Methods, apparatus, equipment, media, and procedures for charge and discharge control products.

Abstract

According to an embodiment of the present disclosure, a method, device, equipment, medium and program product for charge and discharge control are provided. The method includes: determining the expected demand power for the charging facility in the target time period and the expected power of the charging facility in the reference time period for the target time period; obtaining the actual power of the charging facility in the reference time period; determining the power adjustment parameter for the charging facility based on the expected power and the actual power; determining the target demand power of the charging facility in the target time period based on the power adjustment parameter, the expected demand power and the power cost information in the target time period, the target demand power is the charging power or discharging power of the charging facility; and performing the charging operation or discharging operation of the charging facility in the target time period based on the target demand power.

Description

Technical Field

[0001] Example embodiments of the present disclosure generally relate to the field of computers, and more particularly, to a method, apparatus, device, computer-readable storage medium, and computer program product for charge and discharge control. Background Art

[0002] With the rapid development of new energy vehicles, the number of new energy electric vehicles continues to increase, the user demand for charging is strong, and the load demand for charging stations, charging piles and other charging equipment is increasing. In this case, while meeting the charging needs of electric vehicles, it is also necessary to reduce the charging costs of storage and charging stations, ensure the revenue of storage and charging stations, and thus reduce costs and increase efficiency. Summary of the invention

[0003] In a first aspect of the present disclosure, a method for charge and discharge control is provided. The method includes: determining an estimated power demand for a charging facility in a target time period and an estimated amount of electricity for the charging facility in a reference time period for the target time period; obtaining an actual amount of electricity for the charging facility in the reference time period; determining a power adjustment parameter for the charging facility based on the estimated amount of electricity and the actual amount of electricity; determining a target power demand for the charging facility in the target time period based on the power adjustment parameter, the estimated power demand, and electricity cost information in the target time period, the target power demand being the charging power or discharging power of the charging facility; and performing a charging operation or a discharging operation on the charging facility in the target time period based on the target power demand.

[0004] In a second aspect of the present disclosure, a device for charge and discharge control is provided. The device includes: an estimated demand determination module, configured to determine the estimated demand power of the charging facility in the target time period and the estimated power of the charging facility in the reference time period for the target time period; an actual power acquisition module, configured to acquire the actual power of the charging facility in the reference time period; an adjustment parameter determination module, configured to determine the power adjustment parameter for the charging facility based on the estimated power and the actual power; a target demand determination module, configured to determine the target demand power of the charging facility in the target time period based on the power adjustment parameter, the estimated demand power and the power cost information in the target time period, the target demand power being the charging power or discharging power of the charging facility; and an operation execution module, configured to execute the charging operation or discharging operation of the charging facility in the target time period based on the target demand power.

[0005] In a third aspect of the present disclosure, an electronic device is provided. The device includes at least one processing unit; and at least one memory, the at least one memory is coupled to the at least one processing unit and stores instructions for execution by the at least one processing unit. When the instructions are executed by the at least one processing unit, the device executes the method of the first aspect.

[0006] In a fourth aspect of the present disclosure, a computer-readable storage medium is provided, wherein a computer program is stored on the computer-readable storage medium, and the computer program can be executed by a processor to implement the method of the first aspect.

[0007] In a fifth aspect of the present disclosure, a computer program product is provided, which includes computer executable instructions, and when the computer executable instructions are executed by a processor, the method of the first aspect is implemented.

[0008] It should be understood that the contents described in the summary of the present invention are not intended to limit the key features or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

[0010] Figure 1 A schematic diagram showing an example environment in which embodiments of the present disclosure can be implemented;

[0011] Figure 2 A flow chart showing a process for charge and discharge control according to some embodiments of the present disclosure is shown;

[0012] Figure 3 A schematic diagram showing examples of multiple electricity cost intervals according to some embodiments of the present disclosure;

[0013] Figure 4 A block diagram of a device for charge and discharge control according to some embodiments of the present disclosure is shown;

[0014] Figure 5 A block diagram of an electronic device capable of implementing one or more embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0015] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0016] In the description of the embodiments of the present disclosure, the term "including" and similar terms should be understood as open inclusion, that is, "including but not limited to". The term "based on" should be understood as "based at least in part on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The term "some embodiments" should be understood as "at least some embodiments". Other explicit and implicit definitions may be included below. The terms "first", "second", etc. may refer to different or the same objects. Other explicit and implicit definitions may be included below.

[0017] The embodiments of the present disclosure may involve user data, data acquisition and / or use, etc. These aspects are subject to the corresponding laws, regulations and relevant provisions. In the embodiments of the present disclosure, all data collection, acquisition, processing, processing, forwarding, use, etc. are carried out on the premise that the user knows and confirms. Accordingly, when implementing each embodiment of the present disclosure, the type, scope of use, usage scenario, etc. of the data or information that may be involved should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with the relevant laws and regulations. The specific notification and / or authorization method can vary according to the actual situation and application scenario, and the scope of the present disclosure is not limited in this respect.

[0018] The term "in response to" as used herein refers to a state in which a corresponding event occurs or a condition is satisfied. It will be understood that the timing of executing a subsequent action executed in response to the event or condition is not necessarily strongly related to the time when the event occurs or the condition is satisfied. For example, in some cases, the subsequent action may be executed immediately when the event occurs or the condition is satisfied; while in other cases, the subsequent action may be executed some time after the event occurs or the condition is satisfied.

[0019] In order to maximize the comprehensive benefits of microgrid operation, it is often necessary to have high-precision short-term and ultra-short-term load forecasting capabilities, such as electric vehicle charging demand, other microgrid loads, and renewable energy generation forecasts, so as to accurately estimate the charging and discharging needs of the energy storage system. Another example includes real-time acquisition and analysis of power grid electricity price information, such as peak and valley electricity price distribution, electricity price adjustment time, etc. Another example includes key parameters such as the state of charge (SOC), health state (SOH), and temperature of energy storage devices (such as batteries, supercapacitors, etc.). In summary of all kinds of information and parameters, it is necessary to design an optimization algorithm that can find a balance between multiple objectives such as economic efficiency (such as effectively reducing costs), reliability (such as ensuring power supply security), and sustainability (such as reducing carbon emissions) to achieve cost reduction and efficiency improvement. In addition, the algorithm must have the ability to respond quickly to external changes and adjust the control strategy in real time to adapt to these changes.

[0020] At present, for storage and charging microgrid sites equipped with energy storage, power control strategies often adopt manual fixed strategies, intelligent strategies based on load prediction, and offline simulation algorithms.

[0021] For the manual fixed strategy, it is relatively simple and intuitive, does not require complex algorithm development and maintenance, and is easy to understand and execute. This is a low-cost solution for small or initially operated microgrids. And when it is known that the peak and valley differences in electricity prices are large, by presetting fixed charging and discharging time and power at the site, the price difference can be directly used for energy storage and release to achieve economic benefits. Once the fixed strategy is set, its operating mode is relatively fixed, and there may be problems such as insufficient flexibility, inability to adapt to complex electricity price mechanisms, and neglect of overall system optimization. It is not suitable for long-term and refined electricity use in microgrids.

[0022] However, if a fixed strategy is adopted, it cannot make efficient and timely dynamic adjustments based on recent load changes, market price fluctuations, changes in grid status, etc., and may miss the best charging and discharging opportunities, resulting in failure to maximize economic benefits. In view of the complex daily price change mechanism, the artificial fixed strategy is affected by the load of the charging station on that day, and it is difficult to ensure sufficient discharge in the high-price range and sufficient charging in the low-price range, and it is impossible to achieve the best benefits in the complex electricity price mechanism. The artificial fixed strategy usually focuses on the simple economy of energy storage, and ignores the influencing factors such as the cost of various accessories of the microgrid and the aging cost of energy storage batteries, and cannot maximize the overall comprehensive efficiency of the microgrid.

[0023] For intelligent strategies based on load forecasting, the limitations of some manual fixed strategies can be resolved. For example, the control strategy can be adjusted dynamically based on offline load forecasting, which solves the problem of declining revenue caused by periodic changes in load to a certain extent. At the same time, for slightly complex electricity price mechanisms, the charging and discharging time can be dynamically adjusted based on the load forecast results to balance the use of the peak electricity price range and the peak electricity price range, which can achieve full utilization of the high electricity price range to a certain extent. In terms of maintenance, no human intervention is required, and the manual maintenance cost can be reduced by directly issuing it through cloud computing.

