Bidirectional energy control method and intelligent equipment

Abstract

The invention relates to the technical field of power grid interaction, in particular to a two-way energy control method and intelligent equipment, and aims to solve the problem of how to perform charging and discharging control on two-way energy equipment according to power grid load information and peak and valley electricity prices on the basis of an optimal cost principle. The method comprises the steps that the initial electric quantity of an energy storage module is acquired; acquiring historical electrical load information of a set time period; and predicting a power control curve of a set time period according to the initial electric quantity, the historical electric load information and the expected electric quantity based on a cost optimal principle. On the basis of the predicted power control curve, charging and discharging control is performed on the energy storage module through the bidirectional energy equipment, so that the stability of the power grid can be enhanced, the power supply condition of the power grid can be optimized, the electric charge expenditure of the user can be reduced, the user experience can be improved, income can be brought to the user, and the return on investment of the user can be improved. And by adopting a dynamic programming algorithm, the operation speed is improved, and engineering realization is more convenient.

Description

Technical Field

[0001] The present application relates to the field of grid interaction technology, and in particular to a bidirectional energy control method and intelligent device. Background Art

[0002] With the booming economy, electricity demand continues to grow, placing a heavy burden on the power grid. At the same time, the rapid popularization of electric vehicles not only increases the load on the power grid, but also poses new challenges to the stability of the power grid. For users, electricity bills during peak hours are also a considerable expense.

[0003] In order to deal with these problems, it is particularly important to effectively use bidirectional energy equipment such as bidirectional charging piles and bidirectional battery swap stations. Therefore, how to enable bidirectional energy equipment to inject electricity into the grid at the right time according to grid load information and peak and valley electricity prices, improve the power supply of the grid, and reduce users' electricity bills and even bring electricity bill benefits to users has become an urgent problem to be solved.

[0004] Accordingly, the art needs a new bidirectional energy control solution to solve the above problems. Summary of the invention

[0005] In order to overcome the above-mentioned defects, the present application is proposed to solve or at least partially solve the technical problem of how to control the charging and discharging of bidirectional energy equipment based on the cost-optimal principle according to grid load information and peak and valley electricity prices.

[0006] In a first aspect, a bidirectional energy control method is provided, the method comprising: Obtaining the initial power of the energy storage module at the start time of a set time period; Obtaining historical power load information for the set time period; Based on the cost optimization principle, according to the initial power consumption, the historical power load information, and the expected power consumption at the end time of the set time period, a power control curve corresponding to the set time period is predicted; During the set time period, the energy storage module is controlled based on the power control curve.

[0007] In a technical solution of the above-mentioned bidirectional energy control method, the method for obtaining the power control curve includes: Based on the historical power load information, predict the predicted charging power and the predicted discharging power corresponding to each unit time period within the set time period; According to the initial power, the predicted charging power and the predicted discharging power corresponding to each unit time period in the set time period, the power constraint condition, and the working state of the energy storage module, a first number of first power prediction sets and predicted cost values ​​corresponding to each of the first power prediction sets are obtained through a dynamic programming algorithm; Based on the expected power and the cost optimization principle, combined with the predicted cost value, the first power prediction set is screened to obtain the power control curve; The power constraint conditions include a minimum discharge power and a maximum charge power, and the working state of the energy storage module includes a charging state, a discharging state and an idle state.

[0008] In a technical solution of the above-mentioned bidirectional energy control method, the method for obtaining the power control curve further includes: Based on the historical power load information, predict the predicted charging power and the predicted discharging power corresponding to each unit time period within the set time period; Based on the expected power, according to the initial power, the predicted charging power and the predicted discharging power corresponding to each unit time period in the set time period, the power constraint, and the working state of the energy storage module, a second power prediction set of a second quantity is obtained through a dynamic programming algorithm; Obtaining predicted cost values ​​corresponding to each of the second power prediction sets; Based on the cost optimization principle and according to the predicted cost value, the second quantity of second power prediction sets is screened to obtain the power control curve; The power constraint conditions include a minimum discharge power and a maximum charge power, and the working state of the energy storage module includes a charging state, a discharging state and an idle state.

[0009] In a technical solution of the above-mentioned bidirectional energy control method, the method for obtaining the second power prediction set includes: Based on the expected power, according to the initial power, the predicted charging power and the predicted discharging power corresponding to each unit time period in the set time period, the power constraint condition, and the working state of the energy storage module, a second power prediction set of the second quantity is directly obtained through a dynamic programming algorithm; or, According to the initial power, the predicted charging power and the predicted discharging power corresponding to each unit time period within the set time period, the power constraint condition, and the working state of the energy storage module, a third amount of predicted power that may be generated at the end of the set time period is obtained through a dynamic programming algorithm; Comparing the predicted power with the expected power, and screening out a fourth number of predicted power that meets the standard, the predicted power that meets the standard being greater than or equal to the expected power; From the end time of the set time period, each of the predicted up-to-standard electricity quantities is traced back in time to obtain a second electricity quantity prediction set of the second quantity.

[0010] In a technical solution of the above-mentioned bidirectional energy control method, the method for obtaining the predicted cost value includes: The predicted cost value is obtained based on the accumulated charging cost, accumulated discharging cost and accumulated depreciation cost in the set time period.

