Optical charge synergic optimization method, device, medium and system

Abstract

This invention discloses a photovoltaic-storage-load co-optimization method, relating to the field of power dispatching technology, to address the overload of dedicated transformers in existing small and micro-sized industrial parks. The method includes the following steps: receiving photovoltaic forecast data, load forecast data, actual sampling data, production schedules, and meteorological data; dividing the operation scenarios; generating energy storage charging and discharging power or power plans for different electricity price periods under each operation scenario using a preset photovoltaic-storage-load co-optimization method; and adjusting power according to the plans. This invention also discloses a photovoltaic-storage-load co-optimization electronic device, computer storage medium, and system. By formulating different charging and discharging strategies for different scenarios, this invention avoids overload of dedicated transformers in industrial parks, maximizes the operational efficiency of the parks, and simultaneously improves the parks' flexible adjustment capabilities and emergency backup power capacity.

Description

Technical Field

[0001] This invention relates to the field of power dispatching technology, and in particular to a method, equipment, medium, and system for coordinating photovoltaic, energy storage, and load optimization to cope with variable production conditions and complex weather conditions. Background Technology

[0002] Small and micro-sized industrial parks are highly replicable and scalable, and currently have a significant scale. Many business owners have independently invested in and built distributed energy storage or "photovoltaic + energy storage" systems, employing established strategies such as "self-consumption of photovoltaic power" or "peak-shaving and peak-filling" of energy storage to reduce energy costs. However, when using this charging and discharging solution, small and micro-sized industrial parks have failed to fully consider the possibility of factory expansion and production capacity increases, as well as the impact of weather on photovoltaic power generation, leading to overload issues with dedicated transformers. Even when using energy storage systems at full power for "two charging and two discharging" to smooth peak and valley loads, it is still difficult to match complex production loads and random photovoltaic power generation curves. Overcapacity issues still exist during periods of high production load, and even during midday off-peak charging periods, easily leading to equipment insulation aging and depreciation losses, high overcapacity penalties, and other problems, and also posing potential risks to the safe and stable operation of the power distribution network.

[0003] In summary, small and micro industrial parks urgently need an optimized strategy for the coordinated operation of photovoltaic, energy storage, and power generation to adapt to different production conditions and weather conditions, so as to solve the problem of overcapacity of dedicated transformers in the parks while ensuring economic operation. Summary of the Invention

[0004] In order to overcome the shortcomings of the prior art, one of the objectives of this invention is to provide a method for coordinated optimization of photovoltaic storage and load, which optimizes the charging and discharging of each scenario by dividing the operating scenarios.

[0005] One of the objectives of this invention is achieved through the following technical solution:

[0006] A method for coordinated optimization of optical storage and power generation includes the following steps:

[0007] Receive photovoltaic power forecast data, load forecast power data, production schedules, and meteorological data;

[0008] Based on the production schedule and the corresponding meteorological data, the operation scenarios are divided;

[0009] Based on the defined operating scenarios, and combined with the photovoltaic predicted power, load predicted power, and actual sampled power data, a pre-defined photovoltaic-storage-load collaborative optimization method is used to generate energy storage charging and discharging power or power plans for different electricity price periods under each operating scenario.

[0010] The active power of the energy storage system is regulated according to the charging and discharging plan.

[0011] Furthermore, the operating scenarios are four: production day rainy day operating scenario, production day sunny day operating scenario, non-production day rainy day operating scenario, and non-production day sunny day operating scenario.

[0012] Furthermore, when the operating scenario is a cloudy or rainy day during production, the charging and discharging plan generated by the preset photovoltaic-storage-load co-optimization method includes:

[0013] During the peak period from 9:00 to 11:00, energy storage is used for stepped discharge optimization to ensure the discharge power meets the following requirements:

[0014]

[0015] Where i = 12, 13 represent the two time periods 11:00-12:00 and 12:00-13:00; P transform,max Rated power of dedicated transformer for small and micro industrial parks; E discharge E represents the total discharge of the energy storage system during the time period. L，i E represents the predicted total load electricity consumption of the micro-industrial park at time i; pv,ultrashort，i Let be the photovoltaic ultra-short-term predicted power generation at time i; soc max The upper limit of the state of charge of the energy storage system; SOC min This is the state-of-charge limit for energy storage systems; E ES,e This refers to the rated capacity of the energy storage system.

