nd, the 24-hour weather forecast for July 2nd is obtained from an external source. The data storage module then searches for the day with the most similar weather conditions in history (here, May 5th is used as an example). The most similar day in the data storage module is May 5th of that year, and the corresponding pre-charge demand information for that day is 40% of the total power.

[0134] The PCS module (Photovoltaic Power Conditioning System) charges the battery during the nighttime period of low electricity prices, so that the remaining battery power reaches 40% at the end of the nighttime period of low electricity prices.

[0135] The embodiment of the present application further provides a microgrid power system, as shown in FIG5 , wherein the microgrid power system 500 includes:

[0136] Control module 510, power generation component 520 and energy storage component 530;

[0137] The control module 510 is configured to execute the charging control method described in any embodiment of the present application to charge the energy storage component 530;

[0138] The power generation capacity of the power generation component 520 is affected by the power generation environment.

[0139] In some exemplary embodiments, the power generation component 520 includes any one of the following types:

[0140] Photovoltaic power generation components, wind power generation components, and hydropower generation components.

[0141] In some exemplary embodiments, the control module 510 is further configured to obtain actual power generation environment data of the current control cycle and / or power generation environment prediction data of the next control cycle from a third-party server.

[0142] In some exemplary embodiments, the energy storage component 530 includes: a battery.

[0143] In some exemplary embodiments, as shown in FIG6 , the microgrid power system 500 further includes a storage module 540 for storing power generation and consumption record data of each control cycle.

[0144] In some exemplary embodiments, the microgrid power system 500 further includes a PCS (Power Conditioning System) module 550 , configured to receive a charging control instruction to complete charging of the energy storage module 530 .

[0145] In some exemplary embodiments, the control module 510 is further configured to send a charging control instruction to the PCS module 550 according to the pre-charging requirement information during a valley period included in the next control cycle.

[0146] In some exemplary embodiments, the microgrid power system 500 further includes: an electrical load 560 and an electrical box 570. The PCS module 550 is connected to the electrical load 560 via the electrical box 570 to supply power thereto.

[0147] An embodiment of the present application further provides an electronic device, including:

[0148] one or more processors;

[0149] a storage device configured to store one or more programs,

[0150] When the one or more programs are executed by the one or more processors, the one or more processors implement the charging control method as described in any embodiment of the present application.

[0151] An embodiment of the present application further provides a computer storage medium, wherein the storage medium stores a computer program, wherein the computer program is configured to execute the charging control method as described in any embodiment of the present application when running.

[0152] The energy storage control solution provided in the embodiment of the present application fully considers the characteristic that the power generation capacity of the self-generating component is greatly affected by the power generation environment. Based on the pre-charging demand information corresponding to the control cycle with the historical data closest to the power generation environment, the control target control cycle completes pre-charging during the valley period, so that the overall control solution of the microgrid power system can take into account both the maximization of the self-sufficiency rate and the minimization of the electricity cost, further improving the operating energy efficiency of the microgrid power system.

[0153] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In hardware implementations, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all components may be implemented as software executed by a processor, such as a digital signal processor or a microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or temporary medium). As is well known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable, and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, as is well known to those skilled in the art, communication media typically embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

Claims

1. A charging control method, wherein: Applied to microgrid power systems, including: Obtaining power generation environment prediction data for the next control cycle, and determining, from multiple historical control cycles, a historical control cycle in which the actual power generation environment data has the highest matching degree with the power generation environment prediction data, and recording the historical matching cycle; According to the pre-charging demand information corresponding to the historical matching cycle, the grid completes charging of the energy storage components in the microgrid power system during the valley period included in the next control cycle; The microgrid power system includes a power generation component, and the power generation of the power generation component is affected by the power generation environment.

2. The charging control method according to claim 1, wherein: The method further includes: after the current control cycle ends, obtaining power generation, power consumption and actual power generation environment data of the microgrid power system in the current control cycle; Determining pre-charging demand information for the current control cycle according to the power generation and the power consumption and a set optimization principle; The pre-charging requirement information and the actual power generation environment data are saved as power generation and consumption record data corresponding to the current control cycle.

