Energy storage scheduling method, energy management equipment and computer readable storage medium

Abstract

The invention is suitable for the technical field of electric power, and provides an energy storage scheduling method, energy management equipment and a computer readable storage medium, and the method comprises the steps: determining a target electricity price and a target electricity price time period based on a step electricity price time period and a corresponding dynamic electricity price; determining a time period from the current moment to the starting moment of the target electricity price time period as a current energy scheduling time period; based on the dynamic electricity price, taking the lowest electricity cost in the energy scheduling time period as a target, and constructing a target function; constructing a power balance constraint condition based on the predicted power consumption information of the load equipment in the energy scheduling time period, the charging and discharging power of the energy storage equipment and the power grid input power; constructing an energy storage equipment constraint condition based on preset limitation information of the energy storage equipment; based on the power balance constraint condition and the energy storage equipment constraint condition, solving the objective function to obtain an energy storage scheduling strategy; controlling energy storage equipment to work based on the energy storage scheduling strategy; the power consumption condition of the system can be flexibly dispatched to improve the power consumption efficiency.

Description

Technical Field

[0001] The present application belongs to the field of electric power technology, and in particular relates to an energy storage scheduling method, an energy management device, and a computer-readable storage medium. Background Art

[0002] In recent years, promoting the use of renewable energy, reducing energy waste, and lowering dependence on fossil fuels has become a global focus. As users' energy usage patterns change, the trend of connecting demand-side power sources to the grid is becoming increasingly evident.

[0003] However, the large-scale deployment of renewable energy sources like wind and solar power also presents numerous challenges, such as impacting the economic operation of microgrids within power systems. Therefore, as renewable energy is globally promoted, along with the introduction of time-of-use electricity pricing, comes the challenges of high electricity costs and low efficiency. Coordinating system power dispatch based on dynamic time-of-use pricing is becoming increasingly important. Summary of the Invention

[0004] The embodiments of the present application provide an energy storage scheduling method, an energy management device, and a computer-readable storage medium, which can flexibly schedule system power usage to reduce power costs and improve power efficiency.

[0005] In a first aspect, the present application provides an energy storage scheduling method, which is applied to an energy management device, wherein the energy management device is used to control an energy storage device and / or a power grid to supply power to a load device; the method may include:

[0006] Based on the pre-divided tiered electricity price time periods and the dynamic electricity prices corresponding to each tiered electricity price time period, the target electricity price and the target electricity price time period corresponding to the target electricity price are determined; the time period between the current moment and the start moment of the target electricity price time period is determined as the current energy scheduling time period; based on the dynamic electricity prices corresponding to each tiered electricity price time period, an objective function is constructed with the goal of minimizing electricity costs within the energy scheduling time period; predicted electricity consumption information of the load equipment within the energy scheduling time period is obtained; power balance constraints are constructed based on the predicted electricity consumption information, the charging and discharging power of the energy storage equipment, and the grid input power of the power grid; energy storage device constraints are constructed based on the preset limit information of the energy storage equipment; based on the power balance constraints and the energy storage device constraints, the objective function is solved to obtain an energy storage scheduling strategy; and the energy storage equipment is controlled to operate based on the energy storage scheduling strategy.

[0007] In a second aspect, an embodiment of the present application provides an energy storage scheduling device, which may include:

[0008] A first determining unit is configured to determine a target electricity price and a target electricity price time period corresponding to the target electricity price based on pre-divided stepped electricity price time periods and the dynamic electricity prices corresponding to the stepped electricity price time periods;

[0009] A second determining unit is configured to determine a time period between the current moment and the start moment of the target electricity price time period as a current energy scheduling time period;

[0010] A first constructing unit is configured to construct an objective function based on the dynamic electricity price corresponding to each of the stepped electricity price time periods and with the goal of minimizing the electricity cost within the energy scheduling time period;

[0011] an acquiring unit, configured to acquire predicted power consumption information of the load device within the energy scheduling time period;

[0012] a second constructing unit, configured to construct a power balance constraint condition based on the predicted power consumption information, the charge and discharge power of the energy storage device, and the grid input power of the grid;

[0013] A third constructing unit, configured to construct energy storage device constraint conditions based on preset restriction information of the energy storage device;

[0014] a calculation unit, configured to solve the objective function based on the power balance constraint and the energy storage device constraint to obtain an energy storage scheduling strategy;

[0015] A control unit is used to control the energy storage device to operate based on the energy storage scheduling strategy.

[0016] In a third aspect, the present application provides an energy management device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in the first aspect when executing the computer program.

[0017] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in the first aspect is implemented.

[0018] In a fifth aspect, an embodiment of the present application provides a computer program product, which, when executed on a terminal device, enables the terminal device to execute the method described in the first aspect above.

[0019] Compared with the prior art, the present application has the following beneficial effects: the method of the present application determines the target electricity price time period and the current energy scheduling time period based on the stepped electricity price time period and the corresponding dynamic electricity price, and constructs the corresponding objective function and constraints. Finally, the objective function is solved to obtain the energy storage scheduling strategy for controlling the operation of the energy storage equipment. The energy scheduling time period can be flexibly determined based on the stepped changes of the dynamic electricity price with the time period; compared with the traditional method of setting a fixed adjustment period, when the electricity price changes, the electricity consumption can only be adjusted in the next period. The method of the present application flexibly sets the energy scheduling time period according to the current time and dynamic electricity price, which facilitates the timely execution or update of the energy storage scheduling strategy. While reducing electricity costs and improving electricity efficiency, the energy storage scheduling strategy is more in line with the user's actual electricity usage scenario; it has strong ease of use and practicality.

[0020] It can be understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant description of the first aspect and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0022] Figure 1 Schematic diagram of an application scenario of the energy storage scheduling method provided in an embodiment of the present application;

[0023] Figure 2 Schematic diagram of the flow of the energy storage scheduling method provided in the embodiment of the present application;

[0024] Figure 3 This is a schematic diagram of the tiered electricity price time period provided in the embodiment of the present application;

[0025] Figure 4 This is a flow chart of constructing an objective function according to an embodiment of the present application;

[0026] Figure 5 This is a schematic diagram of the implementation process of the particle swarm algorithm provided in the embodiment of the present application;

[0027] Figure 6 Schematic diagram of the structure of the energy storage scheduling device provided in an embodiment of the present application;

[0028] Figure 7 It is a structural diagram of the energy management device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0029] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0030] The continued expansion of renewable energy, including wind and solar power, has also brought numerous challenges with its large-scale application. These include difficulties absorbing localized energy and impacting the economic operation of microgrids within power systems. With the introduction of tiered and peak-valley pricing, consumers will be more motivated to change their electricity usage and more sensitive to rising electricity costs. Therefore, it is increasingly important to provide consumers with accurate electricity usage information based on real-time photovoltaic power generation and peak-valley time-of-use pricing, thereby reducing energy waste and lowering system electricity costs to meet diverse needs.

[0031] The embodiments of the present application provide an energy storage scheduling method that provides users with real-time and accurate electricity usage information through real-time control and timely adjustment of the load scheduling strategy for energy storage charging and discharging behavior, and supports users to independently select electricity usage modes, thereby meeting users' diverse needs for energy utilization forms, while reducing electricity costs, reducing energy waste, and improving electricity efficiency.

[0032] The following introduces the actual application scenarios of the energy storage scheduling method.

