Data processing methods, apparatus, electronic devices and storage media

Abstract

This invention discloses a data processing method, apparatus, electronic device, and storage medium. The method includes: acquiring target electricity load data for a target area within a first preset time period before the current moment; determining the electricity load baseline for the target area within a second preset time period after the current moment based on the target electricity load data; determining the load capacity of the energy storage system associated with the target area within the second preset time period, and the predicted output power of the photovoltaic unit within the second preset time period; updating the electricity load baseline based on the load capacity, the predicted output power, a preset coordination strategy, and a preset peak-shaving objective function, and determining an electricity allocation strategy based on the updated electricity load baseline. The technical solution of this embodiment achieves the effect of maximizing the utilization of source-grid-load-storage resources, with the goal of increasing the value of the microgrid in the target area, while ensuring stable electricity consumption in the target area, and dynamically adjusting the original electricity load baseline.

Description

Technical Field

[0001] This invention relates to the field of microgrid technology, and in particular to a data processing method, apparatus, electronic device, and storage medium. Background Technology

[0002] With the continuous development of my country's power system, the country is currently in a transitional period between the old and new power systems. Market players will shift from a single focus to diversification, power transmission will shift from "generation, transmission, distribution, and consumption" to "source, grid, load, and storage," and the energy mix will shift from primarily thermal power to new energy sources such as wind, solar, and nuclear power. Especially after the establishment of dual-carbon targets, the addition of distributed energy sources such as wind and solar power and energy storage systems necessitates comprehensive consideration and research into source, grid, load, and storage. For microgrids, a common type involves utilizing distributed power sources such as wind power, photovoltaic power, small hydropower, and energy storage systems to generate revenue.

[0003] Existing microgrid applications that include small hydropower mainly rely on the peak-valley difference in electricity prices to generate revenue. This approach involves a limited variety of distributed energy sources and a fixed form, which fails to fully leverage the ability of microgrids to combine multiple new energy sources. Furthermore, it lacks consideration of the actual power load and does not adequately account for peak-shaving revenue. Summary of the Invention

[0004] This invention provides a data processing method, apparatus, electronic device, and storage medium to achieve the effect of dynamically adjusting the original power load baseline while ensuring stable power consumption in the target area and maximizing the utilization of source-grid-load-storage (power source, grid, load, energy storage) resources, with the goal of increasing the value of the microgrid in the target area.

[0005] According to one aspect of the present invention, a data processing method is provided, the method comprising:

[0006] Obtain the target electricity load data for the target area within a first preset time period prior to the current moment;

[0007] Based on the target electricity load data, a baseline for the electricity load of the target area within a second preset time period after the current time is determined; wherein, the electricity load baseline is generated based on the electricity load benchmark value corresponding to each preset time node within the second preset time period;

[0008] Determine the energy storage system associated with the target area under different operating conditions within the second preset time period, and the predicted output power of the photovoltaic unit associated with the target area within the second preset time period;

[0009] Based on the load, the predicted output power, the preset coordination strategy, and the preset peak shaving objective function, the power load baseline is updated, so as to determine the power allocation strategy of the target area at each preset time node within the second preset duration based on the updated power load baseline.

[0010] The preset coordination strategy is the coordinated working strategy of the energy storage system and the photovoltaic unit at various preset time nodes.

[0011] According to another aspect of the present invention, a data processing apparatus is provided, the apparatus comprising:

[0012] The electricity load data acquisition module is used to acquire the target electricity load data of the target area within a first preset time period before the current time.

[0013] The power load baseline determination module is used to determine the power load baseline of the target area within a second preset time period after the current time based on the target power load data; wherein, the power load baseline is generated based on the power load reference value corresponding to each preset time node within the second preset time period;

[0014] The load determination module is used to determine the load of the energy storage system associated with the target area under different operating states within the second preset time period, and the predicted output power of the photovoltaic unit associated with the target area within the second preset time period.

[0015] The power load baseline update module is used to update the power load baseline based on the power load, the predicted output power, the preset coordination strategy, and the preset peak shaving objective function, so as to determine the power allocation strategy of the target area at each preset time node within the second preset time period based on the updated power load baseline.

[0016] The preset coordination strategy is the coordinated working strategy of the energy storage system and the photovoltaic unit at various preset time nodes.

[0017] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0018] At least one processor; and

[0019] A memory communicatively connected to the at least one processor; wherein,

[0020] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the data processing method according to any embodiment of the present invention.

[0021] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the data processing method described in any embodiment of the present invention.

