Multi-service enforceable capacity voucher causal settlement method and device and storage medium

Abstract

This application provides a causal settlement method, apparatus, and storage medium for multi-service fulfillable capacity certificates. The method includes: discretizing energy storage capacity into fulfillable capacity certificates according to nodes and time slices; generating congestion capacity certificates based on distribution network constraints and establishing a binding relationship with the fulfillable capacity certificates; optimizing the combination of fulfillable capacity certificates and congestion capacity certificates during the day-ahead phase to obtain a commitment list and control parameters; issuing control parameters to equipment to execute charging / discharging and reactive power control; collecting active power, reactive power, voltage, and frequency sequences during the metering phase, performing time-frequency decomposition, and generating a certificate-by-certificate receipt based on baselines and causal attribution; aligning the updated execution list with the certificate-by-certificate receipt, completing certificate-by-certificate accounting based on the constraint price corresponding to the congestion capacity certificate, and outputting settlement records organized according to fulfillable capacity certificates for multi-service settlement processing of vehicle-grid-load-storage. This application can achieve multi-service settlement without over-commitment, attributable metering, and auditable consistent settlement.

Description

Technical Field

[0001] This application relates to the field of energy storage technology, and in particular to a method, apparatus and storage medium for causal settlement of multi-service fulfillable capacity certificates. Background Technology

[0002] With the high proportion of new energy sources connected to the grid and the rapid development of electric vehicles, the integration of vehicle-grid-load-storage has become a key path to improve the flexibility of the distribution side. Energy storage can simultaneously participate in multiple services in the distribution network, such as primary frequency regulation, reactive power support, peak shaving and valley filling, and congestion management.

[0003] However, existing projects mostly adopt scenario-based joint optimization or item-by-item superposition scheduling, and network constraints are often handled by exogenous parameters, making it difficult to uniformly constrain them at the commitment level; the metering and settlement sides generally rely on baseline estimation and aggregated electricity accounting, making it difficult to distinguish the specific contributions of different time scales and service types.

[0004] Existing technologies therefore suffer from the following drawbacks: overlapping service commitments and constraint conflicts for the same energy storage resource are difficult to explicitly avoid; there is a lack of a unified orchestration model for service priorities and transferability; distribution network power flow, branch thermal limits, and voltage constraints are difficult to be intrinsically integrated into the entire commitment and settlement process; metering calibers are difficult to separate high-frequency regulation from low-frequency energy transfer, easily leading to duplicate metering and unclear attribution; execution and settlement records lack verifiable documentation, resulting in high auditing and dispute resolution costs. These issues directly affect the feasibility and settlement of multi-service stacking, restricting the sustainable revenue model and financing capabilities of energy storage assets. Summary of the Invention

[0005] In view of this, embodiments of this application provide a multi-service fulfillable capacity certificate causal settlement method, apparatus and storage medium to solve the problems of overlapping and conflicting constraints of multi-service commitments, difficulty in unifying priority arrangement and distribution network constraints, and unclear metering attribution leading to duplicate metering and inconsistent settlement in the prior art.

[0006] A first aspect of this application provides a causal settlement method for multi-service fulfillable capacity certificates, comprising: obtaining network constraints and energy storage capacity based on grid topology and access nodes to form a resource capacity and constraint set; discretizing energy storage capacity into fulfillable capacity certificates according to nodes and time slices, and generating congestion capacity certificates according to distribution network constraints and establishing a binding relationship with the fulfillable capacity certificates; performing combined optimization of fulfillable capacity certificates and congestion capacity certificates in the day-ahead stage, constrained by state of charge, power, energy, ramping and reactive power, and non-overlapping relationships, to obtain a commitment list and control parameters; and issuing control parameters to equipment to perform charging, discharging and reactive power control. The system records execution information associated with the commitment list. In the real-time phase, the commitment list is rearranged with minimal disturbance and the execution list is updated according to the rules of hard priority (non-transferable) and soft priority (transferable). In the metering phase, active power, reactive power, voltage, and frequency sequences are collected, time-frequency decomposition is performed, and baseline and causal attribution are combined to map incremental active power, reactive power, and ramp-up quantities to the corresponding fulfillable capacity certificates according to time slices and nodes, generating a certificate-by-certificate receipt. The updated execution list and the certificate-by-certificate receipt are aligned, and the certificate-by-certificate accounting is completed by combining the constraint price corresponding to the congestion capacity certificate. The settlement records organized according to the fulfillable capacity certificates are output for settlement processing of multiple services such as vehicle-grid-load-storage.

[0007] A second aspect of this application provides a multi-service fulfillable capacity certificate causal settlement device, comprising: an acquisition module, configured to acquire network constraints and energy storage capacity based on grid topology and access nodes, forming a resource capacity and constraint set; a discretization module, configured to discretize energy storage capacity into fulfillable capacity certificates according to nodes and time slices, and generate congestion capacity certificates according to distribution network constraints and establish a binding relationship with the fulfillable capacity certificates; an optimization module, configured to perform combined optimization of fulfillable capacity certificates and congestion capacity certificates in the day-ahead stage, constrained by state of charge, power, energy, ramp and non-functional capacity, and non-overlapping relationships, to obtain a commitment list and control parameters; and a distribution module, configured to distribute control parameters to equipment to execute charging. The system controls discharge and reactive power, and records execution information associated with the commitment list. In the real-time phase, it rearranges the commitment list with minimal disturbance and updates the execution list according to the hard priority non-transferable and soft priority transferable rules. The generation module collects active power, reactive power, voltage and frequency sequences during the metering phase, performs time-frequency decomposition and combines baseline and causal attribution to map incremental active power, reactive power and ramp-up quantities to the corresponding fulfillable capacity certificates according to time slices and nodes, generating a certificate-by-certificate receipt. The output module aligns the updated execution list with the certificate-by-certificate receipt, completes certificate-by-certificate accounting by combining the constraint price corresponding to the congestion capacity certificate, and outputs settlement records organized according to the fulfillable capacity certificate for settlement processing of multiple services such as vehicle-grid-load-storage.

[0008] A third aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method.

[0009] The above-described technical solutions adopted in the embodiments of this application can achieve the following beneficial effects:

[0010] By acquiring network constraints and energy storage capacity based on grid topology and access nodes, a resource capacity and constraint set is formed. Energy storage capacity is discretized into fulfillable capacity certificates according to nodes and time slices, and congestion capacity certificates are generated based on distribution network constraints and bound to the fulfillable capacity certificates. In the day-ahead phase, the fulfillable capacity certificates and congestion capacity certificates are combined and optimized, constrained by state of charge, power, energy, ramp and reactive power, and non-overlapping relationships, to obtain a commitment list and control parameters. Control parameters are issued to equipment to execute charging, discharging, and reactive power control, and execution information associated with the commitment list is recorded. In the real-time phase, the commitment list is rearranged with minimal disturbance and the execution list is updated based on the rules of hard priority (non-transferable) and soft priority (transferable). During the metering phase, active power, reactive power, voltage, and frequency sequences are collected, time-frequency decomposition is performed, and baseline and causal attribution are combined to map incremental active power, reactive power, and ramp-up quantities to corresponding fulfillable capacity certificates by time slice and node, generating a certificate-by-certificate receipt. The updated execution list and certificate-by-certificate receipts are aligned, and certificate-by-certificate accounting is completed by combining the constraint price corresponding to the congestion capacity certificate. Settlement records organized according to fulfillable capacity certificates are output for settlement processing of multiple services including vehicle-grid-load-storage. This application enables multi-service settlement without over-commitment, attributable metering, and auditable consistent settlement. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a flowchart illustrating the causal settlement method for multi-service fulfillable capacity certificates provided in this application embodiment;

[0013] Figure 2 This is a schematic diagram of the multi-service fulfillable capacity certificate causal settlement device provided in the embodiments of this application;

[0014] Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0015] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0016] In existing technologies, distribution-side energy storage generally participates in primary frequency regulation, reactive power support, peak shaving and valley filling, and congestion management through scenario-based joint optimization or item-by-item superposition scheduling. Network constraints are mostly treated as exogenous parameters, and there is a lack of a unified carrier for commitments and settlements. Metering and settlement mainly rely on baseline estimation and aggregated electricity accounting, making it difficult to distinguish the actual contributions of different time scales and service types, resulting in high auditing and dispute resolution costs.

