Aggregation regulation and control method and system for multiple virtual power plants

Abstract

The invention discloses an aggregation regulation and control method and system for multiple virtual power plants, and relates to the technical field of intelligent power grid regulation and control, and the method comprises the steps: enabling a trust anchor to synchronize a link health abstract and preset a safe operation envelope and an autonomous rule when the communication is normal; after link disconnection is detected, an autonomous mode is immediately switched to generate power setting; during the autonomous period, recording power deviation and events and generating a hash track; after the link is recovered, two-way identity verification is completed through quantum key handshake, then a fingerprint set and sparse differences are uploaded, and latest global constraints are received; an aggregation composer reconstructs a system model based on a constraint relaxation matrix and a compensation weight vector, generates an optimized power instruction, smoothly replaces an autonomous instruction with a slip coefficient, recursively allocates spare capacity, and finally realizes the transition from autonomous smooth transition to unified scheduling of each virtual power plant; the method gives consideration to toughness, integrity, fairness and safety, and significantly and integrally improves the distributed energy regulation and control reliability and recovery efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of smart grid regulation, in particular to an aggregation regulation method and system for multiple virtual power plants. BACKGROUND

[0002] With the large-scale access of flexible resources such as photovoltaic, wind power, distributed energy storage and electric vehicles to the distribution network, the operators begin to package and aggregate the dispersed distributed energy through the virtual power plant platform, so that it has the ability to participate in active power dispatch, auxiliary services and multi-layer power market transactions. The existing technology generally adopts the mode of cloud centralized optimization + edge rapid execution: in the case of good communication link, the aggregation editor can collect the operation state and market quotation reported by each virtual power plant in real time, generate unified power instructions through hierarchical and zoned power flow calculation and economic clearing algorithm, and then send them to each trusted anchor or edge controller through edge-cloud link, and finally act on specific inverters, energy storage systems and adjustable loads.

[0003] Therefore, the industry generally configures TLS or VPN for encrypted transmission, and detects the survival of the link through the heartbeat package; but the logs need to be uploaded to the cloud or blockchain in full to ensure traceability, resulting in a large amount of log data. Therefore, when large-scale demonstration is carried out, it is found that there are situations such as power failure of cellular network base stations, misoperation of optical fiber trunks, routing blockage and network attacks on distributed energy. Once the communication quality is poor or even interrupted, the current technology will have a major vulnerability, that is, the central cannot obtain the real-time situation of the trusted anchor point, and cannot perform global security constraints; when the link is restored, the cloud side needs to pull and compare a large amount of logs to know, at this time, the system may be in a state of loss of control or in a semi-loss of control state for a period of time.

[0004] In a scenario where multiple virtual power plants are connected to the distribution network in parallel and rely on centralized cloud optimization, once a long or sudden long-term interruption occurs or the adjustable bandwidth is drastically reduced under control, the central aggregation orchestrator will lose the ability to judge and allocate the true operating boundaries of each virtual power plant, and the one-time overall control of the operating safety boundaries will also be declared invalid. At this time, each virtual power plant will still use the previously established rules to continuously diverge in the vector frequency domain for the purpose of maximizing its own interests, and then generate overlapping power flow backflow, node overvoltage and primary frequency support collapse in the time and space region within a very short period of time; after a long period of operation, it will cause the circuit breaker protection device to continuously operate and batch tripping behavior will occur, and cause continuous power outages affecting adjacent partitions; after the communication signal access is restored, the current log can only be compared with the previous batch of logs to equivalently represent the power generation and load conditions in the same working cycle. The conditions on the energy consumption side and the power supply side cannot be correlated at the same time, and a combination of multiple offline verification devices and manual verification is required to realize the fault judgment of one or two aspects; secondly, the protection device for the control message is more easily bypassed, and the control side cannot perform targeted interception operations on these tampered attack messages.

[0005] Therefore, how can all virtual power plants maintain their respective security boundaries and operate autonomously even when a link is lost? How can data integrity verification be achieved with minimal data changes after a communication loss, and how can the global optimal constraint model be restored as quickly as possible? These are the core issues that limit the reliability and scalability of the aggregated control system for multiple virtual power plants. Summary of the Invention

[0006] (1) Technical problems solved In order to overcome the problems existing in the prior art, the present invention provides an aggregated control method and system for multiple virtual power plants. When the communication is normal, the trust anchor synchronizes the link health summary and presets the safe operation envelope and autonomous rules; when the link is detected to be lost, it immediately switches to the autonomous mode to generate the power setting; the above-mentioned recording work is completed by autonomously recording the power deviation and events and generating a hash trajectory; if the link is restored, the quantum key handshake is used to perform identity authentication between the two parties, and then the fingerprint set and the sparse difference are uploaded to the application server together and accept the latest global constraints; in this process, the aggregated scheduler is called to reconstruct the system model according to the constraint relaxation matrix and the compensation weight vector to generate the optimized power instruction, and finally the slip coefficient is used to smoothly replace the autonomous instruction into the spare capacity according to the formula, thereby completing the smooth transition of each virtual power plant from autonomous to unified scheduling; it can greatly improve the reliability and recovery efficiency of distributed energy control, and solve the technical problems mentioned in the background technology.

[0007] (2) Technical solution To achieve the above objectives, the present invention is implemented through the following technical solutions: An aggregated control method for multiple virtual power plants, comprising: When communication is normal, a trust anchor is configured on each virtual power plant gateway, link health summaries are synchronized to the aggregation orchestrator, and safe operation envelopes and autonomous scheduling rules are pre-loaded to establish decision boundaries. When the trust anchor detects a link interruption, it immediately switches to autonomous mode, calls the safe operation envelope and autonomous scheduling rules to calculate the distributed energy power setting, and ensures that resources operate according to unified constraints; During autonomous operation, the trust anchor continuously records power deviation and event information, generates a cumulative hash root using cryptographic hashing, and stores it locally to form a complete, continuous, and verifiable operation trace; After the link is restored, the trust anchor first completes two-way authentication through quantum key handshake, then uploads the fingerprint quadruple and current state and receives the latest global constraints issued by the aggregation orchestrator; Based on the uploaded power deviation, the aggregation orchestrator reconstructs the system constraints, generates optimized power instructions, and smoothly replaces the autonomous power settings with decreasing slip coefficients, restoring unified scheduling of all virtual power plants.

[0008] Finally, the trust anchor is used to collect communication indicators such as message packet loss rate, round-trip delay, and signal reliability in real time, and linearly normalizes them according to predetermined weight coefficients to obtain the link health. The link health and the safe operation envelope matrix are concatenated and input into the collision-resistant hash function to obtain the link health summary, which is then sent to the aggregation orchestrator for version registration and consistency verification.

[0009] In addition, when the link health is above the threshold, the aggregation orchestrator sends global power flow constraints to the trust anchor. Based on the constraints, the trust anchor constructs an operational safety envelope matrix containing node voltage, node power, and system frequency boundaries. It also generates an autonomous scheduling rule based on the target minimum baseline power offset and the inequality formed by the aforementioned envelope matrix, and generates a rule hash and returns it to the aggregation orchestrator.

[0010] In addition, when the link health is above the threshold, the aggregation orchestrator sends global power flow constraints to the trust anchor. Based on the constraints, the trust anchor constructs an operational safety envelope matrix containing node voltage, node power, and system frequency boundaries. It also generates an autonomous scheduling rule based on the target minimum baseline power offset and the inequality formed by the aforementioned envelope matrix, and generates a rule hash and returns it to the aggregation orchestrator.