[0022] The technical solution of this invention obtains target electricity load data for a target area within a first preset time period before the current moment. Then, based on the target electricity load data, it determines the electricity load baseline for the target area within a second preset time period after the current moment. Furthermore, it determines the load of the energy storage system associated with the target area under different operating states within the second preset time period, and the predicted output power of the photovoltaic units associated with the target area within the second preset time period. Finally, based on the load, the predicted output power, the preset coordination strategy, and the preset peak-shaving objective function, it updates the electricity load baseline. Based on the updated electricity load baseline, it determines the electricity allocation strategy for each preset time node in the target area within the second preset time period. This solves the problems in the prior art where, in grid peak-shaving control, the types of distributed energy participating in peak-shaving control are few, the forms are singular and fixed, and there is a lack of consideration for the actual power load. It achieves the effect of dynamically adjusting the original electricity load baseline by maximizing the utilization of source-grid-load-storage (power source, grid, load, energy storage) resources with the goal of increasing the value of the microgrid in the target area, while ensuring the stability of electricity consumption in the target area.

[0023] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a flowchart of a data processing method provided according to Embodiment 1 of the present invention;

[0026] Figure 2 This is a flowchart of a data processing method provided according to Embodiment 1 of the present invention;

[0027] Figure 3 This is a schematic diagram of the structure of a data processing device according to Embodiment 2 of the present invention;

[0028] Figure 4This is a schematic diagram of the structure of an electronic device that implements the data processing method of the present invention. Detailed Implementation

[0029] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0031] Example 1

[0032] Figure 1 This is a flowchart of a data processing method provided in Embodiment 1 of the present invention. This embodiment is applicable to situations where power load peak-shaving control is performed on a microgrid in a target area based on source-grid-load-storage resources. This method can be executed by a data processing device, which can be implemented in hardware and / or software, and can be configured in a terminal and / or server. Figure 1 As shown, the method includes:

[0033] S110. Obtain the target power load data of the target area within a first preset time period before the current time.

[0034] In this embodiment, the target area can be any area where electricity consumption data analysis and electricity allocation strategy formulation are needed. For example, the target area can be a province, city, county, or enterprise with high electricity demand. The first preset duration can be a predetermined time range used to limit the collection time of the target electricity load data. For example, the first preset duration can be 1 day, 1 week, or 1 month, etc., and this embodiment does not specifically limit this. The target electricity load data can be electricity load data that meets the data analysis requirements after filtering and processing. The electricity load data can be the total electrical power taken from the power system by electrical equipment at any time, or it can be the total electrical power consumed by electrical equipment using electricity.

[0035] In practical applications, when analyzing the electricity consumption of a target area, in order to clearly and concisely display the overall electricity consumption of the target area based on the electricity load data, an electricity load curve corresponding to the target area can be generated based on the electricity load data. Generally, there may be abnormal data such as excessively high or low electricity load data in the target area over a period of time. In this case, in order to obtain an electricity load curve that fits and accurately reflects the electricity consumption of the target area, historical electricity load data of the target area within a first preset time period before the current moment can be collected. Then, based on the historical electricity load data, the target electricity load data can be determined, and thus, an electricity load curve corresponding to the target area can be generated based on the target electricity load data.

[0036] Based on this, in addition to the above technical solutions, the method further includes: obtaining historical electricity load data of the target area within a first preset time period before the current moment, and determining the historical average electricity load based on the historical electricity load data; filtering the historical electricity load data based on the historical average electricity load to obtain the target electricity load data.

[0037] In this embodiment, historical power load data can be the total power consumed by the associated electrical equipment in the target area within a first preset time period before the current moment. The historical power load average can be the ratio between the historical power load data and the number of its corresponding samples, or it can be understood as the average power load of the target area within the first preset time period.

[0038] In practical applications, in order to perform data analysis on the user situation in the target area and determine the optimal power allocation strategy corresponding to the target area based on the data analysis results, historical power load data of the target area within a first preset time period before the current moment can be collected. Then, the collected historical power load data is averaged to obtain the corresponding historical power load average. Further, the historical power load data is filtered based on the historical power load average to remove data samples that are too high or too low and do not meet the requirements. At this point, the filtered historical power load data can be used as the target power load data.

[0039] For example, the process of determining the target electricity load data can be represented by the following formula:

[0040]

[0041]

[0042] Among them, P t It can represent the historical average electricity load within a first preset time period. It can represent historical electricity load data within a first preset time period, with n data samples. This can represent the target electricity load data within a first preset time period, with n data samples. ′ .

[0043] S120. Based on the target power load data, determine the power load baseline of the target area within a second preset time period after the current time.

[0044] In this embodiment, the second preset duration can be any time range following the first preset duration. It should be noted that the length of the second preset duration and the length of the first preset duration can be the same or different; this embodiment does not specifically limit this. For example, if the first preset duration is 1 day, then the second preset duration can be 1 day. The electricity load baseline can be a curve representing the electricity load benchmark of any region, which can represent the basic electricity load situation of any region. The electricity load baseline is generated based on the electricity load benchmark values ​​corresponding to each preset time node within the second preset duration. The preset time nodes can be pre-set nodes used to divide the second preset duration into time periods. For example, if the second preset duration is 1 day, then the second preset duration can include 12 preset time nodes, each preset time node corresponding to each hour, i.e., 0:00 is a preset time node, 1:00 is a preset time node, 2:00 is a preset time node, and so on.

