Operation control method of virtual power plant

Abstract

The invention discloses an operation control method for a virtual power plant, and relates to the technical field of virtual power plants, and the method comprises the steps: obtaining health state parameters of an energy storage system, and calculating health state indexes of the energy storage system through a health state evaluation model; calculating the life decay cost based on the health state index and the battery replacement cost, integrating the life decay cost into the operation regulation and control total cost, and generating a first charging and discharging strategy through multi-time scale optimization control by taking the minimization of the operation regulation and control total cost as a target; an access request of the energy equipment is monitored, the equipment type, the communication protocol and the capability parameters are analyzed through the equipment metadata self-description module, protocol conversion and communication configuration are completed through the protocol adaptive interface, and a protocol adaptive result is generated; adding operation parameters of new access equipment to the health state evaluation model, and extending life attenuation cost calculation to an equipment scene; and adjusting multi-time scale optimization control based on a protocol adaptation result to generate a second charging and discharging strategy.

Description

A method for operation control of a virtual power plant Technical Field

[0001] This invention relates to the field of virtual power plant technology, and in particular to a method for operating and controlling a virtual power plant. Background Technology

[0002] Virtual power plants, as systems that aggregate and coordinate the control of various distributed energy resources such as distributed generation, energy storage systems, and controllable loads through advanced information and communication technologies and software systems, play a crucial role in improving grid flexibility, promoting renewable energy consumption, and reducing grid operating costs. However, existing virtual power plant operation and control methods still face many challenges: In terms of energy storage system operation and control, existing methods often focus on short-term economic benefits while neglecting the lifespan degradation of energy storage devices. Frequent charging and discharging operations, unreasonable charging and discharging depths and rates, and operating temperatures can significantly affect the health of energy storage systems, leading to shortened lifespans and increased replacement and maintenance costs. Currently, there is a lack of a multi-objective optimization method that can comprehensively consider both device health and operating costs. With the continuous increase in the types of distributed energy devices, the heterogeneity of device access is becoming increasingly prominent. Different manufacturers and different types of devices often use different communication protocols and data formats, resulting in poor protocol compatibility, complex configuration, and low communication efficiency when devices are connected to virtual power plant systems. Existing systems typically rely on manual configuration or fixed protocol conversion rules, making it difficult to achieve plug-and-play functionality and flexible expansion of devices. Existing virtual power plant optimization control strategies are mostly focused on a single time scale, such as performing only minute-level real-time power balancing or hourly electricity price arbitrage, lacking a multi-time scale collaborative optimization mechanism from short-term to long-term. This makes it difficult for the system to ensure real-time supply and demand balance while simultaneously extending equipment lifespan and minimizing total lifecycle costs. Therefore, there is an urgent need for a virtual power plant operation control method that can comprehensively consider equipment health status, support flexible access of heterogeneous equipment, and achieve multi-time scale collaborative optimization, in order to improve the economy, reliability, and scalability of virtual power plants. Summary of the Invention

[0003] This application provides a virtual power plant operation control method that solves the problems of insufficient life management of energy storage systems, difficulty in connecting heterogeneous devices, single time scale of operation control strategy, and limited system scalability in the prior art. It achieves the technical effects of full life cycle cost optimization, plug-and-play intelligent access of equipment, multi-time scale collaborative optimization, and adaptive system expansion.

[0004] This application provides a method for the operation control of a virtual power plant, comprising: S1: acquiring health status parameters of an energy storage system, and calculating health status indicators of the energy storage system through a health status assessment model; the health status parameters include charge / discharge depth, charge / discharge rate, operating temperature, and cycle count; S2: calculating lifetime degradation cost based on health status indicators and battery replacement cost, integrating it into the total operation and control cost, and generating a first charge / discharge strategy through multi-timescale optimization control with the goal of minimizing the total operation and control cost; S3: monitoring access requests from energy devices, parsing device type, communication protocol, and capability parameters through a device metadata self-description module, and completing protocol conversion and communication configuration using a protocol adaptation interface to generate protocol adaptation results; S4: adding the operating parameters of newly accessed devices to the health status assessment model, extending lifetime degradation cost calculation to multi-device scenarios; and adjusting multi-timescale optimization control based on the protocol adaptation results to generate a second charge / discharge strategy.