[0011] Furthermore, the trust anchor uses a rolling time window to predict the load in autonomous mode, performs secondary optimization on the power decision vector with the weighted goal of minimum economic deviation and shortest calculation time, and forces the calculation time to not exceed the tolerable time limit to output the autonomous power setting vector.

[0012] Furthermore, the trust anchor uses the serialization period as the time granularity, concatenates the power deviation vector, alarm event vector and manual intervention vector into a unified sequence vector, accumulates the sequence matrix according to the fragment window length and generates a fragment hash, records the start and end time of the fragment, and sends it to the root chain maintenance thread.

[0013] In addition, the root chain maintenance thread will generate the hash value of the current fragment based on recursive hashing and add it to the global track root of the previous round, which is the new root value of this round. The current fragment will be saved using a sliding window method. For fragments beyond this window, only their hash values ​​will be stored in the cold archive index. After the mapping period arrives, all fragments in the cold archive will be rehashed and a compressed root value will be generated.

[0014] In addition, quantum random bit exchange is completed using the BB84 protocol through the trust anchor and aggregation orchestrator. After information coordination and privacy amplification, the quantum session key is obtained, and the key is used to encrypt and decrypt the fingerprint quadruple composed of rule hash, cumulative hash root, global trajectory root and compressed root, and complete the two-way verification of the fingerprint quadruple.

[0015] In addition, after the fingerprint quadruple is verified, the trust anchor generates an index set only for the part of the power difference component that is greater than the perception threshold value, encrypts it, and sends it up. The aggregation orchestrator completes the above-mentioned vector combination reconstruction and exponential decay coefficient determination, and sends them together to the respective master control objects under the slip fusion to form an autonomous power setting vector and a centrally optimized power vector.

[0016] Finally, the aggregation orchestrator calculates the constraint relaxation matrix based on the power deviation cumulative vector and line sensitivity matrix uploaded by the trust anchor, and embeds it together with the compensation weight vector into the security and economic integration optimization model containing economic cost terms and line relaxation penalty terms to obtain the new optimized power vector of each virtual power plant.

[0017] In addition to the above cases, the aggregation orchestrator allocates the system-level backup power to each microgrid in a certain proportion based on the recursive allocation factor of the cumulative power deviation of each virtual power plant, forming an instruction snapshot containing the optimized power vector, constraint relaxation matrix, recursive allocation factor and slip coefficient. It is encrypted using public key cryptography technology and sent to each microgrid. It is also written into the trust anchor mapping area together with the original safe operation envelope matrix and rule hash to update the snapshot version information.

[0018] An aggregated control system for multiple virtual power plants, including: Synchronize the pre-configured module. When communication is normal, configure a trust anchor on each virtual power plant gateway, synchronize the link health summary to the aggregation orchestrator, and pre-load the safe operation envelope and autonomous scheduling rules to establish the decision boundary. The loss of connection autonomous module, when the trust anchor detects a link interruption, immediately switches to autonomous mode, calls the safe operation envelope and autonomous scheduling rules to calculate the distributed energy power setting, and ensures that resources operate according to unified constraints; Deviation recording module: During autonomous operation, the trust anchor continuously records power deviation and event information, uses cryptographic hashing to generate a cumulative hash root and stores it locally to form a complete, continuous and verifiable operation trace; In the secure reconnection module, after the link is restored, the trust anchor first completes two-way authentication through a quantum key handshake, then uploads the fingerprint quadruple and current state and receives the latest global constraints issued by the aggregation orchestrator; In the unified polyphonic module, the aggregation orchestrator reconstructs the system constraints based on the uploaded power deviation, generates optimized power instructions, and smoothly replaces the autonomous power settings with a decreasing slip coefficient, so that each virtual power plant can be restored to unified scheduling.

[0019] (3) Beneficial effects The present invention provides a method and system for aggregated control of multiple virtual power plants, which has the following beneficial effects: The above is the continuous trust of the central anchor. Under normal communication, link health synchronization, safe operation envelope matrix loading and rule hash consolidation are completed. At the moment of loss of connection, the autonomous power setting vector is used to maintain unified boundary operation, and the trajectory hash root is used to record complete on-site changes. Finally, the quantum session key is used to achieve secure channel reconstruction, thus forming a complete closed tough chain to prevent distributed resources from being out of control due to communication anomalies.

[0020] By combining sparse difference sets with quaternion fingerprints, the edge side only needs to report a small amount of data to the aggregation orchestrator, allowing the aggregation orchestrator to reconstruct the complete power deviation vector and pass through multiple historical traces at once, completely independent of the process of massive log return. This reduces bandwidth usage while significantly shortening grid connection time and improving scheduling consistency.

[0021] A difference-driven constraint relaxation matrix is ​​used to map the accumulated power difference within the autonomous cycle into the safety margin of the line, and a compensation weight vector is used to convert the loss within the autonomous cycle into the objective function weight. Both are embedded in the safety and economic integrated optimization model, realizing centralized scheduling to calculate all information including physical information and economic information at one time. In the solution process, the synchronous results of safety verification and profit compensation can be obtained, highlighting the cross-layer collaborative technological innovation.

[0022] According to the time of loss of connection, the exponential slip attenuation coefficient is adaptively adjusted autonomously, the centralized weight and the recursive allocation factor are intelligently tilted according to the accumulated deviation amount, so as to realize the functions of suppressing power steps and voltage surges during the recovery process, giving priority to supporting virtual power plants with the greatest risks, thereby effectively increasing the dynamic stability margin of the distribution network.

[0023] The optimized power instructions, constraint relaxation matrix, recursive allocation factor, slip coefficient and existing safe operation envelope matrix are encapsulated into a five-tuple, and the optimized power instructions and other instructions are transmitted once in the mapping area. Even if the connection is lost again in the next cycle, it can directly fall back to the current autonomous mode and ensure the consistency of parameter versions, eliminating symbol drift and interface mismatch problems across cycles, forming a self-incrementing resilient closed loop.

[0024] The optimized power instructions, constraint relaxation matrix, recursive allocation factor, slip coefficient and existing safe operation envelope matrix are encapsulated into a five-tuple, and the optimized power instructions and other instructions are transmitted once in the mapping area. Even if the connection is lost again in the next cycle, it can directly fall back to the current autonomous mode and ensure the consistency of parameter versions, eliminating symbol drift and interface mismatch problems across cycles, forming a self-incrementing resilient closed loop. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 Schematic diagram of the process of the aggregated control method for multiple virtual power plants of the present invention; Figure 2 This is a schematic diagram of the structure of the aggregated control system for multiple virtual power plants of the present invention. DETAILED DESCRIPTION

[0026] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0027] See also Figure 1 The present invention provides an aggregate control method for multiple virtual power plants, comprising: In the multi-virtual power plant aggregation control system, the central-side aggregation orchestrator is used as the link to complete high-frequency interaction with each virtual power plant gateway via edge-cloud links, realizing real-time updates of the status of multiple virtual power plants and global optimization instruction generation functions.

[0028] However, uncertainty and potential intrusions make large-scale scheduling prone to resilience issues. When a link has problems or is invaded, the scheduling will lack the control capability of collaborative optimization. At this time, local constraints will be released, and reverse currents, frequency fluctuations, and even protection tripping will occur.

[0029] The first step is to use the normal communication window to enable the trust anchor side to complete link quality assessment, envelope loading and rule solidification, so that all virtual power plants have the same trusted decision semantics and output verifiable health summaries to the outside world.