[0045] Generally, the load characteristics of microgrids are characterized by obvious peak-valley-flatness, rapid fluctuations in electricity consumption, relatively small natural power factor, and stable electricity load due to the operation of high-power production equipment with shifts or year-round operation. Therefore, in order to facilitate power workers in quickly determining the electricity load baseline for any area within a preset time period, the average value of electricity load data in a historical time period can be used as the benchmark value for electricity load in any future time period associated with that historical time period. Thus, the corresponding electricity load baseline for that area can be determined based on this benchmark value.

[0046] Optionally, based on the target power load data, the power load baseline of the target area within a second preset time period after the current time is determined, including: based on the target power load data, determining the target power load average value, and using the target power load average value as the power load benchmark value of the target area within a second preset time period after the current time; and determining the power load baseline based on the power load benchmark value.

[0047] In this embodiment, the target average electricity load can be the ratio between the target electricity load data and the corresponding number of samples. The electricity load benchmark value can be data characterizing the benchmark electricity load situation of the target area within a second preset time period.

[0048] In practical applications, after obtaining the target power load data, the target power load data can be averaged to obtain the target power load average. At this time, the target power load average can be used as the power load benchmark value of the target area within the second preset time period after the current time. Furthermore, the corresponding power load baseline can be generated based on the power load benchmark value.

[0049] For example, the target average electricity load can be represented by the following formula:

[0050]

[0051] Among them, P t ′ It can represent the average value of the target electrical load. This can represent the target electricity load data within a first preset time period, with n data samples. ′ .

[0052] S130. Determine the charge of the energy storage system associated with the target area under different operating conditions within a second preset time period, and the predicted output power of the photovoltaic unit associated with the target area within a second preset time period.

[0053] In this embodiment, the energy storage system can be a device for storing electrical energy. Energy storage refers to the process of storing energy through a medium or device and releasing it when needed. Generally, to ensure that all electrical equipment in the target area can operate under stable power load conditions, at least one energy storage system can be set up in the target area. Through the energy storage and buffering of the energy storage system, the electrical equipment in the target area can still operate at a stable output level even when the load fluctuates rapidly. The energy storage system can serve as a backup and transitional system when clean energy generation cannot operate normally. It should be noted that energy storage systems can be classified according to the storage medium, including mechanical energy storage, electrical energy storage, electrochemical energy storage, thermal energy storage, and chemical energy storage. Among them, commonly used energy storage systems can be mechanical energy storage, such as pumped hydro storage, compressed air energy storage, and flywheel energy storage, and electrochemical energy storage, such as lithium-ion battery energy storage, lead-acid battery energy storage, and flow battery energy storage.

[0054] The operating state can include charging or discharging. The charge quantity can be a quantity that characterizes the remaining capacity of the energy storage system, and its numerical definition can be the ratio of the remaining capacity of the energy storage system to the total capacity.

[0055] In practical applications, in order to determine the power storage situation in the target area, and based on the determination of the power load situation in the target area, a comprehensive analysis of the power consumption situation in the target area can be conducted in combination with the power storage situation. This allows us to determine the load of the energy storage system associated with the target area under different operating conditions, and thus conduct source-grid-load-storage coordinated analysis of the target area based on the load.

[0056] Optionally, determining the energy storage system associated with the target area under different states of charge within a second preset time period includes: acquiring the remaining energy storage system at the current moment, as well as the charging parameters, discharging parameters, and rated operating power corresponding to the energy storage system; determining the energy storage system under the charging state within the second preset time period based on the remaining energy storage system, charging parameters, and rated operating power; and determining the energy storage system under the discharging state within the second preset time period based on the remaining energy storage system, discharging parameters, and rated operating power.

[0057] In this embodiment, the remaining charge capacity can be the remaining charge capacity of the energy storage system at the current moment. Charging parameters can be parameters characterizing the charging capability of the energy storage system. For example, charging parameters may include charging efficiency and charging power. Discharging parameters can be parameters characterizing the discharging capability of the energy storage system. For example, discharging parameters may include discharging efficiency and discharging power. Rated operating power can be the output power that the energy storage system can achieve under normal operating conditions. In practical applications, if the actual operating power of the energy storage system is greater than its corresponding rated operating power, the energy storage system may be damaged; if the actual operating power of the energy storage system is less than its corresponding rated operating power, the energy storage system may not operate normally. It should be noted that the charging parameters, discharging parameters, and rated operating power of the energy storage system are all rated parameters of the energy storage system, fixed parameters corresponding to the energy storage system.

[0058] In practical applications, the energy storage system can first be detected at the current moment to obtain its remaining charge. Simultaneously, based on pre-set parameters, the charging parameters, discharging parameters, and rated operating power of the energy storage system can be obtained. Further, the product of charging efficiency and charging power can be determined, and the ratio of this product to the rated operating efficiency can be calculated. Then, the sum of this ratio and the remaining charge can be determined, and this sum can be used as the charge of the energy storage system during the charging state within a second preset time period. Similarly, the product of discharging efficiency and rated operating efficiency can be determined, and the ratio of the discharging power to this product can be determined. Then, the difference between this ratio and the remaining charge can be determined, and this difference can be used as the charge of the energy storage system during the discharging state within the second preset time period.