[0016] Energy storage charging is performed during the off-peak hours of 11:00-13:00, with the charging power meeting the following requirements:

[0017] P charge,j =P transform,max -(P L,j -P pv,j ),

[0018] Among them, P charge,j The energy storage charging power for the j-th cycle; P L,j P represents the total load sampling power of the micro-park during the j-th cycle; pv,j The sampled power of photovoltaic power generation in the j-th cycle;

[0019] During the peak period from 13:00 to 15:00, the system automatically checks the energy storage's state of charge. If the state of charge has reached its upper limit, it goes into standby mode; otherwise, it charges the energy storage system to its upper limit and goes into standby mode. During the peak period from 15:00 to 17:00, the system performs a stepped discharge to the upper limit of the energy storage system's state of charge. During the off-peak period from 22:00 to 8:00 the next day, the system performs low-power charging to the upper limit of the energy storage system's state of charge. At other times, the energy storage system remains in standby mode.

[0020] Furthermore, when the operating scenario is a sunny production day, the same photovoltaic-storage-load co-optimization method is executed as for a cloudy or rainy production day. However, due to the different weather conditions, the generated charging and discharging plans are slightly different from the former.

[0021] Furthermore, when the operating scenario is a non-production day (cloudy or rainy), the charging and discharging plan generated by the preset photovoltaic-storage-load co-optimization method includes:

[0022] During peak hours (8:00-11:00 and 13:00-22:00), energy storage discharge occurs. When the energy storage charge lower limit is reached, the energy storage system goes into standby mode, and the park is powered by both photovoltaic power and the grid. The discharge power meets the following requirements:

[0023] P discharge,j =P L,j -P pv,j , where P discharge,j P is the discharge power in the kth cycle. L,j P represents the total load sampling power of the micro-park in the kth cycle; pv,j The sampled power of photovoltaic power generation in the j-th cycle;

[0024] During off-peak hours (11:00-13:00 and 22:00-8:00 the next day), the energy storage system is charged to its rated power, and then goes into standby mode when it reaches the upper limit of its state of charge.

[0025] Furthermore, when the operating scenario is a sunny, non-production day, the charging and discharging plan generated by the preset photovoltaic-storage-load co-optimization method includes:

[0026] During peak hours (8:00-11:00 and 13:00-22:00), the energy storage system discharges. When the lower limit of energy storage charge is reached, the system goes into standby mode, and power is supplied jointly by photovoltaic power and the grid. The discharge power meets the following requirements:

[0027] Among them, P discharge,j Let P be the discharge power in the j-th cycle. L,j P represents the total load sampling power of the micro-park during the j-th cycle; pv,j The sampled power of photovoltaic power generation in the kth cycle;

[0028] During off-peak hours (11:00-13:00 and 22:00-8:00 the next day), the energy storage system is charged to its rated power, and then goes into standby mode when it reaches the upper limit of its state of charge.

[0029] Furthermore, the stepped discharge is a periodic discharge, in which the discharge power is increased from low to high in a fixed step-size manner, and the discharge satisfies the following:

[0030]

[0031] △P discharge =P discharge,max -P discharge,ave ,

[0032]

[0033] Where t is the discharge time, E discharge P is the planned total discharge amount of the energy storage within hour t; discharge,ave P represents the average discharge power of the stored energy within t hours. discharge，max The maximum discharge power of the stored energy; ΔP discharge This is the difference between the maximum discharge power and the average discharge power of the energy storage.

[0034] A second objective of this invention is to provide an electronic device for performing one of the objectives of the invention, comprising a processor, a storage medium, and a computer program, wherein the computer program is stored in the storage medium and, when executed by the processor, implements the aforementioned optical-storage-load co-optimization method.

