A power distribution optimization method and system based on photovoltaic energy storage equipment

Abstract

This disclosure provides a power distribution optimization method and system based on photovoltaic energy storage devices. The method includes: step S1, obtaining the total remaining photovoltaic energy storage capacity of the photovoltaic energy storage device cluster in the target area during each sunshine period on the target optimization day; step S2, determining whether the last sunshine period on the target optimization day is the full-capacity photovoltaic energy storage period; if the determination result of step S2 is no, then step S3 is executed; step S3, based on the total remaining photovoltaic energy storage capacity of each sunshine period on the target optimization day, determining at least one sunshine period from all sunshine periods on the target optimization day as the daytime charging period, and the grid charging amount of the photovoltaic energy storage device cluster during each daytime charging period, and the sum of the grid charging amount corresponding to all daytime charging periods on the target optimization day is equal to the difference between the preset maximum photovoltaic energy storage capacity and the total remaining photovoltaic energy storage capacity corresponding to the last sunshine period.

Description

Technical Field

[0001] This disclosure relates to the field of photovoltaic energy storage technology, and in particular to a power distribution optimization method and system based on photovoltaic energy storage equipment. Background Technology

[0002] Photovoltaic energy storage devices are devices that can convert light energy into electrical energy and store electrical energy. They are widely used in residential life. Currently, using photovoltaic energy storage devices in conjunction with the power grid system to supply power to loads in a target area has become a common scenario. How to achieve efficient and low-cost power supply in conjunction with photovoltaic energy storage devices and the power grid system is one of the research hotspots in this field. Summary of the Invention

[0003] In a first aspect, embodiments of this disclosure provide a power distribution optimization method based on photovoltaic energy storage devices, which divides a complete day into multiple statistical time periods and determines the corresponding statistical time periods as sunshine periods or non-sunshine periods according to the sunrise and sunset times of the day. The power distribution optimization method includes:

[0004] Step S1: Obtain the total remaining photovoltaic energy storage capacity of the photovoltaic energy storage device cluster in the target area during each solar sunshine period on the target day to be optimized;

[0005] Step S2: Determine whether the last sunshine period of the target day to be optimized is a period of full photovoltaic energy storage. The period of full photovoltaic energy storage refers to the period when the remaining total power of the corresponding photovoltaic energy storage is equal to the preset maximum power of photovoltaic energy storage.

[0006] If the result of step S2 is negative, then step S3 is executed.

[0007] Step S3: Based on the total remaining photovoltaic energy storage capacity corresponding to each sunshine period within the target optimization day, determine at least one sunshine period from all sunshine periods within the target optimization day as a daytime charging period, and the grid charging amount for charging the photovoltaic energy storage device cluster using the grid system during each daytime charging period. The total grid charging amount corresponding to all the daytime charging periods within the target optimization day is equal to the difference between the preset maximum photovoltaic energy storage capacity and the total remaining photovoltaic energy storage capacity corresponding to the last sunshine period.

[0008] In some embodiments, step S3 includes:

[0009] Step S301: Determine whether there is at least one sunshine period during each sunshine period other than the last sunshine period of the target day to be optimized that is the full-capacity period of the photovoltaic energy storage;

[0010] If the judgment result of step S301 is yes, then step S302 is executed; if the judgment result of step S301 is no, then step S303 is executed.

[0011] Step S302: Determine all sunshine periods within the target day to be optimized that are after the last full-capacity photovoltaic energy storage period as rechargeable alternative periods;

[0012] Step S303: Determine each sunshine period within the target day to be optimized as a rechargeable alternative period;

[0013] After steps S302 and S303 are completed, step S304 is executed, where the number of rechargeable alternative time periods determined is denoted as N, where N is a positive integer;

[0014] Step S304: Construct a charging optimization model to represent the charging of the photovoltaic energy storage device cluster using the power grid system during the N rechargeable alternative time periods. The decision variable in the charging optimization model is the amount of grid charging corresponding to the charging of the photovoltaic energy storage device cluster using the power grid system during the N rechargeable alternative time periods. The objective of the charging optimization model is to minimize the total charging cost of charging the photovoltaic energy storage device cluster using the power grid system during the N rechargeable alternative time periods.

[0015] Step S305: Solve the charging optimization model using the first preset target optimization algorithm to obtain the corresponding current optimal solution. The current optimal solution includes the current optimal value of the grid charging amount corresponding to the charging of the photovoltaic energy storage device cluster by the grid system in N rechargeable alternative time periods. The rechargeable alternative time periods with the current optimal value of the grid charging amount not being 0 are taken as the daytime charging time periods.

[0016] In some embodiments, the total charging cost of charging the photovoltaic energy storage device cluster using the power grid system during N available charging time periods is minimized, as expressed by the following formula:

[0017]

[0018] This indicates the total cost of charging. This represents the amount of grid charging required to charge the photovoltaic energy storage device cluster using the grid system during the nth available charging time period. This represents the unit charging cost for charging the photovoltaic energy storage device cluster using the power grid system during the nth available charging time period;

[0019] The constraints of the charging optimization model include:

[0020] Condition 1: During the N available charging periods, the photovoltaic energy storage cluster will not discharge to the grid system.

[0021]

[0022] Condition 2: In the N available charging time slots, the charging power of each available charging time slot cannot exceed the maximum charging power of the photovoltaic energy storage equipment cluster.

[0023]

[0024] in, This represents the duration of the nth available charging time slot. This indicates the maximum charging power of the photovoltaic energy storage device cluster;

[0025] Condition 3: After charging the photovoltaic energy storage equipment cluster using the power grid system during N rechargeable alternative time periods, the remaining total photovoltaic energy storage capacity during the last sunshine period of the target day to be optimized is increased to the preset maximum photovoltaic energy storage capacity.

[0026]

[0027] This represents the total remaining photovoltaic energy storage capacity of the photovoltaic energy storage cluster during the last sunshine period of the target optimization day, without charging the grid system. This indicates the preset maximum capacity of photovoltaic energy storage. It represents the total amount of grid charging used to charge the photovoltaic energy storage device cluster during N available charging time slots;

[0028] Condition 4: In any available charging time period, the total remaining photovoltaic energy storage capacity of the photovoltaic energy storage cluster after completing the grid system charging will not exceed the preset maximum photovoltaic energy storage capacity.

[0029]

[0030] in, This represents the total remaining photovoltaic energy storage capacity of the photovoltaic energy storage device cluster in the nth available charging period when the grid system is not charging. This represents the total amount of grid charging used to charge the photovoltaic energy storage device during the first n available charging periods.

[0031] In some embodiments, the method further includes the following steps prior to step S1:

[0032] Step Sa: Generate a solar sunshine period ...

[0033] The input information of the solar energy storage remaining total power prediction model for the sunshine period includes: the time period identifier of the target sunshine period within the target optimization day, the total regional power consumption of all loads connected to the solar energy storage equipment cluster within the target area during the target sunshine period, the temperature of the target area during the target sunshine period, and the cumulative sunshine duration of the target area within the target optimization day during the target sunshine period; the output of the solar energy storage remaining total power prediction model for the sunshine period is the total remaining solar energy storage power corresponding to the target sunshine period;

[0034] Step S1 includes:

[0035] Step S101: Obtain the first input information corresponding to each sunshine period within the target optimization day. The first input information includes: the time period identifier of the corresponding sunshine period within the target optimization day, the total regional electricity consumption of all loads connected to the photovoltaic energy storage equipment cluster in the target area during the corresponding sunshine period, the temperature of the target area during the corresponding sunshine period, and the cumulative sunshine duration of the target area within the target optimization day at the end of the corresponding sunshine period.

[0036] Step S102: Input the first input information corresponding to each sunshine period of the target day to be optimized into the trained photovoltaic energy storage remaining total power prediction model for the sunshine period, and obtain the photovoltaic energy storage remaining total power corresponding to each sunshine period of the target day to be optimized.

[0037] In some embodiments, step Sa includes:

[0038] Step Sa1: Collect comprehensive information of the first historical time period for the target area during multiple sunshine periods over multiple historical days. The comprehensive information of the first historical time period includes: a time period identifier indicating the position of the corresponding sunshine period in the day; the total electricity consumption of all loads connected to the photovoltaic energy storage equipment cluster in the target area during the corresponding sunshine period; the temperature of the target area during the corresponding sunshine period; the cumulative sunshine duration of the target area on the day at the end of the corresponding sunshine period; and the remaining total photovoltaic energy storage capacity of the photovoltaic energy storage equipment cluster in the target area during the corresponding sunshine period.

[0039] Step Sa2: Use the comprehensive information of the first historical period as training samples. The period identifier, the total electricity consumption of the region, the cumulative sunshine duration, the temperature, and the cumulative sunshine duration in the comprehensive information of the first historical period are used as model inputs. The remaining total electricity of photovoltaic energy storage in the comprehensive information of the first historical period is used as model output. Train the first preset prediction model to obtain the prediction model of the remaining total electricity of photovoltaic energy storage during the sunshine period.

[0040] In some embodiments, step Sa2 includes:

[0041] Step Sa21: Divide all the historical days into 8 groups according to the preset classification rules. The preset classification rules are as follows: statutory holidays are divided into 1 group, and non-statutory holidays are divided into the remaining 7 groups according to 7 different dates from Monday to Sunday.

