WO2021242175A1

WO2021242175A1 - Method and apparatus for controlling charging and discharging of user-side energy storage device, and storage medium thereof

Info

Publication number: WO2021242175A1
Application number: PCT/SG2021/050291
Authority: WO
Inventors: Chenwei Wang; Dong Du
Original assignee: Envision Digital International Pte. Ltd.; Shanghai Envision Digital Co., Ltd.
Priority date: 2020-05-27
Filing date: 2021-05-25
Publication date: 2021-12-02
Also published as: CN111525601B; CN111525601A

Abstract

A method for controlling charging and discharging of a user-side energy storage device includes: acquiring history electricity consumption data under a transformer to which the energy storage device is connected in a history period; generating a typical load curve conforming to changes of electricity consumption power in a target period defined on a monthly basis by performing curve fitting on the history electricity consumption data; calculating a charging sub-period and a discharging sub-period of the energy storage device in the target period by a charging and discharging strategy corresponding to a curve type of the typical load curve; and controlling charging and discharging of the energy storage device, the charging and discharging strategy being a strategy defined based on the curve type to achieve at least one of load shaving and demand reduction.

Description

METHOD AND APPARATUS FOR CONTROLLING CHARGING

AND DISCHARGING OF USER-SIDE ENERGY STORAGE DEVICE, AND STORAGE MEDIUM THEREOF

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of energy storage control, and in particular, relates to a method and an apparatus for controlling charging and discharging of a user-side energy storage device, and a storage medium thereof.

BACKGROUND

[0002] An enterprise with a high demand on electricity consumption will usually establish an energy storage system to reduce electricity consumption in peak period by peak load shaving (hereinafter referred to load shaving) or demand reduction or both, so as to reduce the load of a power grid, that is, to reduce an electrical load of the power grid in a peak period. The load refers to the total electric power consumed by the electric device from the power grid at a moment. [0003] Load shaving means charging an energy storage device at a load peak and discharging the energy storage device at a load valley so as to reduce the load peak and fill the load valley; and demand reduction means reduction of the electric power. In other words, an energy storage system in the enterprise controls the energy storage device to be discharged in the peak period of power consumption by devices and controls the energy storage device to be charged in a valley period of power consumption by devices, which allocates the load in the peak period to the valley period so as to reduce the load of the power grid in the peak period. Alternatively, the load of the power grid at this moment may be reduced by reducing the demand in the peak period. Under general circumstances, an energy storage system operates based on the preset charging and discharging periods, that is, the energy storage device is charged in the preset charging period and discharged in the preset discharging period.

[0004] However, the peak periods of power consumption by devices of enterprises are variable, and the peak periods of power consumption by devices in each day may be different. Therefore, the effect of load shaving or demand reduction or both on reducing the load of the grid realized by fixed charging and discharging periods is poor.

SUMMARY [0005] Embodiments of the present disclosure provide a method and an apparatus for controlling charging and discharging of a user-side energy storage device, a device, and a storage medium thereof, such that the burden of the power grid may be reduced more effectively.

[0006] According to one aspect of the embodiments of the present disclosure, a method for controlling charging and discharging of a user-side energy storage device, applicable in an energy storage system, is provided. The energy storage system includes the energy storage device and a transformer to which the energy storage device is connected, and an electric device is connected to the transformer.

[0007] The method includes: acquiring history electricity consumption data under the transformer in a history period; generating a typical load curve conforming to changes of consumed electric power in a target period by performing curve fitting on the history electricity consumption data, the target period being a period defined on a monthly basis; calculating a charging sub-period and a discharging sub-period of the energy storage device in the target period by a charging and discharging strategy corresponding to a curve type of the typical load curve, the charging and discharging strategy being a strategy defined based on the curve type to achieve at least one of load shaving and demand reduction; and controlling the energy storage device to be charged in the charging sub-period and discharged in the discharging sub-period.

[0008] According to another aspect of the embodiments of the present disclosure, an apparatus for controlling charging and discharging of a user-side energy storage device is provided.

[0009] The apparatus includes: an acquiring module, configured to acquire history electricity consumption data of a transformer to which the energy storage device is connected in a history period; a generating module, configured to generate a typical load curve conforming to changes of consumed electric power in a target period by performing curve fitting on the history electricity consumption data, the target period being a period defined on a monthly basis; a calculating module, configured to calculate a charging sub-period and a discharging sub-period of the energy storage device in the target period by a charging and discharging strategy corresponding to a curve type of the typical load curve, the charging and discharging strategy being a strategy defined based on the curve type to achieve at least one of load shaving and demand reduction; and a controlling module, configured to control the energy storage device to be charged in the charging sub-period and discharged in the discharging sub-period.

[0010] According to another aspect of the embodiments of the present disclosure, a server is provided.

[0011] The server includes: a memory storing at least one executable instruction; and a processor communicably connected to the memory; wherein the processor, when loading and executing the at least one executable instruction, is caused to perform the method for controlling charging and discharging of the user-side energy storage device according to the above aspect or optional embodiments of the present disclosure.

[0012] According to another aspect of the embodiments of the present disclosure, a non-transitory computer-readable storage medium is provided. The non -transitory computer-readable storage medium stores at least one instruction, at least one program, a code set, or an instruction set; wherein the at least one instruction, the at least one program, the code set, or the instruction set, when loaded and executed by a processor, causes the processor to perform the method for controlling charging and discharging of the user-side energy storage device according to the above aspect or optional embodiments of the present disclosure.

[0013] The technical solutions according to the present disclosure at least achieve the following beneficial effects:

[0014] A typical load curve conforming to changes of consumed electric power in a target period is generated by performing curve fitting on the history electricity consumption data of a transformer to which the energy storage device is connected in a history period. A charging and discharging strategy corresponding to a curve type of the typical load curve is configured to calculate a charging sub-period and a discharging sub-period of the energy storage device in the target period. The charging and discharging strategy is a strategy defined based on the curve type to achieve at least one of load shaving and demand reduction. That is, the charging sub-period and the discharging sub-period are calculated with reference to fluctuations of the typical load curve in the target period, and the charging and discharging strategy matches a trend of the typical load curve in a better way, such that the energy storage device is controlled to be charged in the charging sub-period and discharged in the discharging sub-period so as to better achieve at least one of load shaving and demand reduction.