[0005] Furthermore, the health status indicators are calculated using a health status assessment model: ,in, As an indicator of health status, The number of loops. For rated cycle life, This represents the deviation from the average operating temperature. The maximum allowable temperature deviation, The average depth of charge and discharge. For the maximum permissible depth of charge and discharge, For the corresponding weight coefficients, and .

[0006] Furthermore, the formula for calculating the lifetime degradation cost is as follows: ,in, Cost of lifespan degradation, As an indicator of health status, The replacement cost per unit capacity of the battery. This is the attenuation weighting coefficient. The cyclic decay coefficient represents the weight of the impact of a single cycle on maintenance costs; This represents the actual number of loops. Based on maintenance costs.

[0007] Furthermore, the multi-timescale optimization control includes a three-level collaborative optimization strategy: short-term optimization control: executed on a minute-level timescale, with the optimization objective being real-time load response and renewable energy consumption, minimizing the control cost of the current operating cycle by adjusting the charging and discharging power of the energy storage system; medium-term optimization control: executed on an hour-level timescale, with the optimization objective being peak-valley electricity price arbitrage and equipment operating status balance, formulating charging and discharging plans to extend equipment lifespan based on time-of-use electricity price forecasts and equipment health status; and long-term optimization control: executed on a day-level timescale, with the optimization objective being minimizing the entire lifecycle cost, optimizing long-term operating strategies through lifespan decay cost calculation and capacity planning.

[0008] Furthermore, the device metadata self-description module includes: a standardized metadata format that includes device type, communication protocol, capability parameters, and security authentication information; a parsing engine that verifies the metadata format, parses the semantic meaning, and maps protocol relationships; automatically broadcasting metadata when the device is connected, and completing parsing and registration; and when non-standard metadata is detected, initiating a compatibility mode and inferring missing fields using machine learning algorithms.

[0009] Furthermore, the protocol adaptive interface includes: identifying the device communication protocol type based on the protocol information provided by the device metadata self-description module, and realizing intelligent mapping and conversion between non-standard protocols and standard protocols through a two-way conversion mechanism combining rule engine and machine learning; configuring communication parameters according to device capability parameters, establishing a secure encrypted transmission channel, and optimizing communication quality and achieving fault self-recovery through data verification and performance monitoring.

[0010] Furthermore, the protocol adaptation results include: generating a protocol conversion status report containing conversion success status, efficiency indicators, and error codes; providing a standardized communication interface specification with a unified data point mapping table and communication parameter configuration; recording device capability description files of device control modes, performance parameters, and safe operation boundaries; and verification results containing communication quality assessment and control command verification data.

[0011] Furthermore, the operating parameters include basic equipment operating parameters, performance degradation monitoring parameters, environmental adaptation parameters, and safe operating boundary parameters; the basic equipment operating parameters are used to describe the real-time operating status and performance indicators of the equipment; the performance degradation monitoring parameters are used to quantify the equipment's lifespan degradation trend; the environmental adaptation parameters are used to assess the impact of the external environment on equipment operation; and the safe operating boundary parameters are used to define the equipment's safe operation thresholds and emergency handling rules.

[0012] One or more technical solutions provided in this application have at least the following technical effects or advantages: by adopting technical solutions of health status assessment and life decay cost quantification, equipment metadata self-description and protocol adaptive conversion, and multi-time scale optimization control synergy, the comprehensive technical effects of virtual power plant full life cycle cost optimization, plug-and-play access of heterogeneous equipment, multi-objective collaborative optimization and system adaptive expansion are achieved. Attached Figure Description

[0013] Figure 1 is a flowchart of a virtual power plant operation control method according to an embodiment of the present invention. Detailed Implementation

[0014] To facilitate understanding of the present invention, a more complete description of this application will be given below with reference to the accompanying drawings, which illustrate preferred embodiments of the invention. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to enable a more thorough and complete understanding of the disclosure of the present invention.