[0030] The real-time scheduling of virtual power plants is achieved based on cloud-based algorithms. However, the cloud cannot promise that there will never be packet loss on the link. Once a problem occurs on the link, it will be too late to delegate the information to the edge. There is no unified system between the center and the edge, and data drift will occur once the data is transmitted back.

[0031] Therefore, it is necessary to synchronize the safe operation envelope and autonomous scheduling rules to the trust anchor that has been deployed when the link is healthy in advance, and continuously monitor the quality of the link through quantitative indicators on the edge side, so that smooth switching and accurate recovery on the edge side can be achieved at any time.

[0032] Link health assessment, envelope loading, and rule solidification are mutually dependent: envelope synchronization is triggered only when the link health reaches the threshold; only after envelope synchronization is complete can autonomous scheduling rules be formulated based on the data flow status downloaded in the previous step; and the rules are used to influence the calculation logic of the delay prediction item in the link health summary calculation.

[0033] Step 101: When communication is normal, the trust anchor collects packet loss rate, round-trip delay, and signal reliability in real time to generate link health. After synchronization with the aggregation orchestrator, it builds a safe operation envelope matrix based on the issued flow boundaries and solidifies the autonomous scheduling rules and rule hashes, providing a unified and verifiable decision baseline for future autonomous takeover.

[0034] When the communication is normal, the trust anchor will first conduct a detailed quality inspection on the link: message packet loss rate, round-trip delay, and signal reliability. The three-dimensional link health is quantified. Mapping to a unique scalar allows the center side and the edge side to form the same measurement coordinate system and achieve common evaluation. The expression is as follows:

[0035] Including: Link health , value range , the larger the value, the more stable the link; the weight coefficient satisfy , set at initialization according to the service level; Link packet loss rate , value range , represents the probability of message loss per unit time; link delay , value range , is the round trip time of the message; the maximum allowed delay , determined by industry standards and used for normalization; signal reliability , value range , represents the proportion of effective communication; The unified indicator makes the central and edge have the same threshold for health recognition, which provides the basic conditions for subsequent synchronization triggering. Greater than the set threshold When the aggregation orchestrator sends the current global power flow constraints, the trust anchor generates a safe operation envelope matrix that encapsulates the voltage, power, and frequency boundaries. The formula is as follows:

[0036] Among them: This parameter setting includes: ① Number of nodes , represents the number of distributed energy sources included in the virtual power plant; ② voltage upper and lower limits , which is determined by the capacity of the equipment in the distribution network; ③ Upper and lower limits of power , which are determined by the equipment nameplate and safety margin respectively; ④ Upper and lower frequency limits , is stipulated in the current grid operation rules; ⑤Unit matrix , dimension , refers to mapping the edges corresponding to the scalar to each node; ⑥ The size of the safe operation envelope matrix refers to the size of a set of three-dimensional boundaries configured for each energy node.

[0037] Matrixization realizes the calculable and one-key indexing function of the envelope, which facilitates the direct call of subsequent autonomous scheduling. After the synchronization is completed, the current link health of the trust anchor and the generated safe operation envelope matrix Hash compression is performed to obtain a link health summary of a certain length and written back to the aggregation orchestrator. The generated method is as follows:

[0038] in: , is a collision-resistant secure hash function; , indicating series connection; , column-wise matrix vectorization; link health summary , fixed length, used for subsequent identity verification and consistency verification of whether the data is correct.

[0039] By associating quality indicators with the envelope and recording them, no matter how the envelope changes in the future, it will be accompanied by a health certificate and will be traceable and tamper-proof.

[0040] Step 102: After the health and envelope are verified simultaneously, the trust anchor generates an autonomous scheduling rule based on the minimum baseline difference target, and writes the formed hash into the persistent mapping area after cross-verification. After the verification is completed, the envelope, power baseline and hash are linked by means of the efficiency safety cycle and the minimum power quantity policy, providing the required decision-making basis template for edge computing at any time.

[0041] After receiving the confirmation from the aggregation orchestrator, the trust anchor uses the security operation envelope matrix And the current distributed energy benchmark power vector obtained by itself Automatically generate autonomous dispatch rules to impose constraints on the local optimization goal after loss of connection. The core constraint formula is:

[0042] Where: Power decision vector , dimension , the power setting quantity to be solved in the current autonomous optimization, where is the number of distributed energy nodes in the virtual power plant, and each node corresponds to three types of control quantities: active power, reactive power, and frequency; Reference power vector ,The current sampling result is the central benchmark power instruction received and solidified by the trust anchor in the normal communication phase, which records the target output of each node during centralized scheduling, and is the reference center of the minimum deviation in the autonomous period; Two-norm , which measures the degree of deviation; is the unit vector on the right side; the inequality Indicates that all nodes are constrained by the envelope at the same time; Autonomous scheduling rules are established according to the minimum benchmark deviation target, taking into account user comfort and security boundaries. When a terminal loses connection, flexible tracking is carried out. Once the autonomous scheduling rules are solidified, the trust anchor will complete the timestamp of the rule generation. and the last round of link health summary After rehashing, a regular hash is formed , and cross-check with the records on the aggregation orchestrator side:

[0043] If the comparison is consistent, the aggregation orchestrator will return a verification pass flag, otherwise it will trigger retransmission until the hashes are aligned.

[0044] Two-way verification enables the center and edge to have the same cognitive perspective on the envelope and rules, and to achieve synchronization of disconnection and autonomy. After successful verification, the trust anchor will Write to the local persistent mapping area and have its own life cycle , when entering the disconnected state, it can only be If the mapping is referenced internally for a certain period of time, its weight will be downgraded to a safe minimum level to prevent decision drift caused by long-term offline conditions. The mapping envelope, cardinality power, and hash value are bound together using triple properties. This ensures that the mapping envelope has a lifetime and a history of references, and prevents invalid rules from being misused after multiple changes due to link instability.

[0045] It should be noted that when the trust anchor generates or updates certain key data (such as rule hash, link health summary, cumulative hash root, etc.) locally on the gateway, it will send the data to the aggregation orchestrator in the cloud through the communication link; the aggregation orchestrator stores the previous version or confirmed copy of the same data in its own database.

[0046] After the transmission is completed, the aggregation orchestrator will perform a field-by-field and bit-by-bit consistency check on the received value and the stored value. Only when the records on both sides are completely consistent will the aggregation orchestrator return a confirmation message that the verification has passed. If inconsistency is found, it is determined that the data has been tampered with during transmission or the versions are out of sync, and a retransmission or rollback is required.

[0047] After health metrics and envelope mapping in step 101, and rule solidification and summary verification in step 102, a single chain is formed from the central aggregation orchestrator to the edge trust anchor: quality assessment, security envelope, and autonomous rules, using a bidirectional hashing mechanism. This single chain tightly ties together communication quality, operational boundaries, and scheduling goals. This allows the edge to make simple decisions even when disconnected, and to re-verify identity and calculate balances when contact is established.

[0048] After step 1, the whole chain from link health assessment to autonomous rule solidification is achieved. The multi-virtual power plant aggregation control method also has the following three basic conditions: First, by evaluating the link health Form quantitative criteria and finally implement them to communication quality; secondly, use the safe operation envelope matrix Compress the constraints of the entire global trend to each edge node; third, use rule hashing To generate an algorithm for verifying a semantic closed loop. At the same time, under such conditions, the trust anchor can always obtain unified, reliable, and timely judgment parameters when communication is normal, so that it can be used as a basis for future disconnection handover, deviation backtracking, and resynchronization.

