Virtual power plant distributed energy aggregation scheduling method and system

Abstract

This invention relates to a virtual power plant distributed energy aggregation and scheduling method and system, comprising the following steps: First, protocol adaptation and data collection are performed on various distributed energy resources through a unified resource access platform to obtain resource access data. Second, attribute extraction and filing are performed on each distributed energy resource based on the resource access data to obtain resource profile files. Third, the operating status and potential prediction of each distributed energy resource are performed in real time based on the resource profile files to obtain resource adjustable potential data. Fourth, based on the resource adjustable potential data and a preset electricity market price and grid dispatching requirements, a collaborative scheduling strategy is generated and instructions are issued for each distributed energy resource to obtain resource scheduling instructions. This invention solves the technical problem that traditional technologies neglect the actual adjustment needs of the grid at different times and fail to achieve complementary advantages and collaborative optimization among resources, resulting in low scheduling efficiency.

Description

Technical Field

[0001] This invention relates to the field of virtual power plant technology, and in particular to a method and system for distributed energy aggregation and scheduling in a virtual power plant. Background Technology

[0002] Virtual power plants currently face numerous challenges in actual operation. Distributed equipment from different vendors uses their own closed communication protocols and data formats, leading to technical barriers to resource access, severe data silos, and making it difficult for the platform to fully grasp the real-time status and adjustment capabilities of resources. Many virtual power plants lack in-depth resource characteristic mining and continuous tracking mechanisms, making it impossible to accurately assess the response characteristics, reliability levels, and real-time adjustability potential of each resource, directly hindering the scientific nature of dispatch decisions. In the dispatching process, traditional methods often simply and crudely allocate dispatching instructions according to capacity ratios, failing to fully consider the price fluctuations in the electricity market, neglecting the actual adjustment needs of the grid at different times, and failing to achieve complementary advantages and synergistic optimization among resources, resulting in low dispatching efficiency, poor economic benefits, and insufficient resource utilization. Summary of the Invention

[0003] The main technical problem addressed in this application is to provide a virtual power plant distributed energy aggregation and dispatch method and system, which solves the technical problem that traditional technologies neglect the actual adjustment needs of the power grid at different times and fail to achieve complementary advantages and synergistic optimization among resources, resulting in low dispatch efficiency.

[0004] To address the aforementioned technical problems, this application employs a virtual power plant distributed energy aggregation and dispatch method, comprising the following steps: The unified resource access platform is used to perform protocol adaptation and data collection for each distributed energy resource to obtain resource access data. Based on the resource access data, attributes are extracted and files are created for each distributed energy resource to obtain a resource profile file. Based on the resource profile file, the operating status of each distributed energy resource is monitored in real time and its potential is predicted, so as to obtain the resource adjustable potential data. Based on the resource adjustability potential data and combined with the preset electricity market price and grid dispatch requirements, a collaborative dispatch strategy is generated and instructions are issued for each distributed energy resource to obtain resource dispatch instructions.

[0005] Furthermore, the unified resource access platform performs protocol adaptation and data collection on various distributed energy resources to obtain resource access data, including: The heterogeneous communication protocols used by various distributed energy resources are parsed to obtain the format specifications and data structures of each protocol. Based on the format specifications and data structures of each protocol, the preset unified communication protocol template is adapted and adjusted to obtain the adapted unified communication protocol. Based on the adapted unified communication protocol, the real-time operation data of each distributed energy resource is collected periodically through the unified resource access platform to obtain raw collected data. The raw collected data is then integrated and classified, and stored according to resource type and time dimension to obtain resource access data.

[0006] Furthermore, the process of extracting and archiving attributes of each distributed energy resource based on the resource access data to obtain a resource profile file includes: The real-time operating data in the resource access data is parsed to obtain the operating parameters of each distributed energy resource, and the operating parameters are classified and statistically analyzed according to resource type to obtain classified operating parameters; Based on the classification and operation parameters, each distributed energy resource is labeled to obtain resource feature tags. The resource feature tags are then integrated with the corresponding resource access data to obtain a resource profile file.

[0007] Furthermore, the real-time monitoring and potential prediction of the operational status of each distributed energy resource based on the resource profile file, to obtain resource adjustability potential data, includes: The real-time operating parameters in the resource profile are dynamically tracked to obtain the dynamic operating parameter sequence of each distributed energy resource at different times, and the fluctuation analysis of the dynamic operating parameter sequence is performed to calculate the fluctuation amplitude of each dynamic operating parameter sequence in different time periods. Based on the fluctuation range and combined with the historical operating data of each distributed energy resource, the adjustability of each distributed energy resource under the current operating state is evaluated to obtain resource adjustability potential data.

[0008] Furthermore, the generation and issuance of collaborative scheduling strategies for various distributed energy resources based on the resource adjustability potential data combined with preset electricity market prices and grid dispatching requirements, resulting in resource dispatching instructions, includes: By integrating and analyzing the adjustable power range and adjustable time interval in the resource adjustable potential data, a comprehensive index of the adjustable capacity of each distributed energy resource is obtained. The electricity market price is divided into different time periods to obtain time-of-use price data, and the power grid dispatch demand is analyzed to determine the dispatch power demand and dispatch time requirements for each time period. Based on the aforementioned comprehensive adjustable capacity index, time-of-use price data, and dispatch power demand and dispatch time requirements, a preliminary matching of distributed energy resources is performed to form a preliminary matching resource set. Based on the comprehensive index of the adjustability of each resource in the preliminary matching resource set and the time-of-use price data, the cost-benefit ratio of each distributed energy resource participating in the scheduling at different time periods is calculated. The resources in the initial matching resource set are sorted in descending order of cost-effectiveness. Based on the urgency of the power grid dispatching needs, a collaborative dispatching strategy is planned for the sorted resources to determine the dispatching power allocation and dispatching order of each resource in different time periods, thus obtaining a collaborative dispatching strategy scheme. Based on the aforementioned collaborative scheduling strategy, scheduling instructions for each distributed energy resource are generated.