[0017] Therefore, the existing technology has the following problems: when the same energy storage resource is used for multiple services, there is a tendency for commitment overlap and constraint conflict; there is a lack of a unified orchestration mechanism that is compatible with both hard and soft priorities; constraints such as power flow, thermal limits and voltage are difficult to be endogenized in the entire commitment and settlement process; the granularity of metering attribution is insufficient, the frequency band contribution and service mapping are unclear, and duplicate metering and inconsistency in settlement are easy to occur.

[0018] In view of the problems existing in the prior art, this application proposes a causal settlement method for multi-service fulfillable capacity certificates. By using fulfillable capacity certificates as the core, the power, energy, reactive power, and ramping capacity of energy storage at nodes and time slices are discretized into atomic commitments with mutual exclusion identifiers and priorities, and congestion capacity certificates corresponding to distribution network constraints are generated, establishing a binding relationship between the two. In the day-ahead phase, the two types of certificates are combined and optimized, uniformly constrained by state of charge, power, energy, reactive power, ramping capacity, and non-overlapping relationships, outputting a commitment list and control parameters. In the real-time phase, minimum disturbance rearrangement is performed according to hard priority non-transferable rules and soft priority transferable rules, and the execution list is updated. In the metering phase, active power, reactive power, voltage, and frequency sequences are collected, and time-frequency decomposition combined with baseline and causal attribution is used to map incremental active power, reactive power, and ramping capacity to corresponding certificates, generating a certificate-by-certificate receipt. The execution list and receipts are aligned, and combined with the constraint price corresponding to the congestion capacity certificate, certificate-by-certificate accounting is completed, forming a settlement record organized by certificate dimension.

[0019] Through the above-mentioned technical solution of this application, this application realizes the unity of multi-service non-overcommitment and intrinsic constraints, completes the metering attributability and voucher-by-voucher auditable settlement for service and frequency band, improves the feasibility and settlement consistency of multi-service overlay, and provides an executable engineering path for the continuous revenue and large-scale configuration of energy storage assets.

[0020] The technical solution of this application will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0021] Figure 1 This is a flowchart illustrating the causal settlement method for multi-service fulfillable capacity credentials provided in this application embodiment, such as... Figure 1 As shown, the multi-service fulfillable capacity certificate causal settlement method may specifically include:

[0022] S101, based on the power grid topology and access nodes, obtains network constraints and energy storage capacity to form a set of resource capacity and constraints;

[0023] S102, the energy storage capacity is discretized into fulfillable capacity certificates according to nodes and time slices, and congestion capacity certificates are generated according to distribution network constraints and a binding relationship is established with the fulfillable capacity certificates.

[0024] S103, in the day-ahead phase, combines and optimizes the fulfillable capacity certificate and the congestion capacity certificate, constrained by the state of charge, power, energy, ramp and non-functionality, as well as non-overlapping relationships, to obtain the commitment list and control parameters.

[0025] S104: Send control parameters to the equipment to perform charging, discharging and reactive power control, and record the execution information associated with the commitment list. In the real-time stage, according to the hard priority non-transferable and soft priority transferable rules, rearrange the commitment list with minimum disturbance and update the execution list.

[0026] S105: During the metering phase, active power, reactive power, voltage and frequency sequences are collected, time-frequency decomposition is performed and baseline and causal attribution are combined, and incremental active power, reactive power and ramp-up are mapped to the corresponding fulfillable capacity vouchers according to time slices and nodes, generating voucher-by-voucher receipts.

[0027] S106 aligns the updated execution list with the voucher-by-voucher receipt, combines the constraint price corresponding to the congestion capacity voucher to complete the voucher-by-voucher accounting, and outputs the settlement records organized by the fulfillable capacity voucher for the settlement processing of multiple services of vehicle network load storage.

[0028] In some embodiments, network constraints and energy storage capabilities are obtained based on the power grid topology and access nodes to form a set of resource capabilities and constraints, including:

[0029] Based on the phase topology, the relationship between nodes and branches is established and the relationship between metering points and access points is determined, thereby obtaining node constraint parameters;

[0030] Convert node constraint parameters into congestion capacity limits based on power flow sensitivity;

[0031] A multi-dimensional capability envelope containing power, energy, reactive power, and ramp boundary is generated using nodes and time slices as indexes, and a mutual exclusion identifier and priority field are built in.

[0032] The congestion capacity limit is merged with the multidimensional capacity envelope to form a set of resource capabilities and constraints.

[0033] Specifically, firstly, the primary wiring and phase information of the distribution network in the target power supply area are obtained. Based on the parameters of substations, feeders, ring networks, switches and branches, a phase topology model is established. The relationship between each metering point and energy storage access is marked on the topology. Node constraint parameters such as the upper and lower limits of node voltage, the limit of branch thermal stability current, and the limit of phase imbalance are calculated.

[0034] Secondly, based on the power flow sensitivity method, the node constraint parameters are transformed into equivalent capacity limits that can be released or absorbed at different nodes and time slices, forming congestion capacity limits.

[0035] Furthermore, using "node-time slice" as an index, the available active power boundary, energy window, reactive capacity curve, ramp rate limit, state of charge range, and temperature control boundary of the connected energy storage unit are uniformly encapsulated to generate a multi-dimensional capacity envelope containing power, energy, reactive power, and ramp boundary. Mutual exclusion identifiers and priority fields are built into the capacity envelope for mutual exclusion and arrangement basis when the capacity is discretized into fulfillable capacity certificates in the future.

[0036] Finally, the congestion capacity limit and the multidimensional capacity envelope are aligned and merged in the node and time slice dimensions to obtain a set of resource capabilities and constraints, which serves as the sole data source for subsequent certificate generation, day-ahead optimization, and real-time reordering.

[0037] The innovative technical features and technical terms involved in this embodiment are explained below:

[0038] Phase-separated topology and metering points: Phase-separated topology refers to modeling each feeder and branch separately according to phase, explicitly recording the three-phase imbalance, phase coupling and phase connection relationship; metering points include grid connection points, voltage monitoring points and necessary intermediate nodes, which are used for consistent anchoring of subsequent measurement verification and constraint verification.

[0039] Node constraint parameters: These refer to the operational range of a node derived from the phase topology and equipment parameters, including the upper and lower limits of node voltage, branch current limits, allowable phase imbalance values, reactive power voltage characteristic curve parameters, etc.

[0040] Power flow sensitivity and congestion capacity limit: Power flow sensitivity characterizes the impact of injecting active or reactive power at a node on the voltage and branch current of related nodes. By combining node constraint parameters with sensitivity, the upper and lower limits of the equivalent capacity that each node and time slice can promise without triggering constraints can be calculated, i.e., the congestion capacity limit.

[0041] Multidimensional capacity envelope: At the time slice granularity, the active power upper and lower bounds, available energy window, reactive capacity available domain, ramp rate limit, state of charge range and temperature control boundary of the energy storage unit are uniformly represented as a multidimensional boundary set and aligned with the same time base.

[0042] Mutual exclusion identifier and priority field: The mutual exclusion identifier is used to indicate the capability elements that cannot be used in overlapping ways between different services within the same time slice. The priority field records the hard priority and soft priority, which are used for the transfer determination and replacement order determination during subsequent reordering.

[0043] For example, taking a 110 kV distribution network as an example, the substation has two feeders. Feeder 1 includes nodes 1 to 4 and is modeled as a three-phase system. Energy storage device A is connected to a phase of node 2, and energy storage device B is connected to the other two phases of node 3. Both are bidirectional converters with independent reactive power regulation capabilities. The data acquisition period is set to 5 minutes, dividing the day into 288 time slices.

[0044] Step 1: Phase-by-phase topology modeling and metering point determination. Based on the primary wiring diagram and measurement point layout, establish the "node-branch-phase" relationship, identify node voltage monitoring points and grid-connected metering points, and clarify the connection nodes and phases of energy storage devices A and B. Collect branch impedance, conductor cross-section, and switch status to obtain basic network parameters. Calculate the thermal stability current limit for each branch and the allowable voltage range for each node, and set phase imbalance limits according to regional regulations. The above constraints are stored at the "node-phase-time slice" granularity, forming node constraint parameters.

