Block chain-based electrochemical energy storage resource virtual power plant optimal configuration system

Abstract

The invention belongs to the technical field of electric power system operation and control, and discloses an electrochemical energy storage resource virtual power plant optimal configuration system based on a block chain, which comprises the steps of collecting operation state data of each battery energy storage device in a virtual power plant jurisdiction in real time, the operation state data comprising available charge state data and battery temperature data; obtaining a current scheduling demand instruction of a power grid, and analyzing a corresponding service scene type and a target power demand according to the scheduling demand instruction; based on the charge state data and the battery temperature data, calculating a dynamic loss cost value of each battery energy storage device under the business scene type; the scheduling module is used for generating a resource configuration scheduling scheme according to the dynamic loss cost value and a target power demand; executing the resource configuration scheduling scheme through an intelligent contract, and recording an execution result and the dynamic loss cost value into a block chain account book; and the full life cycle service life of the battery energy storage equipment is prolonged.

Description

Technical Field

[0001] This invention relates to the field of power system operation and control technology, and more specifically, to a blockchain-based virtual power plant optimization configuration system for electrochemical energy storage resources. Background Technology

[0002] With the rapid development of distributed energy technology, virtual power plants (VPS) are emerging. As an effective management model for aggregating distributed resources, energy storage plays an increasingly important role in peak shaving, valley filling, frequency regulation, and ancillary services of the power grid. Among them, battery energy storage devices, with their fast response speed and high control precision, have become the most core adjustable resource in virtual power plants.

[0003] In existing virtual power plant dispatching systems, a centralized control strategy is typically used to manage battery energy storage devices within the designated area. The core process generally involves the upper-level dispatch center issuing a total power command, and the system allocating tasks proportionally or using a simple round-robin mechanism based on the rated power or current remaining charge (state of charge) of each battery energy storage device.

[0004] However, the aforementioned methods, lacking detailed consideration of the microscopic health status of energy storage devices, can lead to excessive battery life degradation. Existing systems often overlook the non-linear impact of battery temperature and state of charge (SOC) data on battery aging. For example, forced scheduling when battery temperature is too high or in an accelerated aging zone (such as the edge of overcharge / overdischarge) can cause a sharp decline in battery life, increasing long-term operation and maintenance costs. Furthermore, virtual power plants typically involve multiple stakeholders, including grid companies, aggregators, and distributed energy storage owners. Traditional centralized database models suffer from a "data black box," where disagreements often arise regarding the authenticity of scheduling execution and the calculation results of dynamic loss costs. Data is also susceptible to tampering, leading to difficulties in subsequent revenue settlement and accountability.

[0005] In view of this, the present invention proposes a blockchain-based virtual power plant optimization configuration system for electrochemical energy storage resources to solve the above problems. Summary of the Invention

[0006] To overcome the aforementioned shortcomings of existing technologies and achieve the above objectives, this invention provides the following technical solution: a blockchain-based virtual power plant optimization and allocation system for electrochemical energy storage resources, comprising: The data acquisition module is used to collect real-time operating status data of each battery energy storage device within the virtual power plant area. The operating status data includes available state of charge data and battery temperature data. The instruction parsing module is used to obtain the current dispatching demand instruction of the power grid, and parse out the corresponding business scenario type and target power demand based on the dispatching demand instruction; The cost calculation module is used to calculate the dynamic loss cost value of each battery energy storage device under the business scenario type based on the state of charge data and the battery temperature data. The optimization scheduling module is used to generate a resource configuration scheduling scheme based on the dynamic loss cost value and the target power requirement; The blockchain execution module is used to execute the resource allocation and scheduling scheme through smart contracts and record the execution results and the dynamic loss cost value to the blockchain ledger.

[0007] Furthermore, the instruction parsing module is specifically used for: Extract the response time parameter and duration parameter from the scheduling request instruction; If the response time parameter is less than the first preset time threshold, the business scenario type is determined to be a frequency adjustment scenario; If the duration parameter is greater than the second preset time threshold, the business scenario type is determined to be a peak shaving and valley filling scenario.