[0059] For example, the energy charge of an energy storage system in both charging and discharging states can be represented by the following formula:

[0060]

[0061]

[0062] Among them, SOC chr (t) can represent the charge state of the energy storage system at time t (the t-th time node) within the second preset time period, SOC. chr (t-1) can represent the remaining charge of the energy storage system at time (t-1), η chr It can represent charging efficiency, P chr (t) represents the charging efficiency of the energy storage system at time t, E n It can represent the rated operating power of an energy storage system; SOC disc (t) can represent the charge of the energy storage system in its discharged state at time t, SOCdisc (t-1) can represent the remaining charge of the energy storage system at time (t-1), η disc P can represent the discharge efficiency of an energy storage system. disc E(t) can represent the discharge efficiency of the energy storage system at time t. n It can represent the rated operating power of an energy storage system.

[0063] In this embodiment, while determining the charge of the energy storage system under different operating states within a second preset time period, the predicted output power of the photovoltaic unit associated with the target area within the second preset time period can also be determined.

[0064] Photovoltaic units can be devices that charge and discharge based on photovoltaic power generation technology. Those skilled in the art should understand that photovoltaic power generation is a technology that directly converts light energy into electrical energy using the photovoltaic effect at semiconductor interfaces. It mainly consists of three parts: solar panels (modules), controllers, and inverters. Solar cells are connected in series and then encapsulated for protection to form large-area solar cell modules. Combined with components such as power controllers, this forms a photovoltaic power generation device. For example, a photovoltaic unit can be a solar generator set. The predicted output power value can be the predicted energy provided to the outside world by the photovoltaic unit at any time point within a second preset time period.

[0065] In this embodiment, in order to make the optimized power load baseline of the target area smoother and avoid sharp increases and decreases in power consumption during the midday period (i.e., at least one power consumption peak), a photovoltaic unit can be added to the energy storage system. This allows the energy storage system and the photovoltaic unit to work together to supply power to the target area, thereby ensuring the stable operation of the related electrical equipment in the target area, improving power supply efficiency, and reducing the electricity cost of the target area.

[0066] Optionally, determining the predicted output power of the photovoltaic unit associated with the target area within a second preset time period includes: obtaining the irradiance and ambient temperature of the target area within a first preset time period before the current moment, as well as the rated operating parameters and standard ambient temperature corresponding to the photovoltaic unit; and determining the predicted output power of the photovoltaic unit within a second preset time period after the current moment based on the irradiance, ambient temperature, rated operating parameters, and standard ambient temperature.

[0067] In this embodiment, the illuminance can be the luminous flux of visible light received per unit area of ​​the photovoltaic unit. The ambient temperature can be the external temperature of the target area at various time points within a first preset time period. The rated operating parameters can be pre-defined, fixed parameters of the photovoltaic unit during operation, and these parameters are matched to the photovoltaic unit. Optionally, the rated operating parameters may include the rated output power and standard illuminance. The standard ambient temperature can be the standard ambient temperature value of the target area in the current season. In practical applications, the standard ambient temperature can be related to the geographical location of the target area and the current season, and can change accordingly based on changes in the target area and the current time.

[0068] In practical applications, firstly, the temperature difference between the ambient temperature and the standard ambient temperature can be determined, and the product between the temperature difference and the temperature coefficient can be determined. The product is added to the number 1 to obtain the first value to be processed. Then, the ratio between the light intensity and the standard light intensity can be determined. The ratio, the first value to be processed, and the rated output power are multiplied to obtain the predicted output power of the photovoltaic unit within the second preset time period.

[0069] For example, the predicted output power can be determined based on the following formula:

[0070]

[0071] Among them, P PV (t) can represent the predicted output power of the photovoltaic unit at the t-th time node within the second preset time period, P SET S(t) can represent the rated output power of the photovoltaic unit, and S(t) can represent the irradiance of the photovoltaic unit at time t. set k can represent the standard irradiance of a photovoltaic (PV) unit. T T(t) can represent the temperature coefficient, and T(t) can represent the ambient temperature. SET It can represent the standard ambient temperature.

[0072] In practical applications, when photovoltaic (PV) units are operating, it is necessary to consider whether the output power of the PV units can meet the needs of the power supply equipment. In particular, some equipment needs to be powered by a stable power source throughout the entire operation process (e.g., a pipe rolling mill). This requires that the predicted output power of the PV units should be less than the expected power demand of these equipment. Therefore, after obtaining the predicted output power of the PV units, constraints can be imposed on the predicted output power to ensure that the predicted output power of the PV units is not less than the power demand of the corresponding equipment at the same time, or not less than the minimum charging power of the energy storage system at the same time node.

[0073] Based on this, in addition to the above technical solutions, the method further includes: obtaining the preset power consumption of at least one electrical device associated with the photovoltaic unit, and the minimum charging power corresponding to the energy storage system; and updating the output power prediction value based on at least one preset power consumption and the minimum charging power.