[0024] However, the intelligent strategy based on load prediction is highly dependent on the accuracy of the prediction, and has poor applicability to complex electricity price mechanisms and lacks the ability to evaluate the comprehensive benefits of microgrids. Specifically, cloud-based intelligent strategies often need to rely on accurate load prediction results. If corresponding redundancy is set for high-price electricity intervals in the strategy, it is also easy to cause other electricity price intervals to be unable to fully charge and discharge. Therefore, it has poor applicability for complex electricity price mechanisms and uncertain real-time load changes. At the same time, the attenuation of energy storage batteries is affected by usage behavior, such as charging and discharging power, applicable windows, etc. Intelligent strategies often set fixed charging and discharging power in order to meet energy demand, qualitatively consider the attenuation cost, and other additional dynamic cost elements cannot achieve quantitative cost evaluation. Therefore, intelligent strategies are not applicable in terms of the comprehensive benefits of microgrids. For example, fixed strategies are only used for the price difference profitability of a single point of energy storage.

[0025] For offline simulation algorithms, the goal is to meet the global optimal solution under all constraints and comprehensively consider multi-objective optimization problems. For example, maximizing economic benefits while minimizing battery attenuation costs to maximize the overall comprehensive benefits of the site. The charging and discharging strategies can be dynamically adjusted based on the expected results to quickly respond to external changes such as market electricity prices and load demand, thereby improving system flexibility and adaptability. At the same time, in addition to the single peak-valley price difference benefit, the corresponding constraints and objective functions can be implanted by introducing more factors, such as grid stability, dynamic expansion benefits of energy storage sites, and user satisfaction. At the same time, more scientific data support and decision-making basis are provided.

[0026] However, offline simulation algorithms are highly dependent on the data on the algorithm input side, making them difficult to apply in engineering. The accuracy of the optimization results depends largely on the quality of the input data, including the accuracy of the load forecast and the accuracy of the battery model. Data deviations may cause the optimization results to deviate from reality, but the current site-level load forecast results often have large uncertainties and fluctuations. At the same time, the accuracy of battery status parameters is often difficult to quantify accurately, and is affected by a variety of highly uncertain parameters, which often affects the effectiveness of the simulation algorithm and the applicability of the simulation control strategy. This results in profitability far below expectations, making it difficult to implement on the engineering side.

[0027] The embodiments of the present disclosure propose an improved scheme. According to various embodiments of the present disclosure, the expected demand power for the charging facility in the target time period and the expected amount of electricity for the charging facility in the reference time period for the target time period are determined. The actual amount of electricity of the charging facility in the reference time period is obtained. Based on the expected amount of electricity and the actual amount of electricity, the power adjustment parameters for the charging facility are determined. Based on the power adjustment parameters, the expected demand power, and the electricity cost information in the target time period, the target demand power of the charging facility in the target time period is determined, and the target demand power is the charging power or discharging power of the charging facility. Based on the target demand power, a charging operation or a discharging operation of the charging facility in the target time period is performed.

[0028] In this way, the embodiments of the present disclosure can dynamically adjust the control strategy according to the actual operation data of the charging and storage station and environmental changes, thereby improving the flexibility of charging and discharging of the charging facilities and the robustness to external disturbances. In addition, it can not only achieve good economic benefits, but also adjust the charging and discharging strategy in real time according to the actual state of the battery to avoid overcharging and discharging, thereby effectively balancing economic benefits and battery life maintenance and reducing maintenance costs.

[0029] Figure 1 1 is a schematic diagram of an example environment 100 in which embodiments of the present disclosure can be implemented. Figure 1 As shown, environment 100 involves a service-end device 110 , a charging facility 120 , and an electric vehicle 130 .

[0030] In an embodiment of the present disclosure, the server device 110 may provide background management services for the charging facility 120. Exemplarily, the server device 110 may be deployed with a charging management platform, which is used to monitor and remotely control the charging facility 120 in real time. The charging management platform may utilize the communication connection between the server device 110 and the charging facility 120 to receive charging status information reported by the charging facility 120 (e.g., messages that comply with the IEC60870-5-104 protocol) to monitor the charging status of the charging facility 120 in real time. The charging management platform may also utilize the communication connection between the server device 110 and the charging facility 120 to send a scheduling strategy to the charging facility 120, instructing the charging facility 120 to perform a scheduling operation on the charging process.

[0031] In some embodiments, the server device 110 may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content distribution networks, and big data and artificial intelligence platforms. The server device 110 may include, for example, a computing system / server, such as a mainframe, an edge computing node, a computing device in a cloud environment, and the like.

[0032] The charging facility 120 is used to charge one or more electric vehicles 130. The electric vehicles 130 may include pure electric vehicles, plug-in hybrid vehicles, etc. For example, the electric vehicles 130 may include sedans, SUVs, MPVs, buses, trucks, and other cargo or passenger vehicles, including various watercrafts such as ships and boats, and aircraft, etc. The model of the electric vehicle 130 is not limited here. In this article, the charging facility 120 may be a charging station, which includes one or more charging devices (e.g., charging piles). In some embodiments, the charging facility 120 may be a single charging device.

[0033] The charging facility 120 is a storage and charging facility for electric energy. During the charging process of the charging facility 120, the charging facility 120 (the energy storage module therein) obtains electric energy from the power grid 140. During the discharging process of the charging facility 120, the charging facility 120 releases electric energy to the charging object, such as the electric vehicle 130.

[0034] A communication connection is established between the charging facility 120 and the server device 110. The communication connection can be established in a wired manner or a wireless manner. The communication connection may include, but is not limited to, a Bluetooth connection, a mobile network connection, a Universal Serial Bus (USB) connection, a Wireless Fidelity (WiFi) connection, etc., and the embodiments of the present disclosure are not limited in this respect. In the embodiments of the present disclosure, the charging facility 120 and the server device 110 can realize signaling interaction through the communication connection between the two.

[0035] In some embodiments, the charging facility 120 may further include a charging control unit (TCU), and the TCU may be configured to send charging status information to the charging management platform based on a communication connection between the charging facility 120 and the server device 110 .

[0036] It should be understood that the structure and function of the various elements in the environment 100 are described for exemplary purposes only and do not imply any limitation on the scope of the present disclosure.

[0037] Figure 2FIG. 2 is a flow chart of a process 200 for charge and discharge control according to some embodiments of the present disclosure. The process 200 may be implemented in the server device 110. Figure 1 The environment 100 is described in Figure 2 Process 200 is shown. Process 200 can be implemented to dynamically determine the power demand of charging facility 120 at various time periods to determine the power when performing charging or discharging operations at various time periods.

[0038] In block 210, the server device 110 determines the estimated power demand for the charging facility 120 in the target time period and the estimated power of the charging facility 120 in the reference time period for the target time period. The charging facility 120 discussed herein may include the entire charging station. In some embodiments, the charging facility 120 may also include a single charging device, such as a single charging pile.

[0039] In some embodiments, the reference time period may be a time period before the target time period. For example, the reference time period may be a time period before the target time period. In such an embodiment, since the reference time period is adjacent to the target time period, it is helpful to reliably control the charging operation or discharging operation of the charging facility 120 in the target time period.

[0040] In some embodiments, each time period may include multiple time points. In other words, the target time period and the reference time period may also include a target time point and a reference time point, respectively.

[0041] In some embodiments, the target time period and the reference time period may be included in a plurality of time periods divided from a predetermined time period. The predetermined time period may be, for example, 24 hours a day or other suitable time periods. Assuming that 24 hours a day is divided into time periods of 15 minutes, 96 time periods may be obtained. Here, it may be assumed that the target time period is the i-th time period, and the reference time period may be, for example, the i-1-th time period. In other examples, the reference time period may be other time periods that are considered useful for demand power prediction of the target time period. Accordingly, i may range from 1 to 96. The following specific embodiments will describe the example process of the present disclosure using an example in which the target time period is the i-th time period and the reference time period is the i-1-th time period.