[0008] Furthermore, the cost calculation module is specifically used for: Based on the target power demand and the battery temperature data, determine the heat accumulation risk factor of each battery energy storage device when responding to the scheduling demand command; Based on the target power demand and the rated capacity of the battery energy storage device, calculate the charge / discharge rate required for its response scheduling. The dynamic loss cost value is generated based on the thermal accumulation risk factor, the electrochemical aging influence coefficient, and the charge / discharge rate.

[0009] Furthermore, the methods for obtaining the heat accumulation risk factor include: Based on the target power requirement and the current battery temperature data, determine the estimated heat generation rate corresponding to the battery energy storage device; Based on the estimated heat generation rate and the duration of the scheduling demand command, the predicted temperature value after the battery energy storage device completes the scheduling demand command is predicted. A preset safe temperature threshold is used, and the heat accumulation risk factor is calculated based on the degree of approximation between the predicted temperature value and the safe temperature threshold.

[0010] Furthermore, the methods for obtaining the electrochemical aging influence coefficient include: The available state of charge of battery energy storage devices is divided into a linear aging region, a high-potential nonlinear region, and a low-potential nonlinear region, and different basic loss weights are set for each region. Generate the state-of-charge change trajectory of the battery energy storage device according to the scheduling demand command; Identify the length of the trajectory of the change in the state of charge in the linear aging region, the high-potential nonlinear region, and the low-potential nonlinear region; Using the basic loss weight of each zone as a coefficient, the corresponding span length of each zone is weighted and summed to obtain the electrochemical aging influence coefficient.

[0011] Furthermore, the optimized scheduling module is specifically used for: The battery energy storage devices are prioritized and sorted in order of dynamic loss cost value from low to high to form a candidate queue. The rated power of the battery storage devices in the candidate queue is sequentially accumulated until the accumulated value meets the target power requirement, thereby determining the selected target battery storage device set. Specific power allocation instructions are issued to the target battery energy storage device set to form the resource configuration and scheduling scheme.

[0012] Furthermore, the optimized scheduling module also includes a circuit breaker filtering unit, which is used for: Before the optimization scheduling module prioritizes each battery energy storage device, a maximum tolerable loss threshold is set. Determine whether the dynamic loss cost value of each battery energy storage device exceeds the maximum tolerable loss threshold; If the number exceeds the limit, the corresponding battery energy storage device will be removed from the candidate pool for this scheduling and will not be included in the candidate queue.

[0013] Furthermore, the system also includes a model self-correction module, which is used for: Regularly acquire historical actual capacity degradation data of battery energy storage devices and corresponding historical scheduling records; Based on the span length in the historical scheduling records and the basic loss weight, the cumulative theoretical loss value is calculated; Calculate the aging deviation rate between the historical actual capacity decay data and the cumulative theoretical loss value; If the aging deviation rate exceeds the preset drift threshold, the basic loss weight is corrected.

[0014] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the functions of various modules of the system.

[0015] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the functions of various modules of the system.

[0016] Technical effects and advantages of the present invention: 1. Unlike existing technologies that rely solely on remaining battery capacity for broad-based scheduling, this system comprehensively considers state-of-charge (SOC) and battery temperature data through a cost calculation module. It constructs a multi-dimensional evaluation model that includes thermal accumulation risk factors, electrochemical aging impact coefficients, and charge / discharge rates. The system can accurately identify hidden losses in high / low potential nonlinear regions or high-temperature accumulation risk zones, converting physical damage into economic indicators by calculating dynamic loss costs. This naturally favors scheduling strategies that protect batteries in a "sub-healthy" state, preventing nonlinear degradation caused by overuse and thus maximizing the lifespan of the battery storage device.

[0017] 2. Utilizing the blockchain execution module, this system automatically executes the resource allocation and scheduling scheme's execution process and calculates the dynamic loss cost value through smart contracts, storing the results on the blockchain. The immutability of blockchain technology solves the data trust problem between virtual power plant operators and distributed resource owners, ensuring the authenticity and reliability of each scheduling loss assessment and execution result, and providing an authoritative blockchain ledger basis for subsequent value settlement and liability determination. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the blockchain-based electrochemical energy storage resource virtual power plant optimization configuration system of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1 Please see Figure 1 As shown in this embodiment, the blockchain-based virtual power plant optimization and allocation system for electrochemical energy storage resources includes: The data acquisition module is used to collect real-time operational status data of each battery energy storage device within the virtual power plant's coverage area. This operational status data forms the basis for subsequent cost assessments and specifically includes available state of charge data reflecting remaining power and battery temperature data reflecting the device's thermal characteristics.