[0074] In this embodiment, the electrical equipment can be a device that consumes electrical energy after being powered on, that is, a device that only starts working after being powered on. Accordingly, the preset power consumption can be a power consumption predetermined according to the power consumption needs of the corresponding electrical equipment.

[0075] In practical applications, after obtaining the predicted output power of the photovoltaic unit, the preset power consumption of at least one electrical device associated with the photovoltaic unit and the minimum charging power of the energy storage system can also be obtained. Furthermore, the predicted output power can be constrained according to the preset power consumption and the minimum charging power, and the predicted output power can be updated according to the constraint results.

[0076] For example, the update process for the output power prediction value can be represented by the following formula:

[0077]

[0078] P roll (t)≤P PV (t)

[0079] in, P can represent the minimum charging efficiency of an energy storage system. roll (t) can represent the preset power consumption of electrical equipment associated with the photovoltaic unit.

[0080] S140. Based on the predicted values ​​of load, output power, preset coordination strategy, and preset peak shaving objective function, update the power load baseline, and determine the power allocation strategy for each preset time node in the target area within the second preset time period based on the updated power load baseline.

[0081] In this embodiment, the preset coordination strategy can be a pre-defined strategy for the coordinated operation of the energy storage system and the photovoltaic unit at various preset time nodes. For example, taking a second preset duration of one day, the preset coordination strategy between the energy storage system and the photovoltaic unit can be as shown in Table 1:

[0082] Photovoltaic units Energy storage system 0-5 o'clock No light, no work Charging by purchasing electricity from the grid 6-7 o'clock Output power too low, no power supply Charging by purchasing electricity from the grid 8-11 AM Supply power to electrical equipment Powering other devices 12:00-13:00 Powering the energy storage system Charging from photovoltaic units 2 PM to 7 PM Supply power to electrical equipment until sunset Power other devices until the energy storage limit is reached.

[0083] In this embodiment, the preset peak-shaving target function can be a pre-defined expression corresponding to the desired objective when controlling the electricity load for peak shaving. For example, the preset peak-shaving target function can be the maximum sum of the profit obtained by the microgrid through peak-shaving control and the electricity cost savings through the peak-valley price difference, and can be expressed based on the following formula:

[0084] maxF = F1 + F2

[0085] Here, F1 can represent the profit obtained through peak shaving control, and F2 can represent the electricity cost saved through the peak-valley difference in electricity prices.

[0086] In this embodiment, the power allocation strategy can be a strategy that characterizes the power supply relationship of the target area.

[0087] In practical applications, after determining the predicted values ​​of the energy storage system's charge and the photovoltaic unit's output power, the operating time nodes of the energy storage system and the photovoltaic unit can be determined according to a preset coordination strategy. Then, based on the preset peak-shaving objective function and the predicted values ​​of the charge and output power at the corresponding operating time nodes, the optimal solution corresponding to the preset peak-shaving objective function is determined, and the electricity load baseline is adjusted according to the optimal solution. Thus, based on the adjusted electricity load baseline, the electricity distribution strategy for each preset time node in the target area within the second preset time period can be determined.

[0088] Optionally, the electricity load baseline is updated based on the predicted value of the load, the output power, the preset coordination strategy, and the preset objective function. This includes: determining at least one photovoltaic time node corresponding to the photovoltaic unit and at least one energy storage time node corresponding to the energy storage system based on the preset coordination strategy; and obtaining the clearing attribute value and electricity payment attribute value corresponding to each preset time node in the target area within a second preset time period; and updating the electricity load baseline based on the predicted value of the output power corresponding to at least one photovoltaic time node, the load, clearing attribute value, electricity payment attribute value corresponding to at least one energy storage time node, and the preset peak-shaving objective function.

[0089] In this embodiment, the photovoltaic time node can be the time node corresponding to when the photovoltaic unit supplies power. The energy storage time node can be the time node corresponding to when the energy storage system is working. At least one photovoltaic time node and at least one energy storage time node correspond to at least one preset time node within a second preset duration. The clearing attribute value can be the attribute value when supply and demand are balanced. For example, the clearing attribute value can be the clearing electricity price. The electricity payment attribute value can be the attribute value characterizing the economic value of electricity. For example, the electricity payment attribute value can be the electricity price.

[0090] In practical applications, firstly, based on a preset coordination strategy, at least one photovoltaic time node corresponding to the photovoltaic unit when supplying power and at least one energy storage time node corresponding to the energy storage system when it is working can be determined. Simultaneously, the clearing attribute value and electricity payment attribute value corresponding to each preset time node in the target area within a second preset duration can be determined. Further, based on the method for determining the predicted output power of the photovoltaic unit, the predicted output power value corresponding to each photovoltaic time node is determined. Simultaneously, based on the method for determining the charge of the energy storage system under different working states, the charge of each energy storage time node is determined, i.e., the charge point in the charging state and the charge point in the discharging state. Then, based on the predicted output power value, charge point, clearing attribute value, and electricity payment attribute value, the optimal solution of the preset peak-shaving objective function is determined. This optimal solution can then be used to adjust the electricity load baseline, and the adjusted electricity load baseline is used as the updated electricity load baseline.