[0011] In a technical solution of the above-mentioned bidirectional energy control method, the method for obtaining the predicted cost value further includes: Obtaining the real-time electricity price corresponding to each unit time period within the set time period; Based on the predicted charging power and the real-time electricity price, the charging fee per unit time period corresponding to the unit time period in the charging state is obtained; based on the predicted discharging power and the real-time electricity price, the discharging fee per unit time period corresponding to the unit time period in the discharging state is obtained; or The power change value of each unit time period is obtained, and based on the power change value and the real-time electricity price, the unit time period charging fee or the unit time period discharging fee corresponding to the unit time period is obtained.

[0012] In a technical solution of the above-mentioned bidirectional energy control method, the method for obtaining the predicted cost value further includes: Obtaining a power change value in each unit time period; Based on the power change value and a preset depreciation rate, the unit time period depreciation corresponding to each unit time period is obtained.

[0013] In a technical solution of the above-mentioned bidirectional energy control method, the method further includes: When the predicted cost value includes the profitable predicted cost value, acquiring the power control curve according to the predicted cost value with the largest profit value; When the predicted cost value includes the predicted cost value that does not include a profitable one, the power control curve is obtained according to the predicted cost value with the smallest absolute value.

[0014] In a second aspect, a smart device is provided, comprising at least one processor; and a memory communicatively connected to the at least one processor; wherein a computer program is stored in the memory, and when the computer program is executed by the at least one processor, the method described in any one of the technical solutions of the above-mentioned bidirectional energy control method is implemented.

[0015] In a technical solution of the above-mentioned smart device, the smart device is a bidirectional energy device.

[0016] The above one or more technical solutions of the present application have at least one or more of the following beneficial effects: In the implementation of the bidirectional energy control technical solution provided by this application, by predicting the power control curve based on historical power load information, the expected power at the end of charging, and the cost optimization principle, and controlling the charging and discharging process of the energy storage module through bidirectional energy equipment, it is possible to inject electric energy into the power grid at the appropriate time, enhance the stability of the power grid, and optimize the power supply of the power grid, and reduce the user's electricity bill expenditure, improve the user experience, and even bring benefits to the user and improve the user's return on investment. By adopting a dynamic programming algorithm, compared with the traditional optimization solution algorithm, the amount of data processing can be reduced and the calculation speed can be improved, so that the method of this application is more convenient for engineering implementation. . BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The disclosure of the present application will become more easily understood with reference to the accompanying drawings. It is easy for those skilled in the art to understand that these drawings are only for illustrative purposes and are not intended to limit the scope of protection of the present application.

[0018] Figure 1 It is a flowchart of the main steps of a bidirectional energy control method according to an embodiment of the present application.

[0019] Figure 2 It is a schematic diagram of a typical distribution network system architecture applicable to the bidirectional energy control method of the embodiment of the present application.

[0020] Figure 3 It is a detailed flowchart diagram of step S103 of the bidirectional energy control method according to an embodiment of the present application.

[0021] Figure 4 It is a flowchart of the steps of SOC dynamic planning according to an embodiment of the present application.

[0022] Figure 5 It is a detailed flowchart diagram of step S103 of the bidirectional energy control method according to another embodiment of the present application.

[0023] Figure 6 It is a detailed flowchart diagram of step S502 of the bidirectional energy control method according to another embodiment of the present application.

[0024] Figure 7 It is a flowchart of the steps of SOC dynamic planning according to another embodiment of the present application.

[0025] Figure 8 It is a schematic diagram of the main structure of a smart device according to an embodiment of the present application. DETAILED DESCRIPTION

[0026] Some embodiments of the present application are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present application and are not intended to limit the protection scope of the present application.

[0027] In the description of the present application, "module" and "processor" may include hardware, software or a combination of the two. A module may include hardware circuits, various suitable sensors, communication ports, memory, and may also include software parts, such as program code, or a combination of software and hardware. The processor may be a central processing unit, a microprocessor, an image processor, a digital signal processor or any other suitable processor. The processor has data and / or signal processing functions. The processor may be implemented in software, hardware or a combination of the two. Computer-readable storage media include any suitable medium that can store program code, such as a disk, a hard disk, an optical disk, a flash memory, a read-only memory, a random access memory, etc. The term "A and / or B" means all possible combinations of A and B, such as only A, only B or A and B. The term "at least one A or B" or "at least one of A and B" has a similar meaning to "A and / or B" and may include only A, only B or A and B. The singular terms "one" and "the" may also include plural forms.

[0028] The relevant user personal information that may be involved in the various embodiments of this application is strictly in accordance with the requirements of laws and regulations, following the principles of legality, legitimacy and necessity, based on the reasonable purposes of business scenarios, to process the personal information that users actively provide during the use of products / services or generated due to the use of products / services, as well as the personal information obtained with the user's authorization.

[0029] The user personal information processed by this application will vary depending on the specific product / service scenario, and shall be based on the specific scenario in which the user uses the product / service, and may involve the user's account information, device information, driving information, vehicle information or other related information. This application will treat the user's personal information and its processing with a high degree of diligence.

[0030] This application attaches great importance to the security of user personal information and has taken reasonable and feasible security protection measures that meet industry standards to protect user information and prevent personal information from being accessed, disclosed, used, modified, damaged or lost without authorization.