[0033] See Figure 1 , Figure 1 Schematic diagram of the application scenario of the energy storage scheduling method provided in the embodiment of the present application. Figure 1 As shown, the energy storage power system can include energy storage equipment, load equipment, photovoltaic power generation equipment, inverters, and a mains power grid. The photovoltaic power generation equipment, energy storage equipment, and grid provide power to the load. The photovoltaic power generation equipment and energy storage equipment can exchange energy with the grid via a DC bus and inverter. The photovoltaic power generation equipment can provide power to the energy storage equipment. In some areas, after meeting the power requirements of the energy storage equipment and load equipment, the photovoltaic power generation equipment can also feed excess power to the grid. Energy storage equipment and the grid can also exchange power, and the energy storage equipment is responsible for storing the power converted by the photovoltaic power generation equipment. Furthermore, the energy storage power system can also include an energy management device, which can be a standalone device or a computing unit integrated into the energy storage device. This energy management device can directly obtain charging and discharging information of the energy storage device, power consumption information of the load device, and power generation information of the photovoltaic power generation device, and can also indirectly obtain information from other devices through the energy storage device.

[0034] The energy management device obtains the power consumption information of the load equipment, the charging and discharging information of the energy storage equipment, and the power generation information of the photovoltaic power generation equipment. It can provide timely energy storage scheduling strategies and control the working status of the energy storage equipment based on the dynamically changing tiered electricity prices and real-time photovoltaic power generation information, with the goal of minimizing electricity costs.

[0035] It should be understood that the above-mentioned energy storage power system is merely an illustrative example of the embodiments of the present application. In actual application scenarios, the energy storage power system applying the energy storage scheduling method of the present application may include more or fewer devices, or replace some devices. For example, in some scenarios, the energy storage power system may not include photovoltaic power generation equipment; in other scenarios, photovoltaic power generation equipment may be replaced with wind power generation equipment; and so on. The embodiments of the present application do not specifically limit the devices included in the above-mentioned energy storage power system.

[0036] The specific process of implementing the energy storage scheduling method is introduced below through the embodiments of the present application.

[0037] See Figure 2 , Figure 2 This is a flow chart of the energy storage scheduling method provided by an embodiment of the present application. Figure 1 The energy management device shown is used to control the energy storage device and / or the power grid to supply power to the load device; Figure 2 As shown, the method includes the following steps:

[0038] S201 , based on pre-divided tiered electricity price time periods and dynamic electricity prices corresponding to each tiered electricity price time period, determine a target electricity price and a target electricity price time period corresponding to the target electricity price.

[0039] In some embodiments, a tiered electricity price is a different electricity price that can be divided for different time periods within a specific electricity price cycle (e.g., a day). The tiered electricity price time periods can be divided in advance and can be divided based on the electricity demand in different time periods. For example, during peak hours, due to tight power supply and demand and high marginal power supply costs, electricity prices may be increased accordingly to guide users to save electricity and avoid peak hours; while during off-peak hours, when system supply and demand are loose and marginal power supply costs are low, electricity prices may be relatively low to promote the consumption of new energy and guide users to adjust their loads.

[0040] For example, the electricity prices in different time periods every day change dynamically according to the dynamic electricity prices in the corresponding time periods, that is, different tiered electricity price time periods may correspond to different electricity prices; Figure 3As shown, in an exemplary scenario, the electricity price from 0:00 to 6:00 is A, the electricity price from 6:00 to 14:00 is B, the voltage from 14:00 to 20:00 is C, and the electricity price from 20:00 to 24:00 is D, where D <A<B<C。

[0041] Among them, in order to allow the energy storage scheduling process to include various tiered electricity price time periods, make the energy storage scheduling strategy more comprehensive, and improve the energy utilization rate and the operating efficiency of the power system in various tiered electricity price time periods, the target electricity price can be a lower electricity price in the next electricity price cycle relative to the current moment.

[0042] Understandably, to minimize electricity costs during the energy scheduling period, the energy storage power source is typically consumed completely within the energy scheduling period to minimize utility power usage and lower electricity costs. Therefore, if the target electricity price is high and the energy storage system has a high-power load after the current energy scheduling period, the energy storage system will be forced to use the high-priced utility power to power the load. This may result in lower electricity costs during the energy scheduling period, but higher overall electricity costs.

[0043] However, if the target electricity price is a lower electricity price in the next electricity price cycle, then after the current energy scheduling period, even if the energy storage system has a large load, the energy storage system can use lower-priced city electricity to power the load, thereby maintaining the overall electricity cost at a lower level.

[0044] In some embodiments, the target electricity price may be the lowest electricity price in each tiered electricity price time period on the second day relative to the current moment, and the tiered electricity price time period corresponding to the corresponding lowest electricity price is the target electricity price time period.

[0045] It should be noted that the time periods of each tiered electricity price on the first day can be the same as or different from the time periods of each tiered electricity price on the second day, and the dynamic electricity prices corresponding to the time periods of each tiered electricity price on the two days may also be different or the same. Figure 3 The above is only an example to illustrate the general trend of changes in daily electricity prices, and does not limit the distribution and classification of specific electricity prices.

[0046] In some embodiments, based on the pre-divided tiered electricity price time periods and the dynamic electricity prices corresponding to the tiered electricity price time periods, determining the target electricity price and the target electricity price time period corresponding to the target electricity price includes:

[0047] Based on the pre-divided tiered electricity price time periods and the dynamic electricity prices corresponding to each tiered electricity price time period, the minimum dynamic electricity price is determined as the target electricity price, and the tiered electricity price time period corresponding to the target electricity price is determined as the target electricity price time period.

[0048] For example, Figure 3 As shown, starting from 20:00 in the day, the electricity price starts to become the lowest and continues until midnight on the same day. That is, the electricity price in the tiered electricity price time period from 20:00 to midnight can be used as the target electricity price, and the corresponding tiered electricity price time period from 20:00 to midnight is used as the target electricity price time period.

[0049] It is understandable that for different regions, due to different regulations, the division of each tiered electricity price time period corresponding to the corresponding dynamic electricity price may also be different, and the determined target electricity price time period may also be different.

[0050] S202 : Determine the time period between the current time and the start time of the target electricity price time period as the current energy scheduling time period.

[0051] In some embodiments, the time period between the current moment and the start time of the most recent target electricity price time period can be determined as the current energy scheduling time period. For example, assuming that the current moment has not yet reached the time period corresponding to the lowest electricity price of the day, the lowest electricity price of the day can be determined as the target electricity price, the time period of the lowest electricity price of the day can be determined as the target electricity price time period, and the start time of the time period from the current moment to the lowest electricity price of the day can be determined as the current energy scheduling time period; assuming that the current moment has already passed the time period corresponding to the lowest electricity price of the day, the lowest electricity price of the next day can be determined as the target electricity price, the time period of the lowest electricity price of the next day can be determined as the target electricity price time period, and the start time of the time period from the current moment to the lowest electricity price of the next day can be determined as the current energy scheduling time period.

[0052] In other embodiments, the target electricity price may be the lowest electricity price within the next electricity price cycle relative to the current moment. The lowest electricity price of the current electricity price cycle may also exist in a future tiered electricity price time period within the same electricity price cycle as the current moment. However, in order to ensure that the current energy scheduling time period has a certain length of time to ensure the rationality of energy scheduling, the lowest electricity price within the next electricity price cycle relative to the current moment may be selected as the target electricity price. For example, the lowest electricity price of the next day may be selected as the target electricity price, and the corresponding target electricity price time period may be determined, thereby using the time between the current moment and the start time of the target electricity price time period as the current energy scheduling time period.

