Control method and device, electronic equipment and readable storage medium

Abstract

Embodiments of the present application disclose a control method and device, electronic equipment and a readable storage medium. After obtaining the station-related information of the energy storage device including the charging station in the target time period, such as attribute information, state information, price change information of electric energy and load information of the charging station, the embodiments of the present application determine the electric energy consumption function and the electric energy supplement function of the charging station in each time interval of the target time period according to the price change information of the electric energy, and determine the income function of the charging station according to the functions, and determine the constraint condition of the income function, and then determine the electric energy distribution value of the charging station in each time interval and whether to perform electric energy consumption control in each time interval according to the constraint condition, so as to maximize the absolute value of the income function. The embodiments of the present application can accurately estimate the electric energy distribution value of the charging station and the timing of electric energy consumption control based on the optimization algorithm, thereby reducing the operating cost of the charging station.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and more specifically to a control method, apparatus, electronic device, and readable storage medium. Background Technology

[0002] Users of new energy vehicles can replenish their power at charging stations. Therefore, to ensure a sufficient power supply, charging stations need to pre-store a certain amount of electricity. Simultaneously, the charging stations themselves also consume a certain amount of electricity to maintain the normal operation of the equipment within them. However, electricity pricing is affected by the supply and demand dynamics of the electricity market and is prone to fluctuations. Therefore, current technology lacks effective means to estimate the amount of electricity needed for charging stations, and it is also difficult to determine the appropriate time for charging stations to control their energy consumption. Summary of the Invention

[0003] In view of this, embodiments of the present invention provide a control method, apparatus, electronic device, and readable storage medium to accurately estimate the power allocation value of a charging station and the timing of power consumption control based on an optimization algorithm, thereby reducing the operating cost of the charging station.

[0004] In a first aspect, embodiments of the present invention provide a control method, the method comprising:

[0005] Obtain relevant information about the charging station during a target time period. The relevant information includes the attribute information of the energy storage device at the charging station, the price change information of electricity, the status information of the energy storage device, and the load information of the charging station.

[0006] Based on the price change information, determine the energy consumption function and energy replenishment function of the charging station for each time interval of the target time period;

[0007] The revenue function corresponding to the charging station is determined based on the energy consumption function and the energy replenishment function corresponding to each time interval.

[0008] The constraints of the revenue function are determined based on the attribute information, the state information, and the load information.

[0009] With the goal of maximizing the absolute value of the revenue function, the power allocation value and consumption control status of the charging station in each time interval are determined according to the constraints. The consumption control status indicates whether the energy storage device performs power consumption control in the time interval.

[0010] The energy consumption of the energy storage device is controlled according to the energy allocation value and the consumption control state.

[0011] Secondly, embodiments of the present invention provide a control device, the device comprising:

[0012] The information acquisition unit is used to acquire relevant information about the charging station during a target time period. The relevant information includes the attribute information of the energy storage device of the charging station, the price change information of electricity, the status information of the energy storage device, and the load information of the charging station.

[0013] The first function determination unit is used to determine the energy consumption function and energy replenishment function of the charging station for each time interval of the target time period based on the price change information.

[0014] The second function determination unit is used to determine the revenue function corresponding to the charging station based on the energy consumption function and the energy replenishment function corresponding to each time interval.

[0015] The condition determination unit is used to determine the constraints of the revenue function based on the attribute information, the state information, and the load information.

[0016] An information determination unit is used to determine the power allocation value and consumption control status of the charging station in each time interval according to the constraint conditions with the goal of maximizing the absolute value of the revenue function. The consumption control status indicates whether the energy storage device performs power consumption control in the time interval.

[0017] The control unit is used to control the energy consumption of the energy storage device according to the energy allocation value and the consumption control state.

[0018] Thirdly, embodiments of the present invention provide a computer-readable storage medium storing computer program instructions thereon, which, when executed by a processor, implement the method described in the first aspect.

[0019] Fourthly, embodiments of the present invention provide an electronic device, the device comprising:

[0020] Memory is used to store one or more computer program instructions;

[0021] A processor, wherein the one or more computer program instructions are executed by the processor to implement the method as described in the first aspect.

[0022] Fifthly, embodiments of the present invention provide a computer program product that, when run on a computer, causes the computer to perform the method described in the first aspect.

[0023] This invention, after acquiring relevant information about the charging station, including attribute information, status information, electricity price change information, and charging station load information for the energy storage devices within a target time period, determines the energy consumption function and energy replenishment function of the charging station in each time interval of the target time period based on the electricity price change information. It then determines the charging station's revenue function based on these functions, along with constraints on the revenue function. With the objective of maximizing the absolute value of the revenue function, it determines the energy allocation value of the charging station in each time interval and whether energy consumption control should be implemented in each time interval based on the constraints, thereby controlling the energy consumption of the energy storage devices. This invention can accurately estimate the energy allocation value of the charging station and the timing of energy consumption control based on optimization algorithms, thereby reducing the operating costs of the charging station. Attached Figure Description

[0024] The above and other objects, features and advantages of the present invention will become clearer from the following description of embodiments of the invention with reference to the accompanying drawings, in which:

[0025] Figure 1 This is a flowchart of the control method according to an embodiment of the present invention;

[0026] Figure 2 This is a schematic diagram of the data flow according to an embodiment of the present invention;

[0027] Figure 3 This is a schematic diagram of the price change curve of electricity according to an embodiment of the present invention;

[0028] Figure 4 This is a schematic diagram of the control device according to an embodiment of the present invention;

[0029] Figure 5 This is a schematic diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0030] The present application is described below based on embodiments, but it is not limited to these embodiments. In the detailed description of the present application below, certain specific details are described in detail. Those skilled in the art can fully understand the present application without these details. To avoid obscuring the substance of the present application, well-known methods, processes, flows, elements, and circuits are not described in detail.

[0031] Furthermore, those skilled in the art should understand that the accompanying drawings provided herein are for illustrative purposes only and are not necessarily drawn to scale.