[0042] Step Sa22: For each group, construct the first historical time period comprehensive information set corresponding to the group by combining the first historical time period comprehensive information of all sunshine periods within each historical day included in the group;

[0043] Step Sa23: For each group, use the comprehensive information set of the first historical period corresponding to the group as the training sample set to train the first preset prediction model to obtain the prediction model of the remaining total electricity of photovoltaic energy storage during the sunshine period corresponding to the group.

[0044] Step S102 includes:

[0045] Step S1021: Determine the group to which the target optimization date belongs according to the preset classification rules based on the date of the target optimization date;

[0046] Step S1022: Input the first input information corresponding to each sunshine period within the target optimization day into the photovoltaic energy storage remaining total power prediction model for the sunshine period corresponding to the group to which the target optimization day belongs, to obtain the photovoltaic energy storage remaining total power corresponding to each sunshine period within the target optimization day.

[0047] In some embodiments, it also includes:

[0048] Sb, Generate a model for predicting the remaining total power of photovoltaic energy storage during non-sunlight periods that can predict the total remaining power of photovoltaic energy storage during target non-sunlight periods after the last sunshine period of the day.

[0049] The input information of the photovoltaic energy storage remaining total power prediction model for non-sunlight periods includes: the time period identifier of the target non-sunlight period within the target day to be optimized, the remaining total power of photovoltaic energy storage corresponding to the nearest sunshine period before the target non-sunlight period, the total regional power consumption of all loads connected to the photovoltaic energy storage equipment cluster within the target area during the target non-sunlight period, and the temperature of the target area during the target non-sunlight period; the output of the photovoltaic energy storage remaining total power prediction model for non-sunlight periods is the remaining total power of photovoltaic energy storage corresponding to the target non-sunlight period;

[0050] Step S4: Select non-sunlight periods that fall during the peak electricity consumption period of the power grid within the target optimization day from all non-sunlight periods after the last sunshine period within the target optimization day, and use them as candidate periods for discharge.

[0051] Step S5: Obtain the second input information of the last available dischargeable alternative time period within the target optimization day. The second input information includes: the time period identifier of the last available dischargeable alternative time period, the remaining total power of photovoltaic energy storage corresponding to the nearest sunshine period before the last available dischargeable alternative time period, the total regional power consumption of all loads connected to the photovoltaic energy storage equipment cluster in the target area during the last available dischargeable alternative time period, and the temperature of the target area during the last available dischargeable alternative time period. The remaining total power of photovoltaic energy storage corresponding to the nearest sunshine period before the last available dischargeable alternative time period is taken as the preset maximum power of photovoltaic energy storage.

[0052] Step S6: Input the second input information of the last dischargeable candidate period within the target optimization day into the trained photovoltaic energy storage remaining total power prediction model for the non-sunlight period to obtain the photovoltaic energy storage remaining total power corresponding to the last dischargeable candidate period within the target optimization day.

[0053] Step S7: Determine whether the remaining total photovoltaic energy storage capacity corresponding to the last dischargeable alternative time period within the target optimization day is 0;

[0054] If the result of step S7 is negative, then step S8 is executed.

[0055] Step S8: Determine at least one dischargeable alternative time period from all the dischargeable alternative time periods within the target optimization day as the peak electricity consumption discharge period, and determine the discharge amount of the photovoltaic energy storage equipment cluster to the grid system during each peak electricity consumption discharge period. The total discharge amount of the energy storage cluster corresponding to all the peak electricity consumption discharge periods within the target optimization day is equal to the remaining total photovoltaic energy storage capacity corresponding to the last dischargeable alternative time period within the target optimization day.

[0056] In some embodiments, step Sb includes:

[0057] Step Sb1: Collect comprehensive information on the second historical time period of the target area, which includes multiple non-sunlight periods after the last sunshine period within multiple historical days. The comprehensive information on the second historical time period includes: a time period identifier indicating the position of the corresponding non-sunlight period in the day; the remaining total power of photovoltaic energy storage corresponding to the sunshine period that is closest to the corresponding non-sunlight period; the total power consumption of all loads connected to the photovoltaic energy storage equipment cluster in the target area during the corresponding non-sunlight period; the temperature of the target area during the corresponding non-sunlight period; and the remaining total power of photovoltaic energy storage corresponding to the photovoltaic energy storage equipment cluster in the target area during the corresponding sunshine period.

[0058] Step Sb2: Use the second historical time period comprehensive information as training samples. The time period identifier, the total remaining photovoltaic energy storage capacity corresponding to the nearest sunshine period before the corresponding non-sunshine period, the total power consumption of the region, and the temperature in the second historical time period comprehensive information are used as model inputs. The total remaining photovoltaic energy storage capacity of the photovoltaic energy storage device cluster in the target area corresponding to the sunshine period in the second historical time period comprehensive information is used as model output. Train the second preset prediction model to obtain the prediction model of the total remaining photovoltaic energy storage capacity during non-sunshine periods.

[0059] Step Sb2 includes:

[0060] Step Sb21: Divide all the historical days into 8 groups according to the preset classification rules. The preset classification rules are as follows: statutory holidays are divided into 1 group, and non-statutory holidays are divided into the remaining 7 groups according to 7 different categories of dates from Monday to Sunday.

[0061] Step Sb22: For each group, construct the second historical time period comprehensive information set corresponding to that group by combining the comprehensive information of all non-sunlight periods after the last sunshine period within each historical day included in the group.

[0062] Step Sa23: For each group, use the comprehensive information set of the second historical period corresponding to the group as the training sample set to train the second preset prediction model to obtain the prediction model of the remaining total electricity of photovoltaic energy storage during the non-sunshine period corresponding to the group.

[0063] Step S6 includes:

[0064] Step S601: Determine the group to which the target optimization date belongs according to the preset classification rules based on the date of the target optimization date;

[0065] Step S602: Input the second input information of the last dischargeable candidate period within the target optimization day into the prediction model of the remaining total photovoltaic energy storage power during the non-sunshine period corresponding to the group to which the target optimization day belongs, to obtain the remaining total photovoltaic energy storage power corresponding to the last dischargeable candidate period within the target optimization day.

[0066] In some embodiments, the number of dischargeable candidate time periods selected in step S4 is M, where M is a positive integer; step S8 includes:

[0067] Step S801: Construct a discharge optimization model to represent the discharge of the power grid system by the photovoltaic energy storage device cluster during M dischargeable alternative time periods;

[0068] The decision variable in the discharge optimization model is the discharge amount of the energy storage cluster corresponding to the discharge of the photovoltaic energy storage equipment cluster to the grid in the M dischargeable candidate periods.

[0069] The objective of the discharge optimization model is to maximize the total discharge revenue from the photovoltaic energy storage device cluster to the grid during the M available discharge periods, expressed by the following formula:

[0070]

[0071] This represents the total discharge revenue. This represents the discharge amount of the photovoltaic energy storage cluster when it discharges to the grid during the m-th available discharge period. This represents the unit discharge revenue generated when the photovoltaic energy storage device cluster discharges to the grid during the m-th available discharge period.

[0072] The constraints of the discharge optimization model include:

[0073] Condition 1: During the M available discharge periods, the power grid will not charge the photovoltaic energy storage cluster.

[0074]

[0075] Condition 2: In the M available discharge periods, the discharge power of each available discharge period cannot exceed the maximum discharge power of the photovoltaic energy storage equipment cluster.

[0076]

[0077] in, This represents the duration of the m-th available discharge period. This indicates the maximum discharge power of the photovoltaic energy storage device cluster;

[0078] Condition 3: After the photovoltaic energy storage device discharges to the grid system during M dischargeable alternative time periods, the remaining total amount of photovoltaic energy storage corresponding to the last dischargeable alternative time period decreases to 0.

[0079]

[0080] This represents the total remaining photovoltaic energy storage capacity of the photovoltaic energy storage device cluster during the last available discharge period on the target optimization day, without discharging to the power grid system. This represents the total discharge amount of the photovoltaic energy storage cluster to the power grid system during the M available discharge periods;

[0081] Step S802: Solve the discharge optimization model using the second preset target optimization algorithm to obtain the corresponding current optimal solution. The current optimal solution includes the current optimal value of the discharge amount of the energy storage cluster corresponding to the discharge of the power grid system by the photovoltaic energy storage equipment cluster in the M dischargeable alternative time periods. The dischargeable alternative time periods with the current optimal value of the discharge amount of the corresponding energy storage cluster not being 0 are taken as the peak electricity consumption discharge period.

[0082] In a second aspect, embodiments of this disclosure also provide a power distribution optimization system based on photovoltaic energy storage devices. The power distribution optimization system is configured to implement the power distribution optimization method provided in the first aspect, and the power distribution optimization system includes:

[0083] The acquisition module is configured to acquire the total remaining photovoltaic energy storage capacity of the photovoltaic energy storage device cluster in the target area during each solar sunshine period on the target day to be optimized;

[0084] The judgment module is configured to determine whether the last sunshine period of the target day to be optimized is a full-capacity photovoltaic energy storage period, wherein the full-capacity photovoltaic energy storage period refers to the period during which the remaining total power of the corresponding photovoltaic energy storage is equal to the preset maximum power of the photovoltaic energy storage.