[0091] For example, the preset peak-shaving objective function can be represented by the following formula:

[0092] maxF = F1 + F2

[0093]

[0094]

[0095] Among them, S n P(t) can represent whether the peak control is participated in at the t-th time node, and is a variable of 0 or 1. P3(t) can represent the effective output power at the t-th time node. C1(t) can represent the clearing attribute value at the t-th time node. N can represent the adjustment coefficient.

[0096] Where C2(t) can represent the electricity payment attribute value at time t, P old (t) can represent the target electricity load at time t before the update, P new (t) can represent the target electricity load at time t after the update.

[0097] It should be noted that the effective output power in the above formula can be obtained by constraining the output power prediction of the photovoltaic unit and the charge of the energy storage system. Therefore, before applying the effective output power, the output power prediction and charge can be processed according to the pre-defined constraints to obtain the effective output power.

[0098] For example, the constraint process for effective output power can be represented based on the following formula:

[0099]

[0100] P2(t)=P t ′ -P PV (t)-SOC

[0101] Where P2(t) represents the actual output power at time t, and P1(t) represents the benchmark output power at time t. t ′ P can represent the target electrical load. PV (t) can represent the predicted output power of the photovoltaic unit, and SOC can represent the charge of the energy storage system under different operating conditions.

[0102] For example, based on the above formula, P can be calculated. new (t), at this time P can be... new (t) is the optimal solution of the preset control objective function, and then, according to P new (t) generates a new electricity load baseline, thereby updating the electricity load baseline.

[0103] For example, it can be combined Figure 2 The overall process of the technical solution provided in this embodiment is described as follows: 1. Obtain historical electricity load data; 2. Calculate the average historical electricity load; 3. Remove sample values ​​that do not meet the requirements to obtain target electricity load data; 4. Calculate the average target electricity load and generate an electricity load baseline; 5. Determine the predicted output power of the photovoltaic unit and the charge of the energy storage system; 6. Coordinate the control of the photovoltaic unit and the energy storage system; 7. Substitute the predicted output power, charge, and target electricity load data into the preset peak-shaving objective function to obtain the optimal solution; 8. Adjust the electricity load baseline according to the optimal solution.

[0104] The technical solution of this invention obtains target electricity load data for a target area within a first preset time period before the current moment. Then, based on the target electricity load data, it determines the electricity load baseline for the target area within a second preset time period after the current moment. Furthermore, it determines the load of the energy storage system associated with the target area under different operating states within the second preset time period, and the predicted output power of the photovoltaic units associated with the target area within the second preset time period. Finally, based on the load, the predicted output power, the preset coordination strategy, and the preset peak-shaving objective function, it updates the electricity load baseline. Based on the updated electricity load baseline, it determines the electricity allocation strategy for each preset time node in the target area within the second preset time period. This solves the problems in the prior art where, in grid peak-shaving control, the types of distributed energy participating in peak-shaving control are few, the forms are singular and fixed, and there is a lack of consideration for the actual power load. It achieves the effect of dynamically adjusting the original electricity load baseline by maximizing the utilization of source-grid-load-storage (power source, grid, load, energy storage) resources with the goal of increasing the value of the microgrid in the target area, while ensuring the stability of electricity consumption in the target area.

[0105] Example 2

[0106] Figure 3 This is a schematic diagram of the structure of a data processing device provided in Embodiment 2 of the present invention. Figure 3 As shown, the device includes:

[0107] The power load data acquisition module 210 is used to acquire the target power load data of the target area within a first preset time period before the current time.

[0108] The power load baseline determination module 220 is used to determine the power load baseline of the target area within a second preset time period after the current time based on the target power load data; wherein, the power load baseline is generated based on the power load reference value corresponding to each preset time node within the second preset time period;

[0109] The charge determination module 230 is used to determine the charge of the energy storage system associated with the target area under different operating states within the second preset time period, and the predicted output power of the photovoltaic unit associated with the target area within the second preset time period.

[0110] The power load baseline update module 240 is used to update the power load baseline based on the power load, the predicted output power, the preset coordination strategy, and the preset peak shaving objective function, so as to determine the power allocation strategy of the target area at each preset time node within the second preset duration based on the updated power load baseline.

[0111] The preset coordination strategy is the coordinated working strategy of the energy storage system and the photovoltaic unit at various preset time nodes.