[0031] In this application, energy storage module refers to an energy storage device that can store and release electrical energy by means of electrochemical energy storage, electrical energy storage, etc., including but not limited to lead-acid batteries, lithium-ion batteries, supercapacitors, etc.; and its specific structural form is not limited in this application. As an example, it includes power batteries in new energy vehicles; energy storage batteries in UPS equipment; energy storage batteries in energy storage power stations and energy storage cabinets, etc.

[0032] See attached Figure 1 , Figure 1 FIG. 1 is a flow chart of the main steps of a bidirectional energy control method according to an embodiment of the present application. Figure 1 As shown, the bidirectional energy control method in the embodiment of the present application mainly includes: Step S101: obtaining the initial power of the energy storage module at the start time of a set time period; Step S102: Acquire historical power load information for a set time period; Step S103: Based on the cost optimization principle, according to the initial power consumption, historical power load information, and the expected power consumption at the end of the preset time period, the power control curve corresponding to the set time period is predicted; Step S104: controlling the energy storage module based on the power control curve within a set time period.

[0033] Continue reading Figure 2 , Figure 2 It is a schematic diagram of a typical distribution network system architecture applicable to the bidirectional energy control method of the embodiment of the present application.

[0034] The distribution network system includes one or more branches. Schematically, Figure 2 It includes branch 10 and branch 20, and branch 10 includes branch 11, branch 12, branch 13 and branch 14. Branch 11 is connected to the power load, branch 12 is connected to the energy storage power station, branch 13 is connected to the V2G (Vehicle-to-Grid) charging pile, and branch 14 is connected to the distributed energy system, such as a photovoltaic power station.

[0035] The distribution network system also includes a main grid meter 0, a main branch meter 10, a main branch meter 20, and a main branch meter 11, a branch meter 12, a branch meter 13, and a branch meter 14.

[0036] The server can directly obtain the power metering data including timestamps from each meter, or obtain the power metering data including timestamps from each meter connected to the communication repeater through the communication repeater, and can store the power metering data of each meter to form the historical power load information of the distribution network system. The server can also obtain the real-time electricity price (peak and valley electricity price) from the power company on a regular basis.

[0037] This application does not limit the data communication method between related equipment in the distribution network system such as servers, electricity meters, V2G charging piles, energy storage power stations, etc. As an example, data communication can be carried out by power carrier, 4G / 5G network, etc., and technical personnel in this field can adopt a suitable data communication method according to actual conditions.

[0038] In an application scenario according to an embodiment of the present application, a user uses a V2G charging pile to charge an electric car at night, sets the charging period from 8 pm to 8 am the next day, and expects the SOC of the on-board power battery to be no less than 90% when picking up the car, while achieving optimal cost.

[0039] It should be pointed out that the present application does not limit the method by which the user sets the above-mentioned related parameters. As an example, the user can set them through a smart phone or through the operation panel of the V2G charging pile.

[0040] Step S101: obtaining the initial power of the energy storage module at the start time of a set time period.

[0041] In this embodiment, the user's electric car or on-board power battery can be regarded as the energy storage module of this application. The set time period is the charging period set by the user from 8 pm to 8 am the next day. Accordingly, the start time of the set time period is 8 pm and the end time of the set time period is 8 am the next day. At the end time of the set time period, the expected power is 90% of the SOC of the on-board power battery.

[0042] When the user inserts the charging gun into the charging port of the electric vehicle, the V2G charging pile obtains relevant parameters such as the initial power, battery capacity, maximum battery charging power, and maximum battery discharge power through the BMS of the on-board power battery, and transmits the relevant data to the server, which implements the bidirectional energy control method of the embodiment of the present application.

[0043] It should be noted that the time when the charging gun is inserted into the electric vehicle is usually not exactly the same as the start time of the set time period. When the charging gun is inserted into the electric vehicle earlier than the start time of the set time period, the start time of the set time period remains unchanged; when the charging gun is inserted into the electric vehicle later than the start time of the set time period, the actual time when the charging gun is inserted into the electric vehicle can be used as the start time of the set time period.

[0044] Step S102: Obtain historical power load information for a set time period.

[0045] In this embodiment, according to the time range of the set time period, the server obtains all historical power load information from 8 pm on the current day to 8 am on the next day in the set historical period (for example, the last 10 days). Those skilled in the art can also set the set historical period to other values, such as 15 days or 30 days, according to actual conditions.

[0046] Step S103: Based on the cost optimization principle, according to the initial power consumption, historical power load information, and the expected power consumption at the end of the preset set time period, the power control curve corresponding to the set time period is predicted.

[0047] Continue to refer to the attached Figure 3 , Figure 3 It is a detailed flowchart diagram of step S103 of the bidirectional energy control method according to an embodiment of the present application.

[0048] Step S301: Based on historical power load information, predict the predicted charging power and the predicted discharging power corresponding to each unit time period within a set time period.

[0049] In this embodiment, the set time period can be divided into several consecutive unit time periods according to the unit time length, such as 1 hour. Those skilled in the art can also set different unit time lengths according to actual conditions, such as 30 minutes.

[0050] Moreover, the unit durations of each unit time period within the set time period may be all the same or partially the same. For example, when the electricity price of the set time period has a time-of-use electricity price (peak and valley electricity price), one or more unit durations may be set according to the electricity price time interval range so that the endpoints of the time interval of the time-of-use electricity price coincide with the endpoints of each unit time period. Without departing from the principles of this application, these modified or replaced technical solutions will fall within the scope of protection of this application.