[0049] When communication is normal, the trust anchor has completed the first step: link health , safe operation envelope matrix , reference power vector and rule hashing Solidify into the local mapping area and give each set of mappings a life cycle However, the power communication network is relatively fragile and cannot withstand the risks brought by sudden link interruptions. For example, damage to the grid feeder, power outage of the cellular base station, or malicious attacks can cause the power generation side to be disconnected from the aggregation orchestrator. If the global scheduling results issued by the central optimizer are not available at this time, then the output of the distributed energy side will constantly change with weather conditions and load transients. Without a unified and implementable decision, each edge value will take independent action, resulting in problems such as reverse flow, frequency swings, and voltage over-limit.

[0050] Step 2: When the link is broken, the trust anchor switches to autonomous mode and uses the solidified security operation envelope matrix and the reference power vector To construct the autonomous power setting vector At the same time, the power deviation vector Carry out continuous monitoring and implement encrypted caching to establish a solid data foundation for constraint reconstruction and difference numerical calculation after the final restoration of communication.

[0051] Link loss causes a disconnect between the center and the edge. Local decisions based on local views lack a global view, which may cause a lot of resource competition in the local area. If the edge side only locks the current output, it ignores the dynamic risks caused by changes in renewable load and renewable power generation. An online autonomous optimization framework is needed that can inherit the safe operation envelope matrix given by the central side. At the same time, the safe operation envelope is continuously updated based on the actual data obtained, the solution process of the optimization problem is placed on the edge side, and an acceptable optimization time limit is set. , which can respond to requests for solutions in a timely manner without endless waiting, thus ensuring safe and economical operation during the period of loss of contact.

[0052] When the link monitoring thread detects health Below threshold And if there is no response from the central control unit for several consecutive sampling periods, the autonomous mode will be started. The autonomous mode consists of two parts: the autonomous optimization main line, which is responsible for outputting the autonomous power setting vector ; The second is the deviation tracking main line, constantly calculating the power deviation vector And save this vector and the time corresponding to the vector as part of a ciphertext sequence as a preliminary preparation for the third step of operation trajectory recording.

[0053] Step 201: When the link health is continuously less than the threshold and there is no central response, the trust anchor triggers the disconnection flag and locks the latest triplet; when the rule hash matches, the autonomous optimization model is started and the autonomous power setting vector is output within the specified time. All resources remain within the unified security boundary.

[0054] Once the monitoring thread continues No central heartbeat packet and link health status are received during the period , then the trust anchor will immediately mark the connection lost Set and put the last valid triple in the mapping area Locked down; and also checks the rule hash Is it consistent? If the rule hash If there is no consistency, reduce the power command to the minimum ratio , and will not stop until the rule hash is consistent.

[0055]

[0056] Including: Link health , numerical value , characterizes the quantitative value of communication quality; threshold , numerical value , indicating the disconnection trigger threshold; the number of consecutive cycles , positive integer, anti-shake count; lost connection mark , a binary variable, indicating the current link status; the minimum ratio : Numeric value , the minimum safe output coefficient of the equipment.

[0057] Based on strict loss of contact marking The hash consistency check ignores occasional jitter when determining the loss of connection, ensuring that the edge has obtained a trusted set of global boundary shards when determining the updated global boundary shards; When the target loses contact When the hash is consistent, the trust anchor rolls over Autonomous optimization uses minimizing power deviation as the objective function, balancing power deviation and optimization time to achieve the optimal solution.

[0058]

[0059] Where: Power decision vector , dimension , the autonomous optimization to be solved power settings, the active, reactive and frequency control corresponding to each distributed energy node , dimension , before the loss of central value, trust anchor in the normal period of communication to solidify the centralized scheduling instructions, the minimum deviation of the reference center in the autonomous stage; Autonomous cost function , scalar, that is, the autonomous optimization goal; Weighting coefficient , meet , so that the economy and real-time balance; Calculation time-consuming Is a real number greater than or equal to 0, the edge device completes the actual consumption of the calculation time of autonomous optimization in a rolling window, measured by the trust anchor timer; Safe operation envelope matrix , is a diagonal block matrix constructed by the aggregation orchestrator using the voltage, power and frequency boundaries of each node, that is, the physical safety interval is mapped to a normalized matrix under a unified standard; Optimization time limit , positive value, the longest tolerable optimization time under the loss of connection, determined by the system real-time control cycle requirements; Prediction window , that is, the trend part used to extract the future load curve, is positive; Power upper and lower limit vector , given by the device nameplate.

[0060] Economy and timeliness are written into the objective function to avoid edge overkill, and through hard limit , at any time, the appropriate autonomous power setting vector can be obtained within the controllable time delay .

[0061] Step 202: During the autonomous period, the trust anchor calculates the power deviation vector in real time, and checks the sensitivity of the power deviation vector according to the deviation value step by step; And the power deviation, time and offline state are signed by hash, and the cumulative hash root is updated rolling after each process, and the verifiable power deviation chain is gradually formed based on limited storage.

[0062] A self-autonomous power setting vector , after calculation, the power deviation vector is calculated immediately , detect whether it exceeds the deviation threshold, if Exceeds the deviation threshold , adjust the part exceeding the amount according to the sensitivity coefficient , recalculate until :

[0063] Where: power deviation vector , dimension , real-time deviation dimension; infinite norm , refers to the maximum value of the vector (i.e. the one with the largest absolute value of each component); the deviation threshold , is a positive real number used to define the maximum acceptable single node deviation; sensitivity coefficient , is a positive real number, indicating the step size; iteration index A non-negative integer.

[0064] By power deviation vector Dynamic reduction ensures that the power output in autonomous mode does not deviate too far from the user's expectations, while not exceeding the safety boundary, thus achieving both a good experience and high stability.

[0065] After the deviation check is passed, the trust anchor will Hash signature , and write it to the ring buffer, while updating the deviation cumulative hash root as the reference item in step 3:

[0066] Where: Timestamp , real number, sampling time, deviation signature , fixed-length hash sequence; ,current The cumulative hash root value of the round, The cumulative value of the previous round, the ring buffer area, and the capacity Overwrite old data in a first-in-first-out manner. Utilize the cumulative hash root Rolling updates save logs for quick difference checking for the next communication recovery, preventing recovery from being hindered due to excessive log size.

[0067] Step 2: Connect loss determination - mapping area call - rolling autonomous optimization - power deviation tracking - encrypted cache into a single chain, and set the vector through autonomous power Signature with deviation Parallel generation of safe operation envelope matrix It can be accurately landed in the lost state and provide the minimum and reliable data anchor point for the operation trajectory recording of step three and the quantum key resynchronization of step four.

[0068] Step 2: In the event of communication loss, two parallel main lines are used to ensure uninterrupted scheduling, that is, using the safe operation envelope matrix and the reference power vector Output autonomous power setting vector , the autonomous optimization main line is both economical and can meet the requirements of online real-time computing; at the same time, closely tracking the power deviation vector , establish a deviation chain and use the sensitivity value in the deviation chain, the cached error information and the encrypted hash to build a verifiable deviation chain so that subsequent different liquidation tasks can be carried out smoothly. The two main lines use the same mapping area parameters, and the connection is lost. The simultaneous operation of the driver ensures that the entire process scheduling can produce a unified constraint and obtain the corresponding correction capability. After entering step three, the trust anchor uses the accumulated hash root And the power deviation vector already in the operation log The operation log is expanded to complete the closed-loop operation process in a way that the entire control path is connected without any breakpoints.