[0085] The optimization module is configured to, when the judgment module determines that the last sunshine period in the target optimization day is not a full-capacity period for photovoltaic energy storage, determine at least one sunshine period from all sunshine periods in the target optimization day as a daytime charging period, and the grid charging amount for charging the photovoltaic energy storage device cluster using the grid system during each daytime charging period, based on the remaining total amount of photovoltaic energy storage corresponding to each sunshine period in the target optimization day. The total amount of grid charging corresponding to all the daytime charging periods in the target optimization day is equal to the difference between the preset maximum amount of photovoltaic energy storage and the remaining total amount of photovoltaic energy storage corresponding to the last sunshine period.

[0086] The technical solution disclosed herein first detects the existence of space that can be charged by the power grid system during the sunshine period (i.e., determining whether the last sunshine period of the target optimization day is the full-capacity period of photovoltaic energy storage), and when it is detected that there is space that can be charged by the power grid system during the sunshine period (i.e., the last sunshine period of the target optimization day is not the full-capacity period of photovoltaic energy storage), the power grid system can be used to charge the photovoltaic energy storage equipment cluster during at least part of the sunshine period, so as to appropriately increase the load of the power grid system during the daytime, thereby achieving the purpose of "valley filling" of the power grid system load.

[0087] Furthermore, this disclosure considers that the photovoltaic energy storage equipment cluster itself also generates and stores a certain amount of electricity. Therefore, while achieving valley filling as much as possible, it is also necessary to avoid the problem of charging redundancy caused by the grid system overcharging the photovoltaic energy storage system. Thus, this disclosure limits the total amount of grid charging of the photovoltaic energy storage equipment cluster by the grid system in each sunshine period to be equal to the difference between the preset maximum photovoltaic energy storage capacity and the remaining total photovoltaic energy storage capacity corresponding to the last sunshine period. This ensures that after adopting the measure of charging the photovoltaic energy storage equipment cluster by the grid system, the remaining total photovoltaic energy storage capacity in the last sunshine period of the target day is increased to the preset maximum photovoltaic energy storage capacity. Attached Figure Description

[0088] Figure 1 This is a schematic diagram illustrating the division of a day into multiple periods of sunshine and periods of non-sunshine in this disclosure;

[0089] Figure 2 This is a schematic diagram illustrating a specific application scenario of the technical solution disclosed herein;

[0090] Figure 3 A flowchart illustrating a power distribution optimization method based on photovoltaic energy storage devices provided in this disclosure embodiment;

[0091] Figure 4 A flowchart illustrating another power distribution optimization method based on photovoltaic energy storage devices provided in this disclosure embodiment;

[0092] Figure 5 A flowchart illustrating another power distribution optimization method based on photovoltaic energy storage devices provided in this disclosure embodiment;

[0093] Figure 6 A structural block diagram of a power distribution optimization system based on photovoltaic energy storage equipment provided in this disclosure embodiment;

[0094] Figure 7 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure. Detailed Implementation

[0095] To enable those skilled in the art to better understand the technical solutions of this disclosure, the disclosure will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0096] Unless otherwise defined, the technical or scientific terms used in this disclosure shall have the ordinary meaning understood by one of ordinary skill in the art to which this disclosure pertains. The terms “first,” “second,” and similar terms used in this disclosure do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, the terms “an,” “a,” or “the,” and similar terms do not indicate a quantity limitation, but rather indicate the presence of at least one. The terms “comprising,” “including,” or “containing,” and similar terms mean that the element or information preceding the word encompasses the element or information listed following the word and its equivalents, without excluding other elements or information. The terms “connected,” “linked,” or similar terms are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Many specific details of this disclosure, such as the specific implementation processes of certain steps and certain specific algorithms, are described below to provide a clearer understanding of this disclosure. However, as those skilled in the art will understand, this disclosure may be implemented without adhering to these specific details.

[0097] In the following description, "total remaining photovoltaic energy storage capacity" refers to the sum of the remaining photovoltaic energy storage capacity of all photovoltaic energy storage devices in the photovoltaic energy storage device cluster at the end of the corresponding statistical period. "Historical day" refers to a day that has occurred before the target optimization day, and "target optimization day" refers to a day that has not yet occurred (has not yet arrived).

[0098] In this disclosure, a complete day is pre-divided into multiple statistical periods, and the corresponding statistical periods are determined as sunshine periods or non-sunshine periods according to the sunrise and sunset times of the day. It should be noted that this disclosure assumes that the photovoltaic energy storage device can produce electricity after sunrise, and cannot produce electricity after sunset. Sunshine periods refer to the time when the photovoltaic energy storage device can produce electricity, and non-sunshine periods refer to the time when the photovoltaic energy storage device cannot produce electricity.

[0099] Figure 1 This is a schematic diagram illustrating the division of a day into multiple periods of sunshine and periods of non-sunshine, as described in this disclosure. Figure 1As shown, taking one hour as a statistical period as an example, a day can be divided into 24 statistical periods: 0:00-1:00, 1:00-2:00, ... 23:00-24:00. If the sunrise time of the target area on a certain day is 5:50 and the sunset time is 18:28, then there are 14 sunshine periods on that day: 5:00-6:00 (the photovoltaic energy storage equipment will produce electricity during the period from 5:50 to 6:00, i.e., the first sunshine period), 6:00-7:00, ... 17:00-18:00, 18:00-19:00 (i.e., the last sunshine period), and 10 non-sunshine periods: 0:00-1:00, 1:00-2:00, ... 4:00-5:00, 19:00-20:00, 20:00-21:00, ... 23:00-24:00.

[0100] In practical applications, statistical periods can be divided according to actual needs, and the duration of each statistical period can be the same or different. This disclosure does not impose any restrictions on this.

[0101] Furthermore, in this disclosure, each statistical time period is assigned a different time period identifier according to its location. For example, the time period from 0:00 to 1:00 can be represented by the time period identifier "A1", the time period from 1:00 to 2:00 can be represented by the time period identifier "A2", and so on, the time period from 23:00 to 24:00 can be represented by the time period identifier "A24". Based on the time period identifier, the specific time of day that the corresponding time period refers to can be determined.

[0102] Figure 2 This is a schematic diagram illustrating a specific application scenario of the technical solution disclosed herein. For example... Figure 2 As shown, a photovoltaic energy storage device cluster is configured in the target area. The photovoltaic energy storage device cluster includes multiple photovoltaic energy storage devices arranged in a distributed manner. These photovoltaic energy storage devices are connected to the power supply control equipment. There are multiple loads in the target area. These loads are all connected to at least one photovoltaic energy storage device in the photovoltaic energy storage device cluster. At the same time, these loads are also connected to the power grid system (which can supply power to other residential loads) through the power supply control equipment.

[0103] The power supply controller shall have at least the following functions: 1) control the photovoltaic energy storage equipment cluster to directly supply power to the load in the target area; 2) control the power grid system to charge the photovoltaic energy storage equipment cluster; 3) control the photovoltaic energy storage equipment cluster to discharge to the power grid system; 4) control the power grid system to supply power to the load in the target area.

[0104] In this application scenario, when the photovoltaic (PV) energy storage cluster has stored electrical energy, it will be prioritized to supply power to the loads within the target area; when the PV energy storage cluster has zero stored electrical energy, the power grid system will be used to supply power to the loads within the target area. Specifically, the power supply to the loads within the target area can fall into the following categories:

[0105] Scenario 1: During the sunshine period, when the total power consumption of all loads in the target area (total power consumption in the area per unit time) is less than the total power production of all photovoltaic panels in the photovoltaic energy storage equipment cluster, in response to the control of the power supply control equipment, the photovoltaic panels in the photovoltaic energy storage equipment cluster will not only supply power to the load, but also store the remaining power in the energy storage equipment, and the total remaining power of the photovoltaic energy storage corresponding to the photovoltaic energy storage equipment cluster will increase.

[0106] Scenario 2: During the sunshine period, when the total power consumption of all loads in the target area is greater than the total power output of all photovoltaic panels in the photovoltaic energy storage equipment cluster, and the remaining total power of the photovoltaic energy storage corresponding to the photovoltaic energy storage equipment cluster is not zero, in response to the control of the power supply control equipment, the photovoltaic panels and energy storage equipment in the photovoltaic energy storage equipment cluster will simultaneously supply power to the load, and the remaining total power of the photovoltaic energy storage corresponding to the photovoltaic energy storage equipment cluster will decrease.

[0107] Scenario 3: During the sunshine period, when the total power consumption of all loads in the target area is greater than the total power output of all photovoltaic panels in the photovoltaic energy storage equipment cluster, and the remaining total power of the photovoltaic energy storage corresponding to the photovoltaic energy storage equipment cluster is 0, in response to the control of the power supply control equipment, the power grid system and the photovoltaic panels in the photovoltaic energy storage equipment cluster will simultaneously supply power to the load, and the remaining total power of the photovoltaic energy storage corresponding to the photovoltaic energy storage equipment cluster will remain at 0.

[0108] Scenario 4: During non-sunlight periods, when the total remaining power of the photovoltaic energy storage equipment cluster is not zero, in response to the control of the power supply control equipment, only the energy storage equipment in the photovoltaic energy storage equipment cluster supplies power to the load in the target area.