[0009] Furthermore, based on the aforementioned collaborative scheduling strategy, scheduling instructions for each distributed energy resource are generated, including: Based on the aforementioned collaborative scheduling strategy, the scheduling power allocation and scheduling order of each distributed energy resource are converted into instruction codes to obtain the initial scheduling instruction code. The initial scheduling instruction code is processed for communication protocol adaptation. According to the unified communication protocol adapted to each distributed energy resource, the initial scheduling instruction code is converted into a scheduling instruction that conforms to the corresponding communication protocol format.

[0010] Furthermore, based on the aforementioned collaborative scheduling strategy, the scheduling power allocation and scheduling order of each distributed energy resource are converted into instruction codes to obtain initial scheduling instruction codes, including: The scheduling power allocation data in the cooperative scheduling strategy scheme is numerically quantized, and different power values ​​are converted into corresponding power code values ​​according to preset quantization rules to obtain power code data. The scheduling sequence data in the aforementioned collaborative scheduling strategy scheme is processed by serial number labeling, and a unique serial number is assigned to each distributed energy resource according to the order of resource scheduling to obtain sequential coded data; Based on the power-coded data and sequence-coded data, the power-coded values ​​and sequence-coded values ​​are integrated and arranged according to a preset instruction encoding format rule to obtain the initial scheduling instruction encoding, wherein... The power-coded values ​​are placed in the first half of the initial scheduling instruction code, and the sequential-coded values ​​are placed in the second half of the initial scheduling instruction code.

[0011] The present invention also provides a virtual power plant distributed energy aggregation and dispatch system, comprising: The data acquisition module is used to perform protocol adaptation and data acquisition on various distributed energy resources through a unified resource access platform to obtain resource access data. The filing module is used to extract attributes and file files for each distributed energy resource based on the resource access data, so as to obtain a resource profile file. The prediction module is used to monitor the operating status and predict the potential of each distributed energy resource in real time based on the resource profile file, and obtain resource adjustable potential data. The distribution module is used to generate and distribute collaborative scheduling strategies for each distributed energy resource based on the resource adjustability potential data and the preset electricity market price and grid dispatch requirements, thereby obtaining resource scheduling instructions.

[0012] The above solution uses a unified resource access platform to perform protocol adaptation and data collection for each distributed energy resource, obtaining resource access data. Based on the resource access data, attributes are extracted and files are created for each distributed energy resource, resulting in a resource profile file. Based on the resource profile file, the operating status of each distributed energy resource is monitored in real time and its potential is predicted, resulting in resource adjustable potential data. Based on the resource adjustable potential data and a preset electricity market price and grid dispatching requirements, a collaborative dispatching strategy is generated and instructions are issued for each distributed energy resource, resulting in resource dispatching instructions. This solution solves the technical problems of traditional technologies that neglect the actual adjustment needs of the grid at different times and fail to achieve complementary advantages and collaborative optimization among resources, resulting in low dispatching efficiency. It achieves dual optimization of economy and security, enabling the priority use of lower-cost resources to obtain higher returns during peak electricity price periods, while also meeting the actual adjustment needs of the grid at different times. It overcomes the shortcomings of traditional simple allocation methods, such as poor economic efficiency and low response accuracy, and maximizes the value of resources. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is a schematic diagram of the steps of a virtual power plant distributed energy aggregation and scheduling method in one embodiment of the present invention; Figure 2 This is a structural block diagram of a virtual power plant distributed energy aggregation and dispatch system according to an embodiment of the present invention; Figure 3 This is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0015] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0016] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0017] Specifically, the virtual power plant distributed energy aggregation and scheduling method of this embodiment includes the following steps: like Figure 1 As shown, Figure 1 This invention provides a method for distributed energy aggregation and scheduling of virtual power plants, comprising the following steps: Step S1: Through the unified resource access platform, protocol adaptation and data collection are performed on each distributed energy resource to obtain resource access data.

[0018] Specifically, the unified resource access platform first addresses the issue of inconsistent communication protocols between different devices. This platform incorporates parsing modules for various mainstream communication protocols, such as Modbus TCP, IEC 104, and MQTT. When a photovoltaic inverter or energy storage system connects, the platform automatically matches the corresponding parser based on the protocol type reported by the device, converting the device's proprietary data format into a standard format that the platform can recognize. In other words, the protocol adaptation step expands upon the "unified resource access platform" element in the upper-level solution. The platform automatically identifies protocols using a pre-configured protocol template library. If a charging pile uses a non-standard protocol, technicians can manually add new parsing rules using the protocol configuration tool, defining the starting position and data type of each data field. The data acquisition phase follows immediately after protocol adaptation. The platform sends query commands to each distributed energy resource according to the preset acquisition frequency. Energy storage devices may report SOC and charging / discharging power every 5 seconds, while photovoltaics report power generation and string voltage every minute. After the raw data is parsed by the protocol, it is extracted to form structured data records containing fields such as timestamp, device ID, and operating parameters. This is the actual content of the resource access data. All subsequent analysis and scheduling are based on this real-time acquired data.

[0019] Step S2: Based on the resource access data, extract attributes and create files for each distributed energy resource to obtain a resource profile file.