[0053] For example, Figure 3 As shown, assuming that the current time is 12 noon of the day, the target electricity price is the electricity price of the tiered electricity price period between 20:00 and 24:00 of the next day; the corresponding energy scheduling time period is the time between 12:00 of the current time and 20:00 of the next day, thereby ensuring that the current energy scheduling time period has a certain time length.

[0054] It should be noted that based on the changes in dynamic electricity prices, the energy scheduling time period is adjusted in real time. For example, the lowest electricity price on the second day is adjusted from 20:00 to 24:00 to 18:00 to 22:00, and the corresponding energy scheduling time period can be adjusted to the time between the current moment and 18:00 on the next day. Among them, the ladder electricity price time period can be divided according to specific time points of one or more hours (such as 8 o'clock, 12 o'clock), or it can be divided into other time intervals. The corresponding dynamic electricity price changes can also change every one or more hours, which is not specifically limited here. For example, the division of the ladder electricity price time period on the first and second days and the dynamic electricity prices corresponding to each ladder electricity price time period can be different.

[0055] In some embodiments, determining the time period between the current time and the start time of the target electricity price time period as the current energy scheduling time period includes:

[0056] When the interval between the current moment and the start moment of the next target electricity price time period is greater than the preset duration threshold, the time period between the current moment and the start moment of the next target electricity price time period is determined as the current energy scheduling time period; when the interval between the current moment and the start moment of the next target electricity price time period is less than or equal to the preset duration threshold, the time period between the current moment and the start moment of the next target electricity price time period is determined as the current energy scheduling time period.

[0057] For example, in order to make the energy scheduling time period have a certain length of time, the preset time threshold can be set according to the needs. For example, the preset time can be set to 1 hour, 2 hours, 24 hours, etc. The target electricity price can be the lowest electricity price corresponding to each tiered electricity price time period in a day, such as Figure 3 As shown in FIG, if the preset duration is set to 12 hours, the lowest electricity price time period on the first day is from 20:00 to 24:00, and the current time is 22:00, then the corresponding next target electricity price is the electricity price from 20:00 to 24:00 on the second day, that is, the interval from the current time to the start time of the next target electricity price time period is greater than the preset duration threshold (the interval from 22:00 on the first day to 20:00 on the second day is greater than 12 hours), and the time period between the current time and the start time of the next target electricity price time period is determined as the current energy scheduling time period; Figure 3 As shown, when the lowest electricity price time period on the first day is from 20:00 to 24:00 and the current time is 12:00, the corresponding next target electricity price is the electricity price from 20:00 to 24:00 on the first day, and the time from 12:00 to 20:00 at the current time is less than the preset time threshold, then the time period between the current time and the starting time of the next target electricity price time period (20:00 on the second day) is determined as the current energy scheduling time period (i.e., from 12:00 on the first day to 20:00 on the second day).

[0058] It should be noted that hourly electricity prices may fluctuate as dynamic electricity prices continue to change. When dynamic electricity prices change, the energy scheduling time period can be redefined to replan the energy storage scheduling strategy. When determining the energy scheduling time period, the corresponding energy scheduling time period will also be dynamically adjusted as the current time changes. For example, if the current time is 12:00, the energy scheduling time period will be from 12:00 to 20:00 the next day. If the current time reaches 13:00, the energy scheduling time period will change from 13:00 to 20:00 the next day. If the current time reaches 14:00, the energy scheduling time period will change from 14:00 to 20:00 the next day.

[0059] The time and conditions for rescheduling the energy storage scheduling strategy can be set based on actual needs. For example, in some scenarios, to ensure the real-time nature of the energy storage scheduling strategy, the energy storage scheduling strategy can be replanned every hour, that is, the energy scheduling time period can be dynamically adjusted every hour. In other scenarios, the energy storage scheduling strategy can be replanned based on other preset durations or other trigger conditions, and the energy scheduling time period can be dynamically adjusted, so that subsequent energy storage scheduling strategies can be executed and updated in a timely manner.

[0060] S203 , constructing an objective function based on the dynamic electricity prices corresponding to each tiered electricity price time period and aiming at minimizing the electricity cost within the energy scheduling time period.

[0061] In some embodiments, the objective function is defined for the total cost of using utility power by the energy storage power system during the energy scheduling period. Based on the dynamic electricity prices, electricity usage duration, and electricity usage corresponding to each tiered electricity price period within the energy scheduling period, an objective function is constructed to calculate the total electricity cost during the energy scheduling period.

[0062] The dynamic electricity price can include the price at which users purchase electricity from the utility or sell electricity to the grid during each tiered electricity price period. The amount of electricity consumed during each tiered electricity price period can be determined based on the power exchanged between the energy storage system and the grid (i.e., grid input power) and the corresponding duration. The objective function is the sum of the products of the exchange power, the corresponding duration, and the corresponding dynamic electricity price during each tiered electricity price period.

[0063] S204: Obtain predicted power consumption information of the load device within the energy scheduling time period.

[0064] S205 , constructing a power balance constraint condition based on the predicted power consumption information, the charge and discharge power of the energy storage device, and the grid input power of the grid.

[0065] In some embodiments, the energy management device can obtain historical information of the energy storage device, the load device and the photovoltaic power generation device. The historical information may include one or more of the charging and discharging information of the energy storage device, the power consumption information of the load device and the power generation information of the photovoltaic power generation device within a preset historical time period; based on the historical information within the historical time period, a prediction is made to obtain the predicted power consumption information, the charging and discharging power of the energy storage device and the grid input power of the power grid within the current energy scheduling time period.

[0066] For example, the charge and discharge information, power consumption information, and power generation information can be interface power information corresponding to each device. The energy management device can save the historical information collected in local memory. Before executing the energy scheduling strategy within the energy scheduling period, the energy management device can retrieve the historical information from the local memory and use the historical information as input information to calculate the predicted power information within the energy scheduling period, such as the charge and discharge information of the energy storage device, the predicted power consumption information of the load device, and the power generation information of the photovoltaic power generation device.

[0067] To improve the accuracy of the predicted power information within the energy scheduling time period and the accuracy of the subsequent energy storage scheduling strategy, historical information within the historical time period can be re-acquired each time the energy storage scheduling strategy is updated. For example, historical information within the historical time period can be re-acquired every unit time period (such as every hour) (for example, continuously updating the actual information of photovoltaic power generation equipment and load equipment in the last 24 hours) as input information for the current moment of prediction, making the planned energy storage scheduling strategy more accurate.

[0068] For example, the preset historical time period can be set based on the user's actual application scenario or the actual time point of the current moment, such as the most recent 24 hours or the most recent 12 hours. Taking the most recent 24 hours as an example, assuming that the current moment is 0:00 on the 18th, with the current moment 0:00 on the 18th as the end point, the historical information within the past 24 hours between 0:00 on the 17th and 24:00 on the 17th (i.e., 0:00 on the 18th) is obtained, and the power generation information of the photovoltaic power generation equipment and the power consumption information of the load equipment are used as the input information for the current moment 0:00 on the 18th; when the energy storage scheduling strategy is updated next time, such as 1:00 on the 18th, the statistical historical information is updated to include the information within the one-hour time period from 0:00 on the 18th to 1:00 on the 18th, that is, the historical information within the 24 hours before 1:00 on the 18th (i.e., between 1:00 on the 17th and 1:00 on the 18th) is obtained as input information; thereby, the actual discharge information of the photovoltaic power generation equipment and the power consumption information of the load equipment in the latest historical time period can be used each time the energy storage scheduling strategy is updated.