BRIEF DESCRIPTION OF THE DRAWINGS [0015] For clearer descriptions of the technical solutions according to the embodiments of the present disclosure, the following briefly introduces the accompanying drawings required for describing the embodiments. Apparently, the accompanying drawings in the following description show merely some embodiments of the present disclosure, and a person of ordinary skill in the art may also derive other drawings from these accompanying drawings without creative efforts. [0016] FIG. 1 is a schematic structural diagram of the Internet of things system according to an exemplary embodiment of the present disclosure; [0017] FIG. 2 is a flowchart of a method for controlling charging and discharging of a user-side energy storage device according to an exemplary embodiment of the present disclosure; [0018] FIG. 3 is a flowchart of a method for controlling charging and discharging of a user-side energy storage device according to another exemplary embodiment of the present disclosure;

[0019] FIG. 4 is a schematic diagram of a load curve according to an exemplary embodiment of the present disclosure;

[0020] FIG. 5 is a schematic diagram of a load curve according to another exemplary embodiment of the present disclosure; [0021] FIG. 6 is a schematic diagram of a load curve according to another exemplary embodiment of the present disclosure;

[0022] FIG. 7 is a schematic diagram of a load curve according to another exemplary embodiment of the present disclosure;

[0023] FIG. 8 is a flowchart of a method for controlling charging and discharging of a user-side energy storage device according to another exemplary embodiment of the present disclosure;

[0024] FIG. 9 is a block diagram of an apparatus for controlling charging and discharging of a user-side energy storage device according to an exemplary embodiment of the present disclosure; and [0025] FIG. 10 is a schematic structural diagram of a server according to an exemplary embodiment of the present disclosure.

DETAILED DESCRIPTION

[0026] For clearer descriptions of the objects, technical solutions and advantages in the present disclosure, the implementation of the present disclosure is described in detail below in combination with the accompanying drawings.

[0027] Terms used in the present disclosure are explained as follows:

[0028] Internet of things (IoT): refers to real-time acquisition of any object or process that requires monitoring, connection and interaction via various apparatuses and technologies, such as various information sensors, radio frequency identification technologies, global positioning systems, infrared sensors and laser scanners, and acquisition of acoustic, optical, thermal, electrical, mechanical, chemical, biological, locational and other various desired information. The ubiquitous connection between objects and objects and that between objects and people as well as intelligent perception, identification and management of objects and processes may be implemented via the access of various possible networks. The IoT is an information carrier based on Internet and traditional telecommunication networks and the like, and enables all ordinary physical objects that may be independently addressed to form an interconnected network.

[0029] FIG. 1 shows a schematic diagram of the IoT system according to an embodiment of the present disclosure. The IoT system 100 may include: a server cluster 101 and an IoT device 102

[0030] The server cluster 101 is a cluster that gathers a plurality of servers to calculate and store data information. In the embodiment of the present disclosure, the server cluster 101 includes at least one server. The IoT device 102 refers to a physical device having an IoT communication capability.

[0031] In some embodiments, the IoT device 102 may be an electric device, such as a wind turbine generator, a transformer, a production device, a monitoring device, a processing device, an air conditioner, a refrigerator, and a computer. Alternatively, the IoT device 102 may also be an energy storage device, such as a wind energy storage device, a solar energy storage device, or a water conservancy energy storage device.

[0032] In the embodiment of the present disclosure, the server cluster 101 includes an IoT platform by which the connection and coordinated control of data between the IoT platform and the IoT device 102 may be implemented. Optionally, the IoT platform periodically collects and stores history electricity consumption data of the electric device which includes electric devices connected to the energy storage device. In some embodiments, the history electricity consumption data includes an electric power of the electric device at the k^th moment; alternatively, the electric device is connected to the transformer, and the IoT platform periodically collects and stores total electric power of the electric devices connected to the transformer at the k^th moment, wherein k is a positive integer.

[0033] In the IoT system, the energy storage platform may control the energy storage device to supply power to the electric device. In some embodiments, the IoT platform may be configured to implement the method for controlling charging and discharging of the user-side energy storage device according to the present disclosure in order to supply power to the electric device.

[0034] In some embodiments, an IoT platform acquires history electricity consumption data of a transformer to which the energy storage device is connected in a history period, and predicts a typical load curve conforming to changes of consumed electric power in a target period based on the history electricity consumption data. The typical load curve is analyzed to calculate a charging sub-period and a discharging sub-period of the energy storage device in the target period, so as to better achieve load shaving and demand reduction for the load in the target period. Demand reduction refers to the reduction in demand for power consumption.

[0035] It should be noted that the IoT platform may be provided in one or more servers, which is not limited in the embodiments of the present disclosure. The server cluster 101 may also be nodes of IoT having the functions of receiving and processing information uploaded by the IoT device 102, such as a router or a gateway.

[0036] In some embodiments, the server cluster 101 and the IoT device 102 are connected in a tree topology in which the IoT device 102 is disposed at a leaf node, and the server cluster 101 is disposed at a partial node of a non -leaf node and a root node.

[0037] The IoT device 102 and the server cluster 101 are connected over a wireless or a wired network. For example, an IoT device 102 may be connected to a server cluster 101 and the server clusters 101 may be connected to each other in an IoT device-IoT device connection fashion, that is, a point-to-point (Ad-Hoc) connection fashion, or under coordination of a base station or an access point (AP), which is not limited in the embodiments of the present disclosure. [0038] Those skilled in the art may know that the number of server clusters 101 or IoT devices 102 may be defined according to the actual needs. For example, only one server cluster 101 or IoT device 102 may be configured, or dozens or hundreds of or even more server clusters 101 or IoT devices 102 may be configured. The numbers and types of server clusters 101 or IoT devices 102 are not limited in the embodiments of the present disclosure.

[0039] Under general circumstances, an energy storage system of an enterprise will control the chaiging and discharging of the energy storage device based on the preset charging and discharging periods, that is, the energy storage device is controlled to be chaiged in the preset charging period and discharged in the preset discharging period, such that the energy storage device is controlled to be discharged in the peak period of power consumption by devices and charge in the valley period of power consumption by devices in order to achieve the goal of load shaving. However, the peak periods of power consumption by devices of enterprises are variable, and the peak periods of power consumption by devices in each day may be different. The effect of load shaving on reducing the load of the grid realized by fixed charging and discharging periods is poor. Therefore, the present disclosure provides a method for controlling charging and discharging a user-side energy storage device in order to solve the problems. For detailed implementation modes of the method, reference is made to the description of the following embodiments.