[0020] Specifically, attribute extraction is the implementation process of the "resource profile file" element in the higher-level solution. After the system obtains the resource access data, it first processes static and dynamic parameters separately. Static parameters include basic information such as the device's rated capacity, installation location, and grid connection time, which can be directly read from the registration message reported when the device is initially connected. For example, if the rated capacity of a certain energy storage system is 500kWh, this value will be permanently recorded in the basic information field of the file. The extraction of dynamic parameters is more complex and requires statistical analysis of historical operating data. Taking charging piles as an example, the system will statistically analyze the charging power curves for each time period of the day over the past month, calculate parameters such as average charging time and typical load characteristics. If it is found that the usage frequency of a certain pile is highest from 6 pm to 9 pm every day, the characteristics of this time period will be written into the attribute field. In the filing stage, all the extracted attributes are organized according to a preset template. Each distributed energy resource corresponds to an independent file file. The file contains not only numerical parameters but also information such as the device's response speed level, historical fault records, and maintenance cycle. These contents together constitute the complete content of the resource profile file, which will be consulted for subsequent monitoring and scheduling.

[0021] Step S3: Based on the resource profile file, the operating status of each distributed energy resource is monitored in real time and the potential is predicted to obtain resource adjustable potential data.

[0022] Specifically, the real-time monitoring step is a detailed expansion of the process of acquiring "resource adjustable potential data" from the higher-level solution. The system first retrieves the rated capacity and normal operating parameter range of the equipment recorded in the resource profile file, and then compares the newly collected real-time data with these benchmark values. Taking a 500kWh energy storage system as an example, the file states that its normal SOC range is 20% to 90%. Now, the real-time data shows that the SOC has reached 85%, and the system immediately determines that the device currently has only 25kWh of charging capacity left, while it can still release about 325kWh of electricity. The monitoring process does not only look at a single parameter, but also cross-verifies the logical relationship of multiple data. For example, a photovoltaic inverter reports a current power of 150kW, but the irradiance sensor data for the same period is very low. According to the power generation efficiency curve of this inverter in the file, this power value is obviously too high, and the system will mark this data as abnormal and temporarily determine that the adjustable potential of this device is unreliable.

[0023] Potential prediction is far more complex than simple monitoring. It not only looks at the current state but also anticipates the future adjustment capacity the equipment can provide over a period of time. The prediction module reads historical operating characteristics from the resource profile file and combines them with weather forecast data and load prediction results to make calculations. Taking photovoltaics as an example, the profile records the average hourly output curve of this power station during sunny days over the past three months. If the weather interface indicates that it will be sunny from 2 pm to 4 pm tomorrow, the system will estimate that the photovoltaic power generation during this period will be around 300kW based on the historical curve, and this predicted value will be written into the potential data. The prediction logic for energy storage devices is slightly different. In addition to looking at the SOC change trend, it also considers the impact of charge-discharge cycle count on battery life. If the profile shows that a certain energy storage device has been used for two years and the current cycle count is close to 70% of its design life, the system will discount the device when calculating the adjustable potential. Although it can theoretically release 300kWh, considering battery protection, the actual amount that can be deployed may only be 200kWh.

[0024] The potential prediction of charging piles relies heavily on user behavior patterns. Records show that a particular pile is most frequently used between 6 PM and 9 PM daily. If it's currently 5 PM, the system predicts that the pile will likely be occupied in an hour. Even though it's currently idle, the adjustable potential data will mark it as "not adjustable in the short term" to prevent scheduling commands from failing because the device has already entered charging mode. All these monitoring results and prediction data are ultimately compiled into a table, with one row corresponding to each distributed energy resource. This table includes key fields such as current adjustable capacity, predicted adjustable duration, and reliability level. This is the actual content of the resource's adjustable potential data, and the scheduling module directly reads this table to formulate strategies.

[0025] Step S4: Based on the resource adjustability potential data and combined with the preset electricity market price and grid dispatch requirements, a collaborative dispatch strategy is generated and instructions are issued for each distributed energy resource to obtain resource dispatch instructions.

[0026] Specifically, the collaborative scheduling strategy generation is a detailed implementation of the "resource scheduling instruction" formation mechanism in the higher-level scheme. The system first retrieves the time-of-use electricity price curve for the next 24 hours from the electricity market interface, and simultaneously receives the load demand plan issued by the power grid dispatch center. Assuming that the electricity price reaches its peak of 0.8 yuan / kWh at 2 pm tomorrow and the power grid requires the virtual power plant to provide an upward adjustment capacity of 200kW, the strategy generation module reads the table of resource adjustable potential data and finds that the 500kWh energy storage system can discharge 200kWh, the photovoltaic power station is predicted to output 300kW, and several charging piles are marked as non-adjustable. The next step is the calculation process: the energy storage discharge cost is calculated based on the electricity loss of 0.05 yuan per kilowatt-hour recorded in the file, and the photovoltaic power generation has almost zero cost but unstable output. The system ran a simple optimization algorithm, prioritizing the 150kW output of the photovoltaic system and allowing energy storage to fill the remaining 50kW gap. This satisfied the grid's 200kW demand while minimizing costs. The calculations generated two instruction records: one to limit the photovoltaic inverter's output to 150kW, and the other to instruct the energy storage to discharge at 50kW for one hour. During the instruction issuance phase, these records were returned via the unified resource access platform. The platform then converted these records into control messages of the corresponding format based on the device's protocol type and sent them out. These messages constitute the final form of the resource scheduling instructions.

[0027] In a specific embodiment, the step of performing protocol adaptation and data collection on various distributed energy resources through a unified resource access platform to obtain resource access data includes: The heterogeneous communication protocols used by various distributed energy resources are parsed to obtain the format specifications and data structures of each protocol. Based on the format specifications and data structures of each protocol, the preset unified communication protocol template is adapted and adjusted to obtain the adapted unified communication protocol. Based on the adapted unified communication protocol, the real-time operation data of each distributed energy resource is collected periodically through the unified resource access platform to obtain raw collected data. The raw collected data is then integrated and classified, and stored according to resource type and time dimension to obtain resource access data.