[0069] In step 2, the autonomous power setting vector has been output when the connection is lost. , and continuously calculate the power deviation vector ; and using the deviation signature and cumulative hash root Generate the first layer of data fingerprint locally; the longer the loss of connection time, the greater the change in the distributed energy output curve, the more load fluctuations, and the more frequent on-site alarm events. Simply recording the power deviation vector The entire autonomous process cannot be fully restored. After communication is restored, using only fragmented logs as constraint backtracking can easily lead to power combination errors and frequency verification errors due to inconsistent timing.

[0070] Step 3: During autonomous operation, perform high temporal resolution sampling, serialize and unify the multimodal data streams generated, encrypt the hash chain, and output a verifiable operation trace root. , and locally index it using a sliding window method to ensure data integrity while saving storage.

[0071] The data types in the autonomous stage are complex and diverse, including continuous power deviation vectors , discrete distributed energy alarm events and enumeration manual intervention mark If these events are placed separately, restoring communication requires multi-channel synchronization, wasting bandwidth and making it difficult to decompress the order. Therefore, to reduce complexity, events of different modalities must be encoded on the same timeline and solidified into a sequence using a hash chain. The autonomous phase continues to operate in real time, with new logs constantly generated. However, local storage is limited, and blindly expanding it would squeeze the controller's computing space. Therefore, a sliding index + segmented root approach should be considered to ensure complete traceability. However, given storage limitations, the number of log entries cannot be increased indefinitely.

[0072] Step three is divided into two tightly coupled main lines: the trajectory encoding main line is used to encode different events at the same time granularity Merge into fragment vector , and calculate the fragment hash ; The root chain maintenance main line is used for continuous fragment hashing to roll and converge in order to generate the global track root , and use the dual queue management method to ensure the latest window Replayability of the fragment. Every update of the two main lines is a cumulative hash root of step 2. Update operation to ensure homology synchronization; if the trust anchor in this step detects the corresponding life cycle threshold , it automatically triggers the compression of historical segments, thus avoiding the storage burden caused by a large amount of redundant historical data.

[0073] In step 301, a trust anchor is used to fix the serialization period. On the sequence of power deviation, alarm event, and manual intervention vectorized splicing, the window vector of the fragment sliding is accumulated into the sequence matrix in a fixed serialization period. The fragment hash is generated for the global trace root maintenance thread to input multi-modal events with consistent granularity.

[0074] In order to solve the problem of unifying continuous, discrete and enumeration events, in each serialization cycle In the example, the trust anchors are constructed as sequence vector sequences :

[0075] Where: continuous power deviation vector , dimension , comes from the rolling optimization result of step 2; discrete alarm vector , dimension , each bit represents whether a type of event is triggered, the number of alarm types , positive integer, alarm type defined by the system; Artificially labeled vectors , dimension , encoding the operator's intervention type; a sequence vector , dimension , at the same time Forming multi-mode parallel; number of nodes , positive integer, the number of distributed energy resources in the virtual power plant; the number of manual markings , positive integer, manual intervention action category; serialization cycle , positive real number, ranging from milliseconds to seconds; vector concatenation symbol , which means connecting by columns.

[0076] Vectorized operations represent data of different modalities in the same structure, which can make the subsequent hash function unchanged for the input structure, avoid the problem of hash offset caused by different input data formats, and simplify the parsing logic in the recovery phase.

[0077] After serialization, the trust anchor sets the window length of the segment All sequence vectors within are accumulated, , together form this sequence matrix , and then apply the hash function to get the fragment hash :

[0078] Where: Fragment Hash , fixed-length byte string; segment start and end markers , record the time range for the root chain to maintain the main line reference; fragment window length , a positive real number, typically ranging from a few seconds to tens of seconds; the sequence matrix ,size ; The fragmentation strategy balances granularity and volume: the sequence granularity is determined by the serialization period , the fragment body is determined by the fragment window length During the recovery period, two-level segmentation can be used to select either fast positioning or fine playback according to needs.

[0079] Step 302: The root chain maintenance thread uses a recursive hashing method to concatenate the fragment hashes into a global track root, and uses a sliding window to maintain active fragments, cold-archive the old fragment hashes, and after the fragment mapping expires, cold-hash out a new compressed root and co-source it with the accumulated hash root to maintain historical traceability and storage controllability.

[0080] Generate a new fragment hash After that, the global trace root of the trust anchor will be updated recursively :

[0081] At the same time, the new fragment hash Push it into the active fragment queue along with the time stamp; if the queue length exceeds the window , then store the earliest fragment in the cold archive index and delete the plaintext, retaining its hash value ; Among them: global trajectory root , No. The root value of the update, Corresponding to the previous cycle; active fragment queue, capacity , keep the most recent window replayable; cold archive index, only save the hash reference of the archived fragment.

[0082] The recursive root chain ensures that any modification of a single segment will affect the global trajectory root. The sliding window allows the trust anchor to perform fine-grained playback of recent fragments even with limited storage space, achieving good resource utilization while ensuring security.

[0083] After the update is completed, the root chain will maintain the latest global track root of the main line Write to the cumulative hash root in step 2 and keep them consistent; check the autonomous mapping life cycle and the current running time difference Whether the set upper limit is met.

[0084] If the difference (early warning threshold), the pre-expiry compression process will be started.

[0085] Rehash all cold archive segments from outside the active queue and output the compressed root Write the mapping expiration reminder mark area, which is used in step 4 to complete the verification in one step during quantum key resynchronization and establish the compression root :

[0086] Where: Lifecycle threshold , positive real number, the effective length of the mapping area; running time , positive real number, accumulated since the loss of connection; early warning threshold , a positive real number, used for compression triggering; compression root , fixed-length hash, used for one-time verification, Hash for archive fragments; Lifecycle gating combines data compression and mapping updates while actively protecting storage space, providing a single checkpoint for step 4. During the quantum key recovery phase, there is no need to extract too much cold data to prolong the resynchronization delay.

[0087] When communication is lost, step three is to serialize the cycle The three key events in the fragment window are vectorized with high resolution. Forming hash fragments , continuously cyclically superimposed to form the global trajectory root , synchronously update the global cumulative hash root , synchronously update the global cumulative hash root This structure supports both instantaneous playback and long-term historical compression. Leveraging the linear structure, the large amount of heterogeneous data generated by the autonomous node process occupies a fixed amount of space to retain verifiable records. Furthermore, this data can be used for very simple and rapid consistency checks after the completion of the quantum key handshake, without involving any quantum key handshake.

[0088] At the same time, the sliding window mechanism ensures that the most recent fragments can be used at any time for fault playback or operation and maintenance analysis; the cold archive compression root can effectively prevent the historical expansion problem from occupying edge resources. and mapped to the run cycle Inside, and the power deviation signature generated in step 2 On this basis, the trajectory chain generated in this step realizes the integration of physical security, economic operation and data credibility, providing a practical and verifiable big data underlying basic platform for subsequent quantum key resynchronization, and providing a complete, continuous and traceable autonomous operation picture before multiple virtual power plants resume centralized scheduling.

[0089] From this stage onwards Running cycles, after cumulative online, the global trajectory root is generated and compressed roots , and synchronized with the cumulative hash root, as the communication link gradually recovers after on-site repair or network self-healing, the edge detects that the link health is rapidly increasing, the packet loss rate drops sharply, and the three-way handshake can also run completely.

[0090] Recovery brings both opportunities and risks: if an erroneous forged control message is accepted, all security gains previously obtained in the autonomous phase will be lost; if there is no effective differential synchronization mechanism, the huge amount of historical logs will also delay the speed of grid connection.