[0042] In addition, this division method of dividing a day into 96 time periods can be used for load forecasting, electricity price setting, or time series analysis in energy management systems. It should be understood that in the embodiments of the present disclosure, the predetermined time period can also be divided into other numbers of multiple time periods in other appropriate ways, which is not limited here.

[0043] In some embodiments, the estimated power demand and the estimated power quantity of the charging facility 120 may be predicted using an offline simulation algorithm. For example, the estimated power demand for the charging facility 120 in the target time period and the estimated power quantity of the charging facility 120 in the reference time period for the target time period may be predicted based on historical data for the charging facility 120. The historical data may include, for example, actual data for the charging facility 120 in each time period in the cycle before the current cycle. The offline simulation algorithm for the estimated power demand and the estimated power quantity for each time period will be discussed in detail below.

[0044] In block 220 , the server device 110 obtains the actual power of the charging facility 120 in the reference time period. The actual power of the charging facility 120 in the reference time period may be the battery power of the charging facility 120 monitored in real time.

[0045] In block 230, based on the estimated power and the actual power, the server device 110 determines a power adjustment parameter for the charging facility 120. In some embodiments, the power adjustment parameter may be determined based on a ratio of the actual power to the estimated power.

[0046] Assume that represents the estimated power consumption of the charging facility 120 in the reference time period, and represents the actual power of the charging facility 120 in the reference time period, represents the power adjustment parameter, then the following formula can be used:

[0047] (1)

[0048] In box 240, based on the power adjustment parameter, the expected demand power, and the electricity cost information in the target time period, the service-end device 110 determines the target demand power of the charging facility 120 in the target time period, and the target demand power is the charging power or discharging power of the charging facility 120. As discussed above, if the charging facility 120 includes a single charging device, such as a single charging pile, then the charging power or discharging power of the single charging device can be controlled according to the solution of the present disclosure. The specific method of determining the target demand power will be discussed in detail below.

[0049] In block 250, the service-end device 110 performs a charging operation or a discharging operation on the charging facility 120 in the target time period based on the target demand power. Specifically, if the target demand power is the charging power of the charging facility 120, the service-end device 110 performs a charging operation on the charging facility 120 in the target time period based on the charging power. If the target demand power is the discharging power of the charging facility 120, the service-end device 110 performs a discharging operation on the charging facility 120 in the target time period based on the discharging power.

[0050] In some embodiments, when determining the target demand power, based on the power cost information, the server device 110 can determine a target power cost interval corresponding to the target time period in multiple power cost intervals. In each power cost interval, the charging facility 120 can be specified as allowing charging operations, allowing discharging operations, or allowing charging or discharging operations. Then, the server device 110 can determine the target demand power based on the target power cost interval, the power adjustment parameter, and the expected demand power.

[0051] Exemplarily, the multiple electricity cost intervals may be intervals divided according to the electricity cost of different time periods within a predetermined time period. For example, the multiple electricity cost intervals may be divided according to the electricity cost of different time periods within 24 hours of a day. In some embodiments, each electricity cost interval may include multiple time periods.

[0052] Figure 3 A schematic diagram showing an example 300 of multiple electricity cost intervals according to some embodiments of the present disclosure is shown. Example 300 can be used to implement Figure 2 Process 200. In example 300, multiple electricity cost intervals may include, for example, a valley price (LOW) interval, a common price (COMMON) interval, a peak (HIGH) interval, and a peak (PEEK) interval. The electricity costs of the valley price interval, the common price interval, the peak interval, and the peak interval are from low to high. Such a method of dividing electricity price intervals can be called a peak-high-flat-valley electricity price system, also known as a time-of-use electricity price or a peak-valley electricity price system. This is a dynamically adjusted electricity price mechanism that sets different electricity cost standards according to changes in electricity demand in different time periods of a day. It should be understood that multiple electricity cost intervals can also be obtained based on other electricity price interval division methods, and other electricity price interval division methods can, for example, correspond to other numbers (such as 3, 5, etc.) of electricity cost intervals.

[0053] In example 300, time period 310 and time period 310' may both correspond to a daily electricity price range, but the time sequence of the division of multiple electricity cost ranges is different, so as to represent the division of electricity cost ranges into two different categories.m , where m can be represented as the mth price_b_section electricity price category interval in Example 300. Accordingly, the value range of m is, for example, 1 to 2. In each time period, the whole-day electricity price can be segmented based on the start time of the valley price. Each segmented electricity cost interval can be, for example, represented by price_ordertab k To represent, k can be represented as the serial number corresponding to the electricity cost range in example 300, such as 1~8.

[0054] Exemplarily, in the valley price interval, the charging facility 120 may be designated as allowing charging operations. In the parity interval, the charging facility 120 may be designated as allowing charging or discharging operations. In the peak interval, the charging facility 120 may be designated as allowing discharging operations. In the peak interval, the charging facility 120 may be designated as allowing discharging operations. It should be understood that this is merely exemplary and is not intended to be limiting. Such an example is conducive to reducing the charging cost of the charging facility 120, and in the parity interval, the charging or discharging power of the charging facility 120 can be dynamically controlled according to the actual power of the charging facility 120 and the power demand and power cost of the charging facility 120.

[0055] In addition, in each time period, the parity interval start time can be used as a dividing point for secondary segmentation. For example, in time period 310', interval 311' and interval 312' are divided based on the start time of the parity interval. n To represent such divided intervals, where n can be represented as the nth category interval containing peak electricity price and / or peak electricity price in the mth price_b_section electricity price category interval.

[0056] In such an embodiment, by dividing the predetermined time period into multiple combination category electricity price intervals, dynamic hierarchical constraints on multi-time period electricity demand reservation are implemented, supporting adaptive regulation and control of power within the interval segment.

[0057] The following will take the valley price range (i.e., the first electricity cost range), the parity range (i.e., the second electricity cost range), the peak range (i.e., the third electricity cost range), and the spike range (i.e., the fourth electricity cost range) as examples to discuss specific embodiments of determining the target demand power based on the target electricity cost range, power adjustment parameters, and estimated demand power.

[0058] Before discussing, it should be noted that usually the valley price / parity price of the corresponding electricity price range (i.e., electricity cost range) within a single day is greater than 2 hours or other longer durations, and there is generally no fine-grained electricity price range. In addition, the microgrid storage of the charging station is often not limited by the transformer capacity, and can meet the full charging and discharging capacity within 2 hours. For the electricity price change setting with peak-valley price difference, there is often a long-term valley price, such as the interval from 00:00:00 at night to 8:00:00 in the morning, or other longer durations. Therefore, based on the above characteristics, this solution makes the following two assumptions: 1) Assume that the valley price / parity electricity price range can be fully charged under any state of charge (SOC). 2) Assume that 00:00:00 at night is the starting time of the long-term valley price. In addition, assuming that the power of the charging facility 120 corresponding to the charging condition is greater than 0, the corresponding power under the discharge condition is less than 0.

[0059] In some embodiments, when determining the target required power based on the target electricity cost interval, the power adjustment parameter and the expected required power, if the target electricity cost interval is the first electricity cost interval, the server device 110 can use the determined power adjustment parameter to adjust the expected required power to obtain the adjusted expected required power. The charging facility 120 is designated as allowing charging operations within the first electricity cost interval.

[0060] Assume that To express the expected power demand, to represent the adjusted estimated power demand, and still in To express the power adjustment parameter, the following formula can be used:

[0061] (2)

[0062] Based on the above formula (2), for the charging stage of the charging facility 120, if the actual power in the reference time period (such as the i-1th time period) is greater than the expected power, the charging power of the charging facility 120 can be reduced in the target time period (such as the i-th time period). Conversely, if the actual power in the reference time period is less than the expected power, the charging power of the charging facility 120 can be increased in the target time period. For the discharging stage of the charging facility 120, if the actual power in the reference time period is greater than the expected power, the discharging power is increased in the target time period. If the actual power in the reference time period is less than the expected power, the discharging power is reduced in the target time period.

[0063] In some embodiments, based on the power upper limit of the charging facility 120, the remaining power of the charging facility 120, the remaining time of the first power cost interval from the target time period, and the charging efficiency of the charging facility 120, the server device 110 can determine the lower limit of the charging power for the charging facility 120.