[0028] Specifically, this lower-level solution is a detailed implementation of the "protocol adaptation and data acquisition" stage in the upper-level solution. The parsing operation first requires obtaining the device's protocol documentation. Technicians then break down the message structures of heterogeneous communication protocols such as Modbus TCP and IEC 104. For example, in the Modbus protocol, function code 03 indicates reading the holding register, and data starts from the 7th byte, with each two bytes forming a register value. These rules constitute the protocol's format specification. The data structure part needs to clarify what parameters each register stores. For instance, address 0x0001 might correspond to active power, and 0x0002 to reactive power. Organizing these mapping relationships yields the format specifications and data structures for each protocol.

[0029] The adaptation and adjustment operation targets a preset unified communication protocol template. This template is essentially a standard data frame in JSON format, defining fixed fields such as "Device ID," "Timestamp," "Active Power," and "Reactive Power." The system uses the previously compiled protocol specifications to map the value of Modbus register 0x0001 to the "Active Power" field of the template. A specific information body object in IEC 104 is also mapped to the same field. This process is called adaptation and adjustment. After adjustment, regardless of the original device's protocol, it can be converted to a unified format; this is the adapted unified communication protocol.

[0030] The timed data collection process follows the adapted unified communication protocol. The platform sends a query to the 500kWh energy storage system every 5 seconds. The Modbus message returned by the device is converted into JSON data after protocol transformation, containing information such as SOC 85%, charging power 0kW, and discharging power 0kW. This is the raw collected data. The integration and classification process arranges the data from the same device at different times in chronological order, and stores the data from different devices according to energy storage, photovoltaics, and charging piles. The storage structure uses a time-series database, with each record carrying a resource type tag and a timestamp index. After processing, this becomes the resource access data. Subsequent queries for the operational status of a specific type of device at a specific time can be retrieved directly by index.

[0031] In a specific embodiment, the step of extracting attributes and creating files for each distributed energy resource based on the resource access data to obtain a resource profile file includes: The real-time operating data in the resource access data is parsed to obtain the operating parameters of each distributed energy resource, and the operating parameters are classified and statistically analyzed according to resource type to obtain classified operating parameters; Based on the classification and operation parameters, each distributed energy resource is labeled to obtain resource feature tags. The resource feature tags are then integrated with the corresponding resource access data to obtain a resource profile file.

[0032] Specifically, this lower-level solution is a further refinement of the "attribute extraction and archiving" process in the upper-level solution. The parsing operation directly reads records from the time-series database of resource access data. Taking the 500kWh energy storage system as an example, the system extracts values ​​for fields such as SOC, charging and discharging power, battery temperature, and cycle count; these are the operating parameters. The classification and statistics stage extracts the operating parameters of all energy storage devices for separate statistical analysis. It calculates that the average SOC over the past week was 68%, the maximum charging power reached 450kW, and the average discharging power was around 200kW. For photovoltaic equipment, it collects parameters such as daily power generation, peak power occurrence time, and inverter efficiency. For charging piles, it collects data on daily charging frequency, average charging time, and peak load periods. After processing these data separately according to resource type, the resulting categorized operating parameters are obtained: one set of data for energy storage, another set for photovoltaics, and yet another set for charging piles.

[0033] The labeling process involves assigning characteristic tags to each device based on its operational parameters. Taking energy storage as an example, if the system detects that a device's average SOC remains at 68%, indicating frequent use, it's labeled "High-Frequency Use." If the cycle count has exceeded 70% of its design life, a "Late Life" tag is added. A maximum charging power of 450kW, close to the 0.9C charging rate of a 500kWh rated capacity, is labeled "Strong Fast Charging Capability." For photovoltaic systems, if statistics show that a power station's output is most stable between 10 AM and 3 PM daily, with fluctuations below 5%, it's labeled "Stable Output." If the inverter efficiency drops from 98% to 95% in the last three months, it's labeled "Efficiency Decline," requiring maintenance. For charging piles, if the system detects the highest usage frequency between 6 PM and 9 PM, it's directly labeled "Evening Peak Load." These tags are resource characteristic tags.

[0034] The integration process binds resource feature tags and corresponding resource access data together to create a profile file. The file begins with basic equipment information, including static content such as equipment ID, installation location, and rated parameters. The middle section stores the resource feature tags that were just added, and the last part is a statistical summary of historical operating data. For example, the energy storage system's profile would specify "Equipment No. ES001, Capacity 500kWh, 720 days of operation, 3500 cycles, Tags: High-frequency call / Late lifespan / Strong fast charging capability, Average SOC 68%". This complete profile file is the resource profile file, and the scheduling module can quickly understand the equipment status by checking this file.

[0035] In a specific embodiment, the step of real-time monitoring and potential prediction of the operating status of each distributed energy resource based on the resource profile file to obtain resource adjustability potential data includes: The real-time operating parameters in the resource profile are dynamically tracked to obtain the dynamic operating parameter sequence of each distributed energy resource at different times, and the fluctuation analysis of the dynamic operating parameter sequence is performed to calculate the fluctuation amplitude of each dynamic operating parameter sequence in different time periods. Based on the fluctuation range and combined with the historical operating data of each distributed energy resource, the adjustability of each distributed energy resource under the current operating state is evaluated to obtain resource adjustability potential data.