[0021] The instruction parsing module, serving as the interface for interaction with the power grid, is used to obtain the current dispatching demand instructions from the power grid. This module not only receives instructions but also performs in-depth parsing, resolving the corresponding business scenario type (e.g., determining whether it belongs to frequency regulation or peak shaving) and the target power demand for this dispatch based on the dispatching demand instructions.

[0022] The cost calculation module is the core decision support unit of this system. It calculates the dynamic loss cost of each battery energy storage device based on the state-of-charge data and battery temperature data, combined with the current business scenario type. This dynamic loss cost is no longer a fixed cost per kilowatt-hour, but rather a real-time loss assessment that integrates the current physical state and business scenario.

[0023] The optimization scheduling module generates resource allocation and scheduling schemes based on the dynamic loss cost of each battery energy storage device and the overall target power demand. This module uses algorithms to select the battery energy storage devices with lower costs to meet grid demand.

[0024] The blockchain execution module ensures the reliable execution of the scheduling. This module automatically executes the resource allocation and scheduling scheme through smart contracts and records the execution results and the calculated dynamic loss cost value in an immutable blockchain ledger.

[0025] In power grid dispatching, different business demands have drastically different impact mechanisms on battery energy storage devices. Existing command parsing typically focuses only on power magnitude, neglecting the time dimension. However, short-term, high-frequency power throughput (such as frequency regulation) mainly induces mechanical fatigue and shallow charge-discharge thermal effects, while long-term, continuous power throughput (such as peak shaving) mainly induces deep electrochemical reactions and heat accumulation. If the type of business scenario cannot be accurately distinguished, subsequent calculations of dynamic loss costs will lose their relevance, causing dispatching strategies to deviate from the optimal solution.

[0026] In summary, the system needs to first extract parameters from the received scheduling request instructions, extracting the response time parameter (i.e., the urgency of the time when the power grid requires the virtual power plant to start responding) and the duration parameter (i.e., the duration for which the power grid requires to maintain the power output).

[0027] The system sets a first preset time threshold and a second preset time threshold. Then, it determines whether the extracted response time parameter is less than the first preset time threshold (e.g., 15 seconds or 30 seconds). If it is less than this threshold, it indicates that the instruction has extremely high requirements for response speed, and the system determines the corresponding business scenario type as a frequency regulation scenario. In addition, the system also needs to determine whether the extracted duration parameter is greater than the second preset time threshold (e.g., 1 hour or 2 hours). If it is greater than this threshold, it indicates that the instruction requires the device to perform a long-term energy transfer, and the system determines the corresponding business scenario type as a peak shaving and valley filling scenario.

[0028] In frequency regulation scenarios, the battery plays a minimal role. Therefore, the following technical solutions are used in non-frequency regulation scenarios (up to the first preset time threshold, including frequency regulation scenarios).

[0029] Based on the target power demand and the battery temperature data, the heat accumulation risk factor of each battery energy storage device when responding to scheduling demand commands is determined; the specific steps are as follows: The system determines the estimated heat generation rate of the battery energy storage device based on the current value corresponding to the target power demand and the current internal resistance of the battery, combined with the Bernhard heat generation model.

[0030] Based on the estimated heat production rate Duration extracted from scheduling request instructions Combined with the battery's heat capacity and heat dissipation coefficient Predict the temperature value after the battery energy storage device completes the scheduling demand command. The formula for calculating the predicted temperature value is as follows: In the formula, This indicates the current temperature of the battery.

[0031] The system presets a safe temperature threshold. (For example, 55°C). A heat accumulation risk factor is calculated based on how close the predicted temperature value is to this threshold. In one specific embodiment, the heat accumulation risk factor is obtained as follows: If the predicted temperature value exceeds the safe temperature threshold, the heat accumulation risk factor is set to a maximum value (e.g., 10^6), which means that the battery pack is prohibited from being used. If the predicted temperature value exceeds the safe temperature threshold, then the critical difference between the two is calculated. When the critical difference between the two values ​​is greater than a preset safety margin (e.g., 5°C), the temperature rise is considered safe, and the heat accumulation risk factor is set to 0.001. When the critical difference between the two values ​​is not greater than the preset safety margin, the battery is considered to be on the edge of high risk, and the heat accumulation risk factor is calculated using an exponential mapping function. The functional expression of the exponential mapping function is as follows: In the formula, This represents the calculated heat accumulation risk factor; This represents the adjustment coefficient, with a value greater than 0 and less than 0.5. This represents the critical difference between the two.