[0109] Scenario 5: During non-sunlight periods, when the total remaining power of the photovoltaic energy storage corresponding to the photovoltaic energy storage equipment cluster is 0, in response to the control of the power supply control equipment, only the power grid system supplies power to the loads in the target area.

[0110] Figure 3 A flowchart illustrating a power distribution optimization method based on photovoltaic energy storage devices, provided as an embodiment of this disclosure. Figure 3 As shown, the power distribution optimization method includes

[0111] Step S1: Obtain the total remaining photovoltaic energy storage capacity of the photovoltaic energy storage device cluster in the target area during each sunshine period on the target day to be optimized.

[0112] Step S2: Determine whether the last sunshine period of the target day to be optimized is the period when photovoltaic energy storage is at full capacity.

[0113] Among them, the full-capacity photovoltaic energy storage period refers to the period when the remaining total power of the corresponding photovoltaic energy storage is equal to the preset maximum power of photovoltaic energy storage (i.e., the power corresponding to the current power level of the photovoltaic energy storage equipment cluster is 100%).

[0114] If the result of step S2 is negative, it means that there is space that can be charged by the power grid system during the sunshine period, and then step S3 is executed; if the result of step S2 is positive, it means that there is no space that can be charged by the power grid system during the sunshine period.

[0115] Step S3: Based on the total remaining photovoltaic energy storage capacity corresponding to each sunshine period during the target optimization day, determine at least one sunshine period from all sunshine periods during the target optimization day as the daytime charging period, and the grid charging amount used to charge the photovoltaic energy storage device cluster using the grid system during each daytime charging period.

[0116] Among them, the total grid charging amount corresponding to all daytime charging periods within the target day is equal to the difference between the preset maximum photovoltaic energy storage capacity and the remaining total photovoltaic energy storage capacity corresponding to the last sunshine period.

[0117] According to numerous studies on residential daily load curves, residents' electricity consumption during the day is significantly lower than that during the period after sunset (a peak period for residential electricity consumption occurs after the day ends). In other words, the power grid system experiences a low load period before sunset and a peak load period after sunset, with the load of the power grid system gradually increasing over the period before and after sunset.

[0118] Based on the above phenomena, this disclosure first detects the existence of space that can be charged by the power grid system during the sunshine period (i.e., determining whether the last sunshine period of the target optimization day is the full-capacity period of photovoltaic energy storage). When it is detected that there is space that can be charged by the power grid system during the sunshine period (i.e., the last sunshine period of the target optimization day is not the full-capacity period of photovoltaic energy storage), the power grid system can be used to charge the photovoltaic energy storage equipment cluster during at least part of the sunshine period, so as to appropriately increase the load of the power grid system during the daytime, thereby achieving the purpose of "valley filling" of the power grid system load.

[0119] Furthermore, this disclosure considers that the photovoltaic energy storage equipment cluster itself also generates and stores a certain amount of electricity. Therefore, while achieving valley filling as much as possible, it is also necessary to avoid the problem of charging redundancy caused by the grid system overcharging the photovoltaic energy storage system. Thus, this disclosure limits the total amount of grid charging of the photovoltaic energy storage equipment cluster by the grid system in each sunshine period to be equal to the difference between the preset maximum photovoltaic energy storage capacity and the remaining total photovoltaic energy storage capacity corresponding to the last sunshine period. This ensures that after adopting the measure of charging the photovoltaic energy storage equipment cluster by the grid system, the remaining total photovoltaic energy storage capacity in the last sunshine period of the target day is increased to the preset maximum photovoltaic energy storage capacity.

[0120] It should be noted that in this disclosure, the remaining total amount of photovoltaic energy storage during the last sunshine period of the target day to be optimized is increased to the preset maximum amount of photovoltaic energy storage. This can prepare for the use of photovoltaic energy storage equipment to discharge grid equipment during peak electricity consumption periods, thereby reducing the load on the grid system during peak electricity consumption periods and achieving "peak shaving".

[0121] In some embodiments, step S3 includes the following steps S301 to S305.

[0122] Step S301: Determine whether there is at least one solar term that is a full-capacity solar energy storage period among all solar terms except the last solar term of the target day to be optimized.

[0123] If the judgment result of step S301 is yes, then step S302 is executed; if the judgment result of step S301 is no, then step S303 is executed.

[0124] Step S302: Determine all sunshine periods within the target day that are after the last full-capacity photovoltaic energy storage period as alternative charging periods.

[0125] Step S303: Determine all sunshine periods within the target day to be optimized as alternative charging periods.

[0126] After steps S302 and S303 are completed, step S304 is executed, where the number of available charging time slots is denoted as N, where N is a positive integer.

[0127] Step S304: Construct a charging optimization model to represent the charging of the photovoltaic energy storage device cluster using the power grid system during N rechargeable alternative time periods.

[0128] In the charging optimization model, the decision variable is the amount of grid charging required to charge the photovoltaic energy storage device cluster using the grid system during each of the N available charging time periods.

[0129]

[0130] The goal of the charging optimization model is to minimize the total charging cost of charging the photovoltaic energy storage device cluster using the power grid system during N available charging time slots, which can be expressed by the following formula:

[0131]

[0132] This indicates the total cost of charging. This represents the amount of grid charging required to charge the photovoltaic energy storage device cluster using the grid system during the nth available charging time period. This represents the unit charging cost for charging the photovoltaic energy storage device cluster using the power grid system during the nth available charging period.

[0133] In some embodiments, the constraints of the charging optimization model include:

[0134] Condition 1: During the N available charging periods, the photovoltaic energy storage cluster will not discharge to the grid system.

[0135]

[0136] Condition 2: In the N available charging time slots, the charging power of each available charging time slot cannot exceed the maximum charging power of the photovoltaic energy storage equipment cluster.

[0137]

[0138] in, This represents the duration of the nth available charging time slot. This indicates the maximum charging power of the photovoltaic energy storage device cluster;

[0139] Condition 3: After charging the photovoltaic energy storage equipment cluster using the power grid system during N available charging time periods, the remaining total photovoltaic energy storage capacity during the last sunshine period of the target day to be optimized is increased to the preset maximum photovoltaic energy storage capacity.

[0140]

[0141] This represents the total remaining photovoltaic energy storage capacity of the photovoltaic energy storage cluster during the last sunshine period of the target day for optimization, without charging the grid system. This indicates the preset maximum capacity of photovoltaic energy storage. It represents the total amount of grid charging used to charge the photovoltaic energy storage device cluster during N available charging time slots;

[0142] Condition 4: During any available charging period, the total remaining capacity of the photovoltaic energy storage cluster after completing charging of the grid system will not exceed the preset maximum capacity of the photovoltaic energy storage.

[0143]

[0144] in, This represents the total remaining photovoltaic energy storage capacity of the photovoltaic energy storage cluster in the nth available charging period when the grid system is not being charged. This represents the total amount of grid charging used to charge the photovoltaic energy storage device during the first n available charging periods.

[0145] Step S305: Solve the charging optimization model using the first preset target optimization algorithm to obtain the corresponding current optimal solution. The current optimal solution includes the current optimal value of the grid charging amount corresponding to the charging of the photovoltaic energy storage equipment cluster by the grid system in N rechargeable alternative time periods. The rechargeable alternative time periods with the current optimal value of the grid charging amount not being 0 are taken as daytime charging time periods.

[0146] It should be noted that the first preset target optimization algorithm in this disclosure can be any existing algorithm used for target optimization, such as genetic algorithm, particle swarm optimization algorithm, ant colony optimization algorithm, bat algorithm, etc. The specific process of these target optimization algorithms is conventional technology in this field and will not be described in detail here.

[0147] It should be noted that the above-mentioned step S3, including steps S301 to S305, is only one optional implementation scheme in this disclosure. In practical applications, other methods can also be used to select the daytime charging period. For example, the last sunshine period can be directly used as the daytime charging period, and the grid charging amount during the last sunshine period is equal to the difference between the preset maximum photovoltaic energy storage capacity and the remaining total photovoltaic energy storage capacity corresponding to the last sunshine period. Alternatively, the charging period with the largest remaining total photovoltaic energy storage capacity can be selected from all available charging periods as the first daytime charging period, and the last sunshine period can be selected as the second daytime charging period. The grid charging amount corresponding to the first daytime charging period is Q1, and the grid charging amount for the second daytime charging period is Q2. , , This refers to the maximum value of the remaining total photovoltaic energy storage capacity corresponding to all available charging time periods. In this disclosure, the daytime charging time period determined in step S3 can be one or more, and the grid charging capacity corresponding to each daytime charging time period can be designed accordingly based on its own remaining total photovoltaic energy storage capacity. Examples will not be given here.

[0148] Figure 4 A flowchart illustrating another power distribution optimization method based on photovoltaic energy storage devices provided in this disclosure embodiment. Figure 4 As shown, the power distribution optimization method not only includes steps S1 to S3 in the previous embodiments, but also includes step Sa before step S1.