[0036] Specifically, this lower-level solution further breaks down the "real-time monitoring and potential prediction" operations in the higher-level solution. The dynamic tracking step reads real-time operating parameters from the resource profile file. The system records the SOC value of the 500kWh energy storage every 5 seconds, collecting 7200 data points from 8:00 AM to 5:00 PM. These SOC values ​​arranged by time form the dynamic operating parameter sequence. The output power of the photovoltaic power station is also recorded every minute, accumulating 1440 data points per day to form another sequence. The load power of the charging piles is processed similarly, with different devices forming corresponding parameter sequences according to their respective collection frequencies.

[0037] The fluctuation analysis process uses these dynamic operating parameter sequences to calculate the fluctuation amplitude. Specifically, the day is divided into several time periods, such as 8 AM to 12 PM and 1 PM to 5 PM. Within each time period, the maximum and minimum values ​​of the parameters are calculated as a difference. For example, the energy storage SOC drops from 82% to 76% in the morning, resulting in a fluctuation amplitude of 6 percentage points; in the afternoon, it rises from 76% to 85%, resulting in a fluctuation amplitude of 9 percentage points. Photovoltaic power fluctuates even more in the morning, reaching a maximum of 300kW and a minimum of 150kW, a range of 150kW. In the afternoon, with increased cloud cover, the fluctuation amplitude decreases to 100kW. These calculations represent the fluctuation amplitude for each time period.

[0038] The assessment combines fluctuation range and historical operating data to determine adjustability. The system retrieves historical energy storage data from the archives and finds that the average daily SOC fluctuation of this device over the past month was 15 percentage points. This morning's fluctuation of only 6 percentage points indicates low demand. Combined with the current SOC of 85%, it is calculated that it can discharge another 300kWh or charge 75kWh. This capacity range is then recorded in the resource adjustability potential data. On the photovoltaic side, a 150kW fluctuation was observed this morning. The archives show that typical fluctuations on sunny days are within 100kW. The current fluctuation exceeds this, indicating unstable weather. Therefore, the system discounts the photovoltaic's adjustability potential; although the current output is 300kW, only 200kW is marked as reliable callable capacity. This results in resource adjustability potential data that better reflects the actual situation.

[0039] In a specific embodiment, the step of generating and issuing instructions for coordinated scheduling strategies of various distributed energy resources based on the resource adjustability potential data combined with preset electricity market prices and grid dispatching requirements, to obtain resource scheduling instructions, includes: By integrating and analyzing the adjustable power range and adjustable time interval in the resource adjustable potential data, a comprehensive index of the adjustable capacity of each distributed energy resource is obtained. The electricity market price is divided into different time periods to obtain time-of-use price data, and the power grid dispatch demand is analyzed to determine the dispatch power demand and dispatch time requirements for each time period. Based on the aforementioned comprehensive adjustable capacity index, time-of-use price data, and dispatch power demand and dispatch time requirements, a preliminary matching of distributed energy resources is performed to form a preliminary matching resource set. Based on the comprehensive index of the adjustability of each resource in the preliminary matching resource set and the time-of-use price data, the cost-benefit ratio of each distributed energy resource participating in the scheduling at different time periods is calculated. The resources in the initial matching resource set are sorted in descending order of cost-effectiveness. Based on the urgency of the power grid dispatching needs, a collaborative dispatching strategy is planned for the sorted resources to determine the dispatching power allocation and dispatching order of each resource in different time periods, thus obtaining a collaborative dispatching strategy scheme. Based on the aforementioned collaborative scheduling strategy, scheduling instructions for each distributed energy resource are generated.

[0040] Specifically, this lower-level solution is a detailed expansion of the "coordinated scheduling strategy generation and command issuance" stage in the upper-level solution. The integrated analysis operation first reads the adjustable potential data of resources. The adjustable power range of the 500kWh energy storage system is -300kW to 75kW (negative values ​​indicate discharging, positive values ​​indicate charging), and the adjustable time interval is 24 hours a day. The adjustable power range of the photovoltaic power station is 0 to 200kW (output can only be reduced, not increased), and the time interval is limited to the period from 8 am to 6 pm when there is sunlight. The system merges and processes this information, assigning a comprehensive score to the energy storage system. Fast response speed and flexible time are given 85 points, while photovoltaic power is affected by weather and time is limited, receiving only 60 points. This score is the comprehensive indicator of adjustability.

[0041] The time-of-use pricing (TOU) operation divides the 24-hour electricity price curve provided by the electricity market into several segments. From 8:00 AM to 11:00 AM, the price is 0.5 yuan / kWh, considered a low point; from 11:00 AM to 2:00 PM, it's 0.65 yuan / kWh, considered a flat period; from 2:00 PM to 9:00 PM, it rises to 0.8 yuan / kWh, entering a peak period; and for the remaining time, it falls back to a deep low of 0.4 yuan / kWh. This is the TOU price data. Analysis of grid dispatch demand reveals that from 2:00 PM to 4:00 PM tomorrow, a virtual power plant is required to provide 200kW of increased capacity, which must be responded to within 5 minutes. This 200kW represents the dispatch power demand, and the 5 minutes represents the dispatch time requirement.

[0042] In the initial matching phase, resources are screened based on comprehensive adjustable capacity indicators. A 200kW energy storage system is needed between 2 PM and 4 PM. The energy storage system's response speed meets the 5-minute requirement and its power is sufficient. The 200kW reliable capacity of the photovoltaic system also meets the requirement. However, charging pile records indicate that this time period is likely to be occupied, so it is excluded. Finally, energy storage and photovoltaic are added to the initial matching resource set. When calculating the cost-benefit ratio, discharging 200kW of energy storage for two hours consumes 400kWh of electricity. Based on the recorded electricity loss of 0.05 yuan per kWh, the cost is 20 yuan. During this period, with an electricity price of 0.8 yuan / kWh, the energy can be sold for 320 yuan, resulting in a profit of 300 yuan. The calculated cost-benefit ratio is 15:1. Reducing photovoltaic output by 200kW is equivalent to selling less electricity, with a loss also calculated at 0.8 yuan / kWh, totaling 320 yuan. However, the photovoltaic power generation cost itself is almost zero, making this cost-benefit ratio difficult to calculate. Therefore, the system treats it as opportunity cost and assigns it a negative value.