[0032] Unless the context explicitly requires it, words such as "including" or "contains" throughout the application should be interpreted as including rather than exclusive or exhaustive; that is, meaning "including but not limited to".

[0033] In the description of this application, it should be understood that the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, in the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0034] The solutions described in this specification and embodiments, if involving the processing of personal information, will be processed only under the premise of having a legal basis (such as obtaining the consent of the personal information subject, or being necessary for the performance of a contract), and will only be processed within the scope stipulated or agreed upon. A user's refusal to process personal information beyond what is necessary for basic functions will not affect the user's use of basic functions.

[0035] With the increasing popularity of environmental protection concepts and the continuous development of computer technology, the adoption of new energy equipment is becoming increasingly widespread. Taking new energy vehicles as an example, users can replenish their vehicles' power at charging stations, such as charging-storage microgrid stations. A charging-storage microgrid station is a comprehensive energy station integrating charging facilities, energy storage systems, and microgrid technology. During periods of low electricity demand or when renewable energy generation is abundant, the charging-storage microgrid station obtains low-cost electricity from the main grid on the one hand, and stores excess electricity generated by distributed power sources into the energy storage system on the other. When electric vehicles or other devices need charging, the charging-storage microgrid station can prioritize the allocation of energy from the energy storage system for charging based on real-time power supply and charging demand. Therefore, to ensure sufficient power supply, charging-storage microgrid stations need to pre-store a certain amount of electricity. Simultaneously, to maintain the normal operation of the various devices within the charging-storage microgrid station, the station itself also consumes a certain amount of electricity.

[0036] Electricity prices are mainly divided into two types: medium- and long-term electricity prices and real-time electricity prices. Influenced by the supply and demand relationship in the electricity market, both medium- and long-term electricity prices fluctuate to varying degrees. Charging-storage microgrids utilize the medium- and long-term peak-valley price difference for arbitrage (i.e., peak-valley arbitrage, a strategy to profit from or reduce costs by exploiting the price difference between peak and off-peak electricity in the electricity market). Meanwhile, charging-storage microgrids rely on energy storage batteries to store electricity. However, as the number of charge-discharge cycles increases, the battery's performance degrades, leading to increased costs for storing electricity in charging-storage microgrids.

[0037] Existing technologies mainly employ manual fixed strategies, intelligent strategies based on load forecasting, and offline operations research simulation methods. However, manual fixed strategies cannot dynamically adjust based on changes in charging station load, fluctuations in electricity market prices, and changes in grid conditions, potentially missing optimal charging and discharging opportunities and thus failing to maximize economic benefits and overall efficiency. Intelligent strategies based on load forecasting are highly dependent on forecast accuracy, making it impossible to accurately predict based on complex electricity pricing mechanisms, uncertain real-time load changes, and the degradation of energy storage batteries. The accuracy of offline operations research simulation methods largely depends on the quality of input data, but the load forecast results for charging stations often exhibit significant uncertainty and volatility, and the precision of battery state parameters is often difficult to quantify accurately. Therefore, in engineering practice, it is difficult to accurately assess the charging and discharging timing and capacity of energy storage devices.

[0038] In this embodiment of the invention, a charging and storage microgrid station is used as an example for description. It should be understood that this embodiment is not limited to this. Charging stations that can support the corresponding functions or that can support the corresponding functions with future technological development are all within the protection scope of this embodiment of the invention.

[0039] To address the aforementioned issues, embodiments of the present invention propose a control method, apparatus, electronic device, and readable storage medium to accurately estimate the power allocation value of a charging station and the timing of power consumption control based on an optimization algorithm, thereby reducing the operating costs of the charging station.

[0040] Figure 1 This is a flowchart of the control method according to an embodiment of the present invention. Figure 1 As shown, the method in this embodiment includes the following steps:

[0041] Step S100: Obtain relevant information about the charging stations for the target time period.

[0042] In practical applications, to facilitate the management and maintenance of charging station operations, charging stations periodically upload various data generated during operation to the charging station management platform's server (hereinafter referred to as the server). Therefore, in this step, the server can obtain relevant information about the charging station for the target time period. This relevant information may include the attribute information of the charging station's energy storage devices, electricity price changes, the status information of the energy storage devices, and the charging station's load information.

[0043] The attribute information of the energy storage device can be provided by the manufacturer, obtained by fitting various data collected from the device, or obtained through other methods; this embodiment does not impose any restrictions on this. The status information of the energy storage device can be obtained by the data acquisition module at preset intervals (e.g., 15 minutes). The load information of the charging station can be statistically analyzed using various existing methods, such as calculations based on the rated power and total number of charging devices, or calculations based on the charging amount and duration of each vehicle within a target time period; this embodiment does not impose any restrictions on this.

[0044] Step S200: Determine the energy consumption function and energy replenishment function of the charging station for each time interval of the target time period based on the price change information.

[0045] Step S300: Determine the revenue function corresponding to the charging station based on the energy consumption function and energy replenishment function corresponding to each time interval.

[0046] Step S400: Determine the constraints of the revenue function based on the attribute information, state information, and load information.

[0047] In this embodiment, step S400 can be executed simultaneously with step S200, or simultaneously with step S300, or sequentially with step S200 or step S300. This embodiment does not impose any restrictions on this.

[0048] Step S500: With the goal of maximizing the absolute value of the revenue function, determine the power allocation value and consumption control status of the charging station in each time interval according to the constraints.

[0049] After obtaining the relevant information about the charging stations for the target time period, the server can obtain the revenue function of the charging stations for the target time period based on an optimization algorithm. With the objective of maximizing the absolute value of the revenue function, the server determines the energy allocation value and consumption control status of the charging stations in each time interval of the target time period according to the constraints. The consumption control status is used to characterize whether the energy storage devices are controlling energy consumption in the corresponding time intervals.