[0064] In order to reduce the impact of temporary emergencies at the storage and charging stations, such as temporary equipment maintenance, demand response and other interference during the charging phase, the power cannot be adjusted significantly. At this time, in order to ensure that the demand is fully met at the end of the valley price, the real-time baseline power is integrated for adjustment as follows:

[0065] (3)

[0066] in, represents the lower limit of the charging power for the charging facility 120, Represents 0.25 hours (i.e., a 15-minute period). Indicates the upper limit of the power of the charging facility 120. The upper limit of the power is also the maximum discharge energy of the charging facility 120. Under the current battery health state (SOH state), The product of (battery nominal energy) and SOHE (electricity consumed per unit time) and DOD (Depth of Discharge, battery usage window) is the maximum available energy of the current state of the charging facility 120 DOD refers to the ratio of the actual amount of electricity discharged by the battery in a discharge cycle to its rated capacity, expressed as a percentage. For example, if the battery usage window starts at 3% SOC and ends at 97%, the corresponding battery DOD is 94%.

[0067] in addition, It represents the remaining time of the first electricity cost interval from the target time period, specifically, for example, the number of remaining valley price time periods before executing the i-th time period control strategy. Indicates the remaining power of the charging facility 120, that is, the actual remaining energy value of the battery corresponding to the i-1th time period. Indicates the charging efficiency of the charging facility 120. For the charging efficiency and discharging efficiency, corresponding to the current battery health state, the supplier output or the cloud dynamically calibrates the AC The square root of the value is calculated, that is, the corresponding charging / discharging efficiency value can be or , here it is assumed .

[0068] In some embodiments, based on the adjusted estimated demand power and the lower limit of the charging power, the server device 110 can determine the target demand power of the charging facility 120 in the target time period, where the target demand power is the charging power of the charging facility 120. Therefore, the actual execution power (i.e., the target demand power) in the i-th time period can be:

[0069] (4)

[0070] in, It can be expressed as the target required power. For formula (4), it can be expressed that the larger one is selected from the adjusted estimated required power and the charging power lower limit as the target required power, so that the charging facility 120 is charged based on the target required power in the target time period.

[0071] In some embodiments, when determining the target demand power based on the target electricity cost interval, the power adjustment parameter and the expected demand power, if the target electricity cost interval is the second electricity cost interval, the service-end device 110 may determine whether to use the first power control strategy or the second power control strategy based on whether the time after the duration corresponding to the target electricity cost interval corresponds to the first electricity cost interval. The first electricity cost interval is the lowest electricity cost interval among multiple electricity cost intervals. In the second electricity cost interval, the charging facility 120 is designated as allowing charging or discharging operations. Using the determined first power control strategy or second power control strategy, the service-end device 110 may determine the target demand power based on the power adjustment parameter and the expected demand power.

[0072] In such an embodiment, the charging facility 120 is designated to allow charging or discharging operations in the parity interval (ie, the second electricity cost interval), and due to the impact of real-time load changes, it is necessary to determine whether to use the first power control strategy or the second power control strategy.

[0073] Specifically, it can be determined whether the parity interval of the i-th time period is within For example, the last electricity price interval can be determined based on whether the last electricity price interval is followed by a valley price interval (i.e., the first electricity cost interval). The last electricity price range. Figure 3 For example, it can be determined whether the common price interval is the last electricity cost interval of the time period 310'.

[0074] In some embodiments, when the target demand power is determined based on the power adjustment parameter and the estimated demand power using the first power control strategy, the service-end device 110 may determine the difference between the charging cost and the discharge benefit of the charging facility 120 during the duration of the target electricity cost interval. If the determined difference is greater than a predetermined threshold, the target demand power of the charging facility 120 during the target time period is determined based on the maximum discharge power of the charging facility 120, where the target demand power is the discharge power of the charging facility 120. If the determined difference is greater than a predetermined threshold, the target demand power of the charging facility 120 during the target time period is determined to be zero.

[0075] In such an embodiment, assuming that the parity interval of the i-th time period is If the last electricity price interval is reached and there is profit margin (i.e. the difference between charging cost and discharging profit), the maximum power discharge is performed to ensure that the battery is discharged to the maximum possible level. , otherwise no charging or discharging is performed. In some embodiments, the profit margin may also be limited to be greater than a predetermined threshold. To express the profit space, the corresponding calculation method is as follows:

[0076] = (5)

[0077] in It represents the profit margin when discharging in the parity range and charging in the valley price range, which is calculated as follows:

[0078] (6)

[0079] in represents the redundancy factor.

[0080] The corresponding target power requirement is:

[0081] (7)

[0082] in, Indicates the maximum discharge power of the battery, i is a certain time period, and is associated with the battery SOC value corresponding to this time period. In the current SOH state, the maximum continuous discharge power of the battery output depends on the supplier's BMS control output or cloud dynamic calibration. .

[0083] In some embodiments, when using the second power control strategy to determine the target demand power based on the power adjustment parameters and the expected demand power, the service-side device 110 can determine the baseline demand power of the charging facility 120 within the target time period based on the maximum charging power, maximum discharge power and expected demand power of the charging facility 120, and use the power adjustment parameters to adjust the baseline demand power to obtain the target demand power of the charging facility 120 within the target time period.

[0084] In such an embodiment, it is assumed that the parity interval of the i-th time period is not in The last electricity price interval in the interval (i.e., there is a peak interval or a spike interval after this flat electricity price), the second power control strategy can be adopted. The baseline demand power may include the power corresponding to the discharge stage of the charging facility 120. and corresponding to the charging stage of the charging facility 120 The specific method of determining the target required power based on the reference required power will be discussed below.

[0085] In some embodiments, when the target demand power is determined based on the power adjustment parameter and the estimated demand power using the second power control strategy, if it is determined that the target time period corresponds to the starting time period of the target power cost interval, the server device 110 can compare the remaining power of the charging facility 120 with the reserved power for other power cost intervals outside the target power cost interval. Based on the comparison result, the server device 110 can determine the charging operation, discharging operation, or non-charging and discharging to be performed by the charging facility 120 within the duration corresponding to the target power cost interval.

[0086] In such an embodiment, whether discharge is allowed in the parity interval is determined only in the starting time period of this electricity price interval. The starting time period may include the starting time point. The electricity price range only allows the power value to be adjusted, and does not allow switching between charging and discharging operations.

[0087] In some embodiments, if it is determined that the charging facility 120 is to perform a charging operation within the duration corresponding to the target electricity cost interval, the server device 110 may determine that the target required power within the target time period is the charging power. If it is determined that the charging facility 120 is to perform a discharging operation within the duration corresponding to the target electricity cost interval, the server device 110 may determine that the target required power within the target time period is the discharging power. If it is determined that the charging facility 120 does not perform a charging or discharging operation within the duration corresponding to the target electricity cost interval, the server device 110 may determine that the target required power is zero.

[0088] For example, if To indicate the remaining power of the charging facility 120, To represent the reserved electricity for other electricity cost ranges outside the target electricity cost range, if , which allows the offline simulation algorithm to perform discharge power in the parity interval .like , then the charging power of the offline simulation algorithm is allowed to execute in the parity interval .like ,but , which means that no charging or discharging is required.

[0089] Specifically, if , target power requirement To express, then:

[0090] (8)

[0091] (9)

[0092] The above parity interval is after the discharge condition is executed. Electricity price range, charging is not allowed in the future.

[0093] like , target power requirement To express, then:

[0094] (10)

[0095] (11)

[0096] in and They are respectively the baseline designs for the real-time algorithm side if the offline simulation power base is too low or the charging and discharging are inconsistent, so as to ensure the effectiveness of the real-time regulation.

[0097] like ,but .

[0098] It should also be noted that based on load forecast, in a In order to ensure full utilization of high peak loads, a certain amount of battery energy needs to be reserved in the peak pre-parity interval and / or peak interval (i.e., other electricity cost interval). The reserved value is obtained based on the product of the actual peak load forecast value and the algorithm redundancy coefficient. The high peak reserved power coefficient value is re .exist Within the interval, the subsequent high peak reserved power value greater than the i-th time period is the effective load value of the corresponding peak electricity price interval , denoted as .