[0032] Based on the state of charge (SOC) data, an electrochemical aging impact analysis is performed on the SOC change trajectory of the battery energy storage device in response to this business scenario type, and the electrochemical aging impact coefficient is determined. The specific steps are as follows: The available state of charge (0%-100%) of battery energy storage devices is divided into three physical ranges: High-potential nonlinear region (90%-100%): In this region, side reactions are severe, so a higher base loss weight is set (e.g., 2.0).

[0033] Low-potential nonlinear region (0%-10%): Over-discharge risk is likely to occur in this region, so a higher base loss weight is set (e.g., 1.8).

[0034] Linear aging zone (10%-90%): Battery performance is stable in this zone, and a standard base loss weight (e.g., 1.0) is set.

[0035] Based on the scheduling request instruction, the system simulates the complete path of the battery's State of Charge (SOC) change from its current state of charge (SOC) when the battery executes the instruction in the simulation environment; that is, the SOC change trajectory. The system then identifies the length of this trajectory across the three intervals mentioned above. For example, if the current SOC of the battery is 88%, and the instruction requires charging, the simulation shows that the SOC reaches 95% when the instruction is completed. The trajectory then crosses the linear region (88%-90%, with a crossing length of 2%) and the high-potential region (90%-95%, with a crossing length of 5%).

[0036] Using the basic loss weight of each zone as a coefficient, the electrochemical aging influence coefficient is obtained by weighted summation over the corresponding span length of each zone. .

[0037] The system is based on the analyzed target power demand. and the rated capacity of battery energy storage devices Calculate the charge / discharge ratio required for response scheduling; where the charge / discharge ratio is... The calculation formula can be expressed as: It should be noted that the higher the charge / discharge rate, the higher the Joule heat generated by the battery's internal resistance, and the greater the mechanical stress.

[0038] Constructing the cost function Its function expression is: In the formula, Based on basic depreciation cost, , , These are preset weighting coefficients; This represents the square of the charge / discharge rate; the formula shows that for equipment with higher thermal risk, more significant aging effects, and a larger charge / discharge rate, the calculated dynamic loss cost will increase non-linearly.

[0039] Through the above process, the system constructs a multi-dimensional, refined battery loss assessment model, achieving a leap from macro-level power scheduling to micro-level state perception. Specifically, by predicting the thermal accumulation risk factors after scheduling execution, the system proactively quantifies and avoids the risk of thermal runaway, preventing the battery from operating in a high-temperature critical state. Simultaneously, by identifying the cross-distribution of the state of charge (SOC) change trajectory across the linear aging region and the high-low potential nonlinear region, the system accurately quantifies the electrochemical aging impact coefficients in different SOC ranges, solving the prediction distortion problem caused by the uniformization of battery loss across the entire range in traditional models. This dynamic loss cost calculation mechanism, which integrates charge / discharge rate, thermal safety risk, and electrochemical nonlinear aging, ensures that the virtual power plant can automatically select equipment in optimal operating conditions while meeting grid power demands, minimizing physical losses and extending the full lifespan of battery energy storage assets.

[0040] After obtaining the dynamic loss costs of each battery energy storage device, the final challenge in virtual power plant scheduling is how to efficiently and rationally allocate resources. Existing scheduling strategies often suffer from the following shortcomings: The average power distribution strategy is inefficient: Simply distributing the total power demand equally among all available devices will force some high-loss devices (such as those at high temperatures or on the verge of aging) to operate, thus lowering the overall health of the system.

[0041] Lack of cost-based optimization mechanism: A screening funnel based on real-time marginal cost (dynamic loss cost) has not been built, and it is impossible to guarantee that the total marginal cost of each scheduling is minimized.

[0042] High computational complexity: In large-scale virtual power plant scenarios, if complex global optimization algorithms (such as genetic algorithms and particle swarm algorithms) are used, the computation time is long, making it difficult to meet the real-time requirements of second-level frequency regulation commands.