[0035] A third objective of this invention is to provide a computer-readable storage medium that stores one of the objectives of the invention, wherein a computer program is stored thereon, and when the computer program is executed by a processor, it implements the above-mentioned optical-storage-load co-optimization method.

[0036] The fourth objective of this invention is to provide a photovoltaic-storage-load collaborative optimization system suitable for small and micro-sized industrial parks. This system includes a data acquisition and storage unit, a photovoltaic forecasting unit, a load forecasting unit, a scene identification unit, an energy storage power allocation unit, and an energy storage control unit. The data acquisition and storage unit collects power and meteorological data within the small and micro-sized industrial park. The photovoltaic forecasting unit and the load forecasting unit predict power generation and consumption data and provide data support to the energy storage power allocation unit. The scene identification unit identifies the current operating scene and provides support to the energy storage power allocation unit. The energy storage power allocation unit executes the aforementioned photovoltaic-storage-load collaborative optimization method. The energy storage control unit controls charging and discharging according to the optimization method obtained by the energy storage power allocation unit.

[0037] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0038] This invention, by acquiring photovoltaic power generation and load forecast data, production conditions, and weather conditions, classifies different power consumption scenarios for small and micro industrial parks, optimizes and coordinates photovoltaic-storage-load operation strategies, and generates energy storage charging and discharging plans for various power consumption scenarios. This addresses complex and changing production conditions and weather, fully utilizes the adjustability of the energy storage system, and avoids the problem of overcapacity in dedicated transformers within the park. By using different charging and discharging strategies at different times, it can also improve the economic efficiency of the park and ensure operational economics. Furthermore, this invention proposes a periodic, stepped discharge method, enabling energy storage to reserve a relatively sufficient amount of power during the discharge process while fulfilling a given discharge plan. This not only provides small and micro industrial parks with a longer-lasting backup power source but also provides them with a greater margin for active participation in grid regulation. Attached Figure Description

[0039] Figure 1 This is a flowchart of the optical-storage-load co-optimization method in Implementation Example 1;

[0040] Figure 2 This is a structural block diagram of the electronic device in Embodiment 3;

[0041] Figure 3 This is a structural block diagram of the photovoltaic-storage-load co-optimization system in Example 5. Detailed Implementation

[0042] The present invention will now be described in more detail with reference to the accompanying drawings. It should be noted that the following description of the present invention with reference to the accompanying drawings is merely illustrative and not restrictive. Various embodiments can be combined with each other to form other embodiments not shown in the following description.

[0043] Example 1

[0044] Example 1 provides a photovoltaic-storage-load synergistic optimization method, which aims to collect multi-dimensional data in small and micro parks, refine the charging and discharging power of energy storage at different times according to different operating scenarios, maximize the operating efficiency of photovoltaic and energy storage in the park, avoid equipment aging and depreciation losses and assessment penalties caused by overcapacity of dedicated transformers, and improve the energy efficiency and economy of the park.

[0045] In small industrial parks, distributed photovoltaic and energy storage systems and loads are typically connected to the same low-voltage busbar of the park via switchgear and then connected to the distribution network through a dedicated transformer. Therefore, the dedicated transformer load P transform The total load P of the park L With photovoltaic power P pv Energy storage discharge power P discharge The difference, i.e., P transform =P L -P pv -P discharge If the energy storage is in a charging state, then Pdischarge It is a negative value.

[0046] The main load of small and micro industrial parks is production load, with higher load on production days and lower load on non-production days. Therefore, in this embodiment, the operating scenario is divided into two categories: production days and non-production days. Secondly, the intensity of sunlight will cause different photovoltaic power generation outputs. Therefore, this embodiment uses meteorological data and short-term photovoltaic power forecast P within 48 hours to determine the appropriate power output. pv,short The operating scenarios are divided into sunny and rainy days. In summary, this embodiment identifies diverse scenarios and clusters complex production plans and weather conditions into four typical operating scenarios. It then refines and formulates collaborative optimization strategies for different scenarios and solves the problem of overcapacity of dedicated transformers through the charging and discharging of energy storage systems.