[0069] In some embodiments, based on the usage status of each device in the energy storage power system, it is necessary to add constraints to the energy storage power system. These constraints include power balance constraints. Power balance refers to the balance between the power of the load device, the power of the energy storage device, and the interaction power between the energy storage power system and the grid during each tiered electricity price period within the energy scheduling cycle.

[0070] For example, the expression of the power balance constraint condition may be:

[0071]

[0072] in, It represents the total load power of the load equipment in the tth unit time period within the energy scheduling time period; Indicates the charging power or discharging power of the energy storage device in the tth unit time period within the energy scheduling time period ( Greater than 0 means the energy storage device is charging. Less than 0 indicates that the energy storage device is discharged); It represents the interaction power between the energy storage power system and the power grid in the t-th unit time period during the energy scheduling time period. If the energy storage power system transmits electricity to the power grid, It can be expressed as negative. If the energy storage power system absorbs electricity from the grid, The above parameters can be calculated based on historical information, for example, predicted based on device interface power information in statistical historical information.

[0073] S206: Construct energy storage device constraint conditions based on preset restriction information of the energy storage device.

[0074] In some embodiments, the energy storage device in the energy storage power system can be a battery as an energy storage device. Overcharging or discharging the battery will reduce its service life. Therefore, it is necessary to limit the charging and discharging behavior of the battery based on the charging power, discharging power and capacity status during the charging and discharging process of the energy storage device, that is, to construct energy storage device constraints.

[0075] The preset restriction information of the energy storage device may include a charging power limit, a discharging power limit and / or a capacity state limit of the energy storage device.

[0076] In some embodiments, the energy storage device constraint condition includes a power constraint condition and / or a power constraint condition; constructing the energy storage device constraint condition based on the restriction information of the energy storage device includes:

[0077] Construct power constraints for the energy storage device's charging and discharging power based on the energy storage device's charging power limit and discharging power limit; and / or construct a real-time power expression corresponding to each unit time period of the energy storage device within the energy scheduling time period based on the energy storage device's charging and discharging power; construct power constraints for the energy storage device based on a preset power upper limit, power lower limit, and real-time power expression.

[0078] For example, the charging power of the energy storage device in its actual operating state needs to be within the charging power limit range, for example, the charging power in the actual operating state is less than or equal to the charging power upper limit; the discharge power of the energy storage device in its actual operating state needs to be within the discharge power limit range, for example, the discharge power in the actual operating state is less than or equal to the maximum discharge power. Each unit time period of the energy storage device within the energy scheduling time period can be a time period of each tiered electricity price or a time period within each tiered electricity price time period (such as each hour within each tiered electricity price time period).

[0079] Accordingly, the power constraint of the energy storage device's charge and discharge power can be expressed as:

[0080] 0≤p c ≤p cmax

[0081] p dmax ≤p d ≤0

[0082] Among them, p c Indicates the charging power of the energy storage device in actual working state; p cmax Indicates the upper limit of the charging power of the energy storage device; p d Indicates the discharge power of the energy storage device in actual working state (expressed as a negative value); p dmax Indicates the maximum discharge power of the energy storage device (expressed as a negative value).

[0083] For example, the state of charge (SOC) of an energy storage device represents the ratio of its current battery capacity to its total capacity. The SOC is determined based on the charging power of the energy storage device during charging and the discharging power during discharging. The real-time power expression corresponding to the SOC of the energy storage device in each unit time period within the energy scheduling time period is expressed as follows:

[0084]

[0085] Where δ represents the self-discharge rate of the energy storage device (i.e., the SOC loss of the energy storage device itself when it does not receive any input or output any output). When the time interval is relatively small, the self-discharge rate is close to 0%; η ch Represents the charging efficiency of the energy storage device; η disIndicates the discharge efficiency of the energy storage device; E b_total represents the maximum capacity of the energy storage device; t represents the t-th unit time period within the energy scheduling time period; Δt represents each unit time period. The attribute parameters of the above devices are all known.

[0086] For example, during the entire calculation process, if there is a photovoltaic power generation device, the discharge information of the photovoltaic power generation device does not directly participate in the balance constraint. Instead, the solar energy reservation ratio is used to enable the energy storage device to reserve a corresponding capacity to use solar energy. The capacity that the energy storage device needs to reserve is determined based on the discharge power and discharge time of the photovoltaic power generation device. The corresponding expression is as follows:

[0087]

[0088] in, pv represents the discharge power of the photovoltaic power generation equipment in the tth unit time period within the energy scheduling time period; sum Indicates the total discharge power of photovoltaic power generation equipment during the energy scheduling period; SOC pv It indicates the ratio of the power generation of photovoltaic power generation equipment to the total energy storage capacity during the energy scheduling period, that is, the capacity that the energy storage equipment needs to reserve.

[0089] Accordingly, the energy storage device's power constraint condition is that the capacity state of the energy storage device in actual operation must be between the upper and lower power limits, and the upper power limit is determined based on the maximum capacity of the energy storage device and the capacity that needs to be reserved. The expression of the energy storage device's power constraint condition is expressed as follows:

[0090] SOC min ≤SOC t ≤SOC max -SOC pv

[0091] Among them, SOC t Indicates the capacity state of the energy storage device in the tth unit time period within the energy scheduling time period; SOC min The minimum capacity state of the energy storage device set by the user (i.e., the lower limit of power); SOC max The maximum capacity state of the energy storage device set by the user (i.e., the upper limit of power).

[0092] S207 , solving the objective function based on the power balance constraint and the energy storage device constraint to obtain the energy storage scheduling strategy.

[0093] In some embodiments, the energy management device calculates the predicted power information within the energy scheduling time period based on the historical information of each device in the energy storage power system obtained, such as the predicted power consumption information of the load device, the charging and discharging power of the energy storage device, and the exchange power with the power grid; when the predicted power information meets the power balance constraints and the energy storage device constraints, the predicted power information that minimizes the value of the objective function is selected to determine the energy storage scheduling strategy.

[0094] S208: Control the energy storage device to operate based on the energy storage scheduling strategy.

[0095] Exemplarily, the energy storage scheduling strategy is a strategy for controlling the working state of the energy storage device within the energy scheduling time period, which may include a charging state, a discharging state, and a standby state.

[0096] The charging state may be a state in which the energy storage device obtains electric energy from the power grid, the discharging state may be a state in which the energy storage device supplies power to the load device, and the standby state may be a state in which no electric energy interaction is performed.

[0097] The specific implementation process of constructing the objective function is introduced below.

[0098] like Figure 4 As shown, the flowchart of constructing the objective function provided by the embodiment of the present application is shown; the construction process may include the following steps:

[0099] S401 , based on the dynamic electricity prices corresponding to the various tiered electricity price time periods, determine the dynamic electricity prices corresponding to the various unit time periods within the energy scheduling time period.