[0045] Step 2: Congestion Capacity Limit Conversion. For each time slice, the power flow sensitivity method is used to calculate the rate of change of voltage and critical branch current of adjacent nodes when unit active or reactive power is injected at nodes 2 and 3. The node constraint parameters are multiplied by the sensitivity and a safety margin is added to obtain the upper and lower limits of the equivalent capacity that nodes 2 and 3 can inject or absorb in the corresponding time slice. Active and reactive power are distinguished and recorded independently in the phase dimension. This forms the congestion capacity limit entries, which serve as an endogenous expression of the network's available capacity range.

[0046] Step 3: Generation of Multi-Dimensional Capability Envelope. For energy storage devices A and B, the upper and lower bounds of available active power are calculated for each time slice, and trimmed based on rated power, converter current limiting, temperature control boundaries, and grid connection point limits. Available energy windows are determined based on historical operation and planned scheduling, and state-of-charge evolution boundaries are constructed within adjacent time slices. A reactive power capacity availability domain is generated based on reactive power control curves and voltage operating points. An upper limit for ramp rate is given by combining power and device control characteristics. These boundaries are unified to a single time base, forming a multi-dimensional capability envelope of "power—energy—reactive power—ramp rate". To avoid the reuse of the same capability element across different services, mutually exclusive identifiers are written to the committable elements in the capability envelope in each time slice, and hard and soft priority fields are written according to the operation strategy, serving as the direct basis for subsequent voucher generation and rearrangement.

[0047] Step four involves merging the resource capabilities and constraints into a set. Using nodes and time slices as indices, congestion capacity limits are aligned with multi-dimensional capability envelopes. When a capability envelope exceeds the congestion capacity limit, the capability boundary is truncated according to the network-side limit. When cross-service mutual exclusion relationships exist within the capability envelope, mutual exclusion identifiers are retained, and the mutual exclusion group relationships are recorded in the set. The final result is a "resource capabilities and constraints set," which includes: capability boundaries organized by nodes and time slices, congestion capacity limits, mutual exclusion identifiers, priority fields, and their correspondence with metering points. This set is stored in a structured manner and directly referenced during subsequent credential generation, achieving unification of capability, network, and orchestration information.

[0048] When topology changes or switching states occur, incremental updates to sensitivity and congestion capacity limits should be triggered, and the update timestamps should be written into the set for subsequent optimization identification. When changes in temperature or environmental conditions lead to a decrease in energy storage availability, the multidimensional capacity envelope should be shrunk first, and then merged with the congestion capacity limit to maintain feasibility. When phase imbalance constraints become the dominant limitation, capacity and limits should be recorded separately at the phase level to avoid the hidden over-limit risk caused by three-phase mixing.

[0049] Through the above implementation methods, a “resource capacity and constraint set” can be accurately and uniformly formed without relying on external tables and scripts. This provides a consistent data foundation for the generation of fulfillable capacity certificates, day-ahead combination optimization, and real-time minimum disturbance rearrangement, and lays a verifiable structured foundation for subsequent metering return and certificate-by-certificate settlement.

[0050] In some embodiments, energy storage capacity is discretized into fulfillable capacity certificates according to nodes and time slices, and congestion capacity certificates are generated according to distribution network constraints and bound to the fulfillable capacity certificates, including:

[0051] Capacity slices are generated using nodes and time slices as indexes. Power, energy, reactive power, ramp boundaries, mutual exclusion flags, priorities, and metering verification anchors are written in. Frequency band flags are set to distinguish between frequency modulation components and energy transfer components.

[0052] Based on the power flow sensitivity of the distribution network, the branch thermal limit, voltage deviation threshold and phase imbalance limit, the available capacity of the node is calculated, and a congestion capacity certificate containing capacity limit and constraint type is generated.

[0053] According to the constraint mapping of the same node and the same time slice, each fulfillable capacity certificate is bound to at least one congestion capacity certificate, and linkage binding and alternative sets are set when there is coupling between adjacent nodes.

[0054] The binding relationships, mutual exclusion identifiers, and transfer rules are mirrored and stored in the credential metadata for day-ahead optimization and real-time reordering of references.

[0055] Specifically, firstly, the capacity of the access energy storage is discretized using "node-time slice" as an index to generate capacity slices. Then, the upper and lower limits of power, available energy window, non-functional power domain, and ramp boundary are written into the slices. At the same time, mutual exclusion identifiers, priorities, and metering verification anchor points are written, and frequency band identifiers are configured to distinguish between frequency modulation components and energy transfer components.

[0056] Secondly, based on the power flow sensitivity of the distribution network, branch thermal limits, voltage deviation thresholds, and phase imbalance limits, the available capacity is calculated node by node and time slice by time slice, generating congestion capacity certificates that include capacity limits and constraint types.

[0057] Secondly, according to the constraint mapping of the same node and the same time slice, each fulfillable capacity certificate is bound to at least one congestion capacity certificate; when there is coupling between adjacent nodes, linkage binding and alternative sets are set for subsequent rearrangement.

[0058] Finally, the binding relationships, mutual exclusion identifiers, and transfer rules are uniformly written into the credential metadata for direct reference in day-ahead optimization and real-time reordering.

[0059] The innovative technical features and technical terms involved in this embodiment are explained below:

[0060] Capacity slice: The smallest unit of capacity with node identifier and time slice identifier as keys, including power, energy, reactive power and ramp boundary, and records mutual exclusion identifier and priority, which are used to generate fulfillable capacity certificates in the future.

[0061] Frequency band identification: Frequency domain labels recorded in the capability slice are used to partition high-frequency (for primary frequency modulation and rapid ramping) and low-frequency (for power transfer and peak shaving) capabilities, facilitating the alignment of metering verification and causal attribution.

[0062] Congestion capacity certificate: A network-side available capacity carrier generated based on power flow sensitivity and operational limits, containing capacity limit values ​​and constraint types (such as branch thermal limits, voltage deviations, and phase imbalances).

[0063] Linkage Binding and Replaceable Sets: When there is significant coupling between adjacent nodes in the power flow, the linkage relationship between the credentials is recorded; for each fulfillable capacity credential, a set of similar credentials that can be replaced in the same time slice is listed as a candidate set for real-time minimum disturbance rearrangement.

[0064] Mirroring of transfer rules: The hard priority and soft priority, transfer limit and allocation strategy are structured and solidified, and written into the voucher metadata to ensure that the priority is consistent across the scheduling, metering and settlement sides.

[0065] For example, taking a 10 kV feeder as an example, nodes A, B, and C are set up, all modeled as three-phase. Energy storage device S1 is connected to phase 1 of node B, and energy storage device S2 is connected to phases 2 and 3 of node C. Both support independent reactive power regulation. The scheduling time base is 5 minutes, and one day is divided into 288 time slices.

[0066] Step 1: Capability Slice Generation

[0067] For each time slice, calculate the power boundary (trimmed by rated power, grid connection limit, temperature control boundary, and converter current limit), available energy window (derived from the previous time slice's state of charge and planned endpoint constraints), reactive power domain (determined by the voltage-reactive characteristic curve and the current voltage point), and ramp boundary (determined by the device control rate and grid connection limits) for S1 and S2. Write the following into each "node-time slice-phase" slice: mutual exclusion identifier M (group number of non-overlapping capacity elements for different services in the same time slice), priority P (including hard priority and soft priority), metering verification anchor point R (metering point identifier and timestamp binding method), and frequency band identifier F (0.1-1Hz is designated as the high-frequency domain, corresponding to frequency modulation; less than 0.1Hz is designated as the low-frequency domain, corresponding to energy transfer). Based on the above slices, directly generate the corresponding fulfillable capacity certificate, with each certificate corresponding to a slice.

[0068] Step 2: Congestion capacity certificate generation:

[0069] For each time slice, power flow sensitivity calculations are performed to obtain the rate of change of critical branch current and adjacent node voltage with the injection of unit active or reactive power at nodes B and C. Combining branch thermal limits, voltage deviation thresholds, and phase imbalance limits, the upper and lower limits of the equivalent available capacity at nodes B and C in the corresponding phases are calculated. Three constraint types are distinguished: "active power limit," "reactive power limit," and "phase imbalance limit." Congestion capacity voucher entries are generated, recording the capacity limit values ​​and constraint types.