[0050] Figure 2 This is a schematic diagram of the data flow according to an embodiment of the present invention. Figure 2As shown, the server can obtain the attribute information 21 of the energy storage device, the status information 22 of the energy storage device, the price change information of electricity 23, and the load information 24 of the charging station during the target time period. Based on the attribute information 21, the status information 22, the price change information of electricity 23, and the load information 24 of the charging station, the server can construct an optimization algorithm 25, solve the constructed optimization algorithm 25, and determine the electricity allocation value 26 and consumption control status 27 of the charging station in each time interval.

[0051] To facilitate better calculation of the power allocation value and consumption control status of charging stations, this embodiment divides the target time period into multiple time intervals based on power price change information. Specifically, the target time period can be divided into multiple first time intervals based on medium- and long-term power price change information, and each first time interval can be divided into multiple second time intervals based on real-time power price change information. This yields the revenue function of the charging station under different price change information, which may include a first revenue function under medium- and long-term power price change information and a second revenue function under real-time power price change information. Thus, the power allocation value and charging timing of the charging station in each time interval can be determined through optimization.

[0052] Figure 3 This is a schematic diagram of the price change curve of electricity according to an embodiment of the present invention. Figure 3 As shown, curve L1 represents the change in medium- and long-term electricity prices within a single calendar day. Medium- and long-term electricity prices are categorized into four price types: low, common, high, and peek. Therefore, the server can divide a calendar day into multiple consecutive time intervals based on the time nodes of price type changes, thus dividing the target time period into multiple first time intervals. Curve L2 represents the change in real-time electricity prices within a single calendar day. Real-time electricity prices typically fluctuate more frequently. To facilitate practical calculations, real-time electricity prices can be collected at predetermined sampling intervals. Therefore, the server can further divide each first time interval into multiple time intervals based on the sampling interval of real-time electricity prices, thus dividing each first time interval of the target time period into multiple second time intervals. Taking time interval 31 as an example, the server can divide time interval 31 into multiple second time intervals with a sampling interval of 15 minutes.

[0053] In one optional implementation, the server can determine the revenue function of the charging station under medium- to long-term electricity price information. In this optional implementation, the attribute information of the energy storage devices at the charging station includes the discharge AC efficiency, maximum energy storage capacity, minimum reserved capacity, charging AC efficiency, maximum continuous discharge power, and maximum continuous discharge power of the energy storage devices. The status information of the energy storage devices at the charging station can include the remaining energy of the energy storage devices in each first time interval, and the load information of the charging station can include the first total load of the charging station in each first time interval.

[0054] Among them, discharge AC efficiency characterizes the efficiency of energy storage devices in converting stored chemical energy or other forms of energy into electrical energy, charging AC efficiency characterizes the efficiency of energy storage devices in converting electrical energy into stored chemical energy or other forms of energy, and minimum reserved power capacity characterizes the minimum power capacity reserved to ensure the stable operation of charging stations, cope with emergencies, and meet the power needs of critical equipment. In the optimization process, the status information of the energy storage devices and the load information of the charging station can both be predicted values. The server calculates the average, maximum, and preset quantile values ​​of the charging station or energy storage devices over multiple historical time periods as predicted values ​​for the status information of the energy storage devices and the load information of the charging station. Alternatively, the predicted values ​​can be obtained based on a pre-trained data prediction model, or through other methods. This embodiment does not impose any limitations on these methods.

[0055] Therefore, in step S200, for each first time interval, the server can determine the corresponding first energy consumption function based on the corresponding medium- and long-term electricity price, the energy consumption of the energy storage device, and the AC discharge efficiency of the energy storage device, and determine the corresponding first energy replenishment function based on the corresponding medium- and long-term electricity price and the energy replenishment amount of the energy storage device. Specifically, the first energy consumption function Income for the j-th first time interval... discha,j This can be expressed by the following formula:

[0056] Income discha,j =discha_energy j ×Eff discha *long_price j ;

[0057] Among them, discha_energy j Eff represents the energy consumption of the energy storage devices at the charging station during the j-th time interval. discha The arithmetic square root of the AC discharge efficiency of the energy storage device, long_price jLet be the medium- to long-term electricity price for the j-th first time interval.

[0058] Cost, the first energy replenishment function for the j-th first time interval cha,j This can be expressed by the following formula:

[0059] Cost cha,j =cha_energy j ×long_price j ;

[0060] Among them, cha_energy j This represents the amount of electrical energy replenished by the energy storage devices at the charging station during the j-th first time interval.

[0061] After determining the first energy consumption function and the first energy replenishment function for each first time interval, in step S300, the server can determine the first revenue function corresponding to the charging station based on the sum of the product of the first energy consumption function and the first parameter and the product of the first energy replenishment function and the second parameter for each first time interval. Specifically, the first revenue function f(1) of the charging station in the target time period can be expressed by the following formula:

[0062]

[0063] Where r is the total number of the first time intervals in the target time period, and d j c is the first parameter corresponding to the j-th first time interval. j This is the second parameter corresponding to the j-th first time interval.

[0064] In step S400, one or more of the attribute information, status information, and load information of the energy storage device can be combined to determine the constraints of the first revenue function. In this embodiment, one or more of the following constraints may be included:

[0065] Condition 1: When the medium- and long-term electricity price type is peak price or high price, the energy storage device is not allowed to charge; when the price type is low price, the energy storage device is not allowed to discharge. In other words, the server can determine the charging and discharging state of the energy storage device in each first time interval based on the changing patterns of each first time interval, i.e., the price type. Specifically, Condition 1 can be expressed by the following formula:

[0066] discha_energy j ≤0, if long_price_type j ∈{peek,high};

[0067] cha_energy j≥0, if long_price_type j ∈{low};

[0068] Among them, long_price_type j This represents the price type corresponding to the j-th first time interval.

[0069] Condition 2: The energy storage device can only charge, discharge, or neither charge nor discharge within the same first time interval. In other words, the server can determine the first and second parameters corresponding to each first time interval based on the charging and discharging state of the energy storage device within each first time interval. Specifically, Condition 2 can be expressed by the following formula:

[0070] d j ∈{0,1},d j ∈{0,1}, and d j +c j <=1.