[0015] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains; the terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to limit the invention; the term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0016] Example 1: As shown in Figure 1, a method for operating and controlling a virtual power plant.

[0017] S1: Obtain the health status parameters of the energy storage system and calculate its health status indicators using a health status assessment model. Specifically, health status parameters include depth of charge / discharge, charge / discharge rate, operating temperature, and cycle count. Depth of charge / discharge refers to the ratio of the actual capacity to the rated capacity during a single charge / discharge cycle, reflecting the battery's usage intensity; the average depth of charge / discharge is used to assess the long-term impact. The charge / discharge rate represents the ratio of charge / discharge power to the rated power; excessively high rates accelerate equipment aging. The operating temperature monitors the average operating temperature of the energy storage system and calculates the temperature deviation, i.e., the difference between the actual temperature and the ideal operating temperature. The cycle count records the actual number of cycles, i.e., the cumulative number of charge / discharge cycles, and compares it with the rated cycle life to assess lifespan depletion.

[0018] The health status indicators are calculated using a health status assessment model: ,in, This is a health status indicator; the closer the value is to 1, the better the equipment's condition. The number of loops. For rated cycle life, This represents the deviation from the average operating temperature. The maximum allowable temperature deviation, The average depth of charge and discharge. For the maximum permissible depth of charge and discharge, The corresponding weighting coefficients are adjusted based on historical decay data, and .

[0019] S2: Calculate the lifespan degradation cost based on health status indicators and battery replacement costs, and integrate it into the total operation and control cost. With the goal of minimizing the total operation and control cost, generate the first charge / discharge strategy through multi-timescale optimization control. The formula for calculating the lifespan degradation cost is: ,in, Cost of lifespan degradation, As an indicator of health status, The replacement cost per unit capacity of the battery. This is the degradation weighting coefficient, dynamically adjusted based on battery type, operating history, and environmental factors, with a value range of [0.8, 1.2]; for example, in high-temperature environments... A higher value may be chosen to amplify the attenuation effect. The cyclic decay coefficient represents the weight of the impact of a single cycle on maintenance costs, and is calibrated using historical data. This represents the actual number of cycles, recording the cumulative number of charge-discharge cycles of the energy storage system. This is the baseline maintenance cost, including the cost of routine inspections and component replacements.

[0020] Will As an additional cost added to the total cost function, the total cost minimization objective is expressed as: ,in, To minimize the total cost, For electricity trading costs, For real-time operation and maintenance costs.

[0021] Multi-timescale optimization control employs a three-tiered collaborative strategy, optimizing at minute, hourly, and daily timescales to ultimately generate the first charging and discharging strategy: Short-term optimization control is executed at the minute-level timescale for real-time load response and renewable energy consumption. The goal is to minimize the regulation costs of the current operating cycle, including electricity trading costs, real-time operation and maintenance costs, and penalty costs incurred due to power fluctuations. By monitoring grid load demand and renewable energy generation fluctuations in real time, the charging and discharging power of the energy storage system is dynamically adjusted. For example, charging is prioritized when renewable energy is in surplus, and discharging is performed during peak load periods to smooth out power deviations. Minute-level charging and discharging power commands are generated and used as the basic input for medium-term optimization.

[0022] Mid-term optimization control is executed on an hourly timescale to balance peak-valley electricity price arbitrage with equipment operating status. The goal is to extend the lifespan of energy storage equipment while ensuring economic benefits, by optimizing charge-discharge plans through health status indicators. Hourly charge-discharge plans are developed based on time-of-use electricity price forecasts and equipment health status assessments. For example, charging is performed during off-peak hours and discharging during peak hours, while avoiding high-rate charging and discharging to mitigate lifespan degradation. Hourly charge-discharge plans, including power allocation and equipment maintenance recommendations, are generated and passed to the long-term optimization layer.