[0070] Step 3, Binding Relationships and Linkage Settings:

[0071] On the same "node-time slice-phase" dimension, each fulfillable capacity certificate is bound to at least one congestion capacity certificate. When the power flow sensitivity indicates coupling between node B and node C, the fulfillable capacity certificate of node B is linked to the congestion capacity certificate of node C. For each fulfillable capacity certificate, based on the mutual exclusion identifier M and the frequency band identifier F, a set of alternatives of the same type, frequency band, and phase is listed for minimum disturbance replacement during real-time rearrangement.

[0072] Step 4, Metadata Writing and Mirroring:

[0073] The binding relationship, mutual exclusion identifier M, priority P, transfer rule mirror, and metering return anchor point R are uniformly written into the voucher metadata. The transfer rule mirror includes parameterized definitions of hard priority non-transferable, soft priority transfer upper limit and allocation strategy, which are shared for day-ahead optimization and real-time reordering. The metering return anchor point R and the frequency band identifier F together ensure that the incremental amount after time-frequency decomposition can be accurately aligned to the specific voucher in the subsequent metering stage.

[0074] Through the above implementation methods, energy storage capacity can be sliced ​​and vouched at the granularity of "node-time slice-phase-frequency band". At the same time, distribution network constraints are endogenously incorporated into the commitment layer in the form of congestion capacity vouchers. The binding, linkage and substitutable sets between vouchers provide a direct basis for real-time minimum disturbance rearrangement. The mirroring of mutual exclusion identifiers, priorities and transfer rules ensures consistent caliber across service orchestration. The metering return voucher anchor and frequency band identifier provide verifiable anchors for subsequent causal attribution and voucher-by-voucher settlement, which improves the executability of commitments and the verifiability of settlement in multi-service scenarios.

[0075] In some embodiments, the fulfillable capacity credentials and congestion capacity credentials are combined and optimized at the day-ahead stage, constrained by state of charge, power, energy, ramping and non-functionality, and non-overlapping relationships, to obtain a commitment list and control parameters, including:

[0076] Construct a binding relationship graph with fulfillable capacity certificates and congestion capacity certificates as nodes, and generate a non-overlapping mutually exclusive matrix for the same energy storage unit and the same time slice;

[0077] Time slice sequence constraints are set based on the charge state evolution equation, and consistency and continuity constraints are set for power, energy, reactive power and ramping.

[0078] The congestion capacity limit is determined based on power flow sensitivity and node constraints, and the corresponding constraint shadow price is injected into the objective function.

[0079] Hierarchical targets are set according to hard priority and soft priority. Hard priority is constrained to be non-transferable, while soft priority is characterized by a transfer limit and a penalty coefficient.

[0080] Introduce scenario set robust constraints to cover the bias between load and renewable output forecasts, and retain the reserve margin field and upper and lower limits;

[0081] The solution yields the commitment list and control parameters. The control parameters include charging and discharging power references, reactive power control mode parameters, ramp limits and effective time windows, as well as the distributed images corresponding to mutual exclusion flags and priorities.

[0082] Specifically, firstly, the binding relationship graph and mutual exclusion matrix are constructed: using "node-time slice-phase" as the index, the fulfillable capacity certificate and congested capacity certificate generated in the previous embodiment are mapped to two types of nodes in the graph, with edges representing binding relationships and constraint mappings. For multiple fulfillable capacity certificates of the same energy storage unit within the same time slice, a non-overlapping mutual exclusion matrix is ​​generated based on the mutual exclusion identifier. The matrix entries are Boolean identifiers of "mutually exclusive" or "allowed superposition". If the frequency band identifiers are different but point to the same capacity element, they are marked as mutually exclusive; if they occupy independent active and reactive capacity domains respectively and do not exceed the ramp and state of charge boundaries, they are marked as allowed superposition. For adjacent nodes with linked bindings, linked edges are added to the graph and the alternative set is recorded for subsequent minimum disturbance replacement.

[0083] Furthermore, sequence and consistency constraints are set: One day is divided into 288 time slices, each 5 minutes long. A state-of-charge (POC) evolution equation is established to constrain the POC, charging / discharging power, and energy balance of the previous time slice, ensuring the final POC falls within the feasible range. Consistency and continuity constraints are set for power, energy, reactive power, and ramp-up: power does not exceed the power boundary of the fulfillable capacity certificate; cumulative energy does not exceed the available energy window; reactive power capacity is limited by the reactive power availability domain; and the rate of power change between adjacent time slices does not exceed the ramp-up limit. All sequence constraints are indexed by the energy storage unit, corresponding to each fulfillable capacity certificate it participates in.

[0084] Furthermore, congestion capacity limits and constraint prices are injected: Based on the congestion capacity limits obtained from power flow sensitivity and node constraints, the limit for each "node-time slice-phase" is used as the upper bound of the fulfillable capacity certificate combination for that time slice; when multiple certificates are superimposed at the same location, their superimposed value must not exceed this limit. To reflect the network tension, the constraint price corresponding to the congestion capacity limit is injected as the objective function price coefficient, so that certificate combinations with higher constraint prices are preferentially allocated to nodes and time slices with tighter network constraints.

[0085] Furthermore, the hierarchical objectives and transfer rules are solidified: Hierarchical objectives are set. The first layer ensures the feasibility and satisfaction of hard-priority certificate combinations, and transfer is not allowed at this layer. The second layer optimizes the benefits of soft-priority certificates. Soft priority is represented by a transfer cap and a penalty coefficient. The transfer cap limits the reduction in the amount of fulfillment, and the penalty coefficient imposes costs on the unfulfilled portion. A mutual exclusion matrix serves as a global hard constraint; when soft-priority certificates conflict with hard-priority certificates, the assignment amount of soft-priority certificates is automatically reduced until mutual exclusion and the limit are satisfied.

[0086] Furthermore, robust constraints and reserve margins for the scenario set are implemented: a scenario set containing prediction biases for load and renewable energy output is constructed, for example, selecting three scenarios: "baseline," "upward bias," and "downward bias," and re-evaluating power flow sensitivity and congestion capacity limits under each scenario. Sequence constraints and limit constraints are simultaneously satisfied in all scenarios. To suppress boundary risks, a reserve margin field is retained at the fulfillable capacity certificate level, with upper and lower limits set (e.g., 10% each) to ensure the solution remains feasible across multiple scenarios. For certificates with a set of alternatives for adjacent nodes, equivalent substitution is allowed during scenario switching, but mutual exclusion and continuity constraints must be maintained.

[0087] Further, the solution and control parameter generation are performed: Under the constraints of mutual exclusion matrix, sequence consistency, congestion limits, and scenario set robustness, the solution yields a commitment list and control parameters. The commitment list lists the assigned quantity and priority according to "node—time slice—phase—credential identifier." The control parameters are a device-oriented distribution mirror, including: charging and discharging power reference curves, reactive power control mode parameters and limits, ramp-up limits, and synchronization activation time windows, along with mutual exclusion identifiers and priority fields to maintain consistency with real-time rearrangement.

[0088] For example, in some sample scenarios, taking nodes B and C of a 10 kV feeder as an example, the time slice is 5 minutes, with a total of 288 slices. Energy storage device S1 is connected to phase 1 of node B, and energy storage device S2 is connected to phases 2 and 3 of node C. A frequency regulation-type fulfillable capacity certificate is generated for S1 in the high-frequency domain, and an energy transfer-type fulfillable capacity certificate is generated for S2 in the low-frequency domain. If both S1 and S2 share the active uplink capacity of S1 in the same time slice, they are prohibited from overlapping by a mutual exclusion matrix. The scenario set takes three types of deviations, with a reserve margin of 10% above and below. After solving, in several time slices during the evening peak, the low-frequency energy transfer certificate of S2 is assigned to node C, while the high-frequency frequency regulation certificate of S1 is assigned to node B. There is no conflict between high-frequency and low-frequency within the capacity domain. When the congestion capacity limit of node B tightens, the soft-priority energy transfer certificate automatically shrinks, while the hard-priority frequency regulation certificate remains unchanged. The output control parameters are the charging and discharging reference curves of S1 and S2, reactive mode parameters, ramp limits, and a unified effective time window.