[0051] According to the historical power load information of the electric meter 13, that is, the historical power load information of the electric meter 13 in the branch 13 in the same historical time period corresponding to each unit time period in the set time period, the predicted charging power and the predicted discharging power corresponding to each unit time period in the set time period are obtained.

[0052] As an example, when the charging efficiency is not considered, according to the electric energy E output to the V2G charging pile recorded in each unit time period during the same period of history by the electric meter 13, ch-13 , directly obtain the historical average charging power E corresponding to each unit time period ch-13 / t, where t is the unit time; and the historical average charging power is used as the predicted charging power P required by the current predicted power control curve ch , that is, P ch =E ch-13 / t.

[0053] When considering the charging efficiency, η ch , predict the charging power and the charging energy E of the meter 13 ch-13 The relationship is: P ch =(E ch-13 / t)*η ch .

[0054] Without considering the discharge conversion efficiency, according to the electric energy E output to the power grid (branch 10) recorded by the electric meter 13 in each unit time period during the same period of history dch-13 , and obtain the historical average discharge power E corresponding to each unit time period ch-13 / t, and take the historical average discharge power as the predicted discharge power P required by the current predicted power control curve dch , that is, P dch =E dch-13 / t.

[0055] When considering the discharge efficiency η dch , predict the discharge power and the discharge energy E of the meter 13 dch-13 The relationship is: P dch =(E dch-13 / t) / η dch .

[0056] In another embodiment, the server can also predict the power demand of branch 13 in each unit time period in the set time period based on the historical power load information of all branches in the historical same period branch 10, and obtain the power demand of the V2G charging pile (i.e., the predicted charging power) and the power supply power that needs to be provided to the power grid (predicted discharge power).

[0057] It should be pointed out that the method of predicting the power demand of each power grid branch in different time periods based on the historical data of all power grid branches is a well-known technology in the art and will not be described in detail here.

[0058] Step S302: According to the initial power, the predicted charging power and the predicted discharging power corresponding to each unit time period in the set time period, the power constraint conditions, and the working state of the energy storage module, a first number of first power prediction sets and the predicted cost values ​​corresponding to each first power prediction set are obtained through a dynamic programming algorithm.

[0059] In this embodiment, in each unit time period within the set time period, the vehicle power battery generally has three possible working states: charging state, discharging state and idle state. And during the charging and discharging process, the power of the vehicle power battery should meet the following power constraint conditions: the battery power is greater than or equal to the minimum discharge power, and less than or equal to the maximum charge power.

[0060] Therefore, within the set time period, according to the time sequence, the three possible working states of the vehicle power battery are considered in each unit time period, and the predicted charging power or predicted discharging power in each unit time period is set to a constant value. Through the dynamic programming algorithm, the first quantity of the first power prediction set that may be generated within the set time period is predicted.

[0061] Continue reading Figure 4 , combined with Figure 4 Describe the specific process of dynamic programming of this application. Figure 4 The horizontal coordinate in the middle is the SOC dimension, corresponding to the numerical range of 0 to 100% of the vehicle power battery SOC; the vertical coordinate is the time dimension, corresponding to each unit time period of the set time period.

[0062] The initial charge of the vehicle power battery at the start time of the set time period (time 0, 20:00) is SOC0, for example, 33%. According to the predicted charging power P corresponding to the 0-1 unit time period (20:00-21:00), ch-01 and predicted discharge power P dch-01 , combined with the three working states of the vehicle power battery (charging state, discharging state and idle state), it is predicted that the three possible power levels at time 1 (21:00) are SOC 11 , SOC 12 and SOC 13 .

[0063] Among them, SOC 11 For P dch-01 The remaining battery capacity after discharge; SOC 13 For P ch-01 Battery capacity after charging; SOC 12 The value is the same as SOC0.

[0064] Continue to calculate the predicted charging power P corresponding to the 1-2 unit time period ch-12 and predicted discharge power P dch-12 , and the three working states of the vehicle power battery, respectively, for the SOC that may exist at 1 moment 11 , SOC 12 and SOC 13 Make predictions.

[0065] Get, SOC 11 The three possible charges corresponding to time 2 are SOC 21 , SOC 22 and SOC 23 , SOC 12 The three possible charges corresponding to time 2 are SOC 24 , SOC 25 and SOC 26 , SOC 13 The three possible charges corresponding to time 2 are SOC 27 , SOC 28 and SOC 29 .

[0066] It should be pointed out that in the charging or discharging prediction process of the unit time period (t-1) to t, the time has not yet reached time t, but the power of the on-board power battery has triggered the power constraint condition. At this time, it is necessary to stop the corresponding charging or discharging process according to the type of power constraint condition.

[0067] When the minimum discharge power is triggered, the discharge process is stopped, the SOC at time t is recorded as the minimum discharge power, and the actual discharge time is recorded; when the maximum charge power is triggered, the charging process is stopped, the SOC at time t is recorded as the maximum charge power, and the actual charging time is recorded.