[0040] FIG. 2 shows a flowchart of a method for controlling charging and discharging of a user-side energy storage device according to an exemplary embodiment of the present disclosure. The method is applicable to the server shown in FIG. 1, and includes the following steps.

[0041] In step 201, history electricity consumption data under a transformer to which the energy storage device is connected in a history period is acquired. [0042] The energy storage device refers to a user-side energy storage device. In some embodiments, at least one transformer is connected to the energy storage device, and r electric devices are electrically connected to each transformer; the history electricity consumption data of each electric device is stored in a database, and a server reads the history electricity consumption data of each electric device which is connected to the transformer from the database, and calculate total history electricity consumption data of the transformer at a k^th moment based on history electricity consumption data of r electric devices. Alternatively, history electricity consumption data under each transformer is stored in the database, wherein the history electricity consumption data under the transformer is the total electricity consumption data of r electric devices, the server reads history user data under the transformer from the database, and r is a positive integer. In some embodiments, electricity consumption data may be electric power of an electric device.

[0043] The history period refers to a period before the current moment and associated with a target period; the target period refers to a period after the current moment. Alternatively, the target period is a period defined on a monthly basis. In some embodiments, if a target period is one month after the current moment, the history period may be one month, two months, three months, six months, one year, or the like before the current moment. The duration of the history period is not limited in this embodiment. It should be noted that the target period may also be one day, seven days, fifteen days or the like after the current moment. The duration of the target period is not limited in this embodiment. [0044] In some embodiments, the above history period may be a period before the current moment and associated with a target period; for example, the association may be a corresponding relationship, the target period is January, 2020, and the history period is January in the previous j years; for another example, the target period is January, 2020, and the history period is j months before January, 2020 that match the weather conditions of January, 2020; for yet another example, the target period is January 1, 2020, and the history period is j months before January, 2020 that match the date type and climate type of January, 2020. The date type refers to the same time in different periods. For example, January 1, 2020 and January 1, 2019 are dates with the same date type. The association refers to the association caused by the influencing factors of the change of the electric power, such as festivals, weather, and climate. It should be noted that the duration of an interval between the history period and the target period is not limited in this embodiment.

[0045] In some embodiments, the target period is defined based on the peak period, the valley period and the flat period of electricity prices. In some embodiments, a target period includes several sub-periods divided based on the peak period, the valley period and the flat period of the electricity prices; for example, one sub-period is from 6:00 on June 1, 2020 to 6:00 on June 2, 2020, and the sub-period includes 2 peak periods, 3 flat periods, and 1 valley period, wherein the peak periods are from 8:00 to 11:00 on June 1, 2020, and from 18:00 to 21:00 on June 1, 2020; the valley period is from 22:00 on June 1, 2020 to 6:00 on June 2, 2020; and the flat periods are from 6:00-8:00 on June 1, 2020, from 11:00 to 18:00 on June 1, 2020 and from 21 :00 to 22:00 on June 1, 2020.

[0046] In some embodiments, a server determines a history period based on a target period and acquires history electricity consumption data under a transformer in a history period from the database. In some embodiments, history electricity consumption data may be periodically acquired and stored by a server and stored in the database. For example, an interval between two adjacent data is 2 minutes, 3 minutes, or 5 minutes.

[0047] In step 202, a typical load curve forming to changes of changes of consumed electric power in a target period is generated by performing curve fitting on the history electricity consumption data.

[0048] In some embodiments, curve fitting is performed on the history electricity consumption data by a server based on a curve fitting algorithm, such that a typical load curve conforming to changes of consumed electric power in a target period is generated. For example, the curve fitting is performed by a server on the history electricity consumption data by an interpolation method, a smoothing method, or a least square method. The method for fitting the curve is not limited in this embodiment. The typical load curve is a load curve of electricity consumption of the electric devices under a transformer to which the energy storage device is connected in the target period predicted by a server based on the history electricity consumption data.

[0049] In some embodiments, if a target period is one month after the current moment, a server generates a typical load curve of changes of consumed electric power within the one month.

[0050] In step 203, the charging sub-period and the discharging sub-period of the energy storage device in the target period is calculated by a charging and dischaiging strategy corresponding to a curve type of the typical load curve. [0051] The charging and discharging strategy is a strategy defined based on the curve type to achieve at least one of load shaving and demand reduction. Different types of curves correspond to different charging and discharging strategies. First, the server determines the curve type of the typical load curve, and then determines the charging and discharging strategy corresponding to the curve type, and calculates the charging sub-period and discharging sub-period of the eneigy storage device in the taiget period based on the determined charging and discharging strategy.

Table 1

[0052] In some embodiments, Table 1 shows a corresponding relationship between a curve type and a charging and discharging strategy. A curve type 1 corresponds to a charging and discharging strategy 1, a curve type 2 corresponds to a charging and discharging strategy 2, and a curve type 3 corresponds to a charging and discharging strategy 3 ; if a server determines that the curve type of the typical load curve is curve type 1, the charging and discharging strategy 1 is configured to calculate the charging sub-period and the discharging sub-period of the energy storage device in the target period; if the server determines that the curve type of the typical load curve is curve type 2, the charging and discharging strategy 2 is configured to calculate the charging sub-period and discharging sub-period of the energy storage device in the target period; and if the server determines that the curve type of the typical load curve is curve type 3, the charging and discharging strategy 3 is configured to calculate the charging sub-period and the discharging sub-period of the energy storage device in the target period.

[0053] In some embodiments, a server determines a type of a typical load curve in a day, and then determines a charging and discharging strategy for that day; if the target period is a period defined on a monthly basis, the server determines the curve type of the typical load curve for each day within the target period, then determines a corresponding charging and discharging strategy for each day, and respectively calculate the charging sub-period and the discharging sub-period for each day within the target period. In some embodiments, the charging sub-periods in different days within the target period are different or the same, and/or the discharging sub-periods in different days are different or the same.

[0054] In step 204, the energy storage device is controlled to be charged in the chaiging sub-period and discharged in the discharging sub-period.

[0055] The energy storage device is controlled by a server to be charged in the determined charging sub-period and discharged in the dischaiging sub-period.