[0089] Through the above implementation methods, unified combination optimization based on vouchers is achieved in the day-ahead stage: mutual exclusion matrices are used to avoid the repeated use of the same capacity, congestion capacity limits and constraint prices are used to endogenous network constraints and value signals, hierarchical objectives and transfer rules are used to ensure that hard priorities are not transferred and soft priorities are transferred in a bounded manner, and scenario sets and reserve margins are used to improve the robustness of the plan. Finally, control parameters that can be directly issued and a list of executable commitments are output, providing a consistent data and constraint basis for minimum disturbance rearrangement and subsequent metering and settlement in the real-time stage.

[0090] In some embodiments, control parameters are sent to the device to perform charge / discharge and reactive power control, and execution information associated with the commitment list is recorded, including:

[0091] Generate a device-oriented control image, which includes charging and discharging power reference, reactive power control parameters, ramp limits, effective time windows, and credential identifiers, mutual exclusion identifiers, and priorities corresponding to the commitment list;

[0092] Control mirrors are distributed to nodes and synchronized activation windows and execution sequence numbers are set. Execution receipts and commitment lists are aligned item by item with credential identifiers and registered as execution information entries.

[0093] When an exception occurs or network constraint changes, a local demotion strategy is triggered according to the transfer rule, generating an alternative credential mapping and incorporating it into the execution information.

[0094] Specifically, firstly, after obtaining the list of commitments made before the deadline, the dispatcher generates a control image for each device according to the index of "node - time slice - phase - credential identifier". The control image is constructed with a unified time base and includes: a charging and discharging power reference curve (providing the upper and lower limits and target values ​​of the power reference in 5-minute time slices), reactive power control parameters (power factor target or voltage-reactive power curve parameters and upper and lower limits), ramp limits (upper limit of power change rate between adjacent time slices), effective time window (start timestamp and duration, supporting early loading and delayed activation), and credential identifiers, mutual exclusion identifiers and priority fields that correspond one-to-one with the commitment list.

[0095] The control image also embeds metering verification anchor points, recording the grid-connected metering point number and time synchronization identifier for subsequent verification and metering alignment. To reduce on-site parsing complexity, the image uses a hierarchical field organization: device-level default parameters, time-slice coverage parameters, and abnormal rollback parameters are superimposed at three levels. The device only needs to parse the coverage relationship according to priority to execute.

[0096] Furthermore, the scheduling terminal aggregates control mirrors by node, setting a synchronization activation window and assigning an execution sequence number to each node. The synchronization activation window ensures that multiple devices under the same node complete parameter switching within the same time slice boundary; the window width can be configured from 100 milliseconds to 500 milliseconds. The execution sequence number is a monotonically increasing 64-bit integer used for cross-time period traceability and rearrangement identification. The distribution process includes: completing the first round of distribution at least one time slice before the start of the activation time window; conducting a second confirmation distribution 30 seconds before activation; and conducting a final confirmation distribution and freezing the parameter version 5 seconds before activation.

[0097] Upon receiving the data, the device immediately returns a receipt confirmation and, upon entering the synchronization activation window, returns an execution activation confirmation. This confirmation includes the device clock timestamp, parameter version checksum, current state of charge, converter status variables, and summary checksum. If the device clock deviates from the unified time base by more than 50 milliseconds, the dispatcher issues a time calibration command and confirms its effectiveness again in the next window.

[0098] Furthermore, the dispatcher aligns the execution effectiveness receipts with the commitment list item by item, using the credential identifier as the primary key, and registers them as execution information entries. Each execution information entry records at least: credential identifier, node and time slice, execution sequence number, parameter version, set of readback variables (power reference, reactive power mode, ramp limit, state of charge), metering credential anchor point, mutual exclusion identifier and priority, summary check value, and abnormal event record (such as overcurrent protection triggering, communication retransmission). To enhance traceability, the entry retains a "parameter coverage trajectory," which is the final effective combination among device-level default parameters, time slice coverage parameters, and abnormal rollback parameters. Execution information entries are cached locally for one day and written to the operation ledger in real time to support subsequent metering attribution and settlement verification.

[0099] Furthermore, when a device experiences an execution anomaly within a certain time slice (e.g., temperature control derating, changes in grid connection limits, or communication interruption) or when node congestion limits tighten, the scheduler triggers a local degradation strategy based on the yielding rules. The degradation strategy prioritizes hard priorities as non-yieldable, and performs bounded contraction on soft priorities according to the "yield cap - penalty coefficient". When contraction is insufficient to restore feasibility, alternative credentials are selected from the set of alternatives based on mutual exclusion identifiers and binding relationships, and an alternative credential mapping is generated.

[0100] The replacement mapping records the original voucher identifier, the replacement voucher identifier, the replacement ratio, the triggering reason, the rearranged sequence number, and the causal chain identifier, and incorporates them into the execution information entry of the corresponding time slice. To avoid jitter, the replacement action is completed in one go within the synchronous effective window, and the minimum retention time is set to 1 time slice, during which the equivalent replacement is not triggered repeatedly.

[0101] In the event of communication failure, the device enters "safety hold" mode: it runs for no more than 2 time slices bound by the most recently effective control image and ramp limit, while periodically broadcasting self-test results; during this period, the scheduling terminal prioritizes using alternative sets of the same node or linkage binding credentials of adjacent nodes to complete temporary replacement, and backfills the causal chain after communication is restored.

[0102] For example, in some sample scenarios, taking a 10 kV feeder node B and node C as an example, the time slice is 5 minutes. Energy storage device S1 is connected to phase 1 of node B, and energy storage device S2 is connected to phases 2 and 3 of node C. The commitment list issues high-frequency frequency modulation control images for S1 and low-frequency energy transfer control images for S2 during the evening peak time slice. The synchronization effective window is configured to be 200 milliseconds, and the execution sequence number increments from 000001 of the day. Secondary and final confirmations are completed 30 seconds and 5 seconds before the 19:00 time slice switch, respectively. At 19:25, the voltage constraint of node B tightens, and the dispatcher triggers a downgrading strategy: keeping the hard priority certificate of S1 unchanged, the energy transfer assignment of S2 is reduced by 10% according to the soft priority transfer limit, and the insufficient part is selected from the alternative set of node C to generate alternative certificates for adjacent time slices. All changes were completed in one go within the 19:25 synchronization window, the rearranged sequence number was recorded as 000137, and the corresponding causal chain and digest verification were written into the running ledger.

[0103] Through the above implementation methods, the parameterization and hierarchical organization of the control mirror ensures "synchronization within nodes, simplified parsing on the device side, and cross-process verification"; the synchronization effective window and execution sequence number ensure consistent switching and traceability of multiple devices at time slice boundaries; local demotion based on transfer rules and equivalent replacement of alternative sets enable rapid and controllable rearrangement of anomalies and constraint changes without disrupting hard priorities and mutual exclusion relationships; and full traceability of execution information entries and alternative voucher mappings provides verifiable execution basis for subsequent measurement attribution and voucher-by-voucher settlement.

[0104] In some embodiments, during the real-time phase, the commitment list is rearranged with minimal disturbance and the execution list is updated according to the hard-priority non-delegatizable and soft-priority delegateable rules, including:

[0105] Rearrangement rules are generated based on the non-transferability of hard priority and the transfer limit and penalty coefficient of soft priority.

[0106] Determine an equivalent set of substitutes for each fulfillable capacity certificate based on the mutual exclusion identifier and binding relationship;

[0107] With the goal of minimizing the changes in the control image and charged state trajectory, the time slice that triggers the constraint change is replaced within the synchronization effective window;

[0108] The rearrangement results are verified for feasibility based on the charge state boundary, congestion capacity limit, non-functionality and ramp continuity. If the verification is successful, an alternative credential mapping is generated and the execution list is updated. The rearrangement sequence number and causal chain are recorded and written into the runtime ledger.

[0109] Specifically, node constraints and device status are continuously monitored during runtime. A rescheduling process is triggered when congestion capacity limits are tightened, device limits are reduced, or communication anomalies are detected. The scheduler generates current rescheduling rules based on the mutual exclusion identifier and priority field of each fulfillable capacity certificate in the commitment list, for example:

[0110] Hard priority is non-transferable, keeps the number of assignments unchanged, and only allows equivalent substitutions under the same or stricter constraints;

[0111] Soft priorities can be reduced within the upper limit of the transfer limit, and the unachieved portion will be included in the operating cost according to the penalty coefficient and the transfer ratio will be recorded.

[0112] For multiple soft priority certificates in the same time slice, they are transferred in order of priority from low to high until the constraints are met or the total transfer limit is reached.