[0091] Step four is to use quantum key distribution to ensure the authenticity of the identities of both parties in the link and the confidentiality of the data, realize the status reconciliation between the autonomous stage and the central side with the minimum data increment, download the latest global constraints, and use the local power command slip fusion virtual power plant to re-join the unified scheduling.

[0092] The key stream of quantum key distribution is one-time, unclonable, and unique. If it were incorporated into the scheduling link, any eavesdropper's actions would be instantly detected, subjecting them to the qubit collapse effect. However, due to the limited bandwidth of quantum channels, large amounts of log data cannot be accumulated. Therefore, a symmetric key must first be established using quantum keys. Then, a holistic verification process is performed using a four-tuple of lightweight rule fingerprints, cumulative hash roots, global trace roots, and compressed roots. This predetermines the trusted edge, and finally, only the minimal difference set needs to be uploaded to restore the central model. This approach ensures security while minimizing bandwidth usage and achieving high real-time performance.

[0093] Step 401: Implement the handshake-identity-fingerprint triple chain to verify the trusted channel and ensure data integrity; and step 402: Regain the constraints by reporting the differences, and use the slip fusion closed loop to complete the smooth splicing of the local power command and the central optimization result. Both use the quantum session key and the fingerprint quadruple. Step 401 outputs the pass mark. To decide whether to execute step 402, the process operation is kept seamlessly progressive.

[0094] Step 401: After the link is restored to normal, the trust anchor and the aggregation orchestrator complete the quantum key handshake, and then use the session key to encapsulate the transmission rule hash, cumulative hash root, global trajectory root and compression root information and send them to the central comparison to verify whether the fingerprint is consistent. After the verification is completed, the authentication success mark is returned to the entire cloud-network converged data center network-to-cloud networking system.

[0095] During the link establishment process, the trust anchor and the aggregation orchestrator initiate the BB84 protocol to exchange polarization basis selection, measure the quantum bits, and then remove the inconsistent bits to obtain the original key string. , and then the final quantum session key is obtained through information coordination and privacy amplification:

[0096] Where: Privacy amplification function , the original key string Perform reversible hash mapping, security parameters , is a positive real number that represents the upper bound of acceptable leakage, i.e., the upper bound of acceptable leakage; quantum session key , the length is equivalent to AES-256, where: the original key string , is the bit string measured by the quantum channel; the quantum session key , binary representation, fixed bit length, used to participate in AES encryption in the following steps.

[0097] A large number of high-entropy keys are generated at one time through quantum bits and any eavesdropping behavior is detected by bit error rate statistics, thus ensuring that the data at the session layer has not been tampered with during the fingerprint reconciliation process; after generating the quantum session key After that, it can be converted to use AES-GCM symmetric session layer, and the edge side sends the fingerprint quadruple to the reconciliation server to obtain the fingerprint set :

[0098] The central side calculates the self-stored image , and compare with the existing image, if all are consistent, set :

[0099] Among them: fingerprint set , that is, edge message payload, mirror set , i.e. central archive fingerprint, authentication mark , that is, a binary value, which determines whether the subsequent process is executed.

[0100] The four-tuple fingerprint covers four chains: envelope, power deviation, serialization trajectory, and cold archive compression. Only when any chain in the fingerprint is tampered with will the fingerprint verification fail, thus achieving the purpose of integrity proof with minimum upload and maximum coverage.

[0101] Step 402: After authentication is passed, the trust anchor uploads the sparse power difference component index set greater than the perception threshold to the central end. The central end reconstructs the complete deviation and sends the centrally optimized power vector. The edge end adaptively adjusts the slip attenuation coefficient based on the offline duration to smoothly flatten the autonomous power vector to the instructions of the central end.

[0102] exist Under the premise of With local autonomous power setting vector , and get the difference vector :

[0103] To reduce link load, only upload data greater than the threshold Sparse difference set of :

[0104] Each corresponding component in the set is encrypted with AES-GCM and then sent out.

[0105] Among them: different difference vectors : Dimension , edge-center power difference; threshold : positive real number, greater than zero, upper limit of insensible difference; sparse difference set ,Subset, refers to the index of the uploaded items.

[0106] Upload the difference according to the perception threshold, use exponential compression to make the uploaded data volume lower than the linear growth rate, while not affecting the central end to accurately determine the flow changes in the autonomous stage, and obtain the sparse difference set from the central side. , and reconstruct the complete difference vector from this set , which is compared with the latest global optimized power vector The slip fusion vector is obtained by combining , which is then replaced by the purely global optimization power vector Control, as follows:

[0107] Where: Synchronous time interval , is the actual duration of loss of contact; slip attenuation coefficient , with the synchronization time interval exponentially decreasing; Globally optimized power vector , dimension , the latest central instructions; slip attenuation coefficient , , determines its gradual degree; exponential decay coefficient , is a positive real number, which adjusts the fusion speed; the slip fusion vector , which are instructions executed during the transition period.

[0108] Exponential slip fusion eliminates step changes between autonomous and centralized commands, suppressing voltage surges. The slip attenuation coefficient adaptively decays based on the loss of connection time. The longer the loss of connection, the smaller the remaining autonomous weight, ensuring stability while also taking into account economic efficiency.

[0109] Step 401: Output the quantum session key and certification marks , handed over to 402 as a trusted channel; after the four-element fingerprint verification is completed, only the different vectors are uploaded, and only sparse difference sets are included , greatly reducing the transmission bandwidth; at the same time, the exponential sliding fusion vector By slip attenuation coefficient The autonomous state is smoothly transitioned to centralized optimization, and based on this, the constraint reconstruction and secondary optimization allocation of the system in step five are carried out to achieve dual continuity of data and power.

[0110] Step 4: When achieving link recovery, the following four requirements must be taken into account: zero identity forgery, zero data omission, zero bandwidth waste, and zero power mutation. To ensure that the link cannot be eavesdropped, the central station and the edge station can use a very small amount of data to complete the full-stage consistency verification of this step at any time by using the four-element fingerprint; when uploading the difference vector, only data greater than the threshold value is uploaded, and the massive logs are compressed into several packets; the exponential slip fusion uses adaptive weights Achieve smooth and non-jumpy fusion at the physical level, ensuring that when multiple virtual power plants are connected to the grid, the circuit breaker protection will not be triggered due to low or high voltage. The aggregate orchestrator completes the power flow reconstruction based on the verified trajectory root and uses the slip attenuation coefficient as the transition option weight re-entry optimization model to truly achieve smooth recovery and unified scheduling.

[0111] After confirming that the link is secure after the quantum handshake, the differential vector has been uploaded to the edge side and the exponential slip fusion has been executed on the trust anchor side, these three core information have fallen into the hands of the aggregation orchestrator: the first type is the fingerprint quadruple from each virtual power plant: rule hash , cumulative hash root , global trajectory root , compressed root , used to build the process time chain of the entire historical autonomous period; the second type is the sparse difference set And the corresponding power difference value, showing the size of the gap between the actual autonomous power and the central benchmark and their respective distribution forms; the third category is the slip fusion vector It is running continuously in each trust anchor side and is controlled by the slip attenuation coefficient We are constantly moving closer to the goal of central optimization.

[0112] Step 5. On the central side, the system's constraint reconstruction and global optimization are completed based on the minimum patch principle. The optimized results are distributed to each virtual power plant through recursive allocation and adaptive compensation coefficients, allowing the virtual power plant to smoothly transition from the slip fusion stage to fully unified scheduling, and output the parameter snapshot to the next scheduling cycle.