[0071] Condition 3: The difference between the remaining power of the energy storage device in the previous first time interval and the power consumption in that first time interval is not less than the minimum reserved power of the energy storage device. In other words, the server can determine the maximum power consumption in the current first time interval based on the difference between the remaining power in the previous first time interval and the minimum reserved power. Specifically, Condition 3 can be expressed by the following formula:

[0072] s j-1 -discha_energy j >=s low ;

[0073] Among them, s j-1 Let s be the remaining battery power in the (j-1)th first time interval. low Minimum reserve power for energy storage devices.

[0074] Condition 4: The sum of the product of the energy replenishment amount of the energy storage device in any first time interval and the arithmetic square root of the charging AC efficiency of the energy storage device, and the remaining energy in the previous first time interval, does not exceed the maximum energy storage capacity of the energy storage device. In other words, the server can determine the maximum energy replenishment amount for the current first time interval based on the difference between the maximum energy storage capacity and the remaining energy in the previous first time interval, and the charging AC efficiency. Specifically, Condition 4 can be expressed by the following formula:

[0075] s j-1 +cha_energy j ×Eff cha <=s up ;

[0076] Among them, Eff cha The arithmetic square root of the AC charging efficiency of energy storage devices, s up This represents the maximum electrical energy storage capacity of the energy storage device.

[0077] Condition 5: The amount of energy replenished by the energy storage device in any first time interval does not exceed the product of the energy storage device's maximum continuous charging power and the length of that first time interval, i.e., the length of the first time interval. In other words, for each first time interval, the server can determine the maximum amount of energy replenishment based on the length of the first time interval and the maximum continuous charging power. Specifically, Condition 5 can be expressed by the following formula:

[0078] cha_energy j <=P max_cha ×Δt j ;

[0079] Among them, P max_cha The maximum continuous charging power of the energy storage device, Δt j Let be the first time length of the j-th first time interval.

[0080] Condition 6: The energy consumption of the energy storage device in any first time interval does not exceed the product of the maximum continuous discharge power of the energy storage device and the duration of that first time interval. In other words, for each first time interval, the server can determine the maximum energy consumption based on the first time length and the maximum continuous discharge power of the current first time interval. Specifically, Condition 6 can be expressed by the following formula:

[0081] discha_energy j <=P max_discha ×Δt j ;

[0082] Among them, P max_discha This represents the maximum continuous discharge power of the energy storage device.

[0083] Condition 7: The product of the energy consumption and the discharge AC efficiency of the energy storage device in any first time interval is not higher than the total load of the charging station in that first time interval, i.e., the first total load. In other words, for each first time interval, the server can determine the maximum energy consumption based on the ratio of the corresponding first total load to the discharge AC efficiency. Specifically, Condition 7 can be expressed by the following formula:

[0084] discha_energy j ×Eff discha <=load j ;

[0085] Among them, loadj This represents the first total load of the charging station in the j-th first time interval.

[0086] The optimization algorithm used in this embodiment is Mixed Integer Linear Programming (MILP). During the revenue decision-making process, the energy replenishment amount of the energy storage device during discharge is negative, and the objective of the first energy consumption function is to calculate its minimum value. Conversely, the energy replenishment amount of the energy storage device during charging is positive, and the objective of the first energy replenishment function is also to calculate its minimum value. Therefore, in step S500, the server can use common solution methods, such as branch and bound or cutting plane method, to maximize the absolute value of the first revenue function, and use the optimal energy replenishment amount and optimal energy consumption amount of the charging station in each first time interval as the energy allocation value.

[0087] In another optional implementation, the server can determine the revenue function of the charging station under the implemented electricity price information. In this optional implementation, the attribute information of the energy storage device at the charging station may include the discharge AC efficiency, maximum energy storage capacity, minimum reserved capacity, charging AC efficiency, maximum continuous discharge power, and maximum continuous charging power of the energy storage device. The status information of the energy storage device may include the average discharge power, average charging power, and remaining capacity of the energy storage device in each second time interval. The load information of the charging station includes the total load of the charging station in each second time interval, i.e., the second total load.

[0088] Therefore, in step S200, for each second time interval, the server can determine the corresponding second energy consumption function based on the corresponding real-time energy price, the duration of the second time interval (i.e., the second time length), the discharge AC efficiency of the energy storage device, and the average discharge power, and determine the corresponding second energy replenishment function based on the corresponding real-time energy price, the second time length, and the average charging power of the energy storage device. Specifically, the second energy consumption function Income for the i-th second time interval... discha,i This can be expressed by the following formula:

[0089] Income discha,i =dis_power i ×delta×Eff discha ×flu_price i ;

[0090] Among them, dis_power i Let f_i be the average discharge power of the energy storage device in the i-th second time interval, delta be the second time length of the i-th second time interval, and flu_price be the average discharge power of the energy storage device in the i-th second time interval.i This represents the real-time electricity price for the second time interval.

[0091] The second energy replenishment function Cost for the i-th second time interval cha,i This can be expressed by the following formula:

[0092] Cost cha,i =cha_power i ×delta×flu_price i ;

[0093] Among them, cha_power i Let be the average charging power of the energy storage device in the i-th second time interval.

[0094] After determining the second energy consumption function and the second energy replenishment function for each second time interval, in step S300, the server can determine the second revenue function corresponding to the charging station based on the sum of the products of the second energy consumption function and the first parameter and the second energy replenishment function and the second parameter for each second time interval. Specifically, the second revenue function f(2) of the charging station for the target time period can be expressed by the following formula:

[0095]

[0096] Where h is the total number of second time intervals in the first time interval.

[0097] In step S400, one or more of the attribute information, status information, and load information of the energy storage device can be combined to determine the constraints of the second revenue function. In this embodiment, one or more of the following constraints may be included:

[0098] Condition 1: When the medium- and long-term electricity price type is peak price or high price, the energy storage device is not allowed to charge; when the price type is low price, the energy storage device is not allowed to discharge. In other words, the server can determine the charging and discharging state of the energy storage device in each of the corresponding second time intervals based on the changing patterns of each first time interval, i.e., the price type. Specifically, Condition 1 can be expressed by the following formula:

[0099] discha_power i ≤0, if long_price_type j ∈{peek,high};

[0100] cha_power i ≥0, if long_price_type j ∈{low}.