[0068] As an example, Figure 4 As shown, when predicting in 1 to 2 unit time periods, SOC 11 By predicting the discharge power P dch-12 During discharge, after only t' time, the power of the vehicle power battery has reached the minimum discharge power. At this time, the discharge process should be stopped and the SOC at time 2 should be recorded. 21 The minimum discharge capacity is 5%, for example, and the actual charging time is recorded as t'.

[0069] Similarly, according to the time sequence, continue to predict the charging power P corresponding to the unit time period t~(t+1) ch-t(t+1) and predicted discharge power P dch-t(t+1) , and the three working states of the on-board power battery, based on all the SOC values ​​obtained at time t, predict the SOC that may be generated at time (t+1) (the same SOC values ​​at time t are not combined, and each SOC is predicted according to the three working states respectively), until the prediction of the unit time period from (T-1) to T is completed, and n SOC values ​​corresponding to the end time T of the set time period are obtained (the n SOCs may have the same SOC value), as well as the first quantity of the first power prediction set accordingly.

[0070] The general calculation method of the battery power at time t+1 is: SOC t+1 =SOC t +(E t(t+1) / E) Formula 1 Among them, E is the battery capacity of the vehicle power battery, E t(t+1) It is the amount of electricity discharged or charged by the vehicle power battery in the unit time period t to (t+1).

[0071] E t(t+1) The general calculation method is: Among them, T0 is the charging or discharging time.

[0072] From equations 1 and 2, we can see that when charging, Et(t+1) A positive value indicates that the SOC is at the end of charging. t+1 Increase; when discharging E t(t+1) It is a negative value, and the SOC at the end of discharge is t+1 Reduce.

[0073] It should be noted that when the vehicle power battery is in a charging state or a discharging state throughout the unit time period, T0 is equal to the unit time length of the unit time period. When charging or discharging is stopped due to the triggering of constraint conditions within the unit time period, E t(t+1) The T0 used will use the actual charge or discharge time, such as Figure 4 t′ in .

[0074] In the process of predicting SOC through dynamic programming, record the time from SOC0 to SOC Tx (SOC T1 , SOC T2 ,…,SOC Tn A first quantity of first power prediction sets is obtained by combining all possible paths generated by the method.

[0075] The first number is greater than or equal to n, and the first number is 3 N , N is the number of unit time periods in the set time period. Each first power prediction set {SOC0, SOC 1x ,…,SOC Tx}The number of elements in is N+1.

[0076] When predicting the first quantity of the first power forecast set, the unit time period charging cost or unit time period discharging cost and battery depreciation cost generated in each unit time period can be calculated according to the power change in each unit time period, and the costs can be accumulated.

[0077] As an example, the calculation method of the cost of charging or discharging in the unit time period t to (t+1) (unit time period charging fee or unit time period discharging fee) is: -ct*(E*(SOC t+1 -SOC t )) Formula 3 Among them, c t It is the real-time electricity price in the unit time period from t to (t+1).

[0078] When SOC t+1 Greater than SOC t When SOC is t~(t+1), the vehicle power battery is charged in the unit time period of t~(t+1), and the charging fee per unit time period is negative, which is an expense for the user; when SOC t+1 Less than SOC tWhen t is reached, the on-board power battery discharges in the unit time period of t to (t+1). The discharge fee per unit time period is a positive number, which is income for the user.

[0079] In another embodiment, the charging fee per unit time period or the discharging fee per unit time period may also be calculated directly based on the predicted charging power or the predicted discharging power and the real-time electricity price. Formula 2 can be obtained, and Formula 3 can also be expressed as:

[0080] The cumulative charging fee is obtained by adding up the charging fees of all unit time periods within the set time period; the cumulative discharging fee is obtained by adding up the discharging fees of all unit time periods within the set time period.

[0081] The calculation method of depreciation cost per unit time period of vehicle power battery is: -const dp *abs(SOC t+1 -SOC t ) Formula 5 Among them, const dp is the preset depreciation rate, abs is the absolute value function, and the negative sign indicates that the fee is an expenditure relative to the user.

[0082] For the depreciation rate const dp , taking into account the difference between the depreciation of the battery during charging and discharging, the depreciation rate const dp It can be further expressed as: Among them, const dp-ch is the charging depreciation rate, const dp-dch is the discharge depreciation rate.

[0083] As an example, for a certain type of vehicle-mounted power battery, the charging depreciation rate is 0.04 yuan / KWH, and the discharging depreciation rate is 0.02 yuan / KWH. Those skilled in the art can also set the corresponding depreciation rate according to the actual situation of the energy storage module.

[0084] For each unit time period, when the on-board power battery is in a charging state, the depreciation fee per unit time period is specifically the charging depreciation fee per unit time period; when the on-board power battery is in a discharging state, the depreciation fee per unit time period is specifically the discharging depreciation fee per unit time period.

[0085] According to the unit time period charging fee or unit time period discharging fee and unit time period depreciation fee (unit time period charging depreciation fee or unit time period discharging depreciation fee) of each unit time period corresponding to each first power forecast set, the cumulative charging fee, cumulative discharging fee and cumulative depreciation fee of the set time period are calculated, and then the predicted cost value corresponding to each first power forecast set at the end time T of the set time period is obtained.

[0086] The general calculation method for the predicted cost value is: Where C is the predicted cost value, Including accumulated charging fees and accumulated discharging fees; The accumulated depreciation fee includes the accumulated charging depreciation rate and the accumulated discharging depreciation rate.