[0113] The power deviation in the autonomous phase is not random noise, but reflects the edge's subtle information about local weather, load, and equipment status. If this information is crudely covered by the central side, it is equivalent to abandoning the knowledge from the autonomous phase and deviating from the control target again. On the other hand, deviation compensation without power flow safety verification may introduce new constraint conflict points after the global optimization, so it is necessary to convert the autonomous deviation into a constraint relaxation coefficient and overlap it with the main network's safety margin and market price weight to participate in the global optimization. The process of compensating for differences and absorbing autonomous knowledge is realized during the formula calculation.

[0114] Step 501 uses the two technical means of constrained reconstruction and relaxation coding to convert the sparse difference set and the deviation statistics parsed from the four-element fingerprint into It is converted into a constraint relaxation matrix and a compensation weight vector; then, on this basis, a full period of multi-virtual power plant safety-economic integrated optimization scheduling work is carried out, and the new instructions and new parameters are packaged and saved as a direct basis for subsequent cycle decision-making.

[0115] In step 501, the aggregation orchestrator reconstructs the power deviation vector using the line sensitivity matrix based on the uploaded sparse difference patch; generates a constraint relaxation matrix from the line sensitivity matrix; converts the economic loss during the autonomous period into a compensation weight vector, and simultaneously imports both physical relaxation and economic compensation information into the safety and economic integration optimization.

[0116] The aggregation orchestrator first collects the sparse difference sets generated by all virtual power plants (the superscript represents a virtual power plant), after fingerprint verification, the complete deviation vector is reconstructed:

[0117] Among them: standard basis vector , a unit vector whose length is equal to the dimension of the power decision vector, dimension , No. bit is 1, the rest are 0; Reconstructing the bias vector , dimension , the power difference after completion refers to the virtual power plant The complete power difference vector after the sparse patch is restored has the same dimension as the safe operation envelope matrix, and the order of elements corresponds to the active, reactive and frequency control quantities of each node; the sparse difference set , for virtual power plants The set of power difference entry numbers that are only uploaded during the link recovery phase; Power difference , the difference value of the corresponding index; for the virtual power plant Uploaded The threshold power difference scalar represents the instantaneous deviation of the autonomous power setting from the central reference power at the node; the number of nodes , the number of distributed energy resources within the virtual power plant; Completing the sparse patch to a complete vector and performing homogeneous expansion to facilitate the subsequent constrained reconstruction operation through the matrix, and then the power deviation accumulation vector :

[0118] and line sensitivity matrix Multiply to generate the constraint relaxation matrix :

[0119] Where: Power deviation accumulation vector , represents all the deviations accumulated by the node during the autonomous period; the line sensitivity matrix ,size , used to map node power into power flow changes on the line; constraint relaxation matrix ,size , which is used to give the given slack on each restricted line; the line entry refers to the number of all monitored lines under the virtual power plant.

[0120] The accumulated power difference is converted into line-level slack to dynamically adjust the safety margin size during global optimization, and the effective input of autonomous knowledge is maintained; at the same time, the market price slope vector of the aggregation orchestrator is converted into and the economic deviation vector in the autonomous period The weighted sum of is used as the compensation weight vector:

[0121] Where: Market price slope vector , dimension , comes from the day-ahead market; the economic deviation vector in the autonomous period , obtained by integrating power deviation and real-time electricity price; weight coefficient ,satisfy , according to regulatory agreements, that is, not adjusted according to the market process; the compensation weight vector , dimension , indicating the dimension size used for the objective function.

[0122] Thus, centralized optimization not only takes immediate market signals into account, but also incorporates the marginal losses borne by each virtual power plant during the autonomous period as a compensation factor to ensure economic fairness.

[0123] Step 502, the central side jointly uses the compensation weight vector and the constraint relaxation matrix in the security-economy integrated model to solve the optimization power vector of each virtual power plant, and generates an instruction snapshot containing the power instruction, the relaxation matrix, the allocation factor and the slip coefficient after allocating the standby power according to the cumulative deviation size, and issues and writes it into the edge mapping area for direct calling in the next period.

[0124] The polymeric orchestrator constructs a security-economy integrated optimization model:

[0125]

[0126] Wherein: the number of virtual power plants , the number of accessed virtual power plants; the power decision vector , the optimization variable, the dimension ; the reference power vector , the instruction of the previous period; the security operation envelope matrix , solidified by step one; the relaxation matrix , see above; a norm , indicating the total amount of line relaxation; the trade-off coefficient , a positive real number, balancing economic cost and security relaxation; the upper and lower limits of power , equipment constraints.

[0127] The first half of the objective function minimizes the power deviation according to the economic weight, and the latter part uses a norm to punish the line relaxation, forcing the algorithm to prefer to digest the deviation rather than uncontrolled relaxation; the relaxation matrix is added to the right end in the constraint, allowing limited security margin to be transferred. The model solution is the new power vector of each virtual power plant . In order to avoid the concentration of sudden load, the central side allocates the recursive allocation factor:

[0128] The system-level standby power is injected into the virtual power plant with the most relaxed relaxation to improve the global margin.

[0129] Wherein: the recursive allocation factor , the proportion coefficient, non-negative and sum to one; the cumulative power deviation norm , risk measure.

[0130] The virtual power plant with greater deviation during the autonomous period is regarded as a risk hotspot and is given priority to standby power support, embodying the differentiated resilience compensation mechanism. After optimization, the central side generates an instruction snapshot :

[0131] and transmits it through the session key Encrypted and sent. After receiving, the trust anchor replaces the slip fusion vector part, and will Write to the next cycle mapping area and the old triplet Consists of five snapshots with increasing version numbers.

[0132] Instruction snapshot , centralized optimization of output package; slip coefficient , still decreasing to zero according to the duration of loss of connection; the five-element snapshot serves as the starting baseline for the trust anchor of the next cycle.

[0133] Snapshot solidification horizontally connects new instructions and old security envelope parameters to maintain consistency. If the connection is lost again in the next process cycle, the new version snapshot can be called up by simply connecting to the trust anchor and entering autonomy without further negotiation. This is how closed-loop resilience is enhanced.

[0134] Step 501 maps the difference patches uploaded by each virtual power plant into a constraint relaxation matrix and compensation weight vector Step 502 uses endogenous relaxation-economic target integration optimization to solve , and recursively assign factors Dynamic support for risk hotspots; final instruction snapshot Combined with the old security envelope matrix A snapshot quintuple is formed. During the next week, the trust anchor can directly use the snapshot as a template to continue to perform link health synchronization.

[0135] After completing step five, the final jump from autonomous recovery to fully unified scheduling of multiple virtual power plants is achieved: with the help of matrix relaxation and economic weight coupling, all implicit power and economic information during the autonomous period will be equally incorporated into the global model to obtain the final safe and fair optimization results; increasing the recursive allocation factor to provide more backup support for riskier virtual power plants and improve the resilience of the system; all new and old core parameters are packaged into an instruction snapshot, so that the correct decision version baseline can be quickly found in any communication scenario at any point in time, thereby providing a good idea from basic methods to practical applications for all multiple virtual power plants to achieve the best results in a long-term and stable manner under multiple communication conditions in the future.