[0112] The technical solution of this invention obtains target electricity load data for a target area within a first preset time period before the current moment. Then, based on the target electricity load data, it determines the electricity load baseline for the target area within a second preset time period after the current moment. Furthermore, it determines the load of the energy storage system associated with the target area under different operating states within the second preset time period, and the predicted output power of the photovoltaic units associated with the target area within the second preset time period. Finally, based on the load, the predicted output power, the preset coordination strategy, and the preset peak-shaving objective function, it updates the electricity load baseline. Based on the updated electricity load baseline, it determines the electricity allocation strategy for each preset time node in the target area within the second preset time period. This solves the problems in the prior art where, in grid peak-shaving control, the types of distributed energy participating in peak-shaving control are few, the forms are singular and fixed, and there is a lack of consideration for the actual power load. It achieves the effect of dynamically adjusting the original electricity load baseline by maximizing the utilization of source-grid-load-storage (power source, grid, load, energy storage) resources with the goal of increasing the value of the microgrid in the target area, while ensuring the stability of electricity consumption in the target area.

[0113] Optionally, the device further includes: an average power load determination module and a power load data filtering module.

[0114] The electricity load average determination module is used to obtain historical electricity load data of the target area within a first preset time period before the current time, and determine the historical electricity load average based on the historical electricity load data;

[0115] The electricity load data filtering module is used to filter the historical electricity load data based on the historical average electricity load to obtain the target electricity load data.

[0116] Optionally, the power load baseline determination module 220 includes: a power load reference value determination unit and a power load baseline determination unit.

[0117] The electricity load reference value determination unit is used to determine the target electricity load average value based on the target electricity load data, and to use the target electricity load average value as the electricity load reference value of the target area within the second preset time period;

[0118] The power load baseline determination unit is used to determine the power load baseline based on the power load reference value.

[0119] Optionally, the working state includes a charging state and a charging state, and the charge determination module 230 includes: a remaining charge acquisition unit, a first charge determination unit, and a second charge determination unit.

[0120] The remaining charge acquisition unit is used to acquire the remaining charge of the energy storage system at the current moment, as well as the charging parameters, discharging parameters and rated operating power corresponding to the energy storage system.

[0121] A first charge determination unit is configured to determine the charge of the energy storage system during the charging state within the second preset time period based on the remaining charge, the charging parameters, and the rated operating power; and,

[0122] The second charge determination unit is used to determine the charge of the energy storage system in the discharge state within the second preset time period based on the remaining charge, the discharge parameters and the rated operating power.

[0123] Optionally, the charge determination module 230 may also include an ambient temperature acquisition unit and an output power prediction value determination unit.

[0124] An ambient temperature acquisition unit is used to acquire the light intensity and ambient temperature of the target area within a first preset time period before the current moment, as well as the rated operating parameters and standard ambient temperature corresponding to the photovoltaic unit.

[0125] The output power prediction unit is used to determine the output power prediction value of the photovoltaic unit within a second preset time period after the current moment, based on the light intensity, the ambient temperature, the rated operating parameters, and the standard ambient temperature.

[0126] Optionally, the device further includes: a minimum charging efficiency acquisition module and an output power prediction update module.

[0127] The minimum charging efficiency acquisition module is used to acquire the preset power consumption of at least one electrical device associated with the photovoltaic unit, and the minimum charging power corresponding to the energy storage system.

[0128] The output power prediction update module is used to update the output power prediction value based on the at least one preset power consumption and the minimum charging power.

[0129] Optionally, the electricity load baseline update module 240 includes: a photovoltaic time node determination unit, an attribute value determination unit, and an electricity load baseline update unit.

[0130] Based on the preset coordination strategy, at least one photovoltaic time node corresponding to the photovoltaic unit and at least one energy storage time node corresponding to the energy storage system are determined respectively; wherein, the at least one photovoltaic time node and the at least one energy storage time node correspond to at least one preset time node within the second preset duration; and,

[0131] Obtain the clearing attribute value and electricity payment attribute value of the target area at each preset time node within the second preset time period;

[0132] The electricity load baseline is updated based on the predicted output power value corresponding to the at least one photovoltaic time node, the charge capacity corresponding to the at least one energy storage time node, the clearing attribute value, the electricity payment attribute value, and the preset objective function.

[0133] The data processing apparatus provided in the embodiments of the present invention can execute the data processing method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of executing the method.

[0134] Example 3

[0135] Figure 4 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0136] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0137] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0138] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as data processing methods.

[0139] In some embodiments, the data processing method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or mounted on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the data processing method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the data processing method by any other suitable means (e.g., by means of firmware).