[0023] Long-term optimization control is executed on a daily timescale to minimize total lifecycle costs. Through lifetime degradation cost calculation and capacity planning, long-term operating strategies are optimized to ensure the economics and reliability of the virtual power plant. Capacity planning and strategy adjustments are performed based on long-term load forecasts, equipment aging trends, and replacement costs. For example, the remaining lifespan of the energy storage system is assessed, and future equipment replacement or expansion plans are planned. Daily operating strategies, including capacity adjustments, maintenance scheduling, and cost budgets, are generated to guide short- to medium-term optimization.

[0024] The first charging and discharging strategy is an optimized strategy generated based on the energy storage system's state. First, it obtains the real-time health status parameters of all current energy storage systems, calls the health status assessment model, and calculates the current status of each device. Indicators. Secondly, tiered optimization is performed, on a daily basis, with the goal of minimizing the total lifecycle cost. Initial optimization is then conducted to output the results for the next few days. Budget, The system establishes safety thresholds and a preliminary outline of daily charging and discharging energy demand. Using the long-term layer output as a constraint, optimization calculations are performed hourly, with the goal of minimizing cost. This outputs a charging and discharging power plan accurate to each hour for the next 24 hours, along with the adjustable power range for each hourly segment. Based on the medium-term layer plan, optimization is performed minute-by-minute, with the goal of minimizing cost, within the adjustable range of the hourly plan. This outputs specific charging and discharging power instructions for the next few minutes to tens of minutes. The optimization results of the short-term layer are fed back to the medium-term layer, which determines whether the current hourly plan is optimal in terms of total cost. If not, subsequent plans are fine-tuned in the next optimization cycle. After optimization, the medium-term layer feeds back accumulated equipment loss data to the long-term layer, which assesses the rationality of the long-term budget. Through several rapid iterations, the three optimization objectives converge. Finally, a unified, multi-timescale integrated first charging and discharging strategy is synthesized. This strategy specifically manifests as follows: Long-term dimension: Clearly defining the overall usage intensity and cost control targets for the energy storage system in the coming days.

[0025] Mid-term dimension: A charge / discharge schedule with flexible boundaries, accurate to the hour.

[0026] Short-term dimension: A real-time control algorithm that is adjustable at the minute level based on a medium-term plan.

[0027] The hourly charge / discharge schedule of the medium-term layer is used as the direct output of the first charge / discharge strategy and delivered to the control system for execution, while the short-term and long-term optimization results serve as the basis for generating the schedule and the criteria for dynamic adjustment during operation.

[0028] The technical solutions described in the above embodiments of this application have at least the following technical effects or advantages: This application uses a health status assessment model to comprehensively and quantitatively analyze multiple parameters of the energy storage system, such as the depth of charge and discharge, charge and discharge rate, operating temperature, and number of cycles, accurately calculating health status indicators and providing a scientific basis for equipment life management; based on The indicators and battery replacement costs, through the calculation formula of life decay cost, quantify equipment loss and integrate it into the total cost of operation and control, achieving a balance between short-term economic operation and long-term equipment health, and significantly improving the ability to optimize the whole life cycle cost. Through multi-time scale optimization control, it coordinates minute-level real-time load response, hour-level peak-valley electricity price arbitrage and day-level whole life cycle cost minimization objectives, and generates a first charge and discharge strategy that takes into account real-time performance, economy and reliability. This effectively solves the problems of insufficient life management of energy storage systems and single time scale of operation strategy, and achieves a synergistic improvement in the economics of virtual power plant operation and equipment durability.

[0029] Example 2: In Example 1, the virtual power plant achieved refined operation control of a single energy storage system, but there are still problems such as poor equipment access compatibility and insufficient collaborative management of heterogeneous equipment. This example further supplements Example 1.