[0099] In addition, based on the historical load data of the microgrid station, a prediction model is built to realize the load prediction of each time period of the predetermined time period. For example, the load segmentation and distribution of the predetermined time period can be realized in 96 time periods. To represent the estimated value of the microgrid load. In a certain time period i, if the estimated value of the microgrid load is greater than the actual discharge capacity of the energy storage, the load supported by the actual discharge of the energy storage is used as the actual effective estimated load for subsequent algorithm calculations. The calculation formula for the effective load estimate is as follows:

[0100] (12)

[0101] For the above embodiment, the charging and discharging power values ​​and the positive and negative vectors (corresponding to charging or discharging, respectively) in the parity interval are selected by optimizing the comprehensive benefits in the offline simulation. However, due to the influence of real-time load changes, real-time optimization adjustments and constraints are performed. By adopting a mandatory constraint design, the risk of reduced battery energy utilization that may be caused by real-time load uncertainty and fluctuations is further reduced.

[0102] In some embodiments, when determining the target demand power based on the target electricity cost interval, the power adjustment parameter and the expected demand power, if the target electricity cost interval is the third electricity cost interval and the third electricity cost interval is less than the fourth electricity cost interval, the server device 110 may compare the remaining electricity of the charging facility 120 with the reserved electricity for the fourth electricity cost interval, wherein the charging facility 120 is designated as allowing discharge operation in the third electricity cost interval and the fourth electricity cost interval. If the result of the comparison indicates that the remaining electricity is greater than the reserved electricity, based on the maximum discharge power and the expected demand power of the charging facility 120, the server device 110 may determine the baseline demand power of the charging facility 120 within the target time period, and adjust the baseline demand power using the power adjustment parameter to obtain the target demand power of the charging facility 120 within the target time period, wherein the target demand power is the discharge power of the charging facility 120. If the result of the comparison indicates that the remaining electricity is less than or equal to the reserved electricity, the server device 110 may determine that the target demand power is zero.

[0103] In such an embodiment, the peak interval (i.e., the third electricity cost interval) is an electricity price interval that only allows discharge, and it is necessary to consider reserving energy for the subsequent peak interval (i.e., the fourth electricity cost interval), therefore:

[0104] like , then discharge is allowed, that is: (13)

[0105] (14)

[0106] in, It can represent the reserved power for the fourth power cost range. The remaining power of the charging facility 120 can still be indicated. It can represent the baseline required power of the charging facility 120 within the target time period. The target required power can still be expressed.

[0107] like ,but .

[0108] In some embodiments, when determining the target demand power based on the target electricity cost interval, the power adjustment parameters and the estimated demand power, if the target electricity cost interval is the fourth electricity cost interval, the charging facility 120 is designated as allowing discharge operations in the fourth electricity cost interval, based on the maximum discharge power of the charging facility 120, the service-side device 110 can determine the target demand power of the charging facility 120 within the target time period, where the target demand power is the discharge power of the charging facility 120.

[0109] In such an embodiment, the peak interval has a peak electricity price. In order to consider the volatility of the real-time load in the peak interval, no discharge power limit is imposed in this interval, and demand control is performed with the maximum output power, that is:

[0110] (15)

[0111] in, Indicates the maximum discharge power of the battery. Assuming that the charging power is defined as positive and the discharging power is defined as negative, in the current SOH state, the maximum continuous charging power output by the battery depends on the supplier's control output or dynamic calibration in the cloud. , the maximum continuous discharge power is abs( ), where i is a time period, and the battery SOC value corresponding to this time period is associated.

[0112] It should also be noted that based on load forecast, in a In order to ensure full utilization of the peak load, a certain amount of battery energy needs to be reserved in the peak interval before the peak. The reserved value is obtained based on the product of the effective peak load prediction value and the algorithm redundancy coefficient. The peak reserved power coefficient value is re .exist Within the interval, the subsequent peak reserve power value greater than the i time period is the effective load value of the corresponding peak electricity price interval , which can be written as .

[0113] For the real-time dynamic variable adjustment algorithm on dt day (strategy implementation day, i.e., scheduled time period) discussed above, the real-time dynamic variable adjustment algorithm mainly relies on battery status parameters, each electricity price interval division mark, each electricity price interval effective load data and offline simulation output value. The battery status parameters include , , , , , , , SOHE, among which The remaining energy value of the energy storage at the beginning of the dt day (00:00:00); the offline simulation output value is and ; The electricity price intervals involved include , , ; Important reserved payload data includes , , calculated as follows: (16)

[0114] (17)

[0115] Where m is the mth Interval and nth interval, and They are to ensure peak electricity prices and full use of peak electricity. The redundancy coefficient is set based on actual business needs or analysis experience. Can be interpreted as In order to ensure sufficient electricity consumption at peak electricity prices, it is necessary to reserve a certain amount of electricity when discharging at the pre-parity price or peak electricity price. Can be interpreted as In order to ensure sufficient electricity consumption at peak electricity prices, it is necessary to reserve a certain amount of electricity when discharging at the pre-parity electricity price.

[0116] In addition, considering that a single microgrid charging station cannot obtain accurate and reliable load prediction results, especially considering the characteristics of load prediction changing over time, the optimal solution of the optimization algorithm cannot adapt well to the dynamic adjustment needs in actual operation, resulting in low efficiency or waste of resources in actual operation. The above-mentioned disclosed embodiment adopts a real-time dynamic optimization strategy to dynamically adjust the offline simulation results to ensure optimal benefits.

[0117] Through the real-time dynamic adjustment algorithm in the above disclosed embodiment, it is possible to dynamically adjust the control strategy according to actual operation data and environmental changes (such as market electricity price fluctuations, load mutations, etc.). This improves the flexibility of the charging and discharging control of the charging facility 120 and the robustness to external disturbances, so as to quickly respond and effectively deal with uncertainties.

[0118] In addition, through adaptive predictive control, we can not only pursue the maximization of economic benefits, but also adjust the charging and discharging strategies in real time according to the actual state of the battery (such as charge state, temperature, etc.), avoid excessive charging and discharging, effectively balance economic benefits and battery life maintenance, and reduce maintenance costs.

[0119] In some embodiments, when determining the expected power demand in the target time period and the expected amount of electricity in the reference time period, the server device 110 may determine the initial expected power demand and initial expected amount of electricity for each of the multiple time periods. Based on the initial expected power demand and initial expected amount of electricity for each of the multiple time periods, the server device 110 may determine the difference between the expected charging cost and the expected discharge benefit of the charging facility 120 within a predetermined time period, and update the initial expected power demand and initial expected amount of electricity for each of the multiple time periods by at least changing the difference to the target value, so as to obtain the expected power demand and expected amount of electricity for each of the multiple time periods.

[0120] In such an embodiment, for such an offline mixed integer linear programming algorithm offline simulation stage, an optimization solver (e.g., SCIP_MIXED_INTEGER_PROGRAMMING solver) can be called to construct and solve the mixed integer programming model. Assuming that the objective function is the difference between the comprehensive benefit of discharge electricity and the comprehensive cost of charging electricity (i.e., the difference between the estimated charging cost and the estimated discharge benefit of the charging facility 120 within a predetermined time period), the decision variables corresponding to the target value (e.g., the minimum value) can be optimized. Objective function The calculation of is as follows:

[0121] (18)

[0122] in, It can be expressed as the total discharge benefit, It can represent the total charging cost, and the calculation of the two are as follows:

[0123] (19)

[0124] (20)

[0125] (twenty one)

[0126] During discharge, the power required by the control strategy can be expressed as Considering the hysteresis constraint of the microgrid station, the actual discharge power needs to be subtracted from the hysteresis value, that is, To represent the discharge power hysteresis limit. In the process of energy storage supplying power to the charging pile, due to the unpredictable changes in the load of the charging pile, in order to prevent the energy storage discharge current from flowing back to the grid side, the station often sets a certain redundant power, which can be a discharge power coefficient or a fixed value, recorded as re here, and after normalization and custom function processing, it is .

[0127] In addition, the estimated battery aging cost (or attenuation cost) caused by charging / discharging 1kwh at the corresponding power can be calculated using / It is expressed in "yuan / kw". The parameters are related to the DOD window, ambient temperature and charge / discharge power used by the current battery. The parameters are derived from the life simulation results, in which charging is affected by Value, discharge dependent Value size.