[0113] Set a jitter suppression threshold, for example, when the constraint tightening is less than 2% of the capacity limit, do not trigger a rearrangement, but only record an alert.

[0114] Furthermore, for each candidate rearranged fulfillable capacity certificate, alternative certificates of the same frequency band, phase, and capability domain are retrieved within the same node and time slice based on mutual exclusion identifiers and binding relationships, forming an equivalent substitution set. If the set is insufficient, it is expanded within the linkage binding range of adjacent nodes, but it must be switchable within the synchronization effective window without introducing new mutual exclusion conflicts. The equivalent substitution set is sorted in the following order, for example:

[0115] Prioritize unassigned redundant vouchers from the same node and time slice;

[0116] The next choice is a certificate that is from the same node, an adjacent time slice, and satisfies the requirement of continuous ramping.

[0117] Then select the linked node and the binding certificate for the same time slice;

[0118] Finally, select the linked node and adjacent time slice vouchers.

[0119] Each candidate alternative must include the upper limit of available assignments, an estimate of the impact of the switch on the state of charge, changes in the occupancy of nonfunctional capacity, and the expected percentage of the congestion capacity limit.

[0120] Furthermore, with the goal of controlling the changes in the image and charged state trajectories, a minimum objective for this rearrangement is constructed, for example:

[0121] Objective 1: Minimize the magnitude of changes in the control mirror, prioritizing keeping the issued power reference and reactive power parameters unchanged, and minimizing the total change within the change set when necessary;

[0122] Objective 2: Minimize the deviation of the state of charge trajectory, limit the deviation of a single time slice to no more than a predetermined threshold (e.g., 2% of rated capacity), and the cumulative deviation to no more than the daily limit (e.g., 10% of rated capacity).

[0123] Objective 3: Minimize the number of substitutions, prioritizing a small number of large-value equivalent substitutions over multiple small-value substitutions.

[0124] Perform a one-time replacement within the synchronization window: shrink soft-priority certificates to the feasible range according to the transfer limit, and supplement any shortfall from the equivalent replacement set; for hard-priority certificates, only equivalent replacement is allowed, and the assignment amount is not shrunk. The synchronization window can be configured from 200 milliseconds to 500 milliseconds, and parameter switching, version freezing, and receipt collection are completed within the window.

[0125] Furthermore, after the rearrangement scheme is formed, its feasibility is verified item by item, for example:

[0126] Charge state boundary verification: Verify that the charge state of the current time slice and the next 2 to 3 time slices both fall within the boundary range;

[0127] Congestion capacity limit verification: The cumulative assignment of the same "node-time slice-phase" shall not exceed the limit;

[0128] Reactive power capacity verification: Reactive power assignment is located within the reactive power capacity availability range and is consistent with the voltage-reactive power curve;

[0129] Ramp-up continuity check: The power change rate between adjacent time slots does not exceed the ramp-up limit.

[0130] After all steps are approved, a replacement credential mapping is generated, containing the original credential identifier, replacement credential identifier, replacement ratio, trigger reason, transfer ratio, penalty coefficient reference, and expected state of charge offset. The execution list is then updated, new rearrangement sequence numbers and causal chain identifiers are assigned, and the change image is issued within the synchronization window. Once the device returns an execution effectiveness receipt, the scheduler writes the mapping, receipt, and updated execution list into the runtime ledger.

[0131] For example, in some sample scenarios, taking nodes B and C of a 10 kV feeder as an example, the time slice is 5 minutes. Energy storage device S1 is connected to phase 1 of node B, carrying a hard-priority primary frequency regulation certificate; energy storage device S2 is connected to phases 2 and 3 of node C, carrying a soft-priority energy transfer certificate. At 19:25, node B experiences voltage constraint tightening, and the congestion capacity limit decreases by 8%. After triggering rearrangement:

[0132] The hard priority certificate of S1 is kept unchanged in terms of the number of assignments. Only check whether there is an equivalent replacement for the same node and the same time slice. If no replacement is needed, the original mirror is maintained.

[0133] The soft priority certificates of S2 are reduced by 10% of the transfer limit, of which 6% is used to meet the gap of the tightening limit of Node B, and the remaining 4% is used as a reserve margin.

[0134] If there is still a 2% gap after contraction, select the linkage binding certificate of node C in the same time slice from the equivalent substitution set to make up the gap, and ensure that the climbing continuity and non-functionality are not broken.

[0135] All changes are switched on at once within a 200-millisecond synchronization window, the rearranged sequence number 000185 is assigned, an alternative voucher mapping is generated and written back to the running ledger.

[0136] After switching, the device receipt shows consistent parameter versions, smooth power reference transition, and a current chip offset of 1.2% and a cumulative offset not exceeding the daily limit of 10%.

[0137] Through the above implementation methods, the real-time phase can quickly absorb network and device-side disturbances without changing hard priority fulfillment or excessively adjusting control images and charge state trajectories, and complete equivalent substitution and bounded transfer with minimal disturbances; multi-dimensional feasibility verification ensures that the charge state, congestion capacity, non-functionality, and ramp continuity are still satisfied after rearrangement; the disk records of rearranged sequence numbers, causal chains, and substitution mappings provide verifiable basis for subsequent metering attribution and voucher-by-voucher settlement, thereby improving the operational stability and settlement consistency in multi-service overlay scenarios.

[0138] In some embodiments, active power, reactive power, voltage, and frequency sequences are collected during the metering phase, time-frequency decomposition is performed, and baseline and causal attribution are combined to map incremental active power, reactive power, and ramp-up quantities to corresponding fulfillable capacity vouchers according to time slices and nodes, generating voucher-by-voucher receipts, including:

[0139] Active power, reactive power, voltage and frequency sequences are collected at the grid-connected metering point according to a unified time base.

[0140] A time-frequency decomposition algorithm is performed on the power sequence to obtain the high-frequency component for frequency modulation and the low-frequency component for energy transfer, and the ramp rate is extracted.

[0141] A baseline model is established and corrected based on a list of non-routine events to obtain a baseline sequence;

[0142] Using the frequency band identifier and metering verification anchor point of the fulfillable capacity certificate, the decomposed incremental active power, reactive power and ramp-up amount are aligned by node and time slice and causal attribution is completed.

[0143] The consistency of the attribution results and the equipment receipts are verified and duplicate measurements are eliminated. A receipt for each document containing a time identifier, node identifier and voucher identifier is generated and signed and stored.

[0144] Specifically, synchronous sampling devices are configured at grid-connected metering points to synchronously collect active power, reactive power, phase voltage, and frequency. The metering system adopts a unified time base with 5-minute time slices, totaling 288 time slices per day. The sampling period is set to 100 milliseconds, forming a 10 Hz raw time series. The metering device uses a time synchronization module to control the deviation between the local clock and the unified time base within ±50 milliseconds. Each metering record includes a timestamp, metering point number, node identifier, phase identifier, and summary check value, and is organized and stored according to "node—phase—time slice" for later decomposition and attribution.

[0145] Furthermore, a time-frequency decomposition algorithm is performed on the active power sequence for each "node-phase-time slice" to obtain a multi-scale component set. Components with center frequencies between 0.1 and 1 Hz are defined as high-frequency components, corresponding to primary frequency modulation and rapid ramp-up capability; components with center frequencies less than 0.1 Hz are defined as low-frequency components, corresponding to energy transfer and peak clipping behavior. To improve component stability, the original sequence is first preprocessed with detrending and band-limiting before decomposition. The ramp-up amount is extracted from the first-order difference envelope of the high-frequency components and recorded using two indices: the maximum rate of change and the mean rate of change within the time slice. The reactive power sequence is decomposed using the same method for subsequent reactive power contribution attribution.

[0146] Furthermore, a baseline model is established for the low-frequency component, with input variables including temperature, time period, workday type, historical load, and renewable energy output. The baseline model outputs expected power and reactive power curves according to the "node-phase-time slice" structure, forming a baseline sequence. The baseline sequence is then corrected using a list of non-routine events, including power outages for maintenance, equipment switching, and major events; the corrected baseline is then used for attribution. The increment is defined as the difference between the measured low-frequency component and the corrected baseline, while the increment of the high-frequency component is directly taken from the decomposition results.