[0149] Step Sa: Generate a solar sunshine period ... of solar sunshine period of solar sunshine period of solar sunshine period of solar sunshine period of solar sunshine period of solar sunshine period of solar sunshine period of solar sunshine period of solar sunshine period of solar sunshine period of solar sunshine period of solar sunshine period of solar sunshine period of solar

[0150] The input information for the solar energy storage remaining total power prediction model during sunshine hours includes: the time period identifier of the target sunshine period within the target optimization day, the total regional power consumption of all loads connected to the solar energy storage equipment cluster within the target area during the target sunshine period, the temperature of the target area during the target sunshine period, and the cumulative sunshine duration of the target area within the target optimization day during the target sunshine period; the output of the solar energy storage remaining total power prediction model during sunshine hours is the solar energy storage remaining total power corresponding to the target sunshine period.

[0151] It should be noted that the input data for the prediction model of the remaining total power of photovoltaic energy storage during sunshine hours and the subsequent prediction model of the remaining total power of photovoltaic energy storage during non-sunshine hours in this disclosure are documented. This is because temperature factors will affect the charging and discharging efficiency of photovoltaic energy storage equipment, and thus will affect the remaining total power of photovoltaic energy storage equipment clusters at different times.

[0152] At this point, step S1 includes:

[0153] Step S101: Obtain the first input information corresponding to each sunshine period during the target day to be optimized.

[0154] The first input information includes: the time period identifier of the corresponding sunshine period within the target day to be optimized, the total electricity consumption of all loads connected to the photovoltaic energy storage equipment cluster in the target area during the corresponding sunshine period, the temperature of the target area during the corresponding sunshine period, and the cumulative sunshine duration of the target area within the target day to be optimized at the end of the corresponding sunshine period.

[0155] The time period identifier, which indicates the location of a specific sunshine period within the target day for optimization, can be determined based on pre-configured flag setting rules corresponding to that sunshine period. For example, using... Figure 1 As shown in the diagram, the time period corresponding to the 7-8 AM sunshine period is marked as "A8".

[0156] The total electricity consumption of the target area during a specific statistical period (either during sunny or non-sunny periods) within the target optimization date can be predicted based on the total electricity consumption of the current area during the same period on multiple historical days. For example, the total electricity consumption during that statistical period within the target optimization date can be predicted based on the trend of the total electricity consumption during the same period on multiple consecutive historical days prior to the target optimization date. Alternatively, the average of the total electricity consumption of the current area during the same period on multiple historical days can be taken as the total electricity consumption of the target area during that statistical period within the target optimization date. Of course, the total electricity consumption of the target area during that statistical period within the target optimization date can also be manually set based on actual experience.

[0157] The temperature of the target area during a certain time period (either during sunshine or non-sunshine) on the target day to be optimized can be extracted from the pre-collected weather forecast data of the target area on the target day to be optimized.

[0158] The cumulative sunshine duration of the target area at the end of a certain sunshine period on the target optimization day can be calculated based on the difference between the end time of that sunshine period and the sunrise time of the target optimization day (which can be obtained from the weather forecast data of the target optimization day).

[0159] Step S102: Input the first input information corresponding to each sunshine period of the target day to be optimized into the trained solar energy storage remaining total power prediction model for each sunshine period to obtain the solar energy storage remaining total power corresponding to each sunshine period of the target day to be optimized.

[0160] In this embodiment of the disclosure, a prediction model for the remaining total power of photovoltaic energy storage during sunshine hours can be trained based on historical data, and then the trained prediction model can be used to predict the remaining total power of photovoltaic energy storage corresponding to each sunshine hour of the target day to be optimized.

[0161] In some embodiments, step Sa includes: step Sa1 and step Sa2.

[0162] Step Sa1: Collect comprehensive information on the first historical period of multiple sunshine hours in the target area over multiple historical days.

[0163] The first historical period comprehensive information includes: the period identifier indicating the position of the corresponding sunshine period in the day, the total regional electricity consumption of all loads connected to the photovoltaic energy storage equipment cluster in the target area during the corresponding sunshine period, the temperature of the target area during the corresponding sunshine period, the cumulative sunshine duration of the target area on the day at the end of the corresponding sunshine period, and the remaining total photovoltaic energy storage capacity of the photovoltaic energy storage equipment cluster in the target area during the corresponding sunshine period.

[0164] Step Sa2: Use the comprehensive information of the first historical period as training samples. The time period identifier, total regional electricity consumption, cumulative sunshine duration, temperature and cumulative sunshine duration in the comprehensive information of the first historical period are used as model inputs. The remaining total electricity of photovoltaic energy storage in the comprehensive information of the first historical period is used as model output. Train the first preset prediction model to obtain the prediction model of the remaining total electricity of photovoltaic energy storage during the sunshine period.

[0165] In some embodiments, step Sa2 includes steps Sa21 to Sa23.

[0166] Step Sa21: Divide all historical days into 8 groups according to the preset classification rules. The preset classification rules are: statutory holidays are divided into 1 group, and non-statutory holidays are divided into the remaining 7 groups according to 7 different dates from Monday to Sunday.

[0167] Step Sa22: For each group, construct the first historical time period comprehensive information set corresponding to the group by combining the first historical time period comprehensive information of all sunshine periods within each historical day included in the group.

[0168] Step Sa23: For each group, use the comprehensive information set of the first historical period corresponding to the group as the training sample set to train the first preset prediction model to obtain the prediction model of the remaining total electricity of photovoltaic energy storage during the sunshine period corresponding to the group.

[0169] At this time, step S102 includes: step S1021 and step S1022.

[0170] Step S1021: Determine the group to which the target optimization date belongs according to the preset classification rules based on the date of the target optimization date.

[0171] Step S1022: Input the first input information corresponding to each sunshine period within the target optimization day into the photovoltaic energy storage remaining total power prediction model for the sunshine period corresponding to the target optimization day group, and obtain the photovoltaic energy storage remaining total power corresponding to each sunshine period within the target optimization day.

[0172] In this embodiment, dates are grouped according to statutory holidays and non-statutory holidays from Monday to Sunday, and then trained separately according to the training sample sets corresponding to each group. This effectively distinguishes the differences between different categories of dates. In the prediction stage, the remaining total electricity of photovoltaic energy storage is predicted using the sunshine period corresponding to the group to which the target day to be optimized belongs, which helps to improve the accuracy of the final prediction results.

[0173] Figure 5 A flowchart illustrating another power distribution optimization method based on photovoltaic energy storage devices provided in this disclosure. Figure 5As shown, the power distribution optimization method includes not only steps S1 to S3 in the previous embodiments, but also steps Sb and S4 to S8.

[0174] It should be noted that the execution order of steps Sb and S4-S8 in this embodiment is not limited to the preceding steps S1-S3. For example, steps Sb and S4-S8 can be executed before, after, or simultaneously with steps S1-S3. In this disclosure, it is only necessary to ensure that step Sb is executed before steps S4-S8.

[0175] Sb generates a model for predicting the remaining total power of photovoltaic energy storage during non-sunlight periods, which is capable of predicting the total remaining power of photovoltaic energy storage during target non-sunlight periods following the last sunshine period of the day.

[0176] The input information for the prediction model of the remaining total power of photovoltaic energy storage during non-sunlight periods includes: the time period identifier of the target non-sunlight period within the target day to be optimized, the remaining total power of photovoltaic energy storage corresponding to the nearest sunshine period before the target non-sunlight period, the total regional power consumption of all loads connected to the photovoltaic energy storage equipment cluster in the target area during the target non-sunlight period, and the temperature of the target area during the target non-sunlight period; the output of the prediction model of the remaining total power of photovoltaic energy storage during non-sunlight periods is the remaining total power of photovoltaic energy storage corresponding to the target non-sunlight period.

[0177] In practical applications, step Sb can be executed synchronously with step Sa in the previous embodiment.

[0178] Step S4: Select non-sunlight periods that fall during the peak electricity consumption period of the power grid within the target optimization day from all non-sunlight periods after the last sunshine period within the target optimization day, and use them as candidate periods for discharge.

[0179] Among them, the peak electricity consumption period of the target grid during the day to be optimized can be determined based on the historical load of the grid system (e.g., the residential daily load curve obtained from historical data), or it can be set manually according to the actual situation.

[0180] Step S5: Obtain the second input information for the last available discharge candidate time period within the target day to be optimized.

[0181] The second input information includes: the time period identifier of the last available dischargeable alternative time period, the remaining total photovoltaic energy storage capacity corresponding to the nearest sunshine period before the last available dischargeable alternative time period, the total regional power consumption of all loads connected to the photovoltaic energy storage equipment cluster in the target area during the last available dischargeable alternative time period, and the temperature of the target area during the last available dischargeable alternative time period (for details on how to obtain the time period identifier, total regional power consumption, and temperature of the last available dischargeable alternative time period, please refer to the previous content). The remaining total photovoltaic energy storage capacity corresponding to the nearest sunshine period before the last available dischargeable alternative time period is taken as the preset maximum photovoltaic energy storage capacity (because step S3 can increase the remaining total photovoltaic energy storage capacity of the last sunshine period of the target day to be optimized to the preset maximum photovoltaic energy storage capacity).

[0182] Step S6: Input the second input information of the last dischargeable alternative time period of the target optimization day into the trained photovoltaic energy storage remaining total power prediction model for non-sunlight periods, and obtain the photovoltaic energy storage remaining total power corresponding to the last dischargeable alternative time period of the target optimization day.