[0128] In the above planning algorithm, the following constraints need to be met.

[0129] First, considering the peak-valley price difference as the main goal, we set the restriction that charging is not allowed in the peak interval and peak interval, the restriction that discharging is not allowed in the valley price interval, and no restriction is set in the parity interval, that is:

[0130] like (twenty two)

[0131] like (twenty three)

[0132] in, i=1,2,3，…，96 , is the decision variable corresponding to the i-th time period (Power) value.

[0133] Secondly, we need to In the objective function, the comprehensive cost of charging and the comprehensive benefit of discharging are set respectively, and the energy storage battery can only choose one working condition of charging and discharging at the same time. ， Among them , is 1; otherwise is 0. If it is in a static state, 0, default If it is 0, the corresponding comprehensive discharge income is 0 and will not be included in the function income calculation.

[0134] In some embodiments, when updating the initial estimated power demand and initial estimated power for each of the multiple time periods, the initial estimated power demand and initial estimated power for each of the multiple time periods are updated by satisfying at least one of the following conditions: the remaining power of the charging facility 120 in each time period is not less than the lower limit of the power of the charging facility 120, and is not greater than the upper limit of the power of the charging facility 120. The discharge power of the charging facility 120 in each time period is not greater than the maximum discharge power of the charging facility 120, or the charging power of the charging facility 120 in each time period is not greater than the maximum charging power of the charging facility 120.

[0135] In such an embodiment, the upper and lower limits of the battery energy need to be constrained. The battery power at any time does not exceed the current charging or discharging energy upper limit, that is, during the discharging process, the accumulated discharge energy value is:

[0136] (twenty four)

[0137] in It can indicate the remaining energy value of the battery at the start of the discharge condition; It can represent the cumulative discharge energy value of this discharge condition up to the i-th time period; It can represent the lower limit of the power of the charging facility 120. During the charging process, the upper limit is limited, that is:

[0138] (25)

[0139] in It can be expressed as the remaining energy value of the battery at the starting state of the charging condition; It can be expressed as the cumulative charging energy value from this charging condition to the i-th time period, It can be expressed as the remaining battery energy in the i-th time period; The upper limit of the power of the charging facility 120 may be indicated.

[0140] In addition, the charge / discharge power value needs to be constrained. The battery charge / discharge power at any time cannot exceed the maximum capacity of the battery in the current SOC state, that is, the upper limit of the discharge power. .in It can be expressed as , the maximum discharge power is negative and not less than .in is a positive value. If the absolute value is lower than this value, there is no need to send the discharge power, that is:

[0141] (26)

[0142] in It can be expressed as the upper limit of charging power, that is, the maximum charging power, = ,Right now:

[0143] (27)

[0144] Based on the inputs of battery status parameters, the estimated effective load curve for the scheduled time period, the electricity price curve for the scheduled time period, and related constraints, the mixed integer linear programming algorithm constructed above is used to achieve the optimal control variable Curve output; simultaneously output the remaining battery energy corresponding to each i-th time period in the predetermined time period .

[0145] The disclosed embodiment adopts a method that combines offline algorithm simulation with real-time dynamic adjustment algorithm. On the real-time dynamic adjustment algorithm side, multiple electricity price interval demarcation, power adaptive regulation control, multiple electricity price demand dynamic constraints and redundant or baseline power setting methods are adopted. This can solve the problem of reduced revenue caused by uncertain load fluctuations in storage and charging stations. It has high generalization ability and applicability in various scenarios, and supports the realization of comprehensive overall revenue optimization in practical applications.

[0146] In terms of comprehensive optimization, offline algorithms can make full use of historical data and forecast information, perform comprehensive optimization in an environment without real-time constraints, and design theoretically optimal or near-optimal strategies. Combined with real-time dynamic adjustment algorithms, strategies can be quickly corrected in actual operation to cope with forecast errors and real-time changes, ensuring that the system is always close to the optimal operating state, which is particularly important in long-term operations.

[0147] Through the disclosed embodiments, while retaining the advantage of the offline simulation multi-factor overall comprehensive benefit optimization capability, the real-time dynamic adjustment algorithm solves the interference caused by real-time load uncertainty and fluctuation, improves the generalization ability of the algorithm in high-uncertainty load prediction and dynamic electricity price change scenarios, and can ensure the optimal comprehensive benefits of microgrid sites. In addition, in the real-time algorithm design, the offline simulation decision power variable curve is used as the baseline, mainly relying on the difference between the simulated predicted power and the real-time power, and the remaining demand power and the remaining time relationship of the electricity price, to achieve real-time dynamic adjustment of the power value, and at the same time, based on multi-interval load reservation, the power vector positive and negative value decision is achieved.

[0148] In addition, in terms of adapting to complex environments, for complex systems such as microgrids, adaptive predictive control can handle multi-objective and multi-constraint optimization problems, and is suitable for situations where economic, safety, reliability and environmental factors need to be considered simultaneously.

[0149] Figure 4 A schematic structural block diagram of an apparatus 500 for charge and discharge control according to some embodiments of the present disclosure is shown. The apparatus 400 may be implemented as or included in the server device 110. Each module / component in the apparatus 400 may be implemented by hardware, software, firmware, or any combination thereof.

[0150] like Figure 4 As shown, the device 400 includes an estimated demand determination module 410, which is configured to determine the estimated demand power for the charging facility in the target time period and the estimated power of the charging facility in the reference time period for the target time period; an actual power acquisition module 420, which is configured to acquire the actual power of the charging facility in the reference time period; an adjustment parameter determination module 430, which is configured to determine the power adjustment parameters for the charging facility based on the estimated power and the actual power; a target demand determination module 440, which is configured to determine the target demand power of the charging facility in the target time period based on the power adjustment parameters, the estimated demand power and the power cost information in the target time period, the target demand power being the charging power or discharging power of the charging facility; and an operation execution module 450, which is configured to execute a charging operation or a discharging operation on the charging facility in the target time period based on the target demand power.

[0151] In some embodiments, the target demand determination module 440 is further configured to determine, based on the electricity cost information, a target electricity cost interval corresponding to the target time period in a plurality of electricity cost intervals, wherein in each electricity cost interval the charging facility is designated as allowing charging operations, allowing discharging operations, or allowing charging or discharging operations; and to determine the target demand power based on the target electricity cost interval, the power adjustment parameters, and the estimated demand power.

[0152] In some embodiments, the target demand determination module 440 is further configured to, in response to the target electricity cost interval being the first electricity cost interval, adjust the expected demand power using the determined power adjustment parameters to obtain an adjusted expected demand power, wherein the charging facility is designated as allowing charging operations within the first electricity cost interval; determine a lower limit on the charging power for the charging facility based on the upper limit of the electricity of the charging facility, the remaining time of the first electricity cost interval from the target time period, and the charging efficiency of the charging facility; and determine the target demand power of the charging facility within the target time period based on the adjusted expected demand power and the lower limit of the charging power, wherein the target demand power is the charging power of the charging facility.

[0153] In some embodiments, the target demand determination module 440 is further configured to determine, in response to the target electricity cost interval being the second electricity cost interval, whether the time after the duration corresponding to the target electricity cost interval corresponds to the first electricity cost interval, to use the first power control strategy or the second power control strategy, wherein the first electricity cost interval is the lowest electricity cost interval among multiple electricity cost intervals, and in the second electricity cost interval the charging facility is designated as allowing charging or discharging operations; and determine the target demand power based on the power adjustment parameters and the estimated demand power using the determined first power control strategy or second power control strategy.

[0154] In some embodiments, the target demand determination module 440 is further configured to determine the difference between the charging cost and the discharging benefit of the charging facility within the duration of the target electricity cost interval; in response to the determined difference being greater than a predetermined threshold, determine the target demand power of the charging facility within the target time period based on the maximum discharge power of the charging facility, wherein the target demand power is the discharge power of the charging facility; in response to the determined difference being greater than a predetermined threshold, determine that the target demand power of the charging facility within the target time period is zero.

[0155] In some embodiments, the target demand determination module 440 is further configured to determine a baseline demand power of the charging facility within the target time period based on the maximum charging power, maximum discharge power and estimated demand power of the charging facility; and to adjust the baseline demand power using power adjustment parameters to obtain the target demand power of the charging facility within the target time period.