[0147] Furthermore, based on the frequency band identifier of the fulfillable capacity certificate and the metering return anchor point, attribution is performed in both the high-frequency and low-frequency domains, for example:

[0148] High-frequency domain attribution: Construct an "instruction-response consistency window" within the time slice, match the charge and discharge reference changes in the device execution information entries with the phase and amplitude of the high-frequency components, and include segments with phase errors not exceeding a specified threshold (e.g., ±0.3 seconds) in the incremental active power and ramp-up amount of the corresponding voucher for that time slice.

[0149] Low-frequency domain attribution: The incremental curve of "measured low-frequency component minus baseline" is aligned point by point with the assigned quantity in the commitment list. After removing segments that conflict with mutually exclusive identifiers, the remaining increment is allocated to each certificate in the same frequency band according to the proportion of the certificate assigned quantity.

[0150] Reactive power attribution: Based on the reactive power control mode parameters and the voltage-reactive power relationship, the high-frequency and low-frequency reactive power components are mapped to the corresponding reactive power category certificates.

[0151] For cross-node linked binding vouchers, alignment is first performed within the bound node range, and then the distribution is performed proportionally according to the binding relationship matrix. All attribution results are recorded with "node-time slice-voucher identifier" as the primary key, including three types of values: incremental active power, incremental reactive power, and ramp-up amount.

[0152] Furthermore, to prevent duplicate measurements and inconsistencies in caliber, this embodiment sets up three types of constraint checks, such as:

[0153] Mutual exclusion verification: For multiple credentials of the same "node-time slice-capability domain", the mutual exclusion group is retrieved according to the mutual exclusion identifier. Within the mutual exclusion group, the dual verification of "maximum value criterion" and "sum not exceeding the assigned limit" is performed. The excess part is rolled back in reverse order according to priority.

[0154] Assignment limit verification: The attribution increment of each voucher shall not exceed the available limit corresponding to its assignment limit and congestion capacity limit, whichever is smaller.

[0155] Execution receipt verification: Compare the consistency of the attribution results with the read-back variables of the device execution information entries; if the read-back variables show that the device has not switched to the target parameter version or is in an abnormal rollback, directly remove the attribution increment of that time slice and mark it as "execution inconsistency".

[0156] After verification, the deduplication attribution results are generated, and a summary verification value is generated for each record.

[0157] Furthermore, a voucher-by-voucher receipt is generated for each verified attribution record. The receipt includes at least a time stamp, node stamp, voucher stamp, incremental active power, incremental reactive power, ramp-up amount, metering point number, phase, method stamp, and summary verification value. Each receipt is accompanied by a signature and written to the settlement ledger. Simultaneously, the hashes of the original metering segments and decomposition parameters are written to the runtime ledger for traceability. To support dispute review, the decomposition spectral characteristic quantities and baseline model version number by time slice are saved and recorded in the receipt.

[0158] For example, in some sample scenarios, taking 10 kV feeder node B and node C as examples, the time slice is 5 minutes and the sampling period is 100 milliseconds. From 19:00 to 20:00, node B is responsible for high-frequency modulation certificates, and node C is responsible for low-frequency energy transfer certificates. After decomposition, the average amplitude of the high-frequency component of node B increases in the 19:25 time slice, aligning with the power reference jump of the equipment execution information. The command-response phase error is 0.2 seconds, which is determined to be a valid high-frequency increment, and the corresponding incremental active power and ramp-up amount are included in the frequency modulation certificate of node B. In the same time slice, the low-frequency component of node C shifts upward relative to the baseline. After non-routine event correction, a net increment of 2.5 kW is retained, which is allocated to the energy transfer certificates according to the assigned amount proportion. Mutual exclusion verification shows that there is no overlap in the same capacity domain in this time slice, and the assigned amount upper limit verification and execution receipt verification both pass. The system generates two per-certificate receipts, recording information such as time identifier, node identifier, and certificate identifier, and completes signature storage.

[0159] Through the above implementation methods, the metering stage can separate high-frequency modulation and low-frequency energy transfer under a unified time base, obtain the net increment by combining baseline and non-routine event correction, and achieve causal attribution by voucher based on voucher frequency band identifier and metering receipt anchor point; the triple verification of mutual exclusion, assigned quantity and execution consistency effectively eliminates duplicate metering and caliber differences; the signature and hash trace of each voucher receipt provide verifiable basis for subsequent settlement and auditing, thereby ensuring the attributability of metering and the consistency of settlement in multi-service overlay scenarios.

[0160] In some embodiments, the updated execution list is aligned with the document-by-document receipt, and document-by-document accounting is completed by combining the constraint price corresponding to the congestion capacity document, including:

[0161] Align the updated execution list with the voucher-by-voucher receipts one by one based on voucher identifier, node, and time slice;

[0162] Using the constraint price of the congestion capacity certificate as a coefficient, the incremental active power, reactive power and ramping amount in the receipt are included in the accounting amount of the corresponding fulfillable capacity certificate.

[0163] For entries with alternative document mappings, the accounting volume and constraint price are allocated according to the transfer rules;

[0164] For multiple receipts of the same resource in the same time slice, perform deduplication and consistency checks, generate settlement entries for each voucher, and summarize them to form a settlement record.

[0165] Specifically, using "voucher identifier - node - time slice" as the primary key, the updated execution list and voucher-by-voucher receipts are aligned one by one. The execution list provides the final assigned amount, mutual exclusion identifier, priority, alternative voucher mapping, and parameter version for the current slice; the voucher-by-voucher receipt provides incremental active power, incremental reactive power, ramp-up amount, metering point number, and summary verification value. During alignment, the consistency of parameter versions on both sides and the timestamp falling within the same time slice window are checked. If the deviation exceeds the threshold (e.g., ±50 milliseconds), it is marked as "time inconsistency" and awaits review, and will not participate in this round of calculation.

[0166] Furthermore, for each aligned record, the congestion capacity certificate for the same "node-time slice-phase" is retrieved, and its constraint price is read and used as a pricing factor. Mapping is performed according to service dimensions: incremental active power corresponds to active power constraint price, incremental reactive power corresponds to reactive power constraint price, and ramping amount corresponds to ramping constraint price or an equivalent additional factor. This forms a triplet of accounting amounts: active power accounting amount, reactive power accounting amount, and ramping amount, each equal to the incremental amount in the receipt multiplied by the corresponding constraint price. If multiple congestion capacity certificates exist in the same time slice (e.g., simultaneously subject to voltage and thermal limits), the primary price is selected based on constraint effectiveness priority, and the secondary constraint price is recorded as an additional factor in the supplementary table and recorded in the settlement entry.

[0167] Furthermore, when the execution list includes a substitute voucher mapping, the original voucher and the substitute voucher are treated as two components of the same settlement unit. The posting amount and constraint price are allocated according to the substitution ratio of the mapping record: first, the receipt increment is split between the original voucher and the substitute voucher according to the substitution ratio, and then each is multiplied by the constraint price corresponding to its node and time slice; if the transfer rule defines a penalty coefficient, the penalty coefficient is added to the transferred portion to generate an additional posting amount. To avoid double-counting, after allocation, the sum of the posting amounts of the original voucher and the substitute voucher is checked against the receipt increment; if the difference exceeds a threshold (e.g., 0.5%), the allocation is rolled back and recorded as "allocation anomaly".

[0168] Furthermore, execute multiple receipts for the same resource within the same time slice sequentially:

[0169] Deduplication verification: Deduplicatize by measuring point number and summary verification value;

[0170] Mutual exclusion verification: Only the higher priority accounting entries are retained in the mutual exclusion group, and the rest are rolled back or allocated to zero according to the transfer rules;

[0171] Assignment limit verification: The physical increment corresponding to the accounting quantity shall not exceed the smaller of the final assignment limit of the voucher in the time slice and the congestion capacity limit.

[0172] Version consistency check: If the parameter version of the receipt is inconsistent with the execution list record, the incremental receipt will be directly removed.

[0173] Once approved, the accounting entries for each dimension will be solidified into "voucher-by-voucher settlement items", which include time identifier, node, voucher identifier, three types of accounting entries, the constraint price used, mutual exclusion identifier, substitution relationship and summary verification value.

[0174] Furthermore, using a daily cycle, all voucher-by-voucher settlement entries are aggregated by voucher identifier to generate settlement records. These records include accumulated accounting volumes based on active power, reactive power, and ramp-up dimensions, average constraint prices, weighted constraint prices, alternative allocation details, and a list of exception entries. After aggregation, the data is written to the settlement ledger, while the hashes of the congestion capacity voucher snapshots and price snapshots used for calculation are written to the runtime ledger for audit traceability.