3. The charging control method according to claim 2, wherein: The optimization principle is to maximize the energy self-sufficiency rate and minimize the pre-charging demand during off-peak hours; Alternatively, the optimization principle is the principle of maximum energy self-sufficiency; Alternatively, the optimization principle is the principle of minimizing the pre-charging demand during the valley period; Alternatively, the optimization principle is to maximize the self-consumption rate.

4. The charging control method according to claim 2, wherein: The current control period is divided into multiple sub-periods, the power generation of the current control period includes the power generation of the multiple sub-periods, and the power consumption of the current control period includes the power consumption of the multiple sub-periods; The determining, based on the power generation and the power consumption and in accordance with a set optimization principle, the pre-charging requirement information of the current control cycle includes: Determining the required charging amount for each sub-cycle using a preset optimization algorithm based on the optimization principle according to the power generation amount and the power consumption of the sub-cycles; Determining pre-charging requirement information for the current control cycle based on the required charging amounts of all sub-cycles; The optimization algorithm includes: genetic algorithm, gradient descent method, simulated annealing algorithm, dynamic programming algorithm or linear programming algorithm.

5. The charging control method according to claim 1, wherein: Each of the control cycles is divided into a plurality of sub-cycles, the power generation environment prediction data includes power generation environment prediction data corresponding to the plurality of sub-cycles, and the actual power generation environment data of each control cycle includes actual power generation environment data corresponding to the plurality of sub-cycles; Determining, from a plurality of historical control periods, a historical control period in which the actual power generation environment data has the highest matching degree with the power generation environment prediction data includes: Matching the predicted curve corresponding to the power generation environment prediction data with the actual curves corresponding to the actual power generation environment data of multiple historical control periods to determine a historical control period with the highest curve similarity; The prediction curve is determined based on the power generation environment prediction data of multiple sub-cycles included in the power generation environment prediction data; each actual curve is determined based on the actual power generation environment prediction data of multiple sub-cycles included in the actual power generation environment data of each historical control period.

6. The charging control method according to any one of claims 1 to 5, wherein: The power generation component includes any of the following types: Photovoltaic power generation components, wind power generation components, and hydropower generation components; The pre-charging requirement information includes one or more of the following: Charging time, charging level, total battery percentage and charging level percentage.

7. The charging control method according to claim 6, wherein: The method of completing the charging of the energy storage component in the microgrid power system by the power grid during the valley period included in the next control cycle according to the pre-charging demand information corresponding to the historical matching cycle includes: Determining the pre-charge amount for the next control cycle according to the pre-charge required power, the remaining power of the energy storage component, and the preset energy storage redundancy power; According to the pre-charge amount, the grid completes charging of the energy storage component in the microgrid power system during the valley period included in the next control cycle; Among them, Q=Y-X+P, Q is the pre-charge amount of the next control cycle, Y is the pre-charge demand power determined according to the pre-charge demand information corresponding to the historical matching cycle, X is the remaining power of the energy storage component, and P>=0 is the preset charging power redundancy.

8. The charging control method according to claim 6, wherein: In a case where the power generation component includes a photovoltaic power generation component or a wind power generation component, the power generation environment data includes weather data.

9. A microgrid power system, wherein: include: Control modules, power generation components, and energy storage components; The control module is configured to execute the charging control method according to any one of claims 1 to 8 to charge the energy storage component; The power generation capacity of the power generation component is affected by the power generation environment.

10. The microgrid power system according to claim 9, wherein: The control module is further configured to obtain actual power generation environment data of the current control cycle and / or power generation environment prediction data of the next control cycle from a third-party server.

11. An electronic device, wherein: include: one or more processors; a storage device configured to store one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the charging control method according to any one of claims 1 to 8.

12. A computer storage medium, wherein: The storage medium stores a computer program, wherein the computer program is configured to execute the charging control method according to any one of claims 1 to 8 when running.

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