[0183] It should be noted that after the transition from sunshine hours to non-sunshine hours, the photovoltaic energy storage devices cease generating electricity and prioritize power supply over the grid system. Therefore, the remaining total power of the photovoltaic energy storage device cluster will continue to decrease until the remaining total power of the photovoltaic energy storage device cluster reaches 0, at which point the grid system will begin supplying power.

[0184] Step S7: Determine whether the remaining total power of the photovoltaic energy storage corresponding to the last available discharge period of the target optimization day is 0.

[0185] If the result of step S7 is negative, it indicates that there is still residual electricity in the photovoltaic energy storage cluster at the end of the peak electricity consumption period. Therefore, the photovoltaic energy storage cluster can be used to discharge to the grid system during the peak electricity consumption period to reduce the load on the grid system, thereby achieving load "peak shaving". Then, step S8 is executed. If the result of step S7 is positive, it indicates that there is no residual electricity in the photovoltaic energy storage cluster before the end of the peak electricity consumption period, and load "peak shaving" cannot be performed during the peak electricity consumption period.

[0186] Step S8: Determine at least one dischargeable alternative time period from all dischargeable alternative time periods within the target optimization day as the peak electricity consumption discharge period, and determine the discharge amount of the photovoltaic energy storage equipment cluster to the grid system during each peak electricity consumption discharge period. The total discharge amount of the energy storage cluster corresponding to all peak electricity consumption discharge periods within the target optimization day is equal to the remaining total photovoltaic energy storage capacity corresponding to the last dischargeable alternative time period within the target optimization day.

[0187] Through step S8, after the photovoltaic energy storage device discharges to the grid system during the peak electricity consumption period, the remaining total amount of photovoltaic energy storage corresponding to the last available discharge period drops to 0, thus achieving sufficient load "peak shaving".

[0188] In some embodiments, step Sb includes step Sb1 and step Sb2.

[0189] Step Sb1: Collect comprehensive information on the second historical time period of the target area, which includes multiple non-sunlight periods after the last sunshine period within multiple historical days. The comprehensive information on the second historical time period includes: a time period identifier indicating the position of the corresponding non-sunlight period in the day; the remaining total power of photovoltaic energy storage corresponding to the sunshine period that is closest to the corresponding non-sunlight period; the total power consumption of all loads connected to the photovoltaic energy storage equipment cluster in the target area during the corresponding non-sunlight period; the temperature of the target area during the corresponding non-sunlight period; and the remaining total power of photovoltaic energy storage corresponding to the photovoltaic energy storage equipment cluster in the target area during the corresponding sunshine period.

[0190] Step Sb2: Use the comprehensive information of the second historical period as training samples. The time period identifier, the total remaining power of photovoltaic energy storage corresponding to the nearest sunshine period before the corresponding non-sunshine period, the total power consumption of the region, and the temperature in the comprehensive information of the second historical period are used as model inputs. The total remaining power of photovoltaic energy storage equipment cluster in the target area corresponding to the sunshine period in the second historical period is used as model output. Train the second preset prediction model to obtain the prediction model of the total remaining power of photovoltaic energy storage during non-sunshine periods.

[0191] Step Sb2 includes steps Sb21 to Sa23.

[0192] Step Sb21: Divide all historical days into 8 groups according to the preset classification rules. The preset classification rules are: statutory holidays are divided into 1 group, and non-statutory holidays are divided into the remaining 7 groups according to 7 different dates from Monday to Sunday.

[0193] Step Sb22: For each group, construct the second historical time period comprehensive information set corresponding to that group by combining the comprehensive information of all non-sunlight periods after the last sunshine period within each historical day included in that group.

[0194] Step Sa23: For each group, use the comprehensive information set of the second historical period corresponding to the group as the training sample set to train the second preset prediction model to obtain the prediction model of the remaining total electricity of photovoltaic energy storage during the non-sunshine period corresponding to the group.

[0195] Step S6 includes: step S601 and step S602.

[0196] Step S601: Determine the group to which the target optimization date belongs according to the preset classification rules based on the date of the target optimization date.

[0197] Step S602: Input the second input information of the last dischargeable alternative time period within the target optimization day into the prediction model of the remaining total photovoltaic energy storage power during the non-sunshine period corresponding to the group to which the target optimization day belongs, and obtain the remaining total photovoltaic energy storage power corresponding to the last dischargeable alternative time period within the target optimization day.

[0198] In this embodiment, the days are grouped according to statutory holidays and non-statutory holidays (Monday to Sunday), and then trained separately according to the training sample sets corresponding to each group. This effectively distinguishes the differences between different categories of dates. In the prediction stage, the remaining total electricity of photovoltaic energy storage is predicted using the non-sunshine period corresponding to the group to which the target day to be optimized belongs, which helps to improve the accuracy of the final prediction results.

[0199] In some embodiments, the number of dischargeable candidate time periods selected in step S4 is M, where M is a positive integer; step S8 includes:

[0200] Step S801: Construct a discharge optimization model to represent the discharge of the power grid system by the photovoltaic energy storage device cluster during M dischargeable alternative time periods;

[0201] The decision variables in the discharge optimization model are the discharge amounts of the photovoltaic energy storage equipment clusters that discharge to the grid during the M dischargeable alternative time periods.

[0202] The objective of the discharge optimization model is to maximize the total discharge revenue from the photovoltaic energy storage device cluster to the grid during M available discharge periods, expressed by the following formula:

[0203]

[0204] This represents the total discharge revenue. This represents the discharge amount of the photovoltaic energy storage cluster when it discharges to the grid during the m-th available discharge period. This represents the unit discharge revenue generated when the photovoltaic energy storage device cluster discharges to the grid during the m-th available discharge period.

[0205] The constraints of the discharge optimization model include:

[0206] Condition 1: During the M available discharge periods, the power grid will not charge the photovoltaic energy storage cluster.

[0207]

[0208] Condition 2: In the M available discharge periods, the discharge power of each available discharge period cannot exceed the maximum discharge power of the photovoltaic energy storage equipment cluster.

[0209]

[0210] in, This represents the duration of the m-th available discharge period. This indicates the maximum discharge power of the photovoltaic energy storage device cluster;

[0211] Condition 3: After the photovoltaic energy storage device discharges to the grid system during M rechargeable alternative periods, the remaining total amount of photovoltaic energy storage corresponding to the last rechargeable alternative period drops to 0.

[0212]

[0213] This represents the total remaining photovoltaic energy storage capacity of the photovoltaic energy storage cluster during the last available discharge period on the target optimization day, without discharging to the grid system. This represents the total discharge amount of the photovoltaic energy storage cluster to the power grid system during the M available discharge periods;

[0214] Step S802: Use the second preset target optimization algorithm to find the current optimal solution of the discharge optimization model. The current optimal solution includes the current optimal value of the discharge amount of the energy storage cluster corresponding to the discharge of the photovoltaic energy storage equipment cluster to the power grid system in M ​​dischargeable alternative time periods. The dischargeable alternative time periods with the current optimal value of the discharge amount of the corresponding energy storage cluster not being 0 are taken as peak electricity consumption discharge periods.

[0215] It should be noted that the second preset objective optimization algorithm in this disclosure can be any existing algorithm used for objective optimization, such as genetic algorithm, particle swarm optimization algorithm, ant colony optimization algorithm, bat algorithm, etc. The specific processes of these objective optimization algorithms are conventional techniques in this field and will not be elaborated here. In this disclosure, the first preset objective optimization algorithm and the second preset objective optimization algorithm can be the same or different.

[0216] Figure 6 This is a structural block diagram of a power distribution optimization system based on photovoltaic energy storage equipment, provided as an embodiment of this disclosure. Figure 6As shown, based on the same inventive concept, this disclosure also provides a power distribution optimization system based on photovoltaic energy storage equipment. This power distribution optimization system can implement the power distribution optimization method provided in the previous embodiments. The power distribution optimization system includes: an acquisition module, a judgment module, and an optimization module.

[0217] The acquisition module is configured to acquire the total remaining photovoltaic energy storage capacity of the photovoltaic energy storage device cluster within the target area during each sunshine period on the target day to be optimized.

[0218] The judgment module is configured to determine whether the last sunshine period of the target day to be optimized is the full-capacity photovoltaic energy storage period. The full-capacity photovoltaic energy storage period refers to the period when the remaining total power of the corresponding photovoltaic energy storage is equal to the preset maximum power of the photovoltaic energy storage.

[0219] The optimization module is configured such that when the judgment module determines that the last sunshine period of the target optimization day is not the full-capacity period of photovoltaic energy storage, it determines at least one sunshine period from all sunshine periods of the target optimization day as the daytime charging period, based on the total remaining power of photovoltaic energy storage corresponding to each sunshine period of the target optimization day, and the grid charging amount used to charge the photovoltaic energy storage device cluster by the grid system during each daytime charging period, and the total grid charging amount corresponding to all daytime charging periods of the target optimization day is equal to the difference between the preset maximum photovoltaic energy storage capacity and the total remaining power of photovoltaic energy storage corresponding to the last sunshine period.

[0220] For a detailed description of each of the above modules, please refer to the corresponding content in the previous embodiments, which will not be repeated here.