[0101] Condition 2: The energy storage device can only charge, discharge, or neither charge nor discharge in each of the second time intervals corresponding to the same first time interval. In other words, the server can determine the first and second parameters corresponding to each first time interval based on the charging and discharging state of the energy storage device in each first time interval. Specifically, Condition 2 can be expressed by the following formula:

[0102] d j ∈{0,1},d j ∈{0,1}, and d j +c j <=1.

[0103] Condition 3: The difference between the remaining power of the energy storage device in the preceding second time interval and the power consumption in that second time interval is not less than the minimum reserved power of the energy storage device. The power consumption can be represented by the product of the average discharge power of the energy storage device in any second time interval and the second time length. In other words, the server can determine the maximum value of the average discharge power in the current second time interval based on the difference between the remaining power and the minimum reserved power in the preceding second time interval and the second time length of the current second time interval. Specifically, Condition 3 can be expressed by the following formula:

[0104] s i-1 +discha_power i ×delta>=s low ;

[0105] Among them, s i-1 The remaining power of the energy storage device during the second time interval (i-1) is denoted as .

[0106] Condition 4: The sum of the product of the energy replenishment amount of the energy storage device in any second time interval and the arithmetic square root of the charging AC efficiency of the energy storage device, and the remaining energy in the previous second time interval, does not exceed the maximum energy storage capacity of the energy storage device. The energy replenishment amount can be represented by the product of the average charging power of the energy storage device in any second time interval and the second time length. In other words, the server can determine the maximum energy replenishment amount for the current second time interval based on the difference between the maximum energy storage capacity and the remaining energy in the previous second time interval, the charging AC efficiency of the energy storage device, and the second time length of the current second time interval. Specifically, Condition 4 can be expressed by the following formula:

[0107] s i-1 +cha_power i ×delta×Eff cha <=s up .

[0108] Condition 5: The average charging power of the energy storage device in any second time interval does not exceed the maximum continuous charging power of the energy storage device. In other words, the server can determine the maximum value of the average charging power based on the maximum continuous charging power. Specifically, Condition 5 can be expressed by the following formula:

[0109] cha_power i <=P max_cha .

[0110] Condition 6: The average discharge power of the energy storage device in any second time interval does not exceed the maximum continuous discharge power of the energy storage device. In other words, the server can determine the maximum value of the average discharge power based on the maximum continuous discharge power. Specifically, Condition 6 can be expressed by the following formula:

[0111] discha_power i <=P max_discha .

[0112] Condition 7: The product of the energy consumption and discharge AC efficiency of the energy storage device in any second time interval is not higher than the second total load of the charging station in that second time interval. In other words, for each first time interval, the server can determine the maximum value of the average discharge power based on the ratio of the corresponding second total load to a preset product, where the preset product is the product of the discharge AC efficiency of the energy storage device and the second time length of the second time interval. Specifically, Condition 7 can be expressed by the following formula:

[0113] discha_power i ×delta*Eff discha <=load i .

[0114] Condition 8: The sum of the average discharge power of the energy storage device in each of the second time intervals corresponding to any first time interval and the second time length is not higher than the energy consumption of that first time interval. In other words, the server can determine the minimum energy consumption based on the sum of the products of the average discharge power and the second time length of each of the second time intervals corresponding to the same first time interval. Specifically, Condition 8 can be expressed by the following formula:

[0115]

[0116] Condition 9: The sum of the average charging power of the energy storage device in each of the second time intervals corresponding to any first time interval and the second time length is not higher than the energy replenishment amount in that first time interval. In other words, the server can determine the minimum energy replenishment amount based on the sum of the products of the average charging power and the second time length in each of the second time intervals corresponding to the same first time interval. Specifically, Condition 9 can be expressed by the following formula:

[0117]

[0118] In step S500, the server can also determine the energy consumption state based on common solution methods, aiming to maximize the absolute value of the second revenue function, according to the optimal average discharge power and optimal average charging power of the charging station in each second time interval. Specifically, if the average discharge power of the energy storage device in any second time interval is greater than a preset discharge power threshold, or the average charging power is greater than a preset charging power threshold, the server can determine that the energy consumption state of that second time interval is to perform energy consumption control in that second time interval; otherwise, it determines that the energy consumption state of that second time interval is not to perform energy consumption control in that second time interval.

[0119] It is easy to understand that if there are multiple energy storage devices, the server can determine the constraints of the first and second revenue functions based on the attribute and status information of each energy storage device, and then solve for the energy allocation value and consumption control status of the charging station in each time interval.

[0120] It is also easy to understand that if the target time period includes the time interval that has already been performed before the optimization solution process, the server can determine the constraints based on the actual values ​​generated in that time interval, and will not calculate the power allocation value and / or consumption control status corresponding to that time interval during the optimization solution process.

[0121] For example, if the target time period is 24 hours from 0:00 to 23:59 on January 20th, and the server starts the optimization solution process at 9:00 on January 20th, then the time interval already completed is 9 hours from 0:00 to 8:59. Therefore, when the server executes the optimization solution process, it can determine the constraints based on the actual values ​​generated from 0:00 to 8:59, without calculating the power allocation value and / or consumption control status corresponding to 0:00 to 8:59.

[0122] Step S600: Control the power consumption of the energy storage device according to the power allocation value and consumption control status.

[0123] After determining the power allocation value of the charging station in each first time interval and the consumption control status in each second time interval, in step S600, the server can send the power allocation value and consumption control status to the control unit of the charging station to control the power consumption of the energy storage device, thereby distributing energy.