[0087] Step S303: Based on the expected power and cost optimization principle and in combination with the predicted cost value, the first power prediction set is screened to obtain a power control curve.

[0088] In this embodiment, the first power prediction set is firstly screened according to the expected power, and only the first power prediction set whose SOC value at the end time T of the set time period is greater than or equal to the expected power is retained.

[0089] Based on the cost optimization principle, the first power forecast set that reaches the user's expected power is screened for the second time.

[0090] By comparing the predicted cost values, when the predicted cost values ​​include positive values, it indicates that there are profitable predicted cost values. At this time, the first electricity forecast set corresponding to the predicted cost value with the largest profit value can be screened out.

[0091] The working state and the predicted charging power and the predicted discharging power corresponding to each unit time period are determined according to the first power prediction set, and then the power control curve of the set time period is obtained according to the time sequence combination.

[0092] As an example, Figure 4 As shown in the figure, the first power forecast set that satisfies both the expected power and the optimal cost is {SOC0, SOC 13 , SOC 27 ,…,SOC Tx}, the power control curve corresponding to the first power prediction set can be represented by a set of predicted charging power or predicted discharging power in each unit time period, specifically {P ch-01 , Pd ch-12 ,…}.

[0093] Compared with traditional optimization solution algorithms, dynamic programming algorithms can reduce the amount of data processing and improve the calculation speed, making the method of the present application easier to implement in engineering.

[0094] Continue to refer to the attached Figure 5 , Figure 5 It is a detailed flowchart diagram of step S103 of the bidirectional energy control method according to another embodiment of the present application.

[0095] Step S501: Based on historical power load information, predict the predicted charging power and the predicted discharging power corresponding to each unit time period within a set time period.

[0096] The implementation method of step S501 is the same as that of step S301, which will not be described in detail here. For the specific method, please refer to the above embodiment.

[0097] Step S502: Based on the expected power, according to the initial power, the predicted charging power and the predicted discharging power corresponding to each unit time period in the set time period, the power constraint, and the working state of the energy storage module, a second quantity of second power prediction sets are obtained through a dynamic programming algorithm.

[0098] Continue reading Figure 6 and Figure 7 , Figure 6 is a detailed flow chart of step S502 of the bidirectional energy control method according to another embodiment of the present application, Figure 7 is a schematic flow chart of the steps of SOC dynamic planning according to another embodiment of the present application, combined with Figure 6 and Figure 7 The specific process flow of step S502 is described.

[0099] Step S601: Based on the initial power, the predicted charging power and the predicted discharging power corresponding to each unit time period in the set time period, combined with the power constraint and the working state of the energy storage module, a third amount of predicted power that may be generated at the end of the set time period is obtained through a dynamic programming algorithm.

[0100] Step 601 is an optimization method based on step S302. Step 302 retains all SOC values ​​in the dynamic programming prediction process, that is, if there are multiple SOCs with the same value at time t, they will be used as elements in different first power prediction sets to predict the SOC for the next unit time period.

[0101] Considering that for multiple SOCs with the same value, the SOC values ​​predicted in the next unit time period are all the same, therefore, in step 601 , SOCs with the same value are merged during the dynamic programming prediction process to reduce the amount of data in the dynamic programming prediction process.

[0102] like Figure 7 As shown, when predicting in 1 to 2 unit time periods, SOC 12 Predicted charging power P ch-12 The amount of charge after charging is SOC 26 ;SOC 13 Predicted discharge power P dch-12 The amount of electricity after discharge is SOC 27 .

[0103] SOC 26 and SOC 27 The values ​​of SOC are the same. 26 and SOC 27 Can be combined, when predicting in 2-3 unit time periods, only SOC 26 or SOC 27 According to the three working states of the vehicle power battery and the predicted charging power P corresponding to the 2-3 unit time period, ch-23 and predicted discharge power P dch-23 Conduct power consumption forecasting.

[0104] Referring to Formula 1 and Formula 2 of the above embodiment, the SOC value at each time t is calculated, and at the same time t, the SOC with the same value is combined in the prediction process to obtain the predicted power (SOC m) of the third amount m that may be generated at the end time T of the set time period. T1 , SOC T2 ,…,SOC Tm ).

[0105] For the end time T of the set time period, since there is no need to perform SOC dynamic planning prediction, the SOCs with the same value corresponding to the time T can be merged or not.

[0106] Step S602: Compare the predicted power with the expected power, and screen out a fourth number of predicted power that meets the standard, where the predicted power that meets the standard is greater than or equal to the expected power.

[0107] The predicted power quantities corresponding to the time T are compared with the expected power quantities, and a fourth number of predicted power quantities that meet the standards are obtained by screening, and the predicted power quantities that meet the standards are greater than or equal to the expected power quantities.

[0108] Step S603: From the end of the set time period, each predicted power quantity that meets the standard is traced back in time to obtain a second power quantity prediction set of the second quantity.

[0109] For all the predicted electricity quantities that meet the standards, starting from the end time T of the set time period, each predicted electricity quantity that meets the standards is traced back unit by unit time period in order from the back to the front until SOC0 corresponding to the starting time 0, to obtain a second quantity of second electricity quantity forecast set.