[0100] In some embodiments, each tiered electricity price time period of the day corresponds to a different dynamic electricity price, and each tiered electricity price time period can include several unit time periods; accordingly, after determining the energy scheduling time period, the dynamic electricity price of each unit time period in the energy scheduling time period can be determined.

[0101] For example, the unit time period may be a unit time period for keeping the electricity price unchanged, such as Figure 3 As shown, each unit time period can be one or more hours, and the specific duration is not limited and can be divided or set based on actual application scenarios.

[0102] S402 : Constructing an expression for electricity cost according to the dynamic electricity price corresponding to each unit time period within the energy scheduling time period, the grid input power, and the duration of the unit time period.

[0103] S403: Based on the expression of electricity cost, an objective function is constructed with the goal of minimizing the electricity cost.

[0104] In some embodiments, the dynamic electricity price may include the price at which a user purchases electricity from the utility, and the interactive power between the energy storage power system and the grid may include the grid input power.

[0105] For example, the electricity cost is calculated based on the grid input power, the electricity price corresponding to each unit time period, and the duration of the unit time period. The expression of the electricity cost is as follows:

[0106]

[0107] Among them, Cost is the electricity cost of the energy storage power system using the mains electricity; c t is the electricity price corresponding to the t-th unit time period; It represents the interaction power between the energy storage power system and the grid in the t-th unit time period within the energy scheduling time period, where it is the grid input power; Δt is the unit time period.

[0108] Accordingly, based on the expression of electricity cost and taking the lowest electricity cost as the goal, the expression of the constructed objective function is expressed as follows:

[0109]

[0110] The following introduces the specific implementation process of solving the objective function to obtain the energy storage scheduling strategy.

[0111] In some embodiments, based on the power balance constraint and the energy storage device constraint, solving the objective function to obtain the energy storage scheduling strategy includes:

[0112] Based on the power balance constraints and energy storage equipment constraints, the objective function is solved through a preset optimization algorithm to obtain the energy storage scheduling strategy.

[0113] Exemplarily, the preset optimization algorithm can be a particle swarm optimization algorithm (PSO); the energy management device solves the objective function based on the PSO algorithm with linear parameters, obtains the power data of the energy storage device corresponding to each unit time period within the energy scheduling time period, and satisfies the lowest electricity cost within the energy scheduling time period, thereby obtaining an energy storage scheduling strategy for controlling the working state of the energy storage device.

[0114] In some embodiments, based on power balance constraints and energy storage device constraints, a preset optimization algorithm is used to solve the objective function to obtain an energy storage scheduling strategy, including:

[0115] The charge and discharge power of the energy storage device is used as a variable to generate a particle swarm. Using the particle swarm optimization algorithm, an iterative search is performed under the constraints of power balance constraints and energy storage device constraints to determine the global optimal solution. The global optimal solution includes the expected charge and discharge state and / or expected charge and discharge power value of the energy storage device in each unit time period within the energy scheduling time period. Based on the global optimal solution, the energy storage scheduling strategy within the energy scheduling time period is determined.

[0116] Exemplarily, the charging power or discharging power of the energy storage device is used as a variable to generate a particle swarm. Each unit time period within the energy scheduling time period corresponds to a power variable (particle), and the power variables (particles) corresponding to all unit time periods within the energy scheduling time period constitute a group of particle swarms.

[0117] like Figure 5 As shown in the figure, when the algorithm starts calculating, the initial parameters of the particle swarm optimization algorithm are first input, including the number of iterations, the maximum and minimum update speeds, the individual learning factor, and the group learning factor. The scheduling-related parameters are then optimized, namely, the historical information of each device in the local memory is called up, such as the power generation information of the photovoltaic power generation equipment, the power consumption information of the load equipment, the relevant parameters of the energy storage equipment, and the dynamic electricity price corresponding to each unit time period within the energy scheduling time period. The relevant parameters of the energy storage equipment may include the upper and lower limits of power consumption, the capacity to be reserved, the equipment capacity, the charging power limit, the discharging power limit, the upper and lower limits of the mains power, and the initial SOC.

[0118] The global optimal solution is the value with the best fitness among the left and right particles during the iterative process of the particle swarm optimization algorithm. This refers to the set of power data (i.e., the desired charge and discharge states and / or desired charge and discharge power values) that minimizes electricity costs for all power variables corresponding to all unit time periods within the energy scheduling time period. Based on the global optimal solution, the energy storage scheduling strategy for each unit time period within the energy scheduling time period is determined.

[0119] In some embodiments, when the particle swarm optimization algorithm is used for the first time, the initial position of each particle in the particle swarm is randomly generated; when the particle swarm optimization algorithm is not used for the first time, the position of the global optimal solution obtained in the previous iterative optimization is used as the initial position of each particle in the particle swarm.

[0120] For example, if the particle swarm optimization algorithm starts from a random solution position each time it is cold-started, the solution will be slow and there will be volatility caused by the algorithm itself. Therefore, when planning the energy storage scheduling strategy for the first time, the particle swarm optimization algorithm is cold-started (i.e., the initial power value of the energy storage device is randomly generated). In the solution calculation process of each subsequent re-planning of the energy storage scheduling strategy, the global optimal solution solved in the previous solution can be used as the starting position of the current solution (i.e., the initial power value of the energy storage device), and then iterative calculations can be performed to improve the algorithm's operating efficiency.

[0121] like Figure 5 As shown in the figure, after scheduling the relevant parameters, it is determined whether it is the first scheduling, that is, whether it is the first time to use the particle swarm optimization algorithm. If so, the initial position of each particle in the particle swarm is randomly generated, that is, the power corresponding to each unit time period of the energy storage device in the energy scheduling time period is randomly assigned an initial value; if not, the position of the global optimal solution obtained by iterative optimization after the last startup of the algorithm is used as the initial position of each particle in the particle swarm, and the optimal solution of the previous hour is used as the optimal value of the particle for this optimization.

[0122] For example, it is assumed that the energy storage scheduling strategy is calculated once per unit time period (for example, every hour) to flexibly and promptly respond to uncertain factors such as users' random electricity usage behavior. Therefore, each time the energy storage scheduling strategy is calculated, the optimal solution calculated in the previous unit time period can be obtained as the initial position of each particle corresponding to this unit time period, that is, the initial value of the energy storage device power corresponding to each unit time period within this energy scheduling time period.

[0123] In some embodiments, a particle swarm optimization algorithm is used to iteratively search for the optimal solution under the constraints of power balance and energy storage device constraints to determine the global optimal solution, including:

[0124] The particle swarm optimization algorithm is used to iterate the position of each particle in the particle swarm to determine the first candidate position corresponding to each particle; a second candidate position that meets the power balance constraint and the energy storage device constraint is determined from each first candidate position; based on the objective function, the objective function value corresponding to each second candidate position is calculated; when the objective function value corresponding to the second candidate position is less than the objective function value of the historical optimal solution of the corresponding particle, the current particle position and the historical optimal solution of the corresponding particle are updated based on the second candidate position; when the objective function value corresponding to the second candidate position is less than the objective function value of the global optimal solution, the global optimal solution is updated based on the second candidate position; when the preset iteration end condition is met, the global optimal solution is output; when the preset iteration end condition is not met, the step of iterating the position of each particle in the particle swarm by the particle swarm optimization algorithm is returned to.