[0221] Based on the same inventive concept, this disclosure also provides an electronic device. Figure 7 This is a schematic diagram of the structure of an electronic device according to an embodiment of this disclosure. Figure 7 As shown, this disclosure provides an electronic device including: one or more processors 101, a memory 102, and one or more I / O interfaces 103. The memory 102 stores one or more programs, which, when executed by the one or more processors, enable the one or more processors to implement any of the power distribution optimization methods based on photovoltaic energy storage devices described in the above embodiments; the one or more I / O interfaces 103 are connected between the processor and the memory, configured to enable information interaction between the processor and the memory.

[0222] The processor 101 is a device with data processing capabilities, including but not limited to a central processing unit (CPU); the memory 102 is a device with data storage capabilities, including but not limited to random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), and flash memory (FLASH); the I / O interface (read / write interface) 103 is connected between the processor 101 and the memory 102, and can realize information interaction between the processor 101 and the memory 102, including but not limited to a data bus (Bus).

[0223] In some embodiments, the processor 101, memory 102, and I / O interface 103 are interconnected via bus 104, and thus connected to other components of the computing device.

[0224] In some embodiments, the one or more processors 101 include a field-programmable gate array.

[0225] According to embodiments of this disclosure, a computer-readable medium is also provided. This computer-readable medium stores a computer program, which, when executed by a processor, implements the steps in the power distribution optimization method based on photovoltaic energy storage devices as described in any of the above embodiments.

[0226] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a machine-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), it performs the functions defined above in the system of this disclosure.

[0227] It should be noted that the computer-readable medium disclosed herein may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0228] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0229] It is understood that the above embodiments are merely exemplary embodiments used to illustrate the principles of this disclosure, and this disclosure is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and substance of this disclosure, and these modifications and improvements are also considered to be within the scope of protection of this disclosure.

Claims

1. A power distribution optimization method based on photovoltaic energy storage equipment, characterized in that, The power distribution optimization method divides a full day into multiple statistical periods and determines the corresponding statistical periods as sunshine periods or non-sunshine periods based on the sunrise and sunset times of the day. Step S1: Obtain the total remaining photovoltaic energy storage capacity of the photovoltaic energy storage device cluster in the target area during each solar sunshine period on the target day to be optimized; Step S2: Determine whether the last sunshine period of the target day to be optimized is a period of full photovoltaic energy storage. The period of full photovoltaic energy storage refers to the period when the remaining total power of the corresponding photovoltaic energy storage is equal to the preset maximum power of photovoltaic energy storage. If the result of step S2 is negative, then step S3 is executed. Step S3: Based on the total remaining photovoltaic energy storage capacity corresponding to each sunshine period within the target optimization day, determine at least one sunshine period from all sunshine periods within the target optimization day as a daytime charging period, and the grid charging amount used to charge the photovoltaic energy storage device cluster using the grid system during each daytime charging period, and the sum of the grid charging amounts corresponding to all the daytime charging periods within the target optimization day is equal to the difference between the preset maximum photovoltaic energy storage capacity and the total remaining photovoltaic energy storage capacity corresponding to the last sunshine period; Step S3 includes: Step S301: Determine whether there is at least one sunshine period during each sunshine period other than the last sunshine period of the target day to be optimized that is the full-capacity period of the photovoltaic energy storage; If the judgment result of step S301 is yes, then step S302 is executed; if the judgment result of step S301 is no, then step S303 is executed. Step S302: Determine all sunshine periods within the target day to be optimized that are after the last full-capacity photovoltaic energy storage period as rechargeable alternative periods; Step S303: Determine each sunshine period within the target day to be optimized as a rechargeable alternative period; After steps S302 and S303 are completed, step S304 is executed, where the number of rechargeable alternative time periods determined is denoted as N, where N is a positive integer; Step S304: Construct a charging optimization model to represent the charging of the photovoltaic energy storage device cluster using the power grid system during the N rechargeable alternative time periods. The decision variable in the charging optimization model is the amount of grid charging corresponding to the charging of the photovoltaic energy storage device cluster using the power grid system during the N rechargeable alternative time periods. The objective of the charging optimization model is to minimize the total charging cost of charging the photovoltaic energy storage device cluster using the power grid system during the N rechargeable alternative time periods. Step S305: Solve the charging optimization model using the first preset target optimization algorithm to obtain the corresponding current optimal solution. The current optimal solution includes the current optimal value of the grid charging amount corresponding to the charging of the photovoltaic energy storage device cluster by the grid system in N rechargeable alternative time periods. The rechargeable alternative time periods with the current optimal value of the grid charging amount not being 0 are taken as the daytime charging time periods.

2. The power distribution optimization method according to claim 1, characterized in that, The total charging cost of charging the photovoltaic energy storage device cluster using the power grid system during N available charging time slots is minimized by the following formula: This indicates the total cost of charging. This represents the amount of grid charging required to charge the photovoltaic energy storage device cluster using the grid system during the nth available charging time period. This represents the unit charging cost for charging the photovoltaic energy storage device cluster using the power grid system during the nth available charging time period; The constraints of the charging optimization model include: Condition 1: During the N available charging periods, the photovoltaic energy storage cluster will not discharge to the grid system. Condition 2: In the N available charging time slots, the charging power of each available charging time slot cannot exceed the maximum charging power of the photovoltaic energy storage equipment cluster. in, This represents the duration of the nth available charging time slot. This indicates the maximum charging power of the photovoltaic energy storage device cluster; Condition 3: After charging the photovoltaic energy storage equipment cluster using the power grid system during N rechargeable alternative time periods, the remaining total photovoltaic energy storage capacity during the last sunshine period of the target day to be optimized is increased to the preset maximum photovoltaic energy storage capacity. This represents the total remaining photovoltaic energy storage capacity of the photovoltaic energy storage cluster during the last sunshine period of the target optimization day, without charging the grid system. This indicates the preset maximum capacity of photovoltaic energy storage. It represents the total amount of grid charging used to charge the photovoltaic energy storage device cluster during N available charging time slots; Condition 4: In any available charging time period, the total remaining photovoltaic energy storage capacity of the photovoltaic energy storage cluster after completing the grid system charging will not exceed the preset maximum photovoltaic energy storage capacity. in, This represents the total remaining photovoltaic energy storage capacity of the photovoltaic energy storage device cluster in the nth available charging period when the grid system is not charging. This represents the total amount of grid charging used to charge the photovoltaic energy storage device during the first n available charging periods.

3. The power distribution optimization method according to claim 1, characterized in that, The steps preceding step S1 also include: Step Sa: Generate a solar sunshine period ... The input information of the solar energy storage remaining total power prediction model for the sunshine period includes: the time period identifier of the target sunshine period within the target optimization day, the total regional power consumption of all loads connected to the solar energy storage equipment cluster within the target area during the target sunshine period, the temperature of the target area during the target sunshine period, and the cumulative sunshine duration of the target area within the target optimization day during the target sunshine period; the output of the solar energy storage remaining total power prediction model for the sunshine period is the total remaining solar energy storage power corresponding to the target sunshine period; Step S1 includes: Step S101: Obtain the first input information corresponding to each sunshine period within the target optimization day. The first input information includes: the time period identifier of the corresponding sunshine period within the target optimization day, the total regional electricity consumption of all loads connected to the photovoltaic energy storage equipment cluster in the target area during the corresponding sunshine period, the temperature of the target area during the corresponding sunshine period, and the cumulative sunshine duration of the target area within the target optimization day at the end of the corresponding sunshine period. Step S102: Input the first input information corresponding to each sunshine period of the target day to be optimized into the trained photovoltaic energy storage remaining total power prediction model for the sunshine period, and obtain the photovoltaic energy storage remaining total power corresponding to each sunshine period of the target day to be optimized.

4. The power distribution optimization method according to claim 3, characterized in that, Step Sa includes: Step Sa1: Collect comprehensive information of the first historical time period for the target area during multiple sunshine periods over multiple historical days. The comprehensive information of the first historical time period includes: a time period identifier indicating the position of the corresponding sunshine period in the day; the total electricity consumption of all loads connected to the photovoltaic energy storage equipment cluster in the target area during the corresponding sunshine period; the temperature of the target area during the corresponding sunshine period; the cumulative sunshine duration of the target area on the day at the end of the corresponding sunshine period; and the remaining total photovoltaic energy storage capacity of the photovoltaic energy storage equipment cluster in the target area during the corresponding sunshine period. Step Sa2: Use the comprehensive information of the first historical period as training samples. The period identifier, the total electricity consumption of the region, the cumulative sunshine duration, the temperature, and the cumulative sunshine duration in the comprehensive information of the first historical period are used as model inputs. The remaining total electricity of photovoltaic energy storage in the comprehensive information of the first historical period is used as model output. Train the first preset prediction model to obtain the prediction model of the remaining total electricity of photovoltaic energy storage during the sunshine period.