[0056] In summary, this embodiment provides a method for controlling charging and discharging of a user-side energy storage device. A typical load curve conforming to changes of consumed electric power in a target period is generated by performing curve fitting on the history electricity consumption data of a transformer to which the energy storage device is connected in a history period; and a charging and discharging strategy corresponding to a curve type of the typical load curve is adopted to calculate a charging sub-period and a discharging sub-period of the energy storage device in the target period, wherein the charging and discharging strategy is a strategy defined based on the curve type to achieve at least one of load shaving and demand reduction, i.e., the server predicts the typical load curve in the taiget period by history electricity consumption data, and calculate the charging sub-period and the discharging sub-period with reference to fluctuations of the typical load curve, such that the charging sub-period and the discharging sub-period match a trend of the typical load curve in a better way. Thus, the energy storage device is controlled to be charged in the charging sub-period and discharged in the discharging sub-period so as to better achieve at least one of load shaving and demand reduction, and effectively reduce the cost of electricity consumption by users.

[0057] The process of calculating the charging sub-period and the discharging sub-period is described in detail based on FIG. 2. In some embodiments, the step 203 may include steps 311 to 314, as shown in FIG. 3.

[0058] In step 311, an energy storage capacity and a discharge efficiency of an energy storage device are acquired.

[0059] The energy storage capacity refers to the amount of electricity that may be discharged by the energy storage device, i.e., the amount of electricity that may be stored. The discharge efficiency refers to a ratio between the discharge capacity and the charge capacity (i.e., the energy storage capacity) of the energy storage device . For example, if the charge capacity of the energy storage device is a and the discharge efficiency is 80%, the discharge capacity is 80%a, that is, when the energy storage device is fully charged, the actual discharge capacity is 80%a.

[0060] In step 312, at least one peak period in the typical load curve is determined based on the energy storage capacity and the discharge efficiency.

[0061] The server determines at least one peak period in the typical load curve in a day based on the energy storage capacity and the discharge efficiency. In some embodiments, a server multiplies the energy storage capacity of the energy storage device by the discharge efficiency to acquire the dischaige capacity of the energy storage device; the server determines a straight line parallel to the time axis of the typical load curve, wherein the straight line is configured on one side of the highest load point and encloses into f areas with the typical load curve in the day, the total area of the f areas is the same as the discharge capacity which is acquired by the computation mentioned above, and the area of each area is the electricity consumption of electric devices in the area; and the server determines the period in which each of the f areas is configured, i.e., periods of f peak periods are acquired, wherein f is a positive integer.

[0062] In step 313, the curve type of the typical load curve is determined based on a marked period to which the at least one peak period belongs.

[0063] The marked periods are divided based on at least one of a peak period, a valley period, and a flat period of electricity prices. Optionally, the marked periods include the peak period, the valley period, and the flat period of electricity prices. In some embodiments, based on the electricity prices, 24 hours in each day are divided into three categories of periods, i.e., the peak periods, the valley periods, and the flat periods of the electricity prices.

[0064] A server determines the marked period to which each of the f peak periods belongs, and determines the curve type of the typical load curve in a day based on a type of the marked period. Optionally, the corresponding relationship between at least one peak period and the curve type includes at least one of the following.

[0065] 1) At least one peak period includes a first peak period which belongs to the peak periods, and the typical load curve is of a first single-peak type.

[0066] As shown in FIG. 4, the peak periods are from 8:00 to 11:00 and from 18:00 to

21:00 every day, the valley period is from 22:00 to 6:00 the next day, and the rest periods are the flat periods; if the typical load curve includes a first peak period from ti to ti, and the period from ti to t2 is between 8:00 and 11:00, the typical load curve is of the first single-peak type.

[0067] 2) At least one peak period includes a second peak period which belongs to the flat or the valley periods, and the typical load curve is of a second single-peak type.

[0068] As shown in FIG. 5, the peak periods are from 8:00 to 11:00 and from 18:00 to

21:00 every day, the valley period is from 22:00 to 6:00 the next day, and the rest periods are the flat periods; if the typical load curve includes a second peak period from t3 to t4, and the period from t3 to t₄ is between 12:00 and 18:00, the typical load curve is of the second single-peak type. [0069] 3) At least one peak period includes two third peak periods which belong to the peak periods, and the typical load curve is of a first double-peak type.

[0070] As shown in FIG. 6, the peak periods are from 8:00 to 11:00 and from 18:00 to 21:00 every day, the valley period is from 22:00 to 6:00 the next day, and the rest periods are the flat periods; if the typical load curve includes two third peak periods from t_¾ to tr, and from ti to tx. and the period from tx to ίb is between 8:00 and 11:00 and the period from ti to tx is between 18:00 and 21:00, the typical load curve is of the first double-peak type. [0071] 4) At least one peak period includes two fourth peak periods, one of the fourth peak periods belongs to the peak period and the other of the fourth peak periods belongs to the flat or the valley period, and the typical load curve is of a second double-peak type.

[0072] As shown in FIG. 7, the peak periods are from 8:00 to 11:00 and from 18:00 to

21:00 every day, the valley period is from 22:00 to 6:00 the next day, and the rest periods are the flat periods; if the typical load curve includes two fourth peak periods from t9 to tio and from tn to ti2, wherein the period from t9 to tio is between 8:00 and 11:00 and the period from t7 to tx is between 13:00 and 15:00, the typical load curve is of the second double-peak type.

[0073] 5) Except for the correspondences described in items 1) to 4), the typical load curves are of other types. [0074] In step 314, the charging sub-period and the discharging sub-period is calculated by a charging and discharging strategy corresponding to the curve type of the typical load curve. [0075] In some embodiments, the charging and discharging strategy may include at least one:

[0076] (1) controlling the energy storage device to be discharged and charged once in one day;

[0077] (2) controlling the energy storage device to be dischaiged and chaiged twice in one day; and

[0078] (3) controlling the energy storage device to be charged once and discharged twice in one day. Table 2

[0079] Table 2 shows a corresponding relationship between a curve type and a charging and discharging strategy. The first single-peak type corresponds to a charging and discharging strategy (1), the second single-peak type corresponds to the charging and discharging strategy (1), the first double-peak type corresponds to a charging and discharging strategy (2), the second double-peak type corresponds to a charging and discharging strategy (3), and other types correspond to the charging and discharging strategy (1).

[0080] In some embodiments, a server acquires constraint conditions for charging and discharging of an energy storage device, constructs an objective function based on the charging and discharging strategy and the constraint conditions, and solves the objective function to acquire a charging sub-period and a discharging sub-period.