[0124] This invention, based on dynamic battery status, time-of-use predicted load, spot electricity prices, and stable medium- and long-term electricity prices, employs a mixed-integer linear programming optimization algorithm. It prioritizes utilizing medium- and long-term tiered electricity prices for long-term energy allocation and scheduling, ensuring maximum overall peak-valley revenue and minimum battery aging costs. Subsequently, it utilizes spot electricity prices for short-term energy redistribution, minimizing local electricity costs and battery aging costs. Therefore, the method based on this invention effectively handles the complexity of medium- and long-term tiered electricity prices and futures prices, while simultaneously achieving local energy redistribution and optimizing overall peak-valley revenue and electricity costs.

[0125] This invention, after acquiring relevant information about the charging station, including attribute information, status information, electricity price change information, and load information of the energy storage devices at the charging station during a target time period, determines the electricity consumption function and electricity replenishment function of the charging station for each time interval within the target time period based on the electricity price change information. It then determines the charging station's revenue function based on these functions, along with constraints on the revenue function. With the objective of maximizing the absolute value of the revenue function, it determines the electricity allocation value for the charging station in each time interval and whether electricity consumption control should be implemented in each time interval based on the constraints. This invention can accurately estimate the electricity allocation value and the timing of electricity consumption control for the charging station based on optimization algorithms, thereby reducing the operating costs of the charging station.

[0126] Figure 4 This is a schematic diagram of the control device according to an embodiment of the present invention. Figure 4 As shown, the control device in this embodiment includes an information acquisition unit 401, a first function determination unit 402, a second function determination unit 403, a condition determination unit 404, an information determination unit 405, and a control unit 406.

[0127] The system includes the following components: an information acquisition unit 401 acquires relevant information about the charging station during a target time period, including attribute information of the energy storage device, price change information of electricity, status information of the energy storage device, and load information of the charging station; a first function determination unit 402 determines the energy consumption function and energy replenishment function of the charging station for each time interval during the target time period based on the price change information; a second function determination unit 403 determines the revenue function corresponding to the charging station based on the energy consumption function and energy replenishment function for each time interval; a condition determination unit 404 determines the constraints of the revenue function based on the attribute information, the status information, and the load information; an information determination unit 405 determines the energy allocation value and consumption control status of the charging station for each time interval based on the constraints, with the goal of maximizing the absolute value of the revenue function, whereby the consumption control status indicates whether the energy storage device performs energy consumption control during the time interval; and a control unit 406 performs energy consumption control on the energy storage device based on the energy allocation value and the consumption control status.

[0128] Furthermore, the price change information includes medium- and long-term electricity price change information, the time interval includes a first time interval, which is a time interval obtained by dividing the target time period according to the change pattern of the medium- and long-term electricity price change information, and the attribute information includes the discharge AC efficiency of the energy storage device.

[0129] The first function determination unit 402 includes a first function determination subunit and a second function determination subunit.

[0130] The first function determining subunit is used to determine the corresponding first energy consumption function for each of the first time intervals based on the corresponding medium- and long-term electricity price, the energy consumption of the energy storage device, and the discharge AC efficiency; the second function determining subunit is used to determine the corresponding first energy replenishment function for each of the first time intervals based on the corresponding medium- and long-term electricity price and the energy replenishment amount of the energy storage device.

[0131] Furthermore, the second function determination unit 403 includes a third function determination subunit.

[0132] The third function determining subunit is used to determine the first revenue function corresponding to the charging station based on the sum of the product of the first energy consumption function and the first parameter corresponding to each first time interval and the product of the first energy replenishment function and the second parameter.

[0133] Furthermore, the attribute information also includes the maximum energy storage capacity of the energy storage device, the minimum reserved capacity of the energy storage device, the charging AC efficiency, the maximum continuous discharge power, and the maximum continuous charging power. The status information includes the remaining power of the energy storage device in each of the first time intervals, and the load information includes the first total load of the charging station in each of the first time intervals.

[0134] The condition determination unit 404 includes a first condition determination subunit, a second condition determination subunit, a third condition determination subunit, a fourth condition determination subunit, a fifth condition determination subunit, a sixth condition determination subunit, and a seventh condition determination subunit.

[0135] The first condition determination subunit is used to determine the charging and discharging state of the energy storage device in each of the first time intervals based on the changing pattern of the medium- and long-term electricity price information, wherein the charging and discharging state includes discharging and charging; the second condition determination subunit is used to determine the first parameter and the second parameter based on the charging and discharging state; the third condition determination subunit is used to determine the maximum value of the electricity consumption in the current first time interval based on the difference between the remaining electricity in the previous first time interval and the minimum reserved electricity; the fourth condition determination subunit is used to determine the maximum value of the electricity replenishment in the current first time interval based on the difference between the maximum electricity storage and the remaining electricity in the previous first time interval and the charging AC efficiency; the fifth condition determination subunit is used to determine the maximum value of the electricity replenishment for each of the first time intervals based on the first time length of the current first time interval and the maximum continuous charging power; the sixth condition determination subunit is used to determine the maximum value of the electricity consumption for each of the first time intervals based on the corresponding first time length and the maximum continuous discharging power; and the seventh condition determination subunit is used to determine the maximum value of the electricity consumption for each of the first time intervals based on the ratio of the corresponding first total load to the discharging AC efficiency.

[0136] Furthermore, the information determination unit 405 includes:

[0137] The first solution subunit is used to determine the power allocation value of the charging station in each of the first time intervals based on the constraints, with the objective of maximizing the absolute value of the first revenue function.

[0138] Furthermore, the price change information includes real-time electricity price change information, the time interval includes a second time interval, the second time interval is a period of time obtained by dividing the first time interval, the first time interval is a period of time obtained by dividing the target time interval according to the change pattern of the medium and long-term electricity price change information, the attribute information includes the discharge AC efficiency of the energy storage device, and the status information includes the average discharge power and average charging power of the energy storage device in each second time interval;

[0139] The first function determination unit 402 includes a fourth function determination subunit and a fifth function determination subunit.