[0175] For example, in some sample scenarios, taking nodes B and C of a 10 kV feeder as an example, the time slice is 5 minutes. At 19:25, voucher V1 (node ​​B, frequency regulation type) and voucher V2 (node ​​C, energy transfer type) are aligned. The receipt shows that V1's incremental active power is 30 kW and the ramping amount is 15 kW per minute; the corresponding active power constraint price for node B is 0.42 yuan per kWh, and the ramping constraint price is equivalent to 0.05 yuan per kWh. The active power accounting amount recorded in V1 is 12.6 yuan, and the ramping accounting amount is 0.75 yuan. V2's incremental active power is 25 kW and reactive power is 6 kVar. The corresponding active power constraint price for node C is 0.28 yuan per kWh, and the reactive power constraint price is 0.10 yuan per kVar. The active power accounting amount recorded in V2 is 7.0 yuan, and the reactive power accounting amount is 0.6 yuan. When there is a V2→V3 substitute voucher mapping in the segment, with a substitution ratio of 40%, the V2 receipt increment is first split into 60% and 40%, and multiplied by the constraint price of the node where node C and V3 are located, respectively. The sum of the two parts is then checked against the original receipt increment and recorded. After deduplication and consistency verification pass, voucher-by-voucher settlement entries for V1 and V2 are formed and included in the daily settlement record.

[0176] Through the above implementation methods, precise alignment of the execution list and receipts at the voucher level is achieved. The constrained price of congestion capacity vouchers maps incremental active power, reactive power, and ramp-up volume to verifiable accounting quantities. The rule-based allocation of alternative mappings avoids double-counting and omissions caused by temporary rearrangements. Multiple deduplication and consistency checks ensure that accounting for the same resource within the same time slice is unique and subject to assignment and network limits. This results in voucher-by-voucher, traceable, and auditable settlement records, providing an engineering basis for stable settlement and dispute resolution in multi-service overlay scenarios.

[0177] The following are embodiments of the apparatus described in this application, which can be used to execute the embodiments of the method described in this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method described in this application.

[0178] Figure 2This is a schematic diagram of the multi-service fulfillable capacity certificate causal settlement device provided in an embodiment of this application. Figure 2 As shown, the multi-service fulfillable capacity certificate causal settlement device includes:

[0179] The acquisition module 201 is used to acquire network constraints and energy storage capacity based on the power grid topology and access nodes, and form a set of resource capacity and constraints;

[0180] Discrete module 202 is used to discretize energy storage capacity into fulfillable capacity certificates according to nodes and time slices, and generate congestion capacity certificates according to distribution network constraints and establish a binding relationship with the fulfillable capacity certificates.

[0181] The optimization module 203 is used to perform combined optimization of the fulfillable capacity certificate and the congestion capacity certificate in the day-ahead phase, constrained by the state of charge, power, energy, ramp and non-functional capacity and non-overlapping relationships, to obtain the commitment list and control parameters.

[0182] The distribution module 204 is used to distribute control parameters to the equipment to perform charging and discharging and reactive power control, and record the execution information associated with the commitment list. In the real-time stage, the commitment list is rearranged with minimal disturbance and the execution list is updated according to the hard priority non-transferable and soft priority transferable rules.

[0183] The generation module 205 is used to collect active, reactive, voltage and frequency sequences during the metering stage, perform time-frequency decomposition and combine baseline and causal attribution, map incremental active, reactive and ramp quantities to the corresponding fulfillable capacity vouchers according to time slices and nodes, and generate voucher-by-voucher receipts.

[0184] Output module 206 is used to align the updated execution list with the voucher-by-voucher receipt, combine the constraint price corresponding to the congestion capacity voucher to complete the voucher-by-voucher accounting, and output the settlement records organized by the fulfillable capacity voucher for the settlement processing of multiple services of vehicle network load storage.

[0185] In some embodiments, Figure 2 The acquisition module 201 establishes the relationship between nodes and branches based on the phase topology and determines the relationship between metering points and access points, thereby obtaining node constraint parameters; it converts the node constraint parameters into congestion capacity limits based on power flow sensitivity; it generates a multi-dimensional capacity envelope containing power, energy, reactive power and ramp boundaries using nodes and time slices as indices, and includes built-in mutual exclusion identifiers and priority fields; it merges the congestion capacity limits and the multi-dimensional capacity envelope to form a resource capacity and constraint set.

[0186] In some embodiments, Figure 2The discrete module 202 generates capacity slices indexed by nodes and time slices, writes power, energy, reactive power and ramp boundaries, mutual exclusion identifiers, priorities and metering return anchors, and sets frequency band identifiers to distinguish between frequency modulation components and energy transfer components; calculates the available capacity of nodes based on distribution network power flow sensitivity, branch thermal limits, voltage deviation thresholds and phase imbalance limits, and generates congestion capacity certificates containing capacity limits and constraint types; according to the constraint mapping of the same node and the same time slice, establishes a binding relationship between each fulfillable capacity certificate and at least one congestion capacity certificate, and sets linkage binding and alternative sets when there is coupling between adjacent nodes; and stores the binding relationship, mutual exclusion identifiers and transfer rules in the certificate metadata for day-ahead optimization and real-time reordering references.

[0187] In some embodiments, Figure 2 The optimization module 203 constructs a binding relationship graph with fulfillable capacity certificates and congestion capacity certificates as nodes, and generates a non-overlapping mutually exclusive matrix for the same energy storage unit and the same time slice; it sets time slice sequence constraints based on the charge state evolution equation, and sets consistency and continuity constraints on power, energy, reactive power and ramping; it determines the congestion capacity limit according to power flow sensitivity and node constraints, and injects the corresponding constraint shadow price into the objective function; it sets hierarchical objectives according to hard priority and soft priority, with hard priority constraints being non-transferable, and soft priority being characterized by transfer upper limit and penalty coefficient; it introduces scenario set robust constraints to cover load and renewable output prediction deviations, and retains the reserve margin field and upper and lower limits; it solves to obtain the commitment list and control parameters, including charging and discharging power reference, reactive power control mode parameters, ramping limit and effective time window, and the distributed mirror corresponding to the mutual exclusion identifier and priority.

[0188] In some embodiments, Figure 2 The distribution module 204 generates a device-oriented control image, which includes charging and discharging power references, reactive power control parameters, ramp limits, effective time windows, and credential identifiers, mutual exclusion identifiers, and priorities corresponding to the commitment list. The control image is distributed to nodes and a synchronous effective window and execution sequence number are set. The execution receipts are aligned with the commitment list item by item using the credential identifier and registered as execution information entries. When execution anomalies or network constraint changes occur, a local degradation strategy is triggered according to the transfer rules to generate alternative credential mappings and incorporate them into the execution information.

[0189] In some embodiments, Figure 2The distribution module 204 generates rearrangement rules based on the hard priority non-transferable and soft priority transfer upper limits and penalty coefficients; determines an equivalent replacement set for each fulfillable capacity certificate according to the mutual exclusion identifier and binding relationship; with the goal of minimizing the change in control image and charged state trajectory, it performs replacement on the time slice that triggers the constraint change within the synchronous effective window; it performs feasibility verification on the rearrangement results according to the charged state boundary, congestion capacity limit, non-functionality and ramp continuity, and when it passes, it forms a replacement certificate mapping and updates the execution list, records the rearrangement sequence number and causal chain and writes it into the running ledger.

[0190] In some embodiments, Figure 2 The generation module 205 collects active power, reactive power, voltage, and frequency sequences at the grid-connected metering point according to a unified time base; it performs a time-frequency decomposition algorithm on the power sequence to obtain the high-frequency component for frequency regulation and the low-frequency component for energy transfer, and extracts the ramp rate; it establishes a baseline model and corrects it according to the list of non-routine events to obtain the baseline sequence; using the frequency band identifier of the fulfillable capacity certificate and the metering certificate anchor point, it aligns the decomposed incremental active power, reactive power, and ramp rate by node and time slice and completes causal attribution; it performs consistency verification and duplicate metering elimination on the attribution results and equipment receipts, generates a certificate-by-certificate receipt containing