[0081] In some embodiments, constraint conditions for charging and discharging of an energy storage device include an income from the electricity bill, that is, the saved electricity bill; and a server constructs an objective function that conforms to a charging and discharging strategy based under the condition of maximizing the income. For example, under the premise that the charging and discharging strategy adopted on every day in every month is the same, taking the case where the income maximization is calculated using a natural year as a period as an example, the income in a natural year is equal to a sum of the incomes of 12 natural months, i.e., C_year = max(åC_month_i), wherein C_ ear represents the income of a natural year, C month i represents the income of the i^th natural month, å represents a sum symbol, max() represents a symbol for solving the maximum value of C_year, and i= 1, 2 12.

[0082] For a monthly income, it is necessary to consider the spread income of peak-to-valley arbitrage of energy storage, the income of reduced electricity bills caused by reduction in demand of eneigy storage, the income of government subsidies, and the loss cost of the energy storage device; the spread income of peak-to-valley arbitrage of energy storage refers to the spread income from using electricity which is stored in the valley period in the peak period, and the income of reduced electricity bills caused by reduction in demand of energy storage refers to the income of reduced electricity bills for electricity consumption in a power station due to a reduction in energy storage. Therefore, the income of each month may be expressed as C month = the number of days in the month* (G_d G_u G_s-C_e), wherein G_d represents the income of reduced electricity bills caused by reduction in demand of energy storage, G_u represents the spread income of peak-to-valley arbitrage of energy storage, G_s represents the income of government subsidies, C_e represents the loss cost of energy storage device, and * represents multiplication.

[0083] For computations of G_d, G_u, G_s, C_e, different curve types correspond to different computation methods, as shown below.

[0084] 1. For the first single-peak type, G_d = (D_m-D_t)*P_d, wherein D m represents a maximum load of the month, D_t represents a load value corresponding to the intersection point of the straight line and the typical load curve, and P_d represents the price of electricity in the area where the energy storage device is placed, in CNY/kW (kilowatt);

[0085] G_u = (P_m-P_l)*E_m (P_p-P_l)*E_p, wherein P_p represents the electricity price during peak periods at the area where the energy storage device is placed, in CNY/kWh (kilowatt hour); P_m represents the electricity price during the flat periods at the area where the energy storage device is placed, in CNY/kWh; P_1 represents the electricity price during the valley periods at the area where the energy storage device is placed, in CNY/kWh; E_m represents the total electricity discharged by the eneigy storage device in the flat periods, in kWh; and E_p represents the total electricity discharged by the energy storage device during the peak periods, in kWh;

[0086] G_s = S*G_u, wherein S represents a total energy storage capacity in kWh, and

G_u represents the government subsidy for energy storage per unit capacity in CNY/kWh; and [0087] C_e is equal to the cost for hardware and installation of the energy storage device/the number of charging and discharging cycles in the whole life cycle of the energy storage device, in CNY/kWh; and the number of charging and discharging cycles in the whole life cycle of the energy storage device refers to the total number of cycles that periodically charges and discharges when the energy storage device is used correctly.

[0088] 2. For the second double-peak type, G_d = 0;

[0089] G_u = (P_p-P_l)* pS:

[0090] G_s = S*G_u, wherein S represents a total energy storage capacity in kWh, G_u represents the government subsidy for energy storage per unit capacity in CNY/kWh, and h represents a discharge efficiency; and

[0091] C_e is equal to the cost for hardware and installation of the energy storage device/the number of charging and discharging cycles in the whole life cycle of the energy storage device, in CNY/kWh.

[0092] 3. For the first double-peak type, G_d = (D_m-D_t)*P_d, wherein D m represents a maximum load of the month, D_t represents a load value corresponding to the intersection point of the straight line and the typical load curve, and P_d represents the price of electricity in the area where the energy storage device is placed, in CNY/kW;

[0093] G_u = (P_m-P_l)*E_m (P_p-P_l)*E_p, wherein P_p represents the electricity price during peak periods at the area where the energy storage device is placed, in CNY/kWh; P_m represents the electricity price during the flat periods at the area where the energy storage device is placed, in CNY/kWh; P_1 represents the electricity price during the valley periods at the area where the energy storage device is placed, in CNY/kWh; E_m represents the total electricity discharged by the energy storage device in the flat periods, in kWh; and E_p represents the total electricity discharged by the energy storage device during the peak periods, in kWh;

[0094] G_s = S*G_u, wherein S represents the total energy storage capacity in kWh, and G_u represents the government subsidy for energy storage per unit capacity in CNY/kWh; and [0095] the loss cost of the eneigy storage device C_e = 2* (cost for hardware and installation of the energy storage device/the number of charging and discharging cycles in the whole life cycle of the energy storage device), in CNY/kWh.

[0096] 4. For the second double-peak type, G_d = (D_m-D_t)*P_d, wherein D m represents a maximum load of the month, D_t represents a load value corresponding to the intersection point of the straight line and the typical load curve, and P_d represents the price of electricity in the area where the energy storage device is placed, in CNY/kW;

[0097] G_u = (P_m-P_l)*E_m (P_p-P_l)*E_p, wherein P_p represents the electricity price during peak periods at the area where the energy storage device is placed, in CNY/kWh; P_m represents the electricity price during the flat periods at the area where the energy storage device is placed, in CNY/kWh; P_1 represents the electricity price during the valley periods at the area where the energy storage device is placed, in CNY/kWh; E_m represents the total electricity discharged by the energy storage device in the flat periods, in kWh; and E_p represents the total electricity discharged by the energy storage device during the peak periods, in kWh; [0098] G_s = S*G_u, wherein S represents a total energy storage capacity in kWh, and

G_u represents the government subsidy for energy storage per unit capacity in CNY/kWh; and [0099] C_e is equal to the cost for hardware and installation of the energy storage device/the number of charging and discharging cycles in the whole life cycle of the energy storage device, in CNY/kWh. [00100] 5. For other types, G_d = 0;

[00101] G_u = (P p-PJ* r|S:

[00102] G_s = S*G_u, wherein S represents a total energy storage capacity in kWh, G_u represents the government subsidy for energy storage per unit capacity in CNY/kWh, and h represents a discharge efficiency; and [00103] C_e is equal to the cost for hardware and installation of the energy storage device/the number of charging and discharging cycles in the whole life cycle of the energy storage device, in CNY/kWh.