[0125] For example, Figure 5As shown in Figure 1, after determining the initial position of the particle swarm, the objective function value corresponding to the particles in the current swarm is calculated. At the beginning of the iterative operation, the basic input parameters include the initial optimal fitness value of each particle, the historical optimal fitness value, and the dynamic electricity price.

[0126] The particle swarm optimization algorithm iterates the positions of each particle in the swarm to determine the first candidate position corresponding to each particle. This first candidate position can be multiple candidate power values ​​within the population size. Based on the number of iterations, within a preset maximum number of iterations, the candidate power values ​​are subjected to the power balance constraints and energy storage device constraints to determine the second candidate position that satisfies the constraints, i.e., the candidate power value that satisfies the constraints. The objective function value is then calculated based on the candidate power values ​​that satisfy the constraints.

[0127] Accordingly, when the objective function value calculated based on the candidate power value corresponding to the second candidate position is less than the objective function value corresponding to the historical optimal solution of the corresponding particle, that is, the currently calculated objective function value is better than the historical optimal solution of the particle itself (the optimal fitness value of a single particle itself during the iteration process), the current particle position and the historical optimal solution of the corresponding particle are updated based on the second candidate position, that is, the second candidate position is used as the current particle position, and the candidate power value corresponding to the second candidate position is used as the historical optimal solution.

[0128] Accordingly, when the objective function value corresponding to the second candidate position is less than the objective function value of the global optimal solution, that is, the currently calculated objective function value is better than the global optimal solution position, the global optimal solution is updated based on the second candidate position, that is, the position with the best fitness among all particles is updated, and the power values ​​corresponding to each second candidate position are used as the global optimal solution corresponding to each unit time period in the energy scheduling time period.

[0129] For example, during the iterative operation, after all particles in the population size M are iterated and the maximum number of iterations set is reached, the iterative operation stops, and the particle swarm position of the global optimal solution is updated and output, that is, the power value of the energy storage device corresponding to each unit time period in the energy scheduling time period.

[0130] In the iterative process, n is the current number of iterations, and N is the maximum number of iterations.

[0131] In a possible implementation, during the operation of the particle swarm optimization algorithm, the population size M of the input variables, the maximum number of iterations N, the upper limit w of the inertia weight w, and the number of iterations N are required. max , the lower limit of the inertia weight w min , the upper limit C of the individual learning factor C1 1max , lower limit C 1min , the upper limit C of the group learning factor C2 2max , lower limit C2min ; And a linear decreasing strategy is adopted for the inertia weight w, individual learning factors C1 and C2, satisfying the following expressions:

[0132]

[0133] Among them, p represents any one of the three parameters w, C1, and C2. s Indicates the initial value of the parameter, p e Indicates the end value of the linear decreasing strategy; n represents the current number of iterations. The particle swarm optimization algorithm includes the iterative process of the above parameters in the iterative calculation, and the above parameters also change periodically according to the number of iterations.

[0134] In some embodiments, the global optimal solution includes the expected charge and discharge power of the energy storage device in each unit time period within the energy scheduling time period; based on the global optimal solution, determining the energy storage scheduling strategy within the energy scheduling time period includes:

[0135] The absolute value of the expected charge and discharge power of each unit time period within the energy scheduling time period is compared with the preset power fluctuation threshold, and the expected charge and discharge power with an absolute value less than the power fluctuation threshold is set to 0 to obtain the energy storage scheduling strategy.

[0136] For example, in order to reduce the volatility of the particle swarm optimization algorithm, after the particle swarm optimization algorithm is completed, the power value of the energy storage device obtained by the solution can be calculated. Perform a check, for example, by setting a power threshold. If the absolute value of the calculated optimal solution is less than the threshold, then it is set to 0. The check expression is as follows:

[0137]

[0138] Among them, abs() is to calculate the absolute value, p bthresshhold is the preset power threshold. Due to the volatility of the particle swarm algorithm itself, the power value of the energy storage device solved may fluctuate around 0. Since the fluctuating power value is relatively small, the power threshold is used to limit the power fluctuation of the energy storage device. If it is less than the power threshold, the power of the energy storage device is set to 0, that is, the state of the energy storage device is controlled to be neither charging nor discharging.

[0139] The embodiment of the present application predicts and calculates the energy storage scheduling strategy within the future energy scheduling period by using the information of the user's load devices and photovoltaic power generation equipment within a historical time period (such as the previous day) as a reference; the energy scheduling period is from the current time to the lowest point of the dynamic electricity price on the next day; based on the particle swarm optimization algorithm, within the energy scheduling period, the power of the energy storage device is placed in the backup power ratio (reserved capacity) to minimize the cost of obtaining electricity from the mains, so that the energy storage device can reserve a large capacity before the lowest point of the electricity price, and be charged from the mains at a lower price to meet the power demand of each load device thereafter.

[0140] Exemplarily, the optimization goal of the particle swarm optimization algorithm is to minimize the cost of the energy storage device using the mains electricity during the energy scheduling time period, and after each calculation, the energy storage scheduling strategy for all unit time periods within the energy scheduling time period is given, for example, including three strategies: standby, charging, and discharging, where standby means that the energy storage device is neither charged nor discharged, charging means that the energy storage device is charged, and discharging means that the energy storage device is discharged for use by the load device.

[0141] The energy management device can control the energy storage device to perform energy storage scheduling in each unit time period according to the energy storage scheduling strategy corresponding to the current unit time period.

[0142] For example, taking the unit time period as one hour, the current time is 0:00, and when the lowest electricity price is 0:00 the next day, the energy scheduling time period is 24 hours. The energy storage scheduling strategy obtained is [discharge, standby, standby, standby, standby, standby, charge, standby, charge, standby, standby, standby, charge, standby, charge, standby, discharge, discharge, discharge, standby, standby], according to the first "discharge" of the energy storage scheduling strategy (the energy storage scheduling strategy of the current hour), the energy management device controls the energy storage device to perform the discharge action.

[0143] In addition, in order to deal with emergencies (for example, the energy storage device does not execute according to the predetermined energy storage scheduling strategy), the energy storage scheduling strategy can also be updated according to the status of the energy storage device in each unit time period. Therefore, when energy storage scheduling is performed in each unit time period, historical information of the energy storage device in the historical time period is obtained, and energy storage scheduling is predicted for the energy scheduling time period; and the current moment will continue to approach the lowest point of the electricity price on the next day. When the time interval between the current moment and the lowest point of the electricity price on the next day is less than or equal to the preset time threshold (for example, only one hour left), the lowest point of the electricity price on the third day will continue to be searched backward as the end position of the subsequent energy scheduling time period.

[0144] Through the embodiments of the present application, the energy storage scheduling method implemented based on the particle swarm optimization algorithm can be applied to different power system load scenarios in response to uncertain factors such as dynamic electricity prices and users' random electricity consumption behavior, thereby meeting users' electricity needs while reducing their electricity costs. At the same time, during the iterative operation of the particle swarm optimization algorithm, by using the previous scheduling optimal solution as the starting optimization position for this scheduling, the volatility brought by the algorithm itself can be reduced.

[0145] In addition, the particle swarm optimization algorithm can be deployed on the device side of the energy storage power system. For example, the energy management device can be deployed as a computing unit on the energy storage device, or the algorithm can be deployed directly on the energy storage device to reduce the consumption of device resources. Since the historical information of each device in the energy storage power system can be stored locally, in the absence of a distribution network or network connection, historical information can be directly obtained from the device locally to provide an energy storage scheduling strategy for real-time control of the charging and discharging behavior of the energy storage device, ensuring that the user obtains electricity from the mains at the lowest cost during the energy scheduling period of the device.