[0140] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0141] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0142] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0143] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0144] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0145] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0146] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0147] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A data processing method, characterized in that, include: Obtain the target electricity load data for the target area within a first preset time period prior to the current moment; Based on the target electricity load data, a baseline for the electricity load of the target area within a second preset time period after the current time is determined; wherein, the electricity load baseline is generated based on the electricity load benchmark value corresponding to each preset time node within the second preset time period; Determine the energy storage system associated with the target area under different operating conditions within the second preset time period, and the predicted output power of the photovoltaic unit associated with the target area within the second preset time period; Based on the load, the predicted output power, the preset coordination strategy, and the preset peak shaving objective function, the power load baseline is updated, so as to determine the power allocation strategy of the target area at each preset time node within the second preset duration based on the updated power load baseline. The preset coordination strategy is the coordinated working strategy of the energy storage system and the photovoltaic unit at each preset time node; The step of determining the electricity load baseline of the target area within a second preset time period after the current time based on the target electricity load data includes: determining the average target electricity load based on the target electricity load data, and using the average target electricity load as the reference value of the electricity load of the target area within a second preset time period after the current time; and determining the electricity load baseline based on the reference value of the electricity load. The operating state is either a charging state or a discharging state. Accordingly, determining the energy charge of the energy storage system associated with the target area under different operating states within the second preset time period includes: acquiring the remaining energy charge of the energy storage system at the current moment, as well as the charging parameters, discharging parameters, and rated operating power corresponding to the energy storage system; determining the energy charge of the energy storage system under the charging state within the second preset time period based on the remaining energy charge, the charging parameters, and the rated operating power; and determining the energy charge of the energy storage system under the discharging state within the second preset time period based on the remaining energy charge, the discharging parameters, and the rated operating power. The step of determining the predicted output power of the photovoltaic unit associated with the target area within the second preset time period includes: obtaining the irradiance and ambient temperature of the target area within the first preset time period before the current moment, as well as the rated operating parameters and standard ambient temperature corresponding to the photovoltaic unit; and determining the predicted output power of the photovoltaic unit within the second preset time period after the current moment based on the irradiance, the ambient temperature, the rated operating parameters, and the standard ambient temperature.

2. The method according to claim 1, characterized in that, Also includes: Obtain historical electricity load data of the target area within a first preset time period before the current time, and determine the historical average electricity load based on the historical electricity load data; The historical electricity load data is filtered based on the historical average electricity load to obtain the target electricity load data.

3. The method according to claim 1, characterized in that, Also includes: Obtain the preset power consumption of at least one electrical device associated with the photovoltaic unit, and the minimum charging power corresponding to the energy storage system; The predicted output power value is updated based on the at least one preset power consumption and the minimum charging power.

4. The method according to claim 1, characterized in that, The step of updating the electricity load baseline based on the load capacity, the predicted output power, a preset coordination strategy, and a preset objective function includes: Based on the preset coordination strategy, at least one photovoltaic time node corresponding to the photovoltaic unit and at least one energy storage time node corresponding to the energy storage system are determined respectively; wherein, the at least one photovoltaic time node and the at least one energy storage time node correspond to at least one preset time node within the second preset duration; and, Obtain the clearing attribute value and electricity payment attribute value of the target area at each preset time node within the second preset time period; The electricity load baseline is updated based on the predicted output power value corresponding to the at least one photovoltaic time node, the charge capacity corresponding to the at least one energy storage time node, the clearing attribute value, the electricity payment attribute value, and the preset objective function.

5. A data processing apparatus, characterized in that, include: The electricity load data acquisition module is used to acquire the target electricity load data of the target area within a first preset time period before the current time. The power load baseline determination module is used to determine the power load baseline of the target area within a second preset time period after the current time based on the target power load data; wherein, the power load baseline is generated based on the power load reference value corresponding to each preset time node within the second preset time period; The load determination module is used to determine the load of the energy storage system associated with the target area under different operating states within the second preset time period, and the predicted output power of the photovoltaic unit associated with the target area within the second preset time period. The power load baseline update module is used to update the power load baseline based on the power load, the predicted output power, the preset coordination strategy, and the preset peak shaving objective function, so as to determine the power allocation strategy of the target area at each preset time node within the second preset time period based on the updated power load baseline. The preset coordination strategy is the coordinated working strategy of the energy storage system and the photovoltaic unit at each preset time node; The power load baseline determination module includes: a power load reference value determination unit and a power load baseline determination unit; the power load reference value determination unit is used to determine the target power load average value based on the target power load data, and use the target power load average value as the power load reference value of the target area within the second preset time period; the power load baseline determination unit is used to determine the power load baseline based on the power load reference value; The operating states include a charging state and a charge state. The charge determination module includes: a remaining charge acquisition unit, a first charge determination unit, and a second charge determination unit. The remaining charge acquisition unit is used to acquire the remaining charge of the energy storage system at the current moment, as well as the charging parameters, discharging parameters, and rated operating power corresponding to the energy storage system. The first charge determination unit is used to determine the charge of the energy storage system in the charging state within the second preset time period based on the remaining charge, the charging parameters, and the rated operating power. The second charge determination unit is used to determine the charge of the energy storage system in the discharging state within the second preset time period based on the remaining charge, the discharging parameters, and the rated operating power. The charge determination module further includes: an ambient temperature acquisition unit and an output power prediction value determination unit; the ambient temperature acquisition unit is used to acquire the light intensity and ambient temperature of the target area within a first preset time period before the current moment, as well as the rated operating parameters and standard ambient temperature corresponding to the photovoltaic unit; the output power prediction value determination unit is used to determine the output power prediction value of the photovoltaic unit within a second preset time period after the current moment based on the light intensity, the ambient temperature, the rated operating parameters, and the standard ambient temperature.

6. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the data processing method according to any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the data processing method according to any one of claims 1-4.

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