[0030] S3: Monitor access requests from energy devices, parse device type, communication protocol, and capability parameters through the device metadata self-description module, and complete protocol conversion and communication configuration using the protocol adaptive interface to generate protocol adaptation results; the device metadata self-description module includes: a standardized metadata format containing device type, communication protocol, capability parameters, and security authentication information; a parsing engine that verifies the metadata format, parses semantic meaning, and maps protocol relationships; automatically broadcasts metadata when a device accesses the device, and completes parsing and registration; when non-standard metadata is detected, a compatibility mode is activated and missing fields are inferred using machine learning algorithms.

[0031] Specifically, the module adopts a unified, standardized metadata format, including device type identifying the device category; communication protocol types and versions supported by the device; capability parameters describing the device's performance characteristics, including rated power, response time, adjustment accuracy, maximum charge / discharge rate, and operating voltage range; and security authentication information including digital certificates, encryption keys, access permission levels, and other security elements. All use international standards and machine-readable data structures such as JSON or XML to ensure cross-platform compatibility and scalability.

[0032] The parsing engine performs syntax and structure validation on the received metadata, checking whether required fields are complete and whether the format conforms to specifications. If validation fails, the engine returns an error code and requests retransmission. The engine uses natural language processing techniques to parse the semantic content of the metadata, such as mapping protocol fields to standard entries in the system's internal protocol library. It establishes a mapping table between device protocols and virtual power plant standard protocols, for example, converting device-specific data points into system-wide unified data model points to ensure consistency in data interaction.

[0033] When a device connects to the virtual power plant network, it automatically broadcasts its metadata packets. Upon detecting the broadcast signal, the system triggers the parsing engine to process the metadata. After successful parsing, the engine registers the device information in the virtual power plant's central database and assigns a unique device identifier. Once registration is complete, the system sends an acknowledgment signal to the device and updates its status to online.

[0034] When the parsing engine detects non-standard metadata formats or missing fields, it activates compatibility mode. Compatibility mode integrates machine learning algorithms, training models based on historical access data to infer missing fields. For example, if the device type is missing, the algorithm predicts the device type based on protocol and capability parameters; if the communication protocol version is not specified, the algorithm uses the protocol version of similar devices as a default value. All compatibility mode processing is logged for subsequent analysis and model optimization.

[0035] The protocol adaptive interface includes: identifying the device communication protocol type based on the protocol information provided by the device metadata self-description module, and realizing intelligent mapping and conversion between non-standard protocols and standard protocols through a two-way conversion mechanism combining rule engine and machine learning; configuring communication parameters according to device capability parameters, establishing a secure encrypted transmission channel, and optimizing communication quality and achieving fault self-recovery through data verification and performance monitoring.

[0036] Specifically, based on the protocol information provided by the device metadata self-description module, the protocol recognition engine automatically identifies the device's communication protocol type. A bidirectional conversion mechanism combining a rule engine and machine learning is employed. The rule engine converts data points of the device's specific protocol into data points of the virtual power plant's standard protocol based on mapping rules; the machine learning model is trained using historical conversion data to adaptively learn the mapping patterns of unknown protocols, achieving intelligent conversion. The conversion process includes data format conversion, encoding adjustment, and semantic mapping, and generates mapping logs recording the converted power and error details.

[0037] Communication configuration and secure channel establishment mechanisms ensure the efficiency and security of device communication. Communication parameters, including baud rate, packet size, timeout, and transmission mode, are dynamically configured based on device capability parameters. Authentication and data encryption are performed based on device security authentication information to ensure the confidentiality and integrity of transmissions.