[0146] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0147] Corresponding to the energy storage scheduling method provided in the above embodiment, Figure 6 A schematic structural diagram of the energy storage scheduling device provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0148] Reference Figure 6 , the device comprises:

[0149] The first determining unit 61 is configured to determine a target electricity price and a target electricity price time period corresponding to the target electricity price based on pre-divided tiered electricity price time periods and the dynamic electricity prices corresponding to the tiered electricity price time periods;

[0150] A second determining unit 62 is configured to determine a time period between the current moment and the start moment of the target electricity price time period as a current energy scheduling time period;

[0151] A first constructing unit 63 is configured to construct an objective function based on the dynamic electricity price corresponding to each of the stepped electricity price time periods, with the goal of minimizing the electricity cost within the energy scheduling time period;

[0152] An acquiring unit 64 is configured to acquire predicted power consumption information of the load device within the energy scheduling time period;

[0153] A second constructing unit 65 is configured to construct a power balance constraint condition based on the predicted power consumption information, the charge and discharge power of the energy storage device, and the grid input power of the grid;

[0154] A third constructing unit 66 is configured to construct energy storage device constraint conditions based on preset restriction information of the energy storage device;

[0155] A calculation unit 67 is configured to solve the objective function based on the power balance constraint and the energy storage device constraint to obtain an energy storage scheduling strategy;

[0156] The control unit 68 is used to control the energy storage device to operate based on the energy storage scheduling strategy.

[0157] In one possible implementation, the first construction unit 63 is further used to determine the dynamic electricity price corresponding to each unit time period within the energy scheduling time period based on the dynamic electricity price corresponding to each of the step electricity price time periods; construct an expression for electricity costs based on the dynamic electricity price corresponding to each unit time period within the energy scheduling time period, the grid input power, and the length of the unit time period; and construct an objective function based on the expression for electricity costs with the goal of minimizing the electricity costs.

[0158] In one possible implementation, the energy storage device constraint includes a power constraint and / or a power constraint; the third construction unit 66 is further used to construct a power constraint of the charging and discharging power of the energy storage device based on the charging power limit and the discharging power limit of the energy storage device; and / or, based on the charging and discharging power of the energy storage device, construct a real-time power expression corresponding to each unit time period of the energy storage device within the energy scheduling time period; based on a preset power upper limit value, power lower limit value, and the real-time power expression, construct the power constraint of the energy storage device.

[0159] In a possible implementation, the calculation unit 67 is further configured to solve the objective function based on the power balance constraint and the energy storage device constraint by using a preset optimization algorithm to obtain an energy storage scheduling strategy.

[0160] In one possible implementation, the calculation unit 67, based on the power balance constraint and the energy storage device constraint, is further configured to generate a particle swarm using the charge and discharge power of the energy storage device as a variable; perform iterative optimization under the constraints of the power balance constraint and the energy storage device constraint using a particle swarm optimization algorithm to determine a global optimal solution; the global optimal solution includes the expected charge and discharge state and / or expected charge and discharge power value of the energy storage device in each unit time period within the energy scheduling time period; and determine an energy storage scheduling strategy within the energy scheduling time period based on the global optimal solution.

[0161] In one possible implementation, the computing unit 67 is further configured to randomly generate an initial position of each particle in the particle swarm when the particle swarm optimization algorithm is used for the first time; and to use the position of the global optimal solution obtained in the previous iterative optimization as the initial position of each particle in the particle swarm when the particle swarm optimization algorithm is not used for the first time.

[0162] In one possible implementation, the calculation unit 67 is further used to iterate the position of each particle in the particle swarm through the particle swarm optimization algorithm to determine the first candidate position corresponding to each particle; determine the second candidate position that meets the power balance constraint and the energy storage device constraint from each first candidate position; calculate the objective function value corresponding to each second candidate position based on the objective function; when the objective function value corresponding to the second candidate position is less than the objective function value of the historical optimal solution of the corresponding particle, update the current particle position and historical optimal solution of the corresponding particle based on the second candidate position; when the objective function value corresponding to the second candidate position is less than the objective function value of the global optimal solution, update the global optimal solution based on the second candidate position; when the preset iteration end condition is met, output the global optimal solution; when the preset iteration end condition is not met, return to execute the step of iterating the position of each particle in the particle swarm through the particle swarm optimization algorithm.

[0163] In one possible implementation, the global optimal solution includes the expected charge and discharge power of the energy storage device in each unit time period within the energy scheduling time period; the calculation unit 67 is further used to compare the absolute value of the expected charge and discharge power in each unit time period within the energy scheduling time period with a preset power fluctuation threshold, and set the expected charge and discharge power whose absolute value is less than the power fluctuation threshold to 0 to obtain the energy storage scheduling strategy.

[0164] In one possible implementation, the first determination unit 61 is further used to determine the minimum dynamic electricity price as the target electricity price based on the pre-divided tiered electricity price time periods and the dynamic electricity prices corresponding to each of the tiered electricity price time periods, and to determine the tiered electricity price time period corresponding to the target electricity price as the target electricity price time period.

[0165] In one possible implementation, the second determination unit 62 is further used to determine the time period between the current moment and the start moment of the next target electricity price time period as the current energy scheduling time period when the interval between the current moment and the start moment of the next target electricity price time period is greater than a preset time threshold; and to determine the time period between the current moment and the start moment of the next target electricity price time period as the current energy scheduling time period when the interval between the current moment and the start moment of the next target electricity price time period is less than or equal to the preset time threshold.

[0166] Through the embodiments of the present application, in view of uncertain factors such as dynamic electricity prices and random electricity consumption behaviors of users, the energy storage scheduling method implemented based on the particle swarm optimization algorithm can be applied to different load scenarios of power consumption systems, reducing the user's electricity costs while meeting the user's electricity demand; at the same time, during the iterative operation of the particle swarm optimization algorithm, by using the last scheduling optimal solution as the starting optimization position of this scheduling, the volatility brought by the algorithm itself can be reduced. The particle swarm optimization algorithm can be deployed on the device side of the energy storage power system, for example, the energy management device can be deployed as a computing unit on the energy storage device or the algorithm can be directly deployed on the energy storage device to reduce the consumption of device resources; since the historical information of each device in the energy storage power system can be stored locally, in the absence of a distribution network or a network connection, the historical information can be directly obtained from the local device, providing an energy storage scheduling strategy for real-time control of the charging and discharging behavior of the energy storage device, ensuring that the user obtains electricity from the mains at the lowest cost during the energy scheduling period of the device.

[0167] Figure 7 This is a schematic diagram of the structure of the energy management device 7 provided in one embodiment of the present application. Figure 7 As shown, the energy management device 7 of this embodiment includes: at least one processor 70 ( Figure 7 Only one is shown), a memory 71, and a computer program 72 stored in the memory 71 and executable on the at least one processor 70, wherein the processor 70 implements the steps in the above-described embodiments when executing the computer program 72.

[0168] The energy management device 7 may include, but is not limited to, a processor 70 and a memory 71. Those skilled in the art will appreciate that Figure 7 This is merely an example of the energy management device 7 and does not constitute a limitation on the energy management device 7. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input and output devices, network access devices, etc.