[0043] The sorting operations are ranked from highest to lowest cost-effectiveness ratio, with energy storage (15:1) ranked first and photovoltaics (PV) ranked last due to its negative return. Considering the grid's requirement for a 5-minute response time, the system prioritizes energy storage in the collaborative scheduling strategy planning, allowing it to provide 150kW of output for two hours. The remaining 50kW shortfall is then filled by PV. This approach ensures both response speed and cost control, and the planning results are incorporated into the collaborative scheduling strategy scheme. In the dispatch instruction generation stage, the scheme is translated into specific parameters. An instruction of "discharge power 150kW, duration 120 minutes, start time 14:00" is issued to energy storage, and an instruction of "limited output 50kW, duration 120 minutes, start time 14:00" is issued to PV. These two instructions constitute the final resource scheduling instructions.

[0044] In a specific embodiment, based on the aforementioned collaborative scheduling strategy, scheduling instructions for each distributed energy resource are generated, including: Based on the aforementioned collaborative scheduling strategy, the scheduling power allocation and scheduling order of each distributed energy resource are converted into instruction codes to obtain the initial scheduling instruction code. The initial scheduling instruction code is processed for communication protocol adaptation. According to the unified communication protocol adapted to each distributed energy resource, the initial scheduling instruction code is converted into a scheduling instruction that conforms to the corresponding communication protocol format.

[0045] Specifically, this lower-level scheme refines the specific operation process of "generating resource scheduling instructions" in the upper-level scheme. The instruction encoding conversion stage reads parameters from the collaborative scheduling strategy scheme. The energy storage instruction "discharge power 150kW, duration 120 minutes, start time 14:00" needs to be converted into an encoding format that the machine can recognize. The system encodes the device ID as ES001, the operation type discharge corresponding code 02, the power value 150 as hexadecimal 0x0096, the duration 120 minutes converted to 7200 seconds encoded as 0x1C20, and the start time 14:00 converted to a timestamp 1735459200 according to the preset encoding rules. These parameters are combined into a string "ES001-02-0096-1C20-1735459200", which is the initial scheduling instruction code. The photovoltaic settings, including "limited output of 50kW, duration of 120 minutes, and start-up time of 14:00," are also coded as "PV003-05-0032-1C20-1735459200" according to the same rules. The code 05 for the limited output operation is different from the code 02 for the discharge. The power of 50kW is coded as 0x0032, while other time parameters remain the same.

[0046] The communication protocol adaptation process involves translating the initial scheduling instructions into a message format that the device can receive. The energy storage system uses the Modbus TCP protocol. The adaptation program calls the previously configured Modbus write multi-register function code, writes the power value 0x0096 to register address 0x1000, the duration 0x1C20 to 0x1001, the start timestamp (high and low bits) to 0x1002 and 0x1003, and the operation code 02 to 0x1004 to trigger execution. These register addresses and function codes are combined to form a complete Modbus message. The photovoltaic inverter uses the IEC 104 protocol. The adapter program converts the initial code "PV003-05-0032-1C20-1735459200" into an IEC 104 control command frame. The type identifier is filled with C_SC_NA_1 to indicate single-point control. The information body address points to the active power control object of the inverter. The limit value 0x0032 and the time parameter are arranged in the byte order specified by the IEC 104 protocol. The encapsulation is completed by adding a check bit and a frame tail identifier. This encapsulated IEC 104 message is a scheduling instruction that conforms to the corresponding communication protocol format. The platform sends these two messages to the energy storage and photovoltaic equipment respectively. After receiving them, the equipment executes the corresponding operation according to the instruction.

[0047] In a specific embodiment, the step of converting the scheduling power allocation and scheduling order of each distributed energy resource into an initial scheduling instruction code based on the cooperative scheduling strategy scheme includes: The scheduling power allocation data in the cooperative scheduling strategy scheme is numerically quantized, and different power values ​​are converted into corresponding power code values ​​according to preset quantization rules to obtain power code data. The scheduling sequence data in the aforementioned collaborative scheduling strategy scheme is processed by serial number labeling, and a unique serial number is assigned to each distributed energy resource according to the order of resource scheduling to obtain sequential coded data; Based on the power-coded data and sequence-coded data, the power-coded values ​​and sequence-coded values ​​are integrated and arranged according to a preset instruction encoding format rule to obtain the initial scheduling instruction encoding, wherein... The power-coded values ​​are placed in the first half of the initial scheduling instruction code, and the sequential-coded values ​​are placed in the second half of the initial scheduling instruction code.

[0048] Specifically, this lower-level scheme further breaks down the operational details of the "instruction encoding conversion" in the upper-level scheme. The numerical quantization processing stage focuses on the power allocation data in the coordinated scheduling strategy. The preset quantization rules stipulate that power values ​​are encoded with an accuracy of 0.1kW, with negative values ​​representing discharge and positive values ​​representing charging. For example, the instruction from the energy storage system to discharge 150kW is first converted to a negative number -150 to indicate the discharge direction, then converted to -1500 units with an accuracy of 0.1kW, and finally converted to hexadecimal to obtain 0xFA24. The photovoltaic power output limit of 50kW does not involve the concept of charging and discharging, so it is directly quantized. 50 divided by 0.1 gives 500 units, which are converted to hexadecimal 0x01F4. These hexadecimal values ​​are the power encoding values. The quantization rules also include data length limits. All power codes occupy 4 bytes, with leading zeros added if necessary. 0xFA24 is expanded to 0x0000FA24, and 0x01F4 is expanded to 0x000001F4. The processed data is the power encoding data.