[0140] The fourth function determining subunit is used to determine the corresponding second energy consumption function for each of the second time intervals based on the corresponding real-time energy price, the second time length, the discharge AC efficiency, and the average discharge power; the fifth function determining subunit is used to determine the corresponding second energy replenishment function for each of the second time intervals based on the corresponding real-time energy price, the second time length, and the average charging power.

[0141] Furthermore, the second function determination unit 403 includes a sixth function determination subunit.

[0142] The sixth function determining subunit is used to determine the second revenue function corresponding to the charging station based on the sum of the product of the second energy consumption function and the first parameter corresponding to each second time interval and the product of the second energy replenishment function and the second parameter.

[0143] Furthermore, the attribute information also includes the maximum energy storage capacity of the energy storage device, the minimum reserved capacity of the energy storage device, the charging AC efficiency, the maximum continuous discharge power, and the maximum continuous charging power. The price change information also includes medium- and long-term electricity price change information. The status information includes the remaining power of the energy storage device in each of the second time intervals. The load information includes the second total load of the charging station in each of the second time intervals.

[0144] The condition determination unit 404 includes an eighth condition determination subunit, a ninth condition determination subunit, a tenth condition determination subunit, an eleventh condition determination subunit, a twelfth condition determination subunit, a thirteenth condition determination subunit, a fourteenth condition determination subunit, a fifteenth condition determination subunit, and a sixteenth condition determination subunit.

[0145] The eighth condition determination subunit is used to determine the charging and discharging state of the energy storage device in each of the first time intervals based on the changing pattern of the medium- and long-term electricity price information, wherein the charging and discharging state includes discharging and charging; the ninth condition determination subunit is used to determine the first parameter and the second parameter based on the charging and discharging state; the tenth condition determination subunit is used to determine the maximum value of the average discharge power in the current second time interval based on the difference between the remaining power and the minimum reserved power in the previous second time interval and the second time length of the current second time interval; the eleventh condition determination subunit is used to determine the maximum value of the energy replenishment in the current second time interval based on the difference between the maximum energy storage and the remaining power in the previous second time interval, the charging AC efficiency, and the second time length of the current second time interval; the twelf ... change pattern of the medium- and long-term electricity price information; the ninth condition determination subunit is used to determine the first parameter and the second parameter based on the change pattern of the medium- and long-term electricity price information; the tenth condition determination subunit is used to determine the maximum value of the energy replenishment in the current second time interval based on the change pattern of the medium- and long-term electricity price information; the twelfth condition determination subunit is used to determine the maximum value of the energy replenishment in the current second time interval based on the change pattern of the medium- and long-term electricity price information; the ninth condition determination subunit is used to determine the first parameter and the second parameter based on the change pattern of the medium- and long-term electricity price information; the tenth condition determination subunit is used to determine the maximum value of the energy replenishment in the current second time interval based on the change pattern of the medium- and long- The maximum continuous charging power determines the maximum value of the average charging power; the thirteenth condition determining subunit is used to determine the maximum value of the average discharge power based on the maximum continuous discharge power; the fourteenth condition determining subunit is used to determine the maximum value of the average discharge power for each second time interval based on the ratio of the corresponding second total load to a preset product, where the preset product is the product of the discharge AC efficiency and the second time length; the fifteenth condition determining subunit is used to determine the minimum value of the corresponding energy consumption based on the sum of the products of the average discharge power and the second time length for each second time interval corresponding to the same first time interval; the sixteenth condition determining subunit is used to determine the minimum value of the corresponding energy replenishment based on the sum of the products of the average charging power and the second time length for each second time interval corresponding to the same first time interval.

[0146] Furthermore, the information determination unit 405 includes a second solution subunit.

[0147] The second solution subunit is used to determine the consumption control state of the charging station in each of the second time intervals based on the constraints, with the objective of maximizing the absolute value of the second revenue function.

[0148] This invention, after acquiring relevant information about the charging station, including attribute information, status information, electricity price change information, and load information of the energy storage devices at the charging station during a target time period, determines the electricity consumption function and electricity replenishment function of the charging station for each time interval within the target time period based on the electricity price change information. It then determines the charging station's revenue function based on these functions, along with constraints on the revenue function. With the objective of maximizing the absolute value of the revenue function, it determines the electricity allocation value for the charging station in each time interval and whether electricity consumption control should be implemented in each time interval based on the constraints. This invention can accurately estimate the electricity allocation value and the timing of electricity consumption control for the charging station based on optimization algorithms, thereby reducing the operating costs of the charging station.

[0149] Figure 5 This is a schematic diagram of an electronic device according to an embodiment of the present invention. (For example...) Figure 5 As shown, electronic device 5 is a general-purpose data processing device, which includes a general-purpose computer hardware structure, including at least a processor 501 and a memory 502. The processor 501 and memory 502 are connected via a bus 503. The memory 502 is adapted to store instructions or programs executable by the processor 501. The processor 501 can be a standalone microprocessor or a collection of one or more microprocessors. Thus, the processor 501 executes the instructions stored in the memory 502 to perform the method flow of the embodiments of the present invention as described above, thereby realizing data processing and control of other devices. The bus 503 connects the aforementioned components together, and also connects the aforementioned components to a display controller 504, a display device, and an input / output (I / O) device 505. The input / output (I / O) device 505 can be a mouse, keyboard, modem, network interface, touch input device, motion-sensing input device, printer, and other devices known in the art. Typically, the input / output device 505 is connected to the system via an input / output (I / O) controller 506.

[0150] Those skilled in the art will understand that embodiments of this application can be provided as methods, apparatus (devices), or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0151] This application is described with reference to flowchart illustrations of methods, apparatus (devices), and computer program products according to embodiments of this application. It should be understood that each step in the flowchart can be implemented by computer program instructions.

[0152] These computer program instructions may be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including an instruction means, the implementation process of which is described in the instruction means. Figure 1 The function specified in one or more processes.

[0153] These computer program instructions may also be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing device, produce instructions for implementing processes. Figure 1 A device for a function specified in one or more processes.

[0154] Another embodiment of the present invention relates to a non-volatile storage medium for storing a computer-readable program for use by a computer to execute some or all of the above-described method embodiments.