[0043] To address the aforementioned issues, the optimization scheduling module in this embodiment employs a low-cost priority sorting algorithm based on a greedy strategy. The specific execution steps are as follows: The system obtains the dynamic loss cost value of all currently available battery energy storage devices (this value has been calculated by the cost calculation module based on thermal risk, aging coefficient, etc.).

[0044] The system sorts the battery energy storage devices in ascending order of their dynamic loss cost values, from lowest to highest. A lower cost indicates that the device is currently in a healthier and more suitable state for scheduling (e.g., suitable temperature, within the linear aging zone). After sorting, an ordered candidate queue is formed.

[0045] The system initializes an empty target battery energy storage device set and sets the current accumulated power. Set to 0. The system starts from the head of the candidate queue (i.e., the lowest-cost device) and iterates through each battery storage device in turn: Add the current device's rated power (or current maximum available power) to the accumulated value. And add the device to the target battery energy storage device set.

[0046] Real-time judgment: If If the power requirement exceeds the target power demand, the iteration stops. At this point, the battery storage devices in the set represent the optimal combination for this scheduling.

[0047] For the devices in the target battery energy storage device set, the system makes fine adjustments to the allocation based on the difference between their rated power and the target demand (for example, the last selected device only needs to bear the remaining power gap, rather than operating at full power). Finally, it generates a resource configuration and scheduling scheme containing specific power allocation values ​​and issues specific power allocation instructions to each device in the set.

[0048] By sorting battery resources according to their dynamic loss cost values ​​from low to high, the system ensures that each scheduling prioritizes the use of battery resources in their optimal state (lowest loss, lowest risk). This greedy strategy macroscopically guarantees that the total operating cost within the entire virtual power plant area remains at a minimum, avoiding the operation of battery storage units with defects or high energy consumption. Compared to complex iterative optimization algorithms, the sorting and accumulation logic used in this embodiment is extremely fast (time complexity mainly depends on the sorting algorithm, typically...). This enables the system to easily handle second-level scheduling requirements in scenarios with large-scale device access, making it particularly suitable for frequency regulation scenarios with stringent response speed requirements. Because devices with high dynamic loss costs (such as those at high temperatures or on the verge of overcharge / over-discharge) are placed at the end of the candidate queue, they are only invoked when grid demand is extremely high and low-cost devices are insufficient to support the system. Logically, this forms a soft isolation protection mechanism, preventing battery storage devices in poor condition from prematurely exhausting their lifespan.

[0049] While sorting-based virtual power plant scheduling strategies can optimize resource allocation through a low-cost priority principle, they still lack a hard-line blocking mechanism for extreme high-risk conditions. In extreme scenarios with huge grid power demands, simple sorting and accumulation logic may be forced to call devices at the tail of the queue that are in extremely poor condition (such as temperatures approaching the edge of thermal runaway or SOC in the severe aging zone). This soft protection cannot fundamentally eliminate the risk of operating with defects, which can easily lead to battery thermal runaway or irreversible physical damage, threatening the safe and stable operation of the entire system.

[0050] To address the aforementioned issues, a circuit breaker filtering unit needs to be added to the optimized scheduling module as a pre-emptive safety checkpoint in the scheduling process. The specific execution logic is as follows: Before the scheduling module executes the sorting algorithm, the system pre-sets a maximum tolerable loss threshold. This threshold represents the maximum economic cost or physical risk that the system allows for a single scheduling operation. This threshold can be a fixed value or dynamically adjusted according to the virtual power plant's operating strategy (e.g., lowering the threshold in a mode that pursues ultimate safety).

[0051] The circuit breaker filtering unit iterates through all battery energy storage devices awaiting scheduling, reading their dynamic loss cost values ​​generated by the cost calculation module. The system then checks each device's dynamic loss cost value against the maximum tolerable loss threshold. If the value exceeds the threshold, the device is deemed to be in an unacceptably high-risk or high-loss state (e.g., an excessively high predicted temperature leading to a very high thermal risk factor, thus increasing the total cost). The system directly triggers the circuit breaker mechanism, removing the corresponding battery energy storage device from the current scheduling pool. This means the device will be completely isolated and will not be included in subsequent priority ranking or candidate queues; regardless of grid demand, it will not participate in scheduling during this cycle. If the value does not exceed the threshold, the battery energy storage device remains in the candidate pool and enters the subsequent ranking process.