[0049] The sequence number labeling process is performed on the scheduling order data in the collaborative scheduling strategy. If the strategy specifies that energy storage is prioritized over photovoltaics, the system assigns sequence number 01 to energy storage and sequence number 02 to photovoltaics. These sequence numbers are converted into a fixed-format encoding. The labeling rules define the sequence number as 2 bytes: 01 is converted to hexadecimal 0x0001, 02 to 0x0002, and so on. If a third or fourth device subsequently participates in the scheduling, they are encoded as 0x0003, 0x0004, etc., ensuring that each distributed energy resource receives a unique sequence number. This is the sequential encoding value; a batch of processed sequence number data is collectively called sequential encoded data.

[0050] In the integration and arrangement phase, power code data and sequence code data must be combined according to a preset instruction encoding format rule. The format rule clearly defines that the total length of the initial scheduling instruction code is 8 bytes. The first 4 bytes contain the power code value, and the last 4 bytes contain other information. However, the last 4 bytes are further subdivided. The first 2 bytes contain the sequence code value, and the remaining 2 bytes are reserved for the timestamp or check code. In specific operation, the power code 0x0000FA24 of the energy storage is first placed in the 0th to 3rd byte positions of the encoding, and then the sequence code 0x0001 is placed in the 4th to 5th byte positions. The 6th to 7th bytes are temporarily filled with part of the timestamp information. The resulting string "0000FA24-0001-timestamp" is the initial scheduling instruction code of the energy storage device. On the photovoltaic side, the power code 0x000001F4 is placed in the first half, and the sequence code 0x0002 is placed in the corresponding position in the second half. Similarly, a timestamp is added to form the encoding structure "000001F4-0002-timestamp". Both encodings follow the rule that the first half is power and the second half contains the sequence. Subsequent protocol adaptation programs can directly read this fixed-format encoding to know which bytes represent power and which bytes represent sequence without confusion.

[0051] Please see Figure 2 , Figure 2 This is a schematic diagram of the framework of an embodiment of the virtual power plant distributed energy aggregation and dispatch system of this application. Figure 2 As shown, the virtual power plant distributed energy aggregation and dispatch system includes a data acquisition module 1, used to perform protocol adaptation and data acquisition on each distributed energy resource through a unified resource access platform to obtain resource access data; a filing module 2, used to extract attributes and file each distributed energy resource based on the resource access data to obtain resource profile files; a prediction module 3, used to monitor the operating status and predict the potential of each distributed energy resource in real time based on the resource profile files to obtain resource adjustable potential data; and a dispatch module 4, used to generate collaborative dispatch strategies and issue instructions for each distributed energy resource based on the resource adjustable potential data and preset electricity market prices and grid dispatch requirements to obtain resource dispatch instructions.

[0052] Reference Figure 3 This invention also provides a computer device whose internal structure can be as follows: Figure 3As shown, the computer device includes a processor, memory, display screen, input device, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores the data corresponding to this embodiment. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements the above-described method.

[0053] Those skilled in the art will understand that Figure 3 The structures shown are merely block diagrams of some structures related to the present invention and do not constitute a limitation on the computer devices on which the present invention is applied.

[0054] An embodiment of the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method. It is understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0055] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the present invention and embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0056] In some embodiments, the functions or modules of the apparatus provided in this disclosure can be used to perform the methods described in the above method embodiments. The specific implementation can be referred to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0057] The description of the various embodiments above tends to emphasize the differences between the various embodiments. The similarities or similarities between them can be referred to, and for the sake of brevity, they will not be repeated here.

[0058] In the several embodiments provided in this application, it should be understood that the disclosed methods and apparatus can be implemented in other ways. For example, the apparatus implementations described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms.

[0059] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0060] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0061] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods of various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0062] If the technical solution of this application involves personal information, the product using this technical solution has clearly informed the user of the personal information processing rules and obtained the user's voluntary consent before processing the personal information. If the technical solution of this application involves sensitive personal information, the product using this technical solution has obtained the user's separate consent before processing the sensitive personal information, and also meets the requirement of "express consent". For example, at personal information collection devices such as cameras, clear and prominent signs are set up to inform users that they have entered the scope of personal information collection and that personal information will be collected. If an individual voluntarily enters the collection scope, it is deemed that they have agreed to the collection of their personal information; or on the personal information processing device, with clear signs / information informing users of the personal information processing rules, authorization is obtained from the individual through pop-up information or by asking the individual to upload their personal information; wherein, the personal information processing rules may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the types of personal information processed.

Claims

1. A method for distributed energy aggregation and dispatching in a virtual power plant, characterized in that, Includes the following steps: The unified resource access platform is used to perform protocol adaptation and data collection for each distributed energy resource to obtain resource access data. Based on the resource access data, attributes are extracted and files are created for each distributed energy resource to obtain a resource profile file. Based on the resource profile file, the operating status of each distributed energy resource is monitored in real time and its potential is predicted, so as to obtain the resource adjustable potential data. Based on the resource adjustability potential data and combined with the preset electricity market price and grid dispatch requirements, a collaborative dispatch strategy is generated and instructions are issued for each distributed energy resource to obtain resource dispatch instructions.