[0047] It should be noted that currently, power companies adjust electricity prices into peak, mid-peak, and off-peak prices based on the time of electricity consumption. The time periods described in this embodiment, such as peak periods, are set based on the current electricity price. When the peak, mid-peak, or off-peak electricity price periods change, the energy storage charging and discharging settings can be adjusted according to this embodiment and the actual electricity price billing situation. By combining energy storage charging and discharging with electricity price differences, electricity costs can be saved.

[0048] Specifically, please refer to Figure 1 As shown, a method for coordinated optimization of optical storage and payload includes the following steps:

[0049] S1, Receive photovoltaic power forecast, load power forecast, production schedule and meteorological data;

[0050] The photovoltaic forecast in S1 can be achieved by directly calling the relevant forecast data of the existing small and micro parks or by installing forecast or data acquisition equipment. This embodiment will not elaborate on this conventional technical means. The production operation schedule can be obtained by manual input or by connecting with the working system. Meteorological data can be obtained by connecting to external or internal meteorological systems or by manual input.

[0051] S2. Based on the production schedule and the corresponding meteorological data, divide the operation scenarios;

[0052] In this embodiment, there are four operating scenarios: production day on a cloudy / rainy day, production day on a sunny day, non-production day on a cloudy / rainy day, and non-production day on a sunny day.

[0053] S3. Based on the defined operating scenarios, and combined with the photovoltaic predicted power, load predicted power, and actual sampled power data, a pre-defined photovoltaic-storage-load collaborative optimization method is used to generate energy storage charging and discharging power or power plans for different electricity price periods under each operating scenario.

[0054] Specifically, in scenario S3, when the operating scenario is a cloudy or rainy day during production, the total load of the small industrial park is high, but due to the lack of sunlight, photovoltaic power generation is either stopped or at low power. Because the electricity demand on production days is high, the dedicated transformer load P typically reaches its maximum during the midday period. transform The capacity will not decrease or will only decrease slightly. If the energy storage is charged at full power at this time, it will cause the dedicated transformer to operate under overload. Therefore, in order to avoid overcapacity assessment and ensure that the energy storage achieves maximum profitability during peak and off-peak electricity prices, it is necessary to optimize the charging and discharging strategy of the energy storage during different periods of the production day. In this scenario, the charging and discharging plan includes:

[0055] During the peak period from 9:00 to 11:00, energy storage is used for stepped discharge optimization to ensure the discharge power meets the following requirements:

[0056]

[0057] Where i = 12, 13 represent the two time periods 11:00-12:00 and 12:00-13:00; P transform,max Rated power of dedicated transformer for small and micro industrial parks; E discharge E represents the total discharge of the energy storage system during the time period. L，i E represents the predicted total load electricity consumption of the micro-industrial park at time i; pv,ultrashort，i Let be the photovoltaic ultra-short-term predicted power generation at time i; soc max The upper limit of the state of charge of the energy storage system; SOC min This is the state-of-charge limit for energy storage systems; E ES,e This refers to the rated capacity of the energy storage system.

[0058] The above calculations show that the discharge capacity of the energy storage system between 9:00 and 11:00 is determined by the difference between the predicted total load of the park and the predicted photovoltaic power generation between 11:00 and 13:00. When the difference is greater than the total power capacity that the dedicated transformer can handle during that period, the energy storage system is on standby, discharging at midday to ensure that the dedicated transformer is not overloaded. When the difference is less than the total power capacity that the dedicated transformer can handle during that period, the energy storage system discharges quantitatively according to the calculated value to reduce the park's electricity costs, but the depth of discharge does not exceed the maximum limit. Based on the total discharge capacity E of the energy storage system... discharge In this embodiment, a 15-minute cycle is used, and a stepped discharge method is employed to determine the discharge power P of the energy storage in each cycle within that time period. discharge,j For the stepped discharge strategy, please refer to Example 2.