[0052] By introducing a fuse-based filtering unit and setting a maximum tolerable loss threshold, the system adds a hard safety firewall to its soft sorting optimization. This mechanism can forcibly isolate devices that are usable but have excessive operating costs (such as those on the verge of thermal runaway or severely aged), completely eliminating the possibility of forcibly calling up high-risk devices under extreme scheduling demands. This not only ensures the absolute safety of battery assets and prevents chain reactions caused by individual cell failures, but also reflects the principle of virtual power plant operation that prioritizes safety over profit.

[0053] The system's preset basic loss weights (e.g., 1.0 for the linear region and 2.0 for the high-potential region) are usually based on laboratory calibration data or general empirical values. However, in actual operation, battery degradation is affected by complex factors such as environment and batch consistency, and the theoretically calculated loss values ​​often deviate from the actual degradation in the physical world. In particular, as the battery's service life increases, its aging characteristics will change (e.g., the aging acceleration period arrives). If the system always uses the initial fixed weight parameters, the calculation of dynamic loss costs will become increasingly inaccurate, thus misleading the scheduling strategy and causing resource allocation to deviate from the optimal solution.

[0054] To address the aforementioned issues, the system also needs to introduce a model self-correction module to construct a closed-loop correction mechanism of "prediction-measurement-feedback". The specific steps are as follows: The model self-correction module periodically (e.g., weekly or monthly) obtains historical actual capacity degradation data of the battery energy storage device through BMS (Battery Management System) or periodic capacity verification tests. (For example, the actual capacity decreased by 0.5kWh in the past month). Retrieve the historical scheduling records of the device within the corresponding time period from the database or blockchain ledger, and read the SOC span data recorded in the records.

[0055] Based on the span lengths of each zone in historical scheduling records and the base loss weights used in the current model, the cumulative theoretical loss value for this time period is calculated. The calculation formula is: In the formula, The battery aging characteristic coefficient is an inherent parameter related to the battery chemistry system and material formulation. It is obtained through experimental calibration and is used to convert the state of charge (SOC) stress into the actual capacity loss rate. Indicates the rated capacity of the battery energy storage device; , and They represent the first time. In this scheduling process, the length of the trajectory of the change in state of charge in the corresponding aging interval corresponds to the linear aging region, the high-potential nonlinear region, and the low-potential nonlinear region defined earlier. , and These represent the base loss weights for each aging interval; This represents the cumulative number of scheduling attempts; that is, the total number of historical scheduling records counted by the model self-correction module.

[0056] Aging deviation rate The calculation formula is: In the formula, It is expressed as a dimensionless conversion coefficient, obtained through experimental calibration.

[0057] The system sets a preset drift threshold (e.g., 0.05). If the calculated aging deviation rate is greater than the drift threshold, the base loss weight is increased (e.g., by 10%) to improve the severity of loss prediction; conversely, the base loss weight is decreased (e.g., by 10%). By introducing a model self-correction module, the system establishes a data-driven feedback loop. This mechanism periodically reconciles the "actual degradation in the physical world" with the "theoretical loss in the digital world," automatically identifying and eliminating drift errors in model parameters. Through dynamic adjustment of the basic loss weights, the system ensures that the loss assessment model can self-evolve along with the battery's characteristics throughout its entire lifecycle, maintaining a consistently high level of accuracy in assessing battery health. This guarantees that the virtual power plant can always formulate optimal scheduling strategies based on accurate cost data during long-term operation, avoiding excessive asset overdraft or overprotection due to model distortion.

[0058] A computer device includes a memory and a processor, wherein the memory stores a computer program and the processor executes the computer program to implement the system described above.

[0059] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the system described above.

[0060] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0061] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0062] In the description of this invention, it should be understood that the terms "first," "second," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance.

[0063] In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0064] In the description of this invention, "several" means one or more, and "a large number" means two or more.