[00104] In some embodiments, the constraint conditions for charging and discharging of an energy storage device also include constraints on the maximum charging power, maximum discharging power, charge efficiency, and discharge efficiency of the energy storage device. If the charge efficiency and discharge efficiency are both 90%, a discharge depth of the energy storage device is 80%, that is, the constraint on the remaining power is 0.1S < SOC(t) < 0.9S, wherein SOC(t) represents the remaining amount of electricity of the energy storage device at a moment t; the constraint on charging power is 0 < P_chg < P_chgmax, wherein P_chg represents the charging power of the energy storage device, P_chgmax represents the maximum charging power that may be realized by the energy storage device, P_chg and P_chgmax are determined by parameters of the energy storage device; the constraint on discharging power is 0 < P_out < P_outmax, wherein P_out represents the charging power of the energy storage device, P_outmax represents the maximum discharging power that may be realized by the energy storage device, and P out and P outmax are determined by the parameters of the device.

[00105] In some embodiments, a server performs an iterative solution by using a standard particle swarm algorithm to acquire a charging sub-period and a discharging sub-period.

[00106] In summary, with the method for controlling the charging and discharging of the user-side energy storage device according to this embodiment, the curve type is defined based on the peak periods, valley periods, or flat periods of the electricity price to which the peak period on the typical load curve belongs; while the charging sub-period and the discharging sub-period are calculated with reference to the fluctuations of the typical load curve, the different electricity prices in different periods and the attributes of the energy storage device are also considered, such that the charging sub-period and the discharging sub-period match a trend of the typical load curve in a better way, so as to better achieve at least one of load shaving and demand reduction, and more effectively reduce the expenditure of electricity consumption.

[00107] Referring to FIG. 2, in order to predict and acquire a curve conforms in a better way to a trend of the typical load curve in a target period, the step 202 may also include steps 321 to 323, as shown in FIG. 8.

[00108] In step 321, n groups of history electricity consumption data corresponding to n first moments in the target period are determined from history electricity consumption data. [00109] A group of history electricity consumption data corresponding to each first moment in the n first moments within a target period is determined by a server, wherein n is a positive integer. The history electricity consumption data is generated at a second moment, and at least two different time differences are present between the second moment and the first moment. In some embodiments, a corresponding relationship is established between the first moment and the second moment. For example, if the first moment is 13:15 on February 2, 2020, the second moment may be 13:15 on February 2, 2019, the second moment may be 13:15 on February 2, 2018, or the second time may also be 13:15 on February 6, 2017. Accordingly, if 13: 15 on February 2, 2020 is used as a first moment, n groups of history electricity consumption data include history electricity consumption data at 13:15 on February 2, 2019, history electricity consumption data at 13:15 on February 2, 2018, and history electricity consumption data at 13:15 on February 6, 2017.

[00110] The time differences between the first moment and the second moment are different, and the corresponding weights are also different. Optionally, the smaller the time difference between the first moment and the second moment, the larger the value of the weight. For example, the time difference between 13:15 on February 2, 2020 and 13:15 on February 2, 2019 is one year, and the corresponding weight is hi; and the time difference between 13:15 on February 2, 2020 and February 2, 2018 is two years, and the corresponding weight is I12; and hi is greater than I12.

[00111] In step 322, n target electricity consumption data is acquired by calculating weighted averages of the n groups of history electricity consumption data respectively based on the weights.

[00112] In some embodiments, a corresponding relationship between the time difference and the weight of the first moment and the second moment is established in a server; the weight corresponding to each history electricity consumption data in a group is determined based on the corresponding relationship, and target electricity consumption data is acquired by calculating the weighted average of the history electricity consumption data of the group; and n target electricity consumption data is acquired by performing the above processing on each group of history electricity consumption data in n groups. å

[00113] In some embodiments, the target electricity consumption data P= ^U ^{1 Wv}^^v at the v^th moment, wherein u represents a total of acquired u history electricity consumption data at the v^th moments in u different years, ^Wv represents the weight of the history electricity consumption data in the v^th year, ^Pv represents the load value at the v^th moment in the history electricity consumption data of the v^thyear, v and u are positive integers, and v is less than or equal to u. [00114] It should also be noted that the weights may also be calculated dynamically. In some embodiments, m history electricity consumption data in each group correspond to m second moments, and m weights corresponding to m second moments are calculated based on a mapping relationship among the first moment, the second moment and the weight, wherein m is a positive integer.

[00115] In some embodiments, a weight of history electricity consumption data of the v^th t max — t_v + 1 w = year is å? t_{v w}h_{c rcm}

represents the number of years between the moment when the history electricity consumption data is acquired and the first moment, and tv represents the number of years between the v^th year and the first moment. For example, when target electricity consumption data at 12:00 on March 1, 2020 is calculated based on history electricity consumption data in years from 2015 to 2019, the weight corresponding to each history electricity consumption datum is calculated at first, wherein the weight corresponding to

5 - 1 + 1

Wi = = 5/15

12:00 on March 1, 2019 is 5+ 4 +3 +2 + 1 the weight corresponding to 12:00 on

5 - 2 + 1 w₂ = = 4/15

March 1, 2018 is 5 + 4 + 3 + 2 + 1 the weight corresponding to 12:00 on March

5 - 3 + 1 w₃ = = 3/15

1, 2017 is 5 + 4 + 3 +2 + 1 the weight corresponding to 12:00 on March 1, 2016 5 - 4 + 1 w₄ = = 2/15

5 is + 4 + 3 + 2 + 1 , and the weight corresponding to 12:00 on March 1, 2017 is 5 - 5 + 1 w₆ = = 1/15

5 + 4 + 3 + 2 + 1

[00116] In step 323, a typical load curve is generated by performing curve fitting on n target electricity consumption data.

[00117] In summary, with the method for controlling charging and discharging of the user-side energy storage device according to this embodiment, typical history electricity consumption data is calculated based on a plurality of history electricity consumption data at each moment, and the typical history load curve within a more accurate target period is determined based on typical history electricity consumption data. Thus, more accurate charging and discharging periods of the eneigy storage device are calculated and acquired based on the typical load curve, to better achieve the at least one of load shaving and demand reduction for the amount of electricity consumption by the electric device in the target period, thereby reducing the cost of electricity consumption of users.

[00118] In this method, dynamic weight calculation is also adopted, then more accurate typical history electricity consumption data is calculated based on the weights, and the typical history electricity consumption data is fit to acquire a typical load curve conforming in a better way to the electricity consumption by electric devices in the target period.

[00119] FIG. 9 shows a block diagram of an apparatus for controlling charging and discharging of a user-side energy storage device according to an exemplary embodiment of the present disclosure. The apparatus is implemented as part or entirety of a server by software, hardware, or a combination thereof.