5. The power distribution optimization method according to claim 4, characterized in that, Step Sa2 includes: Step Sa21: Divide all the historical days into 8 groups according to the preset classification rules. The preset classification rules are as follows: statutory holidays are divided into 1 group, and non-statutory holidays are divided into the remaining 7 groups according to 7 different dates from Monday to Sunday. Step Sa22: For each group, construct the first historical time period comprehensive information set corresponding to the group by combining the first historical time period comprehensive information of all sunshine periods within each historical day included in the group; Step Sa23: For each group, use the comprehensive information set of the first historical period corresponding to the group as the training sample set to train the first preset prediction model to obtain the prediction model of the remaining total electricity of photovoltaic energy storage during the sunshine period corresponding to the group. Step S102 includes: Step S1021: Determine the group to which the target optimization date belongs according to the preset classification rules based on the date of the target optimization date; Step S1022: Input the first input information corresponding to each sunshine period within the target optimization day into the photovoltaic energy storage remaining total power prediction model for the sunshine period corresponding to the group to which the target optimization day belongs, to obtain the photovoltaic energy storage remaining total power corresponding to each sunshine period within the target optimization day.

6. The power distribution optimization method according to any one of claims 1 to 5, characterized in that, Also includes: Sb, Generate a model for predicting the remaining total power of photovoltaic energy storage during non-sunlight periods that can predict the total remaining power of photovoltaic energy storage during target non-sunlight periods after the last sunshine period of the day. The input information of the photovoltaic energy storage remaining total power prediction model for non-sunlight periods includes: the time period identifier of the target non-sunlight period within the target day to be optimized, the remaining total power of photovoltaic energy storage corresponding to the nearest sunshine period before the target non-sunlight period, the total regional power consumption of all loads connected to the photovoltaic energy storage equipment cluster within the target area during the target non-sunlight period, and the temperature of the target area during the target non-sunlight period; the output of the photovoltaic energy storage remaining total power prediction model for non-sunlight periods is the remaining total power of photovoltaic energy storage corresponding to the target non-sunlight period; Step S4: Select non-sunlight periods that fall during the peak electricity consumption period of the power grid within the target optimization day from all non-sunlight periods after the last sunshine period within the target optimization day, and use them as candidate periods for discharge. Step S5: Obtain the second input information of the last available dischargeable alternative time period within the target optimization day. The second input information includes: the time period identifier of the last available dischargeable alternative time period, the remaining total power of photovoltaic energy storage corresponding to the nearest sunshine period before the last available dischargeable alternative time period, the total regional power consumption of all loads connected to the photovoltaic energy storage equipment cluster in the target area during the last available dischargeable alternative time period, and the temperature of the target area during the last available dischargeable alternative time period. The remaining total power of photovoltaic energy storage corresponding to the nearest sunshine period before the last available dischargeable alternative time period is taken as the preset maximum power of photovoltaic energy storage. Step S6: Input the second input information of the last dischargeable candidate period within the target optimization day into the trained photovoltaic energy storage remaining total power prediction model for the non-sunlight period to obtain the photovoltaic energy storage remaining total power corresponding to the last dischargeable candidate period within the target optimization day. Step S7: Determine whether the remaining total photovoltaic energy storage capacity corresponding to the last dischargeable alternative time period within the target optimization day is 0; If the result of step S7 is negative, then step S8 is executed. Step S8: Determine at least one dischargeable alternative time period from all the dischargeable alternative time periods within the target optimization day as the peak electricity consumption discharge period, and determine the discharge amount of the photovoltaic energy storage equipment cluster to the grid system during each peak electricity consumption discharge period. The total discharge amount of the energy storage cluster corresponding to all the peak electricity consumption discharge periods within the target optimization day is equal to the remaining total photovoltaic energy storage capacity corresponding to the last dischargeable alternative time period within the target optimization day.

7. The power distribution optimization method according to claim 6, characterized in that, Step Sb includes: Step Sb1: Collect comprehensive information on the second historical time period of the target area, which includes multiple non-sunlight periods after the last sunshine period within multiple historical days. The comprehensive information on the second historical time period includes: a time period identifier indicating the position of the corresponding non-sunlight period in the day; the remaining total power of photovoltaic energy storage corresponding to the sunshine period that is closest to the corresponding non-sunlight period; the total power consumption of all loads connected to the photovoltaic energy storage equipment cluster in the target area during the corresponding non-sunlight period; the temperature of the target area during the corresponding non-sunlight period; and the remaining total power of photovoltaic energy storage corresponding to the photovoltaic energy storage equipment cluster in the target area during the corresponding sunshine period. Step Sb2: Use the second historical time period comprehensive information as training samples. The time period identifier, the total remaining photovoltaic energy storage capacity corresponding to the nearest sunshine period before the corresponding non-sunshine period, the total power consumption of the region, and the temperature in the second historical time period comprehensive information are used as model inputs. The total remaining photovoltaic energy storage capacity of the photovoltaic energy storage device cluster in the target area corresponding to the sunshine period in the second historical time period comprehensive information is used as model output. Train the second preset prediction model to obtain the prediction model of the total remaining photovoltaic energy storage capacity during non-sunshine periods. Step Sb2 includes: Step Sb21: Divide all the historical days into 8 groups according to the preset classification rules. The preset classification rules are as follows: statutory holidays are divided into 1 group, and non-statutory holidays are divided into the remaining 7 groups according to 7 different categories of dates from Monday to Sunday. Step Sb22: For each group, construct the second historical time period comprehensive information set corresponding to that group by combining the comprehensive information of all non-sunlight periods after the last sunshine period within each historical day included in the group. Step Sa23: For each group, use the comprehensive information set of the second historical period corresponding to the group as the training sample set to train the second preset prediction model to obtain the prediction model of the remaining total electricity of photovoltaic energy storage during the non-sunshine period corresponding to the group. Step S6 includes: Step S601: Determine the group to which the target optimization date belongs according to the preset classification rules based on the date of the target optimization date; Step S602: Input the second input information of the last dischargeable candidate period within the target optimization day into the prediction model of the remaining total photovoltaic energy storage power during the non-sunshine period corresponding to the group to which the target optimization day belongs, to obtain the remaining total photovoltaic energy storage power corresponding to the last dischargeable candidate period within the target optimization day.

8. The power distribution optimization method according to claim 6, characterized in that, The number of potential discharge time periods selected in step S4 is M, where M is a positive integer; Step S8 includes: Step S801: Construct a discharge optimization model to represent the discharge of the power grid system by the photovoltaic energy storage device cluster during M dischargeable alternative time periods; The decision variable in the discharge optimization model is the discharge amount of the energy storage cluster corresponding to the discharge of the photovoltaic energy storage equipment cluster to the grid in the M dischargeable candidate periods. The objective of the discharge optimization model is to maximize the total discharge revenue from the photovoltaic energy storage device cluster to the grid during the M available discharge periods, expressed by the following formula: This represents the total discharge revenue. This represents the discharge amount of the photovoltaic energy storage cluster when it discharges to the grid during the m-th available discharge period. This represents the unit discharge revenue generated when the photovoltaic energy storage device cluster discharges to the grid during the m-th available discharge period. The constraints of the discharge optimization model include: Condition 1: During the M available discharge periods, the power grid will not charge the photovoltaic energy storage cluster. Condition 2: In the M available discharge periods, the discharge power of each available discharge period cannot exceed the maximum discharge power of the photovoltaic energy storage equipment cluster. in, This represents the duration of the m-th available discharge period. This indicates the maximum discharge power of the photovoltaic energy storage device cluster; Condition 3: After the photovoltaic energy storage device discharges to the grid system during M dischargeable alternative time periods, the remaining total amount of photovoltaic energy storage corresponding to the last dischargeable alternative time period decreases to 0. This represents the total remaining photovoltaic energy storage capacity of the photovoltaic energy storage device cluster during the last available discharge period on the target optimization day, without discharging to the power grid system. This represents the total discharge amount of the photovoltaic energy storage cluster to the power grid system during the M available discharge periods; Step S802: Solve the discharge optimization model using the second preset target optimization algorithm to obtain the corresponding current optimal solution. The current optimal solution includes the current optimal value of the discharge amount of the energy storage cluster corresponding to the discharge of the power grid system by the photovoltaic energy storage equipment cluster in the M dischargeable alternative time periods. The dischargeable alternative time periods with the current optimal value of the discharge amount of the corresponding energy storage cluster not being 0 are taken as the peak electricity consumption discharge period.

9. A power distribution optimization system based on photovoltaic energy storage equipment, characterized in that, The power distribution optimization system is configured to implement the power distribution optimization method as described in any one of claims 1 to 8, and the power distribution optimization system includes: The acquisition module is configured to acquire the total remaining photovoltaic energy storage capacity of the photovoltaic energy storage device cluster in the target area during each solar sunshine period on the target day to be optimized; The judgment module is configured to determine whether the last sunshine period of the target day to be optimized is a full-capacity photovoltaic energy storage period, wherein the full-capacity photovoltaic energy storage period refers to the period during which the remaining total power of the corresponding photovoltaic energy storage is equal to the preset maximum power of the photovoltaic energy storage. The optimization module is configured to, when the judgment module determines that the last sunshine period in the target optimization day is not a full-capacity period for photovoltaic energy storage, determine at least one sunshine period from all sunshine periods in the target optimization day as a daytime charging period, and the grid charging amount for charging the photovoltaic energy storage device cluster using the grid system during each daytime charging period, based on the remaining total amount of photovoltaic energy storage corresponding to each sunshine period in the target optimization day. The total amount of grid charging corresponding to all the daytime charging periods in the target optimization day is equal to the difference between the preset maximum amount of photovoltaic energy storage and the remaining total amount of photovoltaic energy storage corresponding to the last sunshine period.

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