[0110] During the backtracking process, for each merged SOC, it is necessary to backtrack the related SOC at the previous moment. Therefore, each predicted power quantity that meets the standard may backtrack multiple second power quantity prediction sets, that is, the second number is greater than or equal to the fourth number.

[0111] As an example, Figure 7 As shown, SOC Tm Back to SOC 26 When there is only one path, but due to SOC 26 and SOC 27 A merge has been performed, so when backtracking from time 3 to time 2, SOC 26 There will be two paths SOC 26 →SOC 12 and SOC 26 →SOC 13 Accordingly, SOC Tm Backtracking will also generate two paths, namely two second power prediction sets, which are: {SOC0, SOC 12 , SOC 26 ,…,SOC Tx} and {SOC0, SOC 13 , SOC 26 ,…,SOC Tx}.

[0112] Step 503: Obtain the predicted cost value corresponding to each second electricity quantity prediction set.

[0113] The calculation method of the predicted cost value corresponding to each second power prediction set is the same as the calculation method of the predicted cost value in the aforementioned embodiment, and will not be repeated here.

[0114] After the electricity forecast is completed, first execute steps S602 and S603, filter the predicted electricity according to the expected electricity, and backtrack to obtain the second electricity forecast set, and then execute step S503 to only calculate the predicted cost value corresponding to the second electricity forecast set that meets the expected electricity. This also reduces the amount of data that needs to be processed in this application.

[0115] Step 504: Based on the cost optimization principle and according to the predicted cost value, a second number of second power prediction sets are screened to obtain a power control curve.

[0116] The implementation method of step 504 is the same as the implementation method of the aforementioned step S303. For the detailed process, please refer to the embodiment of step S303, which will not be repeated here.

[0117] In another embodiment, based on the expected power, according to the initial power, the predicted charging power and the predicted discharging power corresponding to each unit time period in the set time period, combined with the power constraint conditions and the working status of the energy storage module, a second quantity of second power prediction set is directly obtained through a dynamic programming algorithm.

[0118] First, referring to the method of the aforementioned embodiment, SOCs with the same value in the dynamic planning prediction process are not merged, and a first quantity of first power prediction sets are obtained, but the prediction cost value is not calculated.

[0119] After obtaining the first quantity of first power forecast sets, the first quantity of first power forecast sets are screened according to the expected power to obtain the second quantity of second power forecast sets. At this time, the power SOC corresponding to the end time T of the set time period in each second power forecast set is greater than or equal to the expected power.

[0120] Continuing to refer to the method of the above step 503 and step 504, a power control curve corresponding to the set time period is obtained.

[0121] Step S104: controlling the energy storage module based on the power control curve within a set time period.

[0122] In this embodiment, the server sends the power control curve to the V2G charging pile, and within the set time period, the V2G charging pile controls the charging and discharging of the vehicle power battery to achieve the optimal cost.

[0123] In another embodiment, the bidirectional energy control method of the present application can also be implemented by a V2G charging pile.

[0124] The V2G charging pile obtains historical power load information and real-time electricity prices from the server, and predicts the power control curve for the set time period. When the set time period arrives, the charging and discharging of the on-board power battery is controlled based on the power control curve.

[0125] Through the method of the present application, the control method of the V2G charging pile is optimized, which can reduce the electricity bill expenditure of electric vehicle users, thereby improving the user experience.

[0126] In another embodiment, the bidirectional energy control method of the present application is also applicable to backup energy systems of communication base stations, server rooms, etc.

[0127] Under the premise of ensuring the amount of energy stored, combined with data such as peak and valley electricity prices and historical electricity load information, the energy storage device of the backup energy system can be charged and discharged. This can not only inject electricity into the power grid at the right time, enhance the stability of the power grid, and optimize the power supply of the power grid, but also bring benefits to users and improve their return on investment.

[0128] It should be pointed out that in the relevant calculations in the above embodiments, the measurement units need to be unified. As an example, the time unit is unified as hours, and the electricity unit is unified as KWH. Accordingly, the real-time electricity price is yuan / kwh, and the depreciation rate is yuan / kwh. Technical personnel in this field may also adopt other measurement units according to actual conditions.

[0129] It should be pointed out that although the various steps in the above embodiments are described in a specific order, those skilled in the art can understand that in order to achieve the effect of the present application, different steps do not have to be executed in such an order. They can be executed simultaneously (in parallel) or in other orders. These adjusted schemes are equivalent to the technical schemes described in this application, and therefore will also fall within the scope of protection of this application.

[0130] It is understood by those skilled in the art that all or part of the processes in the method for implementing the above-mentioned embodiment of the present application can also be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium may include: any entity or device, medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal and software distribution medium, etc. that can carry the computer program code.

[0131] Another aspect of the present application also provides a smart device.

[0132] In an embodiment of an intelligent device according to the present application, the intelligent device may include at least one processor; and a memory connected to the at least one processor; wherein a computer program is stored in the memory, and when the computer program is executed by the at least one processor, the bidirectional energy control method described in any of the above embodiments is implemented. The intelligent device 8 described in the present application may include bidirectional energy devices such as V2G charging piles, UPS, energy storage cabinets, and energy storage power stations. See the attached Figure 8 , Figure 8 8 shows by way of example that the memory 81 and the processor 82 are communicatively connected via a bus.

[0133] In some embodiments of the present application, the smart device may further include at least one sensor, which is used to sense information, such as electrical power information. The sensor is communicatively connected to any type of processor mentioned in the present application.