[0059] Energy storage charging is performed during the off-peak hours of 11:00-13:00, with the charging power meeting the following requirements:

[0060] P charge,j =P transform,max -(P L,j -P pv,j ),

[0061] Where, pcharge,j The energy storage charging power for the j-th cycle; p L,j p represents the total load sampling power of the micro-park during the j-th cycle; pv,j Let P be the sampled power of photovoltaic power generation in the j-th cycle. As mentioned above, during this period, the energy storage will dynamically charge according to the calculated power until it reaches the upper limit of its state of charge, thus controlling the load of the park's dedicated transformer to not exceed its maximum rated power. Similarly, when the difference between the park's total load and photovoltaic power generation exceeds the maximum rated power of the dedicated transformer, P... charge,j A negative value indicates that the energy storage is in a discharge state, ensuring that the dedicated transformer in the park does not operate under overload.

[0062] During the peak period from 13:00 to 15:00, the system automatically checks the energy storage's state of charge. If the state of charge has reached its upper limit, it goes into standby mode; otherwise, it charges the energy storage system to its upper limit and goes into standby mode. During the peak period from 15:00 to 17:00, the system performs a stepped discharge to the upper limit of the energy storage system's state of charge. During the off-peak period from 22:00 to 8:00 the next day, the system performs low-power charging to the upper limit of the energy storage system's state of charge. At other times, the energy storage system remains in standby mode.

[0063] When the operating scenario is a sunny production day, the energy storage charge / discharge plan is generated by referring to the photovoltaic-storage-load coordinated optimization method for cloudy / rainy production days. It is worth noting that when the photovoltaic installed capacity of the small industrial park exceeds the energy storage capacity, the sunlight is good in this scenario, and the photovoltaic system generates power at full or high power. During the midday period, the dedicated transformer load P... transform Compared to the maximum rated power P transform，max There is sufficient margin, so the energy storage can be charged at full power during the midday period without causing the park's dedicated transformer to exceed its capacity. Therefore, the charging and discharging plan calculated by the photovoltaic-storage-load co-optimization method will be as follows: during peak electricity price periods (9:00-11:00, 15:00-17:00), the energy storage will be discharged from the upper limit of the state of charge to the lower limit of the state of charge in a stepped manner, and charged at full power to the upper limit of the state of charge from 11:00-13:00 and from 22:00 to 8:00 the next day.

[0064] When the installed capacity of photovoltaic power is less than the energy storage capacity, even if the photovoltaic power is at full capacity, it cannot meet the full-power charging conditions of energy storage at noon. Therefore, the charging and discharging schedule is similar to that of a cloudy or rainy day during production.

[0065] When the operating scenario is a non-production day with cloudy or rainy weather, there is still a small amount of basic electricity load with relatively low fluctuations. Therefore, in this scenario, the park is mainly powered by coordinating energy storage and photovoltaic power generation. The charging and discharging plan includes:

[0066] During peak hours (8:00-11:00 and 13:00-22:00), energy storage discharge occurs. When the energy storage charge lower limit is reached, the energy storage system goes into standby mode, and the park is powered by both photovoltaic power and the grid. The energy storage discharge power meets the following requirements:

[0067] P discharge,j =P L,j -P pv,j , where P discharge,j Let P be the discharge power in the j-th cycle. L,j P represents the total load sampling power of the micro-park during the j-th cycle; pv,j The sampled power of photovoltaic power generation in the j-th cycle;

[0068] During off-peak hours (11:00-13:00 and 22:00-8:00 the next day), the energy storage system is charged to its rated power, and then goes into standby mode when it reaches the upper limit of its state of charge.