2. The virtual power plant distributed energy aggregation and dispatch method according to claim 1, characterized in that, The process of adapting protocols and collecting data from various distributed energy resources through a unified resource access platform to obtain resource access data includes: The heterogeneous communication protocols used by various distributed energy resources are parsed to obtain the format specifications and data structures of each protocol. Based on the format specifications and data structures of each protocol, the preset unified communication protocol template is adapted and adjusted to obtain the adapted unified communication protocol. Based on the adapted unified communication protocol, the real-time operation data of each distributed energy resource is collected periodically through the unified resource access platform to obtain raw collected data. The raw collected data is then integrated and classified, and stored according to resource type and time dimension to obtain resource access data.

3. The virtual power plant distributed energy aggregation and dispatch method according to claim 1, characterized in that, The process of extracting attributes and creating files for each distributed energy resource based on the resource access data yields a resource profile file, including: The real-time operating data in the resource access data is parsed to obtain the operating parameters of each distributed energy resource, and the operating parameters are classified and statistically analyzed according to resource type to obtain classified operating parameters; Based on the classification and operation parameters, each distributed energy resource is labeled to obtain resource feature tags. The resource feature tags are then integrated with the corresponding resource access data to obtain a resource profile file.

4. The virtual power plant distributed energy aggregation and dispatch method according to claim 1, characterized in that, The real-time monitoring and potential prediction of the operational status of each distributed energy resource based on the resource profile file, to obtain resource adjustability potential data, includes: The real-time operating parameters in the resource profile are dynamically tracked to obtain the dynamic operating parameter sequence of each distributed energy resource at different times, and the fluctuation analysis of the dynamic operating parameter sequence is performed to calculate the fluctuation amplitude of each dynamic operating parameter sequence in different time periods. Based on the fluctuation range and combined with the historical operating data of each distributed energy resource, the adjustability of each distributed energy resource under the current operating state is evaluated to obtain resource adjustability potential data.

5. The virtual power plant distributed energy aggregation and dispatch method according to claim 1, characterized in that, The process of generating and issuing coordinated dispatch strategies for various distributed energy resources based on the resource adjustability potential data, combined with preset electricity market prices and grid dispatch requirements, yields resource dispatch instructions, including: By integrating and analyzing the adjustable power range and adjustable time interval in the resource adjustable potential data, a comprehensive index of the adjustable capacity of each distributed energy resource is obtained. The electricity market price is divided into different time periods to obtain time-of-use price data, and the power grid dispatch demand is analyzed to determine the dispatch power demand and dispatch time requirements for each time period. Based on the aforementioned comprehensive adjustable capacity index, time-of-use price data, and dispatch power demand and dispatch time requirements, a preliminary matching of distributed energy resources is performed to form a preliminary matching resource set. Based on the comprehensive index of the adjustability of each resource in the preliminary matching resource set and the time-of-use price data, the cost-benefit ratio of each distributed energy resource participating in the scheduling at different time periods is calculated. The resources in the initial matching resource set are sorted in descending order of cost-effectiveness. Based on the urgency of the power grid dispatching needs, a collaborative dispatching strategy is planned for the sorted resources to determine the dispatching power allocation and dispatching order of each resource in different time periods, thus obtaining a collaborative dispatching strategy scheme. Based on the aforementioned collaborative scheduling strategy, scheduling instructions for each distributed energy resource are generated.

6. The virtual power plant distributed energy aggregation and dispatch method according to claim 5, characterized in that, Based on the aforementioned collaborative scheduling strategy, scheduling instructions for each distributed energy resource are generated, including: Based on the aforementioned collaborative scheduling strategy, the scheduling power allocation and scheduling order of each distributed energy resource are converted into instruction codes to obtain the initial scheduling instruction code. The initial scheduling instruction code is processed for communication protocol adaptation. According to the unified communication protocol adapted to each distributed energy resource, the initial scheduling instruction code is converted into a scheduling instruction that conforms to the corresponding communication protocol format.

7. The virtual power plant distributed energy aggregation and dispatch method according to claim 6, characterized in that, The method based on the cooperative scheduling strategy involves converting the scheduling power allocation and scheduling order of each distributed energy resource into instruction codes to obtain initial scheduling instruction codes, including: The scheduling power allocation data in the cooperative scheduling strategy scheme is numerically quantized, and different power values ​​are converted into corresponding power code values ​​according to preset quantization rules to obtain power code data. The scheduling sequence data in the aforementioned collaborative scheduling strategy scheme is processed by serial number labeling, and a unique serial number is assigned to each distributed energy resource according to the order of resource scheduling to obtain sequential coded data; Based on the power-coded data and sequence-coded data, the power-coded values ​​and sequence-coded values ​​are integrated and arranged according to a preset instruction encoding format rule to obtain the initial scheduling instruction encoding, wherein... The power-coded values ​​are placed in the first half of the initial scheduling instruction code, and the sequential-coded values ​​are placed in the second half of the initial scheduling instruction code.

8. A virtual power plant distributed energy aggregation and dispatch system, characterized in that, include: The data acquisition module is used to perform protocol adaptation and data acquisition for various distributed energy resources through a unified resource access platform to obtain resource access data. The filing module is used to extract attributes and file each distributed energy resource based on the resource access data to obtain a resource profile file. The prediction module is used to monitor the operating status and predict the potential of each distributed energy resource in real time based on the resource profile file, and obtain resource adjustable potential data. The distribution module is used to generate and distribute collaborative scheduling strategies for each distributed energy resource based on the resource adjustability potential data and the preset electricity market price and grid dispatch requirements, thereby obtaining resource scheduling instructions.

9. A computer device, characterized in that, The device includes a memory and a processor that are coupled to each other. The memory stores program instructions, and the processor executes the program instructions to implement the virtual power plant distributed energy aggregation and scheduling method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The system stores program instructions that can be executed by a processor, the program instructions being used to implement the virtual power plant distributed energy aggregation and scheduling method according to any one of claims 1 to 7.

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