[0065] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0066] All formulas in this manual are dimensionless and calculated numerically. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0067] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A blockchain-based optimal configuration system for virtual power plant of electrochemical energy storage resources, characterized in that, include: The data acquisition module is used to collect real-time operating status data of each battery energy storage device within the virtual power plant area. The operating status data includes available state of charge data and battery temperature data. The instruction parsing module is used to obtain the current dispatching demand instruction of the power grid, and parse out the corresponding business scenario type and target power demand based on the dispatching demand instruction; The cost calculation module is used to calculate the dynamic loss cost value of each battery energy storage device under the business scenario type based on the state of charge data and the battery temperature data. The optimization scheduling module is used to generate a resource configuration scheduling scheme based on the dynamic loss cost value and the target power requirement; The blockchain execution module is used to execute the resource allocation and scheduling scheme through smart contracts and record the execution results and the dynamic loss cost value in the blockchain ledger. 2.The system of claim 1, wherein, The instruction parsing module is specifically used for: Extract the response time parameter and duration parameter from the scheduling request instruction; If the response time parameter is less than the first preset time threshold, the business scenario type is determined to be a frequency adjustment scenario; If the duration parameter is greater than the second preset time threshold, the business scenario type is determined to be a peak shaving and valley filling scenario. 3.The blockchain-based electrochemical energy storage resource virtual power plant optimization configuration system of claim 2, wherein, The cost calculation module is specifically used for: Based on the target power demand and the battery temperature data, determine the heat accumulation risk factor of each battery energy storage device when responding to the scheduling demand command; Based on the target power demand and the rated capacity of the battery energy storage device, calculate the charge / discharge rate required for its response scheduling. The dynamic loss cost value is generated based on the thermal accumulation risk factor, the electrochemical aging influence coefficient, and the charge / discharge rate. 4.The system of claim 3, wherein, The methods for obtaining the heat accumulation risk factor include: Based on the target power requirement and the current battery temperature data, determine the estimated heat generation rate corresponding to the battery energy storage device; Based on the estimated heat generation rate and the duration of the scheduling demand command, the predicted temperature value after the battery energy storage device completes the scheduling demand command is predicted. A preset safe temperature threshold is used, and the heat accumulation risk factor is calculated based on the degree of approximation between the predicted temperature value and the safe temperature threshold. 5.The blockchain-based electrochemical energy storage resource virtual power plant optimization configuration system of claim 4, wherein, Methods for obtaining the electrochemical aging effect coefficient include: The available state of charge of battery energy storage devices is divided into a linear aging region, a high-potential nonlinear region, and a low-potential nonlinear region, and different basic loss weights are set for each region. Generate the state-of-charge change trajectory of the battery energy storage device according to the scheduling demand command; Identify the length of the trajectory of the change in the state of charge in the linear aging region, the high-potential nonlinear region, and the low-potential nonlinear region; Using the basic loss weight of each zone as a coefficient, the corresponding span length of each zone is weighted and summed to obtain the electrochemical aging influence coefficient. 6.The system of claim 5, wherein, The optimized scheduling module is specifically used for: The battery energy storage devices are prioritized and sorted in order of dynamic loss cost value from low to high to form a candidate queue. The rated power of the battery storage devices in the candidate queue is sequentially accumulated until the accumulated value meets the target power requirement, thereby determining the selected target battery storage device set. Specific power allocation instructions are issued to the target battery energy storage device set to form the resource configuration and scheduling scheme. 7.The blockchain-based electrochemical energy storage resource virtual power plant optimization configuration system of claim 6, wherein, The optimized scheduling module further includes a circuit breaker filtering unit, which is used for: Before the optimization scheduling module prioritizes each battery energy storage device, a maximum tolerable loss threshold is set. Determine whether the dynamic loss cost value of each battery energy storage device exceeds the maximum tolerable loss threshold; If the number exceeds the limit, the corresponding battery energy storage device will be removed from the candidate pool for this scheduling and will not be included in the candidate queue.

8. The blockchain-based virtual power plant optimization allocation system for electrochemical energy storage resources according to claim 7, characterized in that, The system also includes a model self-correction module, which is used for: Regularly acquire historical actual capacity degradation data of battery energy storage devices and corresponding historical scheduling records; Based on the span length in the historical scheduling records and the basic loss weight, the cumulative theoretical loss value is calculated; Calculate the aging deviation rate between the historical actual capacity decay data and the cumulative theoretical loss value; If the aging deviation rate exceeds the preset drift threshold, the basic loss weight is corrected.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the functions of each module of the system as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the functions of each module of the system as described in any one of claims 1 to 8.

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