[00120] The apparatus includes: an acquiring module 401, configured to acquire history electricity consumption data under a transformer to which the eneigy storage device is connected in a history period; a generating module 402, configured to generate a typical load curve conforming to changes of consumed electric power in a target period by performing curve fitting on the history electricity consumption data to, the taiget period being a period defined on a monthly basis; a calculating module 403, configured to calculate a charging sub-period and a discharging sub-period of the energy storage device in the target period by a chaiging and discharging strategy corresponding to a curve type of the typical load curve, the charging and discharging strategy being a strategy defined based on the curve type to achieve at least one of load shaving and demand reduction; and a controlling module 404, configured to control the energy device be charged in the charging sub-period and discharged in the discharging sub-period.

[00121] In some embodiments, the calculating module 403 includes: an acquiring sub-module 4031, configured to acquire an energy storage capacity and a discharge efficiency of an energy storage device; a first determining sub-module 4032, configured to determine at least one peak period in a typical load curve based on the energy storage capacity and the discharge efficiency, wherein the first determining sub-module 4032 is configured to determine a curve type of the typical load curve based on a marked period to which the at least one peak period belongs, and the marked period is divided based on at least one of a peak period, a valley period, and a flat period of electricity prices; and a first calculating sub-module 4033, configured to calculate a charging sub-period and a discharging sub-period by a charging and discharging strategy corresponding to the curve type.

[00122] In some embodiments, marked periods include an peak period, an valley period, and a flat period of electricity prices; and a corresponding relationship between at least one peak period and a curve type includes at least one of: at least one peak period including a first peak period which belongs to the peak periods, and the typical load curve being of a first single-peak type, at least one peak period including a second peak period which belongs to the flat or the valley periods, and the typical load curve being of a second single-peak type, at least one peak period including two third peak periods which belong to the peak periods, and the typical load curve being of a first double-peak type, at least one peak period including two fourth peak periods, one of the fourth peak periods belonging to the peak period and the other of the fourth peak periods belonging to the flat or the valley period, and the typical load curve being of a second double-peak ty; and types other than the first single-peak type, the second single-peak type, the first double-peak type, and the second double-peak type.

[00123] In some embodiments, the first calculating sub-module 4033 is configured to acquire a constraint condition for charging and discharging of an energy storage device, construct an objective function based on the charging and discharging strategy and the constraint condition, and acquire a charging sub-period and a discharging sub-period by solving the objective function. [00124] In some embodiments, the generating module 402 includes: a second determining sub-module 4021, configured to determine n groups of history electricity consumption data corresponding to n first moments in a target period from the history electricity consumption data which is generated at a second moment, at least two different time differences being present between the second moment and the first moment, and different time differences corresponding to different weights; a second calculating sub-module 4022, configured to acquire n target electricity consumption data by calculating weighted averages of the n groups of history electricity consumption data respectively based on the weights; and a generating sub-module 4023, configured to generate a typical load curve by performing perform curve fitting on the n target electricity consumption data, wherein n is a positive integer.

[00125] In some embodiments, m history electricity consumption data in each group corresponds to the m second moments, wherein m is a positive integer; and the second calculating sub-module 4022 is further configured to calculate weighted averages of n groups of history electricity consumption data respectively based on weights to acquire n target electricity consumption data, and calculate m weights corresponding to m second moments based on a mapping relationship between the first moment and the second moment, and the weight.

[00126] In summary, this embodiment provides an apparatus for controlling chaiging and discharging of a user-side energy storage device. A typical load curve conforming to changes of consumed electric power in a target period is generated by performing curve fitting on the history electricity consumption data under a transformer to which the eneigy storage device is connected in a history period; and a charging and discharging strategy corresponding to a curve type of the typical load curve is configured to calculate a charging sub-period and a discharging sub-period of the energy storage device in the target period, wherein the charging and discharging strategy is a strategy defined based on the curve type to achieve at least one of load shaving and demand reduction, i.e., the server predicts a typical load curve in the target period by using history electricity consumption data, and calculate the charging sub-period and the discharging sub-period with reference to fluctuations of the typical load curve, such that the charging sub-period and the discharging sub-period match a trend of the typical load curve in a better way. Thus, the energy storage device is controlled to be charged in the charging sub-period and discharged in the discharging sub-period to better achieve at least one of load shaving and demand reduction, and effectively reduce the cost of electricity consumption by users.

[00127] FIG. 10 shows a schematic structural diagram of a server according to an embodiment of the present disclosure. The server is configured to perform the method for controlling charging and discharging of the user-side energy storage device according to the embodiment described above.

[00128] The server 500 includes a central processing unit (CPU) 501, a system memory 504 including a random-access memory (RAM) 502 and a read-only memory (ROM) 503, and a system bus 505 which connects the system memory 504 and the central processing unit 501. The server 500 further includes a basic input/output (I/O) system 506 that helps for transmitting information among various devices within a computer, and a mass storage device 507 for storing an operating system 513, an application 514 and other program modules 515.

[00129] The basic input/output system 506 includes a display 508 for displaying information and such input devices 509 as a mouse and a keyboard for a user to input information. The display 508 and the input devices 509 are connected to the central processing unit 501 by an input and output controller 510 which is connected to the system bus 505. The basic input/output system 506 may further include an input and output controller 510 for receiving and processing inputs from a plurality of other devices such as a keyboard, a mouse, and an electronic stylus. Similarly, the input and output controller 510 further provides an output device which outputs to a display screen, a printer or one of other types of output devices.

[00130] The mass storage device 507 is connected to the central processing unit 501 by a mass storage controller (not shown) which is connected to the system bus 505. The mass storage device 507 and an associated computer-readable medium thereof provide non-volatile storage for the server 500. In other words, the mass storage device 507 may include a computer-readable medium (not shown) such as a hard disk or a compact disc read-only memory (CD-ROM) drive. [00131] Generally, the computer-readable medium may include computer storage medium and communication medium. The computer storage medium includes volatile, non-volatile, removable, or non-removable medium implemented by any method or technology for storing information such as computer-readable instructions, data structures, program modules, or other data. The computer storage media include RAMs, ROMs, erasable programmable read-only memories (EPROMs), electrically erasable programmable read-only memories (EEPROMs), flash memories or other solid-state storage technologies, CD-ROMs, digital versatile discs (DVDs) or other optical storage devices, cassettes, tapes, disk storage devices, or other magnetic storage devices. It is obvious that those skilled in the art may know that the computer storage media are not limited to the above categories. The system memory 504 and the mass storage device 507 may be collectively referred to as a memory.