[0155] That is, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program specifying the relevant hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0156] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. For those skilled in the art, the present invention can be modified and varied in various ways. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within the scope of protection of the present invention.

Claims

1. A control method, characterized in that, The method includes: Obtain relevant information about the charging station during a target time period. The relevant information includes the attribute information of the energy storage device at the charging station, the price change information of electricity, the status information of the energy storage device, and the load information of the charging station. Based on the price change information, determine the energy consumption function and energy replenishment function of the charging station for each time interval of the target time period; The revenue function corresponding to the charging station is determined based on the energy consumption function and the energy replenishment function corresponding to each time interval. The constraints of the revenue function are determined based on the attribute information, the state information, and the load information. With the goal of maximizing the absolute value of the revenue function, the power allocation value and consumption control status of the charging station in each time interval are determined according to the constraints. The consumption control status indicates whether the energy storage device performs power consumption control in the time interval. The energy consumption of the energy storage device is controlled according to the energy allocation value and the consumption control state.

2. The method according to claim 1, characterized in that, The price change information includes medium- and long-term electricity price change information, the time interval includes a first time interval, which is a time interval divided according to the change pattern of the medium- and long-term electricity price change information. The attribute information includes the discharge AC efficiency of the energy storage device. The step of determining the energy consumption function and energy replenishment function of the charging station for each time interval of the target time period based on the price change information includes: For each of the first time intervals, a corresponding first energy consumption function is determined based on the corresponding medium- and long-term electricity price, the energy consumption of the energy storage device, and the discharge AC efficiency. For each of the first time intervals, a corresponding first energy replenishment function is determined based on the corresponding medium- and long-term electricity price and the energy replenishment amount of the energy storage device.

3. The method according to claim 2, characterized in that, The step of determining the revenue function corresponding to the charging station based on the energy consumption function and energy replenishment function corresponding to each of the time intervals includes: The first revenue function corresponding to the charging station is determined by the sum of the product of the first energy consumption function and the first parameter corresponding to each first time interval and the product of the first energy replenishment function and the second parameter.

4. The method according to claim 3, characterized in that, The attribute information also includes the maximum energy storage capacity of the energy storage device, the minimum reserved power of the energy storage device, the charging AC efficiency, the maximum continuous discharge power, and the maximum continuous charging power. The status information includes the remaining power of the energy storage device in each of the first time intervals. The load information includes the first total load of the charging station in each of the first time intervals. The constraints for determining the revenue function based on the attribute information, the state information, and the load information include: The charging and discharging states of the energy storage device in each of the first time intervals are determined based on the changing patterns of the medium- and long-term electricity price information. The charging and discharging states include discharging and charging. The first parameter and the second parameter are determined based on the charge / discharge state; The maximum value of the energy consumption in the current first time interval is determined based on the difference between the remaining energy and the minimum reserved energy in the previous first time interval. The maximum value of the energy replenishment amount in the current first time interval is determined based on the difference between the maximum energy storage amount and the remaining energy in the previous first time interval and the charging AC efficiency. For each of the first time intervals, the maximum value of the energy replenishment is determined based on the first time length of the current first time interval and the maximum continuous charging power. For each of the first time intervals, the maximum value of the energy consumption is determined based on the corresponding first time length and the maximum continuous discharge power. For each of the first time intervals, the maximum value of the energy consumption is determined based on the ratio of the corresponding first total load to the discharge AC efficiency.

5. The method according to claim 4, characterized in that, The step of determining the energy allocation value and consumption control state of the charging station in each time interval, with the objective of maximizing the absolute value of the revenue function, according to the constraints, includes: With the goal of maximizing the absolute value of the first revenue function, the power allocation value of the charging station in each of the first time intervals is determined according to the constraints.

6. The method according to claim 1, characterized in that, The price change information includes real-time electricity price change information, the time interval includes a second time interval, which is a time period obtained by dividing the first time interval, and the first time interval is a time period obtained by dividing the target time period according to the change pattern of medium and long-term electricity price change information, the attribute information includes the discharge AC efficiency of the energy storage device, and the status information includes the average discharge power and average charging power of the energy storage device in each of the second time intervals; The step of determining the energy consumption function and energy replenishment function of the charging station for each time interval of the target time period based on the price change information includes: For each of the second time intervals, the corresponding second energy consumption function is determined based on the corresponding real-time energy price, the second time length, the discharge AC efficiency, and the average discharge power. For each of the second time intervals, a corresponding second energy replenishment function is determined based on the corresponding real-time energy price, the second time length, and the average charging power.

7. A control device, characterized in that, The device includes: The information acquisition unit is used to acquire relevant information about the charging station during a target time period. The relevant information includes the attribute information of the energy storage device of the charging station, the price change information of electricity, the status information of the energy storage device, and the load information of the charging station. The first function determination unit is used to determine the energy consumption function and energy replenishment function of the charging station for each time interval of the target time period based on the price change information. The second function determination unit is used to determine the revenue function corresponding to the charging station based on the energy consumption function and the energy replenishment function corresponding to each time interval. The condition determination unit is used to determine the constraints of the revenue function based on the attribute information, the state information, and the load information. An information determination unit is used to determine the power allocation value and consumption control status of the charging station in each time interval according to the constraint conditions with the goal of maximizing the absolute value of the revenue function. The consumption control status indicates whether the energy storage device performs power consumption control in the time interval. The control unit is used to control the energy consumption of the energy storage device according to the energy allocation value and the consumption control state.

8. A computer-readable storage medium storing computer program instructions thereon, characterized in that, The computer program instructions, when executed by a processor, implement the method as described in any one of claims 1-6.

9. An electronic device, characterized in that, The device includes: Memory is used to store one or more computer program instructions; A processor, wherein the one or more computer program instructions are executed by the processor to implement the method as described in any one of claims 1-6.

10. A computer program product, characterized in that, When the computer program product is run on a computer, it causes the computer to perform the method as described in any one of claims 1-6.

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