[0038] Communication quality optimization and fault self-recovery mechanisms ensure continuous, stable, and efficient communication operation. Data integrity and accuracy are verified in real-time through data validation, and retransmission or correction is automatically requested when errors are detected. A performance monitoring system continuously tracks key indicators and generates real-time reports for analysis. Based on monitoring data, communication parameters are adjusted to cope with network fluctuations, such as reducing the data rate or enabling redundant transmission when packet loss increases. The fault self-recovery function is automatically triggered when connection interruption, timeout, or other faults are detected: reconnection mechanisms and backup path switching ensure rapid recovery; all fault events and recovery processes are logged.

[0039] The protocol adaptation results include: generating a protocol conversion status report containing successful conversion status, efficiency indicators, and error codes; providing a standardized communication interface specification with a unified data point mapping table and communication parameter configuration; recording device capability description files of device control modes, performance parameters, and safe operating boundaries; and verification results including communication quality assessment and control command verification data.

[0040] Specifically, the protocol conversion status report uses Boolean values ​​to indicate whether the protocol conversion was successfully completed. For example, a success status of 1 indicates that the device protocol has been fully mapped to the virtual power plant standard protocol; 0 triggers an error code mechanism. Efficiency metrics are used to quantify performance data during the protocol conversion process, including conversion latency from protocol identification to conversion completion; the number of data packets processed per unit time; and CPU and memory usage during the conversion process. Error codes provide detailed fault diagnosis information, categorized as follows: Protocol mismatch error (E101): The device protocol is incompatible with the system protocol library; Data format error (E102): Data fields are missing or have an invalid format; Security authentication failure (E103): Digital certificate verification or encryption key is invalid. Error codes are accompanied by descriptions and solution suggestions, such as E101: It is recommended to update the protocol library or enable compatibility mode.

[0041] A unified data point mapping table defines the mapping relationship between the device's raw data points and the standard data model of the virtual power plant, such as mapping register addresses to standard data points, and supports many-to-one mapping. Communication parameter configuration provides standardized communication settings, including transmission parameters, network parameters, and security encryption parameters.

[0042] The equipment capability description file uses a standardized format such as JSON or XML, and includes the operating modes supported by the equipment, such as constant power mode, constant voltage / constant current mode, and frequency modulation mode; quantitative performance indicators, such as dynamic response time, regulation accuracy, and maximum output capacity; and safe operating thresholds, such as voltage and temperature boundaries, and emergency handling rules, to ensure that the equipment operates efficiently within safe limits. This data is directly used for multi-timescale optimized control, such as referencing equipment capability limitations when generating charging and discharging strategies to avoid overload or failure, thereby improving system reliability and equipment lifespan.

[0043] Based on real-time monitoring and data analysis, communication quality assessment data is generated, including packet loss rate, latency, jitter, signal strength, and bandwidth utilization; control command verification data, such as command success rate, number of timeout retries, and execution deviation; these are used for fault diagnosis, system optimization, and compliance checks. Combined with equipment capability description files, a closed-loop feedback mechanism is formed, enabling the system to dynamically adjust communication parameters and operating strategies, such as adjusting charging and discharging plans or triggering maintenance alarms based on performance deviations.

[0044] S4: Add the operating parameters of the newly connected device to the health status assessment model, and extend the calculation of life decay cost to multi-device scenarios; adjust the multi-timescale optimization control based on the protocol adaptation results to generate a second charging and discharging strategy, the adjustment range of which includes short-term load response, medium-term energy allocation and long-term maintenance scheduling.

[0045] The operating parameters include basic equipment operating parameters, performance degradation monitoring parameters, environmental adaptation parameters, and safe operating boundary parameters. Specifically, basic equipment operating parameters are used to monitor the equipment's operating status and performance indicators in real time, providing basic operational data, including real-time power data, voltage / current data, operating efficiency indicators, and status indicators. Performance degradation monitoring parameters are used to quantify the equipment's lifespan degradation trend, aging, and performance degradation, including cycle count, capacity degradation rate, efficiency degradation indicators, and internal resistance changes. Environmental adaptation parameters are used to assess the impact of the external environment on equipment operation, ensuring the equipment's reliability in complex environments, including temperature data, humidity data, vibration / shock data, and climatic conditions. Safe operating boundary parameters define the equipment's safe operating thresholds and emergency handling rules to prevent equipment overload or damage, including voltage boundaries, current boundaries, temperature boundaries, and emergency handling rules.