[0134] In some embodiments of the present application, the smart device may further include at least one communication module, which is used to obtain data such as energy storage module data and real-time electricity prices. The communication module is communicatively connected to any type of processor mentioned in the present application. Optionally, the communication module includes but is not limited to a Wi-Fi module, a CAN module, and a 4G / 5G module.

[0135] So far, the technical solution of the present application has been described in conjunction with an embodiment shown in the accompanying drawings, but it is easy for those skilled in the art to understand that the protection scope of the present application is obviously not limited to these specific embodiments. Without departing from the principles of the present application, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present application.

Claims

1. A bidirectional energy control method, characterized in that: The method comprises: Obtaining the initial power of the energy storage module at the start time of a set time period; Obtaining historical power load information for the set time period; Based on the cost optimization principle, according to the initial power consumption, the historical power load information, and the expected power consumption at the end time of the set time period, a power control curve corresponding to the set time period is predicted; During the set time period, the energy storage module is controlled based on the power control curve.

2. The bidirectional energy control method according to claim 1, characterized in that: The method for acquiring the power control curve includes: Based on the historical power load information, predict the predicted charging power and the predicted discharging power corresponding to each unit time period within the set time period; According to the initial power, the predicted charging power and the predicted discharging power corresponding to each unit time period in the set time period, the power constraint condition, and the working state of the energy storage module, a first number of first power prediction sets and predicted cost values ​​corresponding to each of the first power prediction sets are obtained through a dynamic programming algorithm; Based on the expected power and the cost optimization principle, combined with the predicted cost value, the first power prediction set is screened to obtain the power control curve; The power constraint conditions include a minimum discharge power and a maximum charge power, and the working state of the energy storage module includes a charging state, a discharging state and an idle state.

3. The bidirectional energy control method according to claim 1, characterized in that: The method for obtaining the power control curve also includes: Based on the historical power load information, predict the predicted charging power and the predicted discharging power corresponding to each unit time period within the set time period; Based on the expected power, according to the initial power, the predicted charging power and the predicted discharging power corresponding to each unit time period in the set time period, the power constraint, and the working state of the energy storage module, a second power prediction set of a second quantity is obtained through a dynamic programming algorithm; Obtaining predicted cost values ​​corresponding to each of the second power prediction sets; Based on the cost optimization principle and according to the predicted cost value, the second quantity of second power prediction sets is screened to obtain the power control curve; The power constraint conditions include a minimum discharge power and a maximum charge power, and the working state of the energy storage module includes a charging state, a discharging state and an idle state.

4. The bidirectional energy control method according to claim 3, characterized in that: The method for acquiring the second power prediction set includes: Based on the expected power, according to the initial power, the predicted charging power and the predicted discharging power corresponding to each unit time period in the set time period, the power constraint condition, and the working state of the energy storage module, a second power prediction set of the second quantity is directly obtained through a dynamic programming algorithm; or, According to the initial power, the predicted charging power and the predicted discharging power corresponding to each unit time period within the set time period, the power constraint condition, and the working state of the energy storage module, a third amount of predicted power that may be generated at the end of the set time period is obtained through a dynamic programming algorithm; Comparing the predicted power with the expected power, and screening out a fourth number of predicted power that meets the standard, the predicted power that meets the standard being greater than or equal to the expected power; From the end time of the set time period, each of the predicted up-to-standard electricity quantities is traced back in time to obtain a second electricity quantity prediction set of the second quantity.

5. The bidirectional energy control method according to claim 2 or 3, characterized in that: The method for obtaining the predicted cost value includes: The predicted cost value is obtained based on the accumulated charging cost, accumulated discharging cost and accumulated depreciation cost in the set time period.

6. The bidirectional energy control method according to claim 5, characterized in that: The method for obtaining the predicted cost value also includes: Obtaining the real-time electricity price corresponding to each unit time period within the set time period; Based on the predicted charging power and the real-time electricity price, the charging fee per unit time period corresponding to the unit time period in the charging state is obtained; based on the predicted discharging power and the real-time electricity price, the discharging fee per unit time period corresponding to the unit time period in the discharging state is obtained; or The power change value of each unit time period is obtained, and based on the power change value and the real-time electricity price, the unit time period charging fee or the unit time period discharging fee corresponding to the unit time period is obtained.

7. The bidirectional energy control method according to claim 5, characterized in that: The method for obtaining the predicted cost value also includes: Obtaining a power change value in each unit time period; Based on the power change value and a preset depreciation rate, the unit time period depreciation corresponding to each unit time period is obtained.

8. The bidirectional energy control method according to claim 2 or 3, characterized in that: The method further comprises: When the predicted cost value includes the profitable predicted cost value, acquiring the power control curve according to the predicted cost value with the largest profit value; When the predicted cost value includes the predicted cost value that does not include a profitable one, the power control curve is obtained according to the predicted cost value with the smallest absolute value.

9. A smart device, characterized in that: include: at least one processor; and, a memory communicatively coupled to the at least one processor; Wherein, a computer program is stored in the memory, and when the computer program is executed by the at least one processor, the bidirectional energy control method according to any one of claims 1 to 8 is implemented.

10. The smart device according to claim 9, characterized in that: The smart device is a bidirectional energy device.

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