[0069] When the operating scenario is a sunny, non-production day, the basic electricity load is low, and the photovoltaic system operates at full or high power, providing conditions for surplus electricity to be fed into the grid. Therefore, in this scenario, by coordinating energy storage and photovoltaic power generation, the operational economy of the small industrial park is maximized. The charging and discharging plan includes:

[0070] During peak hours (8:00-11:00 and 13:00-22:00), energy storage discharge occurs. When the energy storage charge lower limit is reached, the energy storage system goes into standby mode. The small industrial park is powered by both photovoltaic power and the grid. The energy storage discharge power meets the following requirements:

[0071] Among them, P discharge,j Let P be the discharge power in the j-th cycle. L,j P represents the total load sampling power of the micro-park during the j-th cycle; pv,j The sampled power of photovoltaic power generation in the j-th cycle;

[0072] When the photovoltaic power generation is greater than the total load sampling power of the park, the photovoltaic system is self-consumed, and the surplus power is fed into the grid to generate revenue. When the photovoltaic power generation is less than or equal to the total load sampling power of the park, the energy storage system is prioritized to discharge during peak electricity price periods to work with the photovoltaic system to meet the basic load of the park. When the state of charge limit is reached, the energy storage system goes into standby mode, and the park is powered by both the photovoltaic system and the grid.

[0073] During off-peak hours (11:00-13:00 and 22:00-8:00 the next day), the energy storage system is charged to its rated power, and then goes into standby mode when it reaches the upper limit of its state of charge.

[0074] S4. Perform active power regulation on the energy storage system according to the charging and discharging plan.

[0075] The control in S4 is a conventional technical means, and will not be described in detail in this embodiment.

[0076] In summary, the energy storage charging and discharging method in this embodiment collects multi-factor resource information and, by optimizing the control strategy, matches different production conditions and weather conditions in small and micro parks. This not only solves the problem of overcapacity of dedicated transformers in parks but also ensures the economic efficiency of operation in small and micro parks, making it universally applicable and scalable.

[0077] Example 2

[0078] This embodiment mainly explains and illustrates the stepped discharge method.

[0079] The energy storage system at a given total discharge E dischaege Under this premise, this embodiment uses a 15-minute cycle to gradually increase the discharge power from low to high in a step-by-step manner. Under this discharge strategy, energy storage can not only complete the predetermined discharge plan for each time period, but also reserve more electricity than the fixed power discharge during the discharge process, thus providing the power grid or park with more sufficient regulation and backup power capabilities.

[0080] Let the energy storage state of charge limit be [soc] m in,soc max The total discharge time during this period is t hours, with each cycle lasting 15 minutes, totaling 4t discharge cycles. The discharge formula satisfies:

[0081]

[0082] △P discharge =P discharge,max -p discharge,ave ,

[0083]

[0084] Where t is the discharge time, E discharge p is the planned total discharge amount of the energy storage within hour t; discharge,ave P represents the average discharge power of the stored energy within t hours. discharge，max The maximum discharge power of the stored energy; ΔP discharge This represents the difference between the maximum discharge power and the average discharge power of the energy storage. From the above equation, it can be seen that within the period T∈[0, (4t-1)], the remaining capacity of the energy storage under the stepped discharge method is greater than that under the constant power discharge method.

[0085] Example 3

[0086] Figure 2 This is a schematic diagram of the structure of an electronic device provided in Embodiment 3 of the present invention, as shown below. Figure 2 As shown, the electronic device includes a processor 210, a memory 220, an input device 230, and an output device 240; the number of processors 210 in the computer device can be one or more. Figure 2Taking a processor 210 as an example; the processor 210, memory 220, input device 230, and output device 240 in the electronic device can be connected via a bus or other means. Figure 2 Taking the example of a connection between China and Israel via a bus.

[0087] The memory 220, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules. The processor 210 executes various functional applications and data processing of the electronic device by running the software programs, instructions, and modules stored in the memory 220, thereby realizing the optical-storage-load co-optimization method of Embodiments 1 and 2 described above.

[0088] The memory 220 may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a given function; the data storage area may store data created based on terminal usage. Furthermore, the memory 220 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory, or other non-volatile solid-state storage device. In some instances, the memory 220 may further include memory remotely located relative to the processor 210, which can be connected to the electronic device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0089] Input device 230 can be used to receive input user identity information, forecast data, and weather data, etc. Output device 240 may include display devices such as a display screen.