[0046] The operating parameters of the new equipment are obtained through the equipment metadata self-description module and protocol adaptive interface, and standardized into a unified format. These parameters are input into the health status assessment model in real time, and the model calculates the SOH index for each device based on multiple parameters: ,in, Indicates the first The model processes parameters from multiple devices simultaneously and outputs the parameters for each device. value.

[0047] The calculation of lifespan degradation costs has been expanded from single-device scenarios to multi-device scenarios, and integrated into the total cost of operation and control. ,in, Total lifetime degradation cost, For the number of devices, For the first Unit replacement cost of each piece of equipment This is the attenuation weighting coefficient. The cyclic decay coefficient is... This represents the actual number of loops. Based on maintenance costs.

[0048] Will Adding this to the total cost of operation and control, the objective of minimizing the total cost becomes: ,in, For electricity trading costs, To ensure real-time operation and maintenance costs, it guarantees the optimization of the entire lifecycle cost in multi-device scenarios.

[0049] Based on the protocol adaptation results, multi-timescale optimization control is adjusted to generate a second charging and discharging strategy. Short-term optimization control adjusts the charging and discharging power of the energy storage system based on real-time data from the protocol adaptation results to optimize real-time load response and renewable energy consumption. For example, if communication latency is high, the control frequency is reduced to avoid instability. Medium-term optimization control utilizes performance parameters and safety boundaries from the equipment capacity description file to formulate charging and discharging plans to achieve peak-valley electricity price arbitrage and equipment state balancing. For example, the charging and discharging depth is adjusted based on equipment health status to extend lifespan. Long-term optimization control optimizes long-term operating strategies based on lifespan degradation cost calculations and capacity planning. Validation data from the protocol adaptation results is used to assess equipment reliability, influencing maintenance scheduling and capacity expansion decisions. The adjusted optimization control generates a second charging and discharging strategy that comprehensively considers multiple equipment states and protocol adaptation results. The strategy output includes: minute-level charging and discharging power adjustment instructions; hourly charging and discharging schedules, including power allocation and equipment maintenance recommendations; and daily operating strategies covering capacity adjustment and maintenance scheduling.

[0050] The technical solutions in the above embodiments of this application have at least the following technical effects or advantages: This application adopts a technical solution in which the device metadata self-description module and the protocol adaptive interface work together. It automatically parses the device type, communication protocol and capability parameters through standardized metadata format, and uses a two-way conversion mechanism combining rule engine and machine learning to realize the intelligent mapping conversion from non-standard protocol to standard protocol, thus completing the plug-and-play configuration of device access; furthermore, it integrates the operating parameters of newly accessed devices into the health status assessment model, extends the calculation of life decay cost to multi-device scenarios, and dynamically adjusts multi-time scale optimization control based on protocol adaptation results, generating a second charging and discharging strategy that takes into account short-term load response, medium-term energy distribution and long-term maintenance scheduling. This achieves the comprehensive technical effects of intelligent access of heterogeneous devices, collaborative management of multiple devices, adaptive expansion of the system and optimization of the whole life cycle cost, effectively solving the problems of poor device compatibility and insufficient heterogeneous collaboration in virtual power plants, and improving economy, reliability and scalability.

[0051] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for operating and controlling a virtual power plant, characterized in that, include: S1: Obtain the health status parameters of the energy storage system, and calculate the health status index of the energy storage system through the health status assessment model; the health status parameters include depth of charge and discharge, charge and discharge rate, operating temperature and number of cycles; S2: Calculate the life decay cost based on health status indicators and battery replacement cost, and integrate it into the total operation and control cost. With the goal of minimizing the total operation and control cost, generate the first charge and discharge strategy through multi-timescale optimization control. S3: Monitors access requests from energy devices, parses device type, communication protocol and capability parameters through the device metadata self-description module, and completes protocol conversion and communication configuration using the protocol adaptive interface to generate protocol adaptation results; S4: Add the operating parameters of newly connected devices to the health status assessment model to extend the calculation of lifespan decay costs to multi-device scenarios; Based on the protocol adaptation results, the multi-timescale optimization control is adjusted to generate a second charging and discharging strategy.