[0169] The processor 70 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor may be a microprocessor or any conventional processor.

[0170] In some embodiments, the memory 71 may be an internal storage unit of the energy management device 7, such as a hard disk or memory of the energy management device 7. In other embodiments, the memory 71 may also be an external storage device of the energy management device 7, such as a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the energy management device 7. Furthermore, the memory 71 may also include both an internal storage unit and an external storage device of the energy management device 7. The memory 71 is used to store an operating system, an application, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program. The memory 71 may also be used to temporarily store data that has been output or is to be output.

[0171] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the process of the above-mentioned method embodiment by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can at least include: any entity or device capable of carrying computer program code to the camera / terminal device, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal, and software distribution medium. For example, a USB flash drive, mobile hard drive, magnetic disk, or optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals or telecommunication signals.

[0172] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0173] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned various method embodiments can be implemented.

[0174] An embodiment of the present application provides a computer program product. When the computer program product is run on a mobile terminal, the mobile terminal can implement the steps in the above-mentioned various method embodiments when executing the computer program product.

[0175] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0176] In the embodiments provided in this application, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are merely illustrative. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0177] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0178] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A method for energy storage scheduling, characterized in that: Applied to an energy management device, the energy management device is used to control an energy storage device and / or a power grid to supply power to a load device, the method comprising: Determine a target electricity price and a target electricity price time period corresponding to the target electricity price based on the pre-divided stepped electricity price time periods and the dynamic electricity prices corresponding to the stepped electricity price time periods; Determine the time period between the current time and the start time of the target electricity price time period as the current energy scheduling time period; Based on the dynamic electricity prices corresponding to the tiered electricity price time periods, an objective function is constructed with the goal of minimizing the electricity cost within the energy scheduling time period; Obtaining predicted power consumption information of the load device within the energy scheduling time period; Constructing a power balance constraint condition based on the predicted power consumption information, the charging and discharging power of the energy storage device, and the grid input power of the grid; Constructing energy storage device constraint conditions based on preset restriction information of the energy storage device; Based on the power balance constraint and the energy storage device constraint, solving the objective function to obtain an energy storage scheduling strategy; The energy storage device is controlled to operate based on the energy storage scheduling strategy.

2. The method according to claim 1, wherein the objective function is constructed based on the dynamic electricity price corresponding to each of the step electricity price time periods and with the goal of minimizing the electricity cost within the energy scheduling time period, comprising: Determine the dynamic electricity price corresponding to each unit time period within the energy scheduling time period based on the dynamic electricity price corresponding to each of the step electricity price time periods; Constructing an expression for electricity cost according to the dynamic electricity price corresponding to each unit time period within the energy scheduling time period, the grid input power and the duration of the unit time period; Based on the expression of the electricity cost, an objective function is constructed with the goal of minimizing the electricity cost.

3. The method according to claim 1, characterized in that The energy storage device constraint condition includes a power constraint condition and / or a power constraint condition; The step of constructing energy storage device constraint conditions based on preset restriction information of the energy storage device includes: Constructing a power constraint condition for the charging and discharging power of the energy storage device based on the charging power limit and the discharging power limit of the energy storage device; and / or Constructing a real-time power expression corresponding to each unit time period of the energy storage device within the energy scheduling time period based on the charge and discharge power of the energy storage device; Based on the preset upper power limit value, lower power limit value, and the real-time power expression, the power constraint condition of the energy storage device is constructed.

4. The method according to claim 1, characterized in that The step of solving the objective function based on the power balance constraint condition and the energy storage device constraint condition to obtain an energy storage scheduling strategy includes: Based on the power balance constraint condition and the energy storage device constraint condition, the objective function is solved by a preset optimization algorithm to obtain an energy storage scheduling strategy.

5. The method according to claim 4, characterized in that The objective function is solved by a preset optimization algorithm based on the power balance constraint condition and the energy storage device constraint condition to obtain an energy storage scheduling strategy, including: Using the charge and discharge power of the energy storage device as a variable, generating a particle swarm; By using a particle swarm optimization algorithm, iterative optimization is performed under the constraints of the power balance constraint and the energy storage device constraint to determine a global optimal solution; the global optimal solution includes the expected charge and discharge state and / or expected charge and discharge power value of the energy storage device in each unit time period within the energy scheduling time period; Based on the global optimal solution, an energy storage scheduling strategy within the energy scheduling time period is determined.

6. The method according to claim 5, characterized in that The method further comprises: When the particle swarm optimization algorithm is used for the first time, the initial position of each particle in the particle swarm is randomly generated; When the particle swarm optimization algorithm is not used for the first time, the position of the global optimal solution obtained in the last iterative optimization is used as the initial position of each particle in the particle swarm.

7. The method according to claim 6, characterized in that The particle swarm optimization algorithm is used to iteratively search for the optimal solution under the constraints of the power balance constraint and the energy storage device constraint to determine the global optimal solution, including: Iterate the position of each particle in the particle swarm by using the particle swarm optimization algorithm to determine the first candidate position corresponding to each particle; Determine a second candidate position that meets the power balance constraint condition and the energy storage device constraint condition from each of the first candidate positions; Based on the objective function, calculating the objective function value corresponding to each of the second candidate positions; When the objective function value corresponding to the second candidate position is less than the objective function value of the historical optimal solution of the corresponding particle, updating the current particle position and the historical optimal solution of the corresponding particle based on the second candidate position; When the objective function value corresponding to the second candidate position is less than the objective function value of the global optimal solution, updating the global optimal solution based on the second candidate position; When a preset iteration end condition is met, outputting the global optimal solution; When the preset iteration end condition is not met, returning to the step of iterating the position of each particle in the particle swarm by using the particle swarm optimization algorithm.

8. The method according to claim 5, characterized in that The global optimal solution includes the expected charge and discharge power of the energy storage device in each unit time period within the energy scheduling time period; The step of determining the energy storage scheduling strategy within the energy scheduling time period based on the global optimal solution includes: The absolute value of the expected charge and discharge power in each unit time period within the energy scheduling time period is compared with the preset power fluctuation threshold, and the expected charge and discharge power whose absolute value is less than the power fluctuation threshold is set to 0 to obtain the energy storage scheduling strategy.

9. The method according to claim 1, characterized in that The determining of the target electricity price and the target electricity price time period corresponding to the target electricity price based on the pre-divided stepped electricity price time periods and the dynamic electricity prices corresponding to the stepped electricity price time periods includes: Based on the pre-divided stepped electricity price time periods and the dynamic electricity prices corresponding to each of the stepped electricity price time periods, the minimum dynamic electricity price is determined as the target electricity price, and the stepped electricity price time period corresponding to the target electricity price is determined as the target electricity price time period.

10. The method according to claim 1, characterized in that The step of determining the time period between the current time and the start time of the target electricity price time period as the current energy scheduling time period includes: When the interval between the current moment and the start moment of the next target electricity price time period is greater than the preset time threshold, the time period between the current moment and the start moment of the next target electricity price time period is determined as the current energy scheduling time period; When the interval between the current moment and the start time of the next target electricity price time period is less than or equal to the preset time threshold, the time period between the current moment and the start time of the next target electricity price time period is determined as the current energy scheduling time period.

11. An energy management device, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 10 are implemented.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 10 are implemented.

Images