[0090] Example 4

[0091] Embodiment 7 of the present invention also provides a storage medium containing computer-executable instructions, which can be used by a computer to execute an optical-storage-charge collaborative optimization method, the method comprising:

[0092] Receive photovoltaic power forecast data, load forecast power data, production schedules, and meteorological data;

[0093] Based on the production schedule and the corresponding meteorological data, the operation scenarios are divided;

[0094] Based on the photovoltaic predicted power, load predicted power, and actual sampled power data, generate energy storage charging and discharging power or power plans for each time period in each operating scenario.

[0095] The active power of the energy storage system is regulated according to the charging and discharging plan.

[0096] Of course, the computer-executable instructions provided in the embodiments of the present invention are not limited to the method operations described above, but can also perform related operations in the optical storage-load co-optimization method provided in any embodiment of the present invention.

[0097] Example 5

[0098] Example 5 provides a photovoltaic-storage-load collaborative optimization system for small and micro parks.

[0099] Please refer to Figure 3 As shown, a photovoltaic-storage-load co-optimization system is characterized by comprising a data acquisition and storage unit, a photovoltaic forecasting unit, a load forecasting unit, a scene identification unit, an energy storage power allocation unit, and an energy storage control unit. The data acquisition and storage unit is used to collect power data and meteorological data within a small industrial park. The photovoltaic forecasting unit and the load forecasting unit are used to forecast electricity consumption data and provide data support to the energy storage power allocation unit. The scene identification unit is used to identify operating scenes and provide support to the energy storage power allocation unit. The energy storage power allocation unit is used to execute the photovoltaic-storage-load co-optimization method described in Embodiments 1 and 2. The energy storage control unit is used to control charging and discharging according to the optimization method obtained by the energy storage power allocation unit.

[0100] The main difference between this system and existing technologies is the addition of a scene identification unit to facilitate the identification and acquisition of weather data. It also incorporates a photovoltaic-storage-load co-optimization method and a stepped discharge method in the energy storage power allocation unit to avoid overcapacity issues of dedicated transformers in industrial parks while ensuring operational economy. Under the premise of completing a given discharge plan, it can reserve a relatively sufficient amount of power during the discharge process, which not only provides a longer-lasting backup power source for small and micro industrial parks, but also provides them with more sufficient capacity to participate in grid regulation.

[0101] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause an electronic device (which may be a mobile phone, personal computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0102] For those skilled in the art, various other corresponding changes and modifications can be made based on the technical solutions and concepts described above, and all such changes and modifications should fall within the protection scope of the claims of this invention.

Claims

1. A method for coordinated optimization of photovoltaic storage and load capacity, characterized in that, Includes the following steps: Receive photovoltaic power forecast, load forecast power data, actual sampled power data, production schedule and meteorological data; Based on the production schedule and the corresponding meteorological data, the operation scenarios are divided into four categories: production day rainy day operation scenario, production day sunny day operation scenario, non-production day rainy day operation scenario, and non-production day sunny day operation scenario. Based on the defined operating scenarios, and combined with the photovoltaic predicted power, load predicted power, and actual sampled power data, a pre-defined photovoltaic-storage-load collaborative optimization method is used to generate energy storage charging and discharging power or power plans for different electricity price periods under each operating scenario. When the operating scenario is a cloudy or rainy production day, the charging and discharging plan generated by the preset photovoltaic-energy storage-load collaborative optimization method includes: During the peak period from 9:00 to 11:00, energy storage is used for stepped discharge optimization to ensure the discharge power meets the following requirements: in, This represents two time slots: 11:00-12:00 and 12:00-13:

00. Rated power of dedicated transformers for small and micro industrial parks; This represents the total discharge of the energy storage system within the specified time period. For the first Predicted total load and electricity consumption for small and micro industrial parks at any time; For the first Real-time photovoltaic ultra-short-term power generation forecast; This represents the upper limit of the state of charge of the energy storage system. This is the limit of the state of charge of the energy storage system; This refers to the rated capacity of the energy storage system. Energy storage charging is performed during the off-peak hours of 11:00-13:00, with the charging power meeting the following requirements: ， in, For the first One cycle of energy storage charging power; For the first Total load sampling power of small and micro parks in each cycle; For the first Photovoltaic power generation sampling power per cycle; During the peak period from 13:00 to 15:00, the system automatically checks the energy storage charge status. If the charge status has reached the upper limit, it will standby. Otherwise, it will charge to the upper limit of the energy storage system charge status and then standby. During the peak period from 15:00 to 17:00, it will perform stepped discharge to the lower limit of the energy storage system charge status. During the off-peak period from 22:00 to 8:00 the next day, it will perform low-power charging to the upper limit of the energy storage system charge status. During other periods, the energy storage system will remain in standby mode. When the operating scenario is a non-production day (rainy day), the charging and discharging plan generated by the preset photovoltaic-energy storage-load collaborative optimization method includes: During peak hours (8:00-11:00 and 13:00-22:00), energy storage discharge occurs. When the energy storage charge lower limit is reached, the energy storage system goes into standby mode, and the park is powered by both photovoltaic power and the grid. The discharge power meets the following requirements: ,in, For the first Discharge power per cycle, For the first Total load sampling power of small and micro parks in each cycle; For the first Photovoltaic power generation sampling power per cycle; During off-peak hours of 11:00-13:00 and 22:00-8:00 the next day, the energy storage system is charged to its rated power, and then put into standby mode when the energy storage system reaches its maximum state of charge. When the operating scenario is a sunny, non-production day, the charging and discharging plan generated by the preset photovoltaic-storage-load co-optimization method includes: During peak hours (8:00-11:00 and 13:00-22:00), energy storage discharge occurs. When the energy storage system reaches its state of charge limit, it goes into standby mode. The small industrial park is powered by both photovoltaic power and the grid. The energy storage discharge power meets the following requirements: ,in, For the first Discharge power per cycle, For the first Total load sampling power of small and micro parks in each cycle; For the first Photovoltaic power generation sampling power per cycle; During off-peak hours of 11:00-13:00 and 22:00-8:00 the next day, the energy storage system is charged to its rated power, and then put into standby mode when the energy storage system reaches its maximum state of charge. The active power of the energy storage system is regulated according to the charging and discharging plan.

2. The photoelectric storage-charge co-optimization method as described in claim 1, characterized in that, When the operating scenario is a sunny production day, the charging and discharging plan generated by the preset photovoltaic-energy storage collaborative optimization method includes: The same photovoltaic-storage-load co-optimization method as that used on cloudy or rainy days during production is employed to generate charging and discharging plans.

3. The photoelectric storage-charge co-optimization method as described in claim 1 or 2, characterized in that, The stepped discharge is a periodic discharge, in which the discharge power is increased from low to high in a fixed step-size manner, and the discharge satisfies the following: ， ， ， in, Discharge time, For energy storage The planned total discharge amount given within one hour; For energy storage Average discharge power per hour; This represents the maximum discharge power of the stored energy. This is the difference between the maximum discharge power and the average discharge power of the energy storage.

4. An electronic device comprising a processor, a storage medium, and a computer program, wherein the computer program is stored in the storage medium, characterized in that, When the computer program is executed by the processor, it implements the optical storage-load co-optimization method according to any one of claims 1 to 3.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the optical storage-load co-optimization method according to any one of claims 1 to 3.

6. A photovoltaic-storage-load co-optimization system, characterized in that, It includes a data acquisition and storage unit, a photovoltaic forecasting unit, a load forecasting unit, a scene identification unit, an energy storage power allocation unit, and an energy storage control unit. The data acquisition and storage unit is used to collect power data and meteorological data within the small industrial park. The photovoltaic forecasting unit and the load forecasting unit are used to forecast power generation and consumption data and provide data support for the energy storage power allocation unit. The scene identification unit is used to identify the current operating scene and provide support for the energy storage power allocation unit. The energy storage power allocation unit is used to execute the photovoltaic-storage-load collaborative optimization method according to any one of claims 1 to 3. The energy storage control unit is used to control charging and discharging according to the optimization method obtained by the energy storage power allocation unit.

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