2. The operation control method for a virtual power plant as described in claim 1, characterized in that, The health status indicators are calculated using a health status assessment model: ,in, As an indicator of health status, The number of loops. For rated cycle life, This represents the deviation from the average operating temperature. The maximum allowable temperature deviation, The average depth of charge and discharge. For the maximum permissible depth of charge and discharge, For the corresponding weight coefficients, and 。 3. The operation control method for a virtual power plant as described in claim 1, characterized in that, The formula for calculating the lifespan decay cost is as follows: ,in, Cost of lifespan degradation, As an indicator of health status, The replacement cost per unit capacity of the battery. This is the attenuation weighting coefficient. The cyclic decay coefficient represents the weight of the impact of a single cycle on maintenance costs; This represents the actual number of loops. Based on maintenance costs.

4. The operation control method for a virtual power plant as described in claim 1, characterized in that, The multi-timescale optimization control includes a three-level collaborative optimization strategy: Short-term optimization control: executed on a minute-level timescale, with the optimization objective being real-time load response and renewable energy consumption, minimizing the control cost of the current operating cycle by adjusting the charging and discharging power of the energy storage system; Medium-term optimization control: executed on an hour-level timescale, with the optimization objective being peak-valley electricity price arbitrage and equipment operating status balance, formulating charging and discharging plans to extend equipment lifespan based on time-of-use electricity price forecasts and equipment health status; Long-term optimization control: executed on a day-level timescale, with the optimization objective being minimizing the entire lifecycle cost, optimizing long-term operation strategies through lifespan degradation cost calculation and capacity planning.

5. The operation control method for a virtual power plant as described in claim 1, characterized in that, The device metadata self-description module includes: a standardized metadata format containing device type, communication protocol, capability parameters, and security authentication information; a parsing engine that verifies the metadata format, parses the semantic meaning, and maps protocol relationships; automatic broadcasting of metadata when a device connects, and completion of parsing and registration; and activation of compatibility mode and inference of missing fields using machine learning algorithms when non-standard metadata is detected.

6. The operation control method for a virtual power plant as described in claim 1, characterized in that, The protocol adaptive interface includes: identifying the device communication protocol type based on the protocol information provided by the device metadata self-description module, and realizing intelligent mapping and conversion between non-standard protocols and standard protocols through a two-way conversion mechanism combining rule engine and machine learning; configuring communication parameters according to device capability parameters, establishing a secure encrypted transmission channel, and optimizing communication quality and achieving fault self-recovery through data verification and performance monitoring.

7. The operation control method for a virtual power plant as described in claim 1, characterized in that, The protocol adaptation results include: generating a protocol conversion status report containing successful conversion status, efficiency indicators, and error codes; providing a standardized communication interface specification with a unified data point mapping table and communication parameter configuration; recording device capability description files of device control modes, performance parameters, and safe operating boundaries; and verification results including communication quality assessment and control command verification data.

8. The operation control method for a virtual power plant as described in claim 1, characterized in that, The operating parameters include basic equipment operating parameters, performance degradation monitoring parameters, environmental adaptation parameters, and safe operating boundary parameters; the basic equipment operating parameters are used to describe the real-time operating status and performance indicators of the equipment; the performance degradation monitoring parameters are used to quantify the equipment's lifespan degradation trend; and the environmental adaptation parameters are used to assess the impact of the external environment on equipment operation. The safe operation boundary parameters are used to define the safe operation thresholds and emergency handling rules of the equipment.

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