[0136] See also Figure 2 The present invention provides an aggregated control system for multiple virtual power plants, including: Synchronize the pre-configured module. When communication is normal, configure a trust anchor on each virtual power plant gateway, synchronize the link health summary to the aggregation orchestrator, and pre-load the safe operation envelope and autonomous scheduling rules to establish the decision boundary. The lost autonomous module switches to autonomous mode immediately when the trust anchor detects link interruption, invokes the secure operation envelope and autonomous scheduling rule to calculate the distributed energy power setting, and ensures that resources operate according to unified constraints; The deviation recording module records power deviation and event information during autonomous operation, generates an accumulated hash root by using an encryption hash, and stores the accumulated hash root locally to form a complete and continuous verifiable operation track; The secure reconnection module first completes two-way identity authentication through quantum key handshake after link recovery, then uploads a fingerprint four-tuple and a current state, and receives the latest global constraints issued by the aggregated orchestrator; The unified re-tuning module reconstructs system constraints according to uploaded power deviation, generates optimized power instructions, and replaces the autonomous power setting with a decreasing slip coefficient to make each virtual power plant resume unified scheduling.

[0137] Those skilled in the art can appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0138] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working process of the system, device and unit described above can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0139] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0140] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on a plurality of network units. According to actual needs, some or all of the units can be selected to achieve the purpose of the embodiment scheme.

[0141] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for aggregated control of multiple virtual power plants, characterized by: include, When communication is normal, a trust anchor is configured on each virtual power plant gateway, link health summaries are synchronized to the aggregation orchestrator, and safe operation envelopes and autonomous scheduling rules are pre-loaded to establish decision boundaries. When the trust anchor detects a link interruption, it immediately switches to autonomous mode, calls the safe operation envelope and autonomous scheduling rules to calculate the distributed energy power setting, and ensures that resources operate according to unified constraints; During autonomous operation, the trust anchor continuously records power deviation and event information, generates a cumulative hash root using cryptographic hashing, and stores it locally to form a complete, continuous, and verifiable operation trace; After the link is restored, the trust anchor first completes two-way authentication through quantum key handshake, then uploads the fingerprint quadruple and current state and receives the latest global constraints issued by the aggregation orchestrator; The aggregate orchestrator reconstructs system constraints based on the uploaded power deviation, generates optimized power instructions, and smoothly replaces the autonomous power setting with a decreasing slip coefficient, so that each virtual power plant can resume unified scheduling.

2. The method for aggregated control of multiple virtual power plants according to claim 1, characterized in that: The trust anchor collects three communication indicators in real time: message packet loss rate, round-trip delay, and signal reliability rate, and linearly normalizes them according to preset weight coefficients to generate link health. The link health is then connected in series with the safe operation envelope matrix and input into the collision-resistant hash function to form a link health summary. The summary is then sent to the aggregation orchestrator to complete version registration and consistency verification.

3. The method for aggregated control of multiple virtual power plants according to claim 2, characterized in that: When the link health is above the threshold, the aggregation orchestrator issues global power flow constraints. The trust anchor constructs a safe operation envelope matrix based on the constraints, including node voltage, node power, and system frequency boundaries. It then generates an inequality autonomous scheduling rule with the minimum baseline power deviation as the target and the envelope matrix as the constraint. It then generates a rule hash and sends it back to the aggregation orchestrator.

4. The method for aggregated control of multiple virtual power plants according to claim 3, characterized in that: When the trust anchor detects that the link health is lower than the trigger threshold and there is no central response during the continuous monitoring cycle, it sets the loss of connection flag and locks the envelope matrix and autonomous rules that have passed the hash check the most recently. If the rule hash is inconsistent with the local record, the power of each distributed energy source will be reduced to the minimum ratio until the hash is consistent.

5. The method for aggregated control of multiple virtual power plants according to claim 4, characterized in that: In autonomous mode, the trust anchor uses a rolling time window to predict the load, performs secondary optimization on the power decision vector with the weighted goal of minimum economic deviation and shortest calculation time, and forces the calculation time to not exceed the tolerable time limit to output the autonomous power setting vector.

6. The method for aggregated control of multiple virtual power plants according to claim 5, characterized in that: The trust anchor uses the serialization period as the time granularity, splices the power deviation vector, alarm event vector and manual intervention vector into a unified sequence vector, accumulates them according to the fragment window length to form a sequence matrix and generates a fragment hash, records the start and end time of the fragment and sends it to the root chain maintenance thread.

7. The method for aggregated control of multiple virtual power plants according to claim 6, characterized in that: The root chain maintenance thread concatenates the latest fragment hash with the global trace root of the previous round through recursive hashing to form a new root value, and saves active fragments in a sliding window manner. Fragments outside the window only retain the hash value and write it into the cold archive index. At the same time, all cold archive fragments are re-hashed to generate a compressed root before the mapping expires.

8. The method for aggregated control of multiple virtual power plants according to claim 7, characterized in that: The trust anchor and the aggregation orchestrator use the BB84 protocol to complete quantum random bit exchange, and obtain the quantum session key after information coordination and privacy amplification. The corresponding key is then used to encrypt and transmit the fingerprint quadruple consisting of the rule hash, cumulative hash root, global trajectory root and compressed root and complete two-way verification.

9. The method for aggregated control of multiple virtual power plants according to claim 8, characterized in that: After the fingerprint quadruple is verified, the trust anchor only generates an index set for the power difference component that exceeds the perception threshold and uploads it encrypted. The aggregation orchestrator reconstructs the complete power deviation vector and slip-merges the autonomous power setting vector and the centrally optimized power vector with an exponential decay coefficient.

10. The method for aggregated control of multiple virtual power plants according to claim 9, characterized in that: The aggregation orchestrator generates a constraint relaxation matrix based on the power deviation cumulative vector and line sensitivity matrix uploaded by each trust anchor. This matrix and the compensation weight vector are embedded in a safety-economy integrated optimization model that includes economic cost terms and line relaxation penalty terms to obtain the new optimized power vector of each virtual power plant.

11. The method for aggregated control of multiple virtual power plants according to claim 8, characterized in that: The aggregation orchestrator calculates the recursive allocation factor based on the l-norm of the cumulative power deviation of each virtual power plant, proportionally allocates the system-level backup power, and generates an instruction snapshot containing the optimized power vector, constraint relaxation matrix, recursive allocation factor and slip coefficient. The instruction snapshot is encrypted and sent down and written into the trust anchor mapping area together with the original safe operation envelope matrix and rule hash to complete the snapshot version upgrade.

12. An aggregated control system for multiple virtual power plants, characterized by: include, Synchronize the pre-configured module. When communication is normal, configure a trust anchor on each virtual power plant gateway, synchronize the link health summary to the aggregation orchestrator, and pre-load the safe operation envelope and autonomous scheduling rules to establish the decision boundary. The loss of connection autonomous module, when the trust anchor detects a link interruption, immediately switches to autonomous mode, calls the safe operation envelope and autonomous scheduling rules to calculate the distributed energy power setting, and ensures that resources operate according to unified constraints; Deviation recording module: During autonomous operation, the trust anchor continuously records power deviation and event information, uses cryptographic hashing to generate a cumulative hash root and stores it locally to form a complete, continuous and verifiable operation trace; In the secure reconnection module, after the link is restored, the trust anchor first completes two-way authentication through a quantum key handshake, then uploads the fingerprint quadruple and current state and receives the latest global constraints issued by the aggregation orchestrator; In the unified polyphonic module, the aggregation orchestrator reconstructs the system constraints based on the uploaded power deviation, generates optimized power instructions, and smoothly replaces the autonomous power settings with a decreasing slip coefficient, so that each virtual power plant can be restored to unified scheduling.

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