[0156] In some embodiments, the target demand determination module 440 is further configured to, in response to determining that the target time period corresponds to the starting time period of the target electricity cost interval, compare the remaining electricity of the charging facility with the reserved electricity for other electricity cost intervals outside the target electricity cost interval; based on the comparison result, determine whether the charging facility is to perform a charging operation, a discharging operation, or no charging or discharging within the duration corresponding to the target electricity cost interval; if it is determined that the charging facility is to perform a charging operation within the duration corresponding to the target electricity cost interval, determine that the target demand power within the target time period is the charging power; if it is determined that the charging facility is to perform a discharging operation within the duration corresponding to the target electricity cost interval, determine that the target demand power within the target time period is the discharging power; and if it is determined that the charging facility is not to perform a charging or discharging operation within the duration corresponding to the target electricity cost interval, determine that the target demand power is zero.

[0157] In some embodiments, the target demand determination module 440 is further configured to compare the remaining power of the charging facility with the reserved power for the fourth power cost interval in response to the target power cost interval being the third power cost interval and the third power cost interval being less than the fourth power cost interval, wherein the charging facility is designated as allowing discharge operations in the third power cost interval and the fourth power cost interval; if the result of the comparison indicates that the remaining power is greater than the reserved power, determine a baseline demand power of the charging facility within the target time period based on the maximum discharge power and the estimated demand power of the charging facility, and use the power adjustment parameters to adjust the baseline demand power to obtain the target demand power of the charging facility within the target time period, wherein the target demand power is the discharge power of the charging facility; and if the result of the comparison indicates that the remaining power is less than or equal to the reserved power, determine the target demand power to be zero.

[0158] In some embodiments, the target demand determination module 440 is further configured to determine the target demand power of the charging facility within the target time period based on the maximum discharge power of the charging facility in response to the target electricity cost interval being the fourth electricity cost interval, in which the charging facility is designated as allowing discharge operations. The target demand power is the discharge power of the charging facility.

[0159] In some embodiments, the target time period and the reference time period are included in a plurality of time periods divided from a predetermined time period, and the estimated demand determination module 410 is further configured to respectively determine the initial estimated demand power and the initial estimated electricity for each of the plurality of time periods; and determine the difference between the estimated charging cost and the estimated discharge benefit of the charging facility within the predetermined time period based on the initial estimated demand power and the initial estimated electricity for each of the plurality of time periods; and update the initial estimated demand power and the initial estimated electricity for each of the plurality of time periods at least by changing the difference value to the target value to obtain the estimated demand power and the estimated electricity for each of the plurality of time periods.

[0160] In some embodiments, the estimated demand determination module 410 is further configured to update the initial estimated demand power and the initial estimated power for each of the multiple time periods by ensuring that at least one of the following conditions is satisfied: the remaining power of the charging facility in each time period is not lower than the lower limit of the power of the charging facility, and is not higher than the upper limit of the power of the charging facility; the discharge power of the charging facility in each time period is not higher than the maximum discharge power of the charging facility, or the charging power of the charging facility in each time period is not higher than the maximum charging power of the charging facility.

[0161] The units and / or modules included in the device 400 can be implemented in various ways, including software, hardware, firmware, or any combination thereof. In some embodiments, one or more units and / or modules can be implemented using software and / or firmware, such as machine executable instructions stored on a storage medium. In addition to or as an alternative to machine executable instructions, some or all of the units and / or modules in the device 600 can be implemented at least in part by one or more hardware logic components. As an example and not limitation, exemplary types of hardware logic components that can be used include field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0162] Figure 5 1 shows a block diagram of an electronic device 500 in which one or more embodiments of the present disclosure may be implemented. It should be understood that Figure 5 The electronic device 500 shown is merely exemplary and should not constitute any limitation on the functionality and scope of the embodiments described herein. Figure 5 The electronic device 500 shown can be used to implement Figure 1 The server device 110.

[0163] like Figure 5As shown, the electronic device 500 is in the form of a general-purpose computing device. The components of the electronic device 500 may include, but are not limited to, one or more processors or processing units 510, a memory 520, a storage device 530, one or more communication units 540, one or more input devices 550, and one or more output devices 560. The processing unit 510 may be an actual or virtual processor and is capable of performing various processes according to a program stored in the memory 520. In a multi-processor system, multiple processing units execute computer executable instructions in parallel to improve the parallel processing capability of the electronic device 500.

[0164] The electronic device 500 typically includes a plurality of computer storage media. Such media may be any accessible media accessible to the electronic device 500, including but not limited to volatile and non-volatile media, removable and non-removable media. The memory 520 may be a volatile memory (e.g., a register, a cache, a random access memory (RAM)), a non-volatile memory (e.g., a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), flash memory), or some combination thereof. The storage device 530 may be a removable or non-removable medium, and may include a machine-readable medium, such as a flash drive, a disk, or any other medium, which may be capable of being used to store information and / or data (e.g., training data for training) and may be accessed within the electronic device 500.

[0165] The electronic device 500 may further include additional removable / non-removable, volatile / non-volatile storage media. Figure 5 As shown in , a disk drive for reading or writing from a removable, non-volatile disk (e.g., a "floppy disk") and an optical drive for reading or writing from a removable, non-volatile optical disk may be provided. In these cases, each drive may be connected to the bus (not shown) by one or more data media interfaces. The memory 520 may include a computer program product 525 having one or more program modules that are configured to perform various methods or actions of various embodiments of the present disclosure.

[0166] The communication unit 540 enables communication with other computing devices via a communication medium. Additionally, the functions of the components of the electronic device 500 can be implemented in a single computing cluster or multiple computing machines that can communicate via a communication connection. Therefore, the electronic device 500 can operate in a networked environment using a logical connection with one or more other servers, a network personal computer (PC), or another network node.

[0167] The input device 550 may be one or more input devices, such as a mouse, a keyboard, a tracking ball, etc. The output device 560 may be one or more output devices, such as a display, a speaker, a printer, etc. The electronic device 500 may also communicate with one or more external devices (not shown) through the communication unit 540 as needed, such as a storage device, a display device, etc., communicate with one or more devices that allow a user to interact with the electronic device 500, or communicate with any device that allows the electronic device 500 to communicate with one or more other computing devices (e.g., a network card, a modem, etc.). Such communication may be performed via an input / output (I / O) interface (not shown).

[0168] According to an exemplary implementation of the present disclosure, a computer-readable storage medium is provided, on which computer-executable instructions are stored, wherein the computer-executable instructions are executed by a processor to implement the method described above. According to an exemplary implementation of the present disclosure, a computer program product is also provided, which is tangibly stored on a non-transitory computer-readable medium and includes computer-executable instructions, and the computer-executable instructions are executed by a processor to implement the method described above.

[0169] Various aspects of the present disclosure are described herein with reference to the flowcharts and / or block diagrams of the methods, devices, equipment, and computer program products implemented according to the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer-readable program instructions.

[0170] These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processing unit of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0171] Computer-readable program instructions can be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, so that the instructions executed on the computer, other programmable data processing apparatus, or other device implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0172] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to multiple implementations of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of a module, program segment or instruction includes one or more executable instructions for realizing the logical function of the specification. In some implementations as replacements, the function marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square boxes can actually be executed substantially in parallel, and they can sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be realized by a special hardware-based system that performs the function or action of the specification, or can be realized by a combination of special hardware and computer instructions.

[0173] The above descriptions of various implementations of the present disclosure are exemplary, non-exhaustive, and not limited to the disclosed implementations. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described implementations. The selection of terms used herein is intended to best explain the principles of the implementations, practical applications, or improvements to the technology in the marketplace, or to enable other persons of ordinary skill in the art to understand the various implementations disclosed herein.