[00132] According to various embodiments of the present disclosure, the server 500 may also be connected to a remote computer on the network via a network such as the Internet to run. That is, the server 500 may be connected to a network 512 by a network interface unit 511 which is connected to the system bus 505, or the network interface unit 511 may also be configured to be connected to other types of networks or remote computer systems (not shown).

[00133] The serial numbers of the above embodiments of the present disclosure are only for description, and do not represent the superiorities of the embodiments.

[00134] Those skilled in the art can understand that all or part of the steps of implementing the above embodiments may be completed by hardware, or may be completed by related hardware instructed by a program, and the program may be stored in a computer-readable storage medium. The storage medium mentioned above may be a read-only memory, a magnetic disk, an optical disk, or the like.

[00135] Detailed above are merely optional embodiments of the present disclosure, and are not intended to limit the present disclosure. Any modifications, equivalent substitutions or improvements that are made within the spirit and principle of the present disclosure should all be included in the protection scope of the present disclosure.

Claims

CLAIMS What is claimed is:

1. A method for controlling charging and discharging of a user-side energy storage device, applicable to an energy storage system, the energy storage system comprising the user-side energy storage device and a transformer to which the energy storage device is connected, an electric device being connected to the transformer, the method comprises: acquiring history electricity consumption data under the transformer in a history period; generating a typical load curve conforming to changes of consumed electric power in a taiget period by performing curve fitting on the history electricity consumption data, the target period being a period defined on a monthly basis; calculating a charging sub-period and a discharging sub-period of the energy storage device in the target period by a charging and discharging strategy corresponding to a curve type of the typical load curve, the charging and discharging strategy being a strategy defined based on the curve type to achieve at least one of load shaving and demand reduction; and controlling the energy storage device to be chaiged in the charging sub-period and discharged in the discharging sub-period.

2. The method according to claim 1, wherein calculating the charging sub-period and the discharging sub-period of the energy storage device in the target period by the charging and discharging strategy comprises: acquiring an energy storage capacity and a discharge efficiency of the energy storage device; determining at least one peak period in the typical load curve based on the energy storage capacity and the discharge efficiency; determining the curve type of the typical load curve based on a marked period to which the at least one peak period belongs, the marked period being divided based on at least one of a peak period, a valley period, and a flat period of electricity prices; and calculating the charging sub-period and the discharging sub-period by the charging and discharging strategy corresponding to the curve type.

3. The method according to claim 2, wherein the marked periods comprise a peak period, an valley period, and a flat period; and a corresponding relationship between the at least one peak period and the curve type comprises: the at least one peak period comprising a first peak period which belongs to the peak period, and the typical load curve being of a first single-peak type; the at least one peak period comprising a second peak period which belongs to the flat or the valley period, and the typical load curve being of a second single-peak type; the at least one peak period comprising two third peak periods which belong to the peak period, and the typical load curve being of a first double-peak type; the at least one peak period comprising two fourth peak periods, one of the fourth peak periods belonging to the peak period and the other of the fourth peak periods belonging to the flat or the valley period, and the typical load curve being of a second double-peak type; and types other than the first single-peak type, the second single-peak type, the first double-peak type, and the second double-peak type.

4. The method according to claim 2, wherein calculating the charging sub-period and the discharging sub-period by the charging and discharging strategy corresponding to the curve type comprises: acquiring a constraint condition for charging and discharging the energy storage device; constructing an objective function based on the charging and discharging strategy and the constraint condition; and acquiring the charging sub-period and the discharging sub-period by solving the objective function.

5. The method according to claims 1 to 4, wherein generating the typical load curve conforming to the changes of the consumed electric power in the target period by performing the curve fitting on the history electricity consumption data comprises: determining n groups of history electricity consumption data corresponding to n first moments in the target period from the history electricity consumption data which is generated at a second moment, at least two different time differences being present between the second moment and the first moment, and different time differences corresponding to different weights; calculating weighted averages of the n groups of history electricity consumption data respectively based on the weights to acquire n target electricity consumption data; and generating the typical load curve by performing the curve fitting on the n target electricity consumption data, wherein n is a positive integer.

6. The method according to claim 5, wherein m history electricity consumption data in each group correspond to m second moments, wherein m is a positive integer; and prior to calculating weighted averages of the n groups of history electricity consumption data respectively based on the weights to acquire the n target electricity consumption data, the method further comprises: calculating m weights corresponding to the m second moments based on a mapping relationship between the first moment and the second moment, and the weight.

7. An apparatus for controlling charging and discharging of a user-side energy storage device, comprising: an acquiring module, configured to acquire history electricity consumption data of a transformer to which the energy storage device is connected in a history period; a generating module, configured to generate a typical load curve conforming to changes of consumed electric power in a target period by performing curve fitting on the history electricity consumption data to, the target period being a period defined on a monthly basis; a calculating module, configured to calculate a charging sub-period and a discharging sub-period of the energy storage device in the target period by a charging and discharging strategy corresponding to a curve type of the typical load curve, the charging and discharging strategy being a strategy defined based on the curve type to achieve at least one of load shaving and demand reduction; and a controlling module, configured to control the energy storage device to be charged in the charging sub-period and discharged in the discharging sub-period.

8. The apparatus according to claim 7, wherein the calculating module comprises: an acquiring sub-module, configured to acquire an energy storage capacity and a discharge efficiency of the energy storage device; a determining sub-module, configured to determine at least one peak period in the typical load curve based on the energy storage capacity and the discharge efficiency; wherein the determining sub-module is configured to determine the curve type of the typical load curve based on a marked period to which the at least one peak period belongs, the marked period being divided based on at least one of a peak period, a valley period, and a flat period of electricity prices; and a calculating module, configured to calculate the charging sub-period and the discharging sub-period by the charging and discharging strategy corresponding to the curve type.

9. A server, comprising: a memory storing at least one executable instruction; and a processor communicably connected to the memory; wherein the processor, when loading and executing the at least one executable instruction, is caused to perform the method for controlling chaiging and discharging of the user-side energy storage device as defined in any one of claims 1 to 6.

10. A non -transitory computer-readable storage medium storing at least one instruction, at least one program, a code set, or an instruction set therein, wherein the at least one instruction, the at least one program, the code set, or the instruction set, when loaded and executed by a processor, causes the processor to perform the method for controlling charging and discharging of the user-side energy storage device as defined in any one of claims 1 to 6.