Claims

1. A method for charge and discharge control, characterized in that: include: determining an estimated demand power for a charging facility in a target time period and an estimated power consumption of the charging facility in a reference time period for the target time period, the target time period and the reference time period being included in a plurality of time periods divided from a predetermined time period, the reference time period including a time period before the target time period; Acquire an actual power of the charging facility in the reference time period, wherein the actual power of the charging facility in the reference time period includes a battery power of the charging facility monitored in real time; Determining a power adjustment parameter for the charging facility based on the estimated power and the actual power; Based on the electricity cost information within the target time period, determining a target electricity cost interval corresponding to the target time period from among a plurality of electricity cost intervals, wherein in each electricity cost interval the charging facility is designated as allowing a charging operation, allowing a discharging operation, or allowing a charging or discharging operation; Determine a target required power of the charging facility within the target time period based on the target electricity cost interval, the power adjustment parameter and the estimated required power, wherein the target required power is the charging power or the discharging power of the charging facility; as well as performing a charging operation or a discharging operation on the charging facility in the target time period based on the target required power, Determining the estimated required power and the estimated power includes: Determine respectively the initial estimated required power and the initial estimated power for each of the multiple time periods; Determining a difference between an estimated charging cost and an estimated discharging benefit of the charging facility within the predetermined time period based on the initial estimated required power and the initial estimated power quantity of each of the plurality of time periods; and The initial estimated power requirements and initial estimated electricity quantities for each of the plurality of time periods are updated at least by changing the difference value to a target value, so as to obtain the estimated power requirements and estimated electricity quantities for each of the plurality of time periods.

2. The method according to claim 1, characterized in that Determining the target required power based on the target electricity cost interval, the power adjustment parameter, and the estimated required power includes: In response to the target electricity cost interval being a first electricity cost interval, adjusting the estimated demand power using the determined power adjustment parameter to obtain an adjusted estimated demand power, wherein the charging facility is designated as allowing charging operations within the first electricity cost interval; determining a lower limit of charging power for the charging facility based on an upper limit of power of the charging facility, a remaining power of the charging facility, a remaining time of the first power cost interval from the target time period, and a charging efficiency of the charging facility; and Based on the adjusted estimated demand power and the charging power lower limit, a target demand power of the charging facility within the target time period is determined, wherein the target demand power is the charging power of the charging facility.

3. The method according to claim 1, characterized in that Determining the target required power based on the target electricity cost interval, the power adjustment parameter, and the estimated required power includes: In response to the target power cost interval being the second power cost interval, determining whether to use the first power control strategy or the second power control strategy based on whether a time after the duration corresponding to the target power cost interval corresponds to the first power cost interval, The first electricity cost interval is the lowest electricity cost interval among the multiple electricity cost intervals, and the charging facility is designated as allowing charging or discharging operations in the second electricity cost interval; and The target required power is determined based on the power adjustment parameter and the estimated required power by using the determined first power control strategy or the second power control strategy.

4. The method according to claim 3, characterized in that Determining the target required power based on the power adjustment parameter and the estimated required power by using the first power control strategy includes: Determine the difference between the charging cost and the discharging benefit of the charging facility within the duration of the target electricity cost interval; In response to the determined difference being greater than a predetermined threshold, determining a target required power of the charging facility within the target time period based on a maximum discharge power of the charging facility, wherein the target required power is a discharge power of the charging facility; and In response to the determined difference being greater than a predetermined threshold, it is determined that the target required power of the charging facility within the target time period is zero.

5. The method according to claim 3, characterized in that Using the second power control strategy to determine the target required power based on the power adjustment parameter and the estimated required power includes: determining a reference required power of the charging facility within the target time period based on the maximum charging power, the maximum discharging power and the estimated required power of the charging facility; and The power adjustment parameter is used to adjust the reference required power to obtain the target required power of the charging facility within the target time period.

6. The method according to claim 5, characterized in that Using the second power control strategy to determine the target required power based on the power adjustment parameter and the estimated required power further includes: In response to determining that the target time period corresponds to the start time period of the target electricity cost interval, comparing the remaining electricity of the charging facility with the reserved electricity for other electricity cost intervals outside the target electricity cost interval; Based on the comparison result, determining whether the charging facility is to perform a charging operation, a discharging operation, or not to perform charging or discharging within a duration corresponding to the target electricity cost interval; If it is determined that the charging operation to be performed by the charging facility is within the duration corresponding to the target electricity cost interval, it is determined that the target required power is the charging power within the target time period; If it is determined that the charging facility is to perform a discharging operation within the duration corresponding to the target electricity cost interval, determining that the target required power within the target time period is the discharging power; and If it is determined that the charging facility does not perform charging or discharging operations within the duration corresponding to the target electricity cost interval, the target required power is determined to be zero.

7. The method according to claim 1, characterized in that Determining the target required power based on the target electricity cost interval, the power adjustment parameter, and the estimated required power includes: In response to the target electricity cost interval being a third electricity cost interval and the third electricity cost interval being smaller than a fourth electricity cost interval, comparing the remaining electricity of the charging facility with the reserved electricity for the fourth electricity cost interval, wherein the charging facility is designated as allowing a discharge operation in the third electricity cost interval and the fourth electricity cost interval; If the comparison result indicates that the remaining power is greater than the reserved power, determining a reference required power of the charging facility within the target time period based on the maximum discharge power of the charging facility and the estimated required power, and Using the power adjustment parameter to adjust the reference required power to obtain the target required power of the charging facility within the target time period, wherein the target required power is the discharge power of the charging facility; and If the comparison result indicates that the remaining power is less than or equal to the reserved power, the target required power is determined to be zero.

8. The method according to claim 1, characterized in that Determining the target required power based on the target electricity cost interval, the power adjustment parameter, and the estimated required power includes: In response to the target electricity cost interval being the fourth electricity cost interval, in which the charging facility is designated as allowing discharge operation, the target demand power of the charging facility within the target time period is determined based on the maximum discharge power of the charging facility, wherein the target demand power is the discharge power of the charging facility.

9. The method according to claim 1, characterized in that: Updating the initial estimated required power and the initial estimated power for each of the multiple time periods includes: The initial estimated required power and the initial estimated power for each of the multiple time periods are updated by satisfying at least one of the following conditions: The remaining power of the charging facility in each time period is not less than the lower limit of the power of the charging facility, and is not more than the upper limit of the power of the charging facility; The discharge power of the charging facility in each time period is not higher than the maximum discharge power of the charging facility, or the charging power of the charging facility in each time period is not higher than the maximum charging power of the charging facility.

10. A device for charge and discharge control, comprising: an estimated demand determination module configured to determine an estimated demand power for a charging facility in a target time period and an estimated power of the charging facility in a reference time period for the target time period, the target time period and the reference time period being included in a plurality of time periods divided from a predetermined time period, the reference time period including a time period before the target time period; an actual power acquisition module, configured to acquire an actual power of the charging facility in the reference time period, wherein the actual power of the charging facility in the reference time period includes a battery power of the charging facility monitored in real time; an adjustment parameter determination module, configured to determine a power adjustment parameter for the charging facility based on the estimated power and the actual power; a target demand determination module, configured to determine a target electricity cost interval corresponding to the target time period from among a plurality of electricity cost intervals based on the electricity cost information within the target time period, wherein in each electricity cost interval the charging facility is designated as allowing charging operation, allowing discharging operation, or allowing charging or discharging operation; Determine a target required power of the charging facility within the target time period based on the target electricity cost interval, the power adjustment parameter and the estimated required power, wherein the target required power is the charging power or the discharging power of the charging facility; as well as an operation execution module configured to execute a charging operation or a discharging operation on the charging facility in the target time period based on the target required power, and The estimated demand determination module is further configured to: Determine respectively the initial estimated required power and the initial estimated power for each of the multiple time periods; Determine the difference between the estimated charging cost and the estimated discharging benefit of the charging facility within the predetermined time period based on the initial estimated required power and the initial estimated power of each of the multiple time periods; as well as The initial estimated power requirements and initial estimated electricity quantities for each of the plurality of time periods are updated at least by changing the difference value to a target value, so as to obtain the estimated power requirements and estimated electricity quantities for each of the plurality of time periods.

11. An electronic device, comprising: at least one processing unit; as well as At least one memory, the at least one memory being coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions causing the electronic device to perform the method according to any one of claims 1 to 9 when executed by the at least one processing unit.

12. A computer-readable storage medium having a computer program stored thereon, wherein the computer program can be executed by a processor to implement the method according to any one of claims 1 to 9.

13. A computer program product comprising computer executable instructions, wherein the computer executable instructions, when executed by a processor, implement the method according to any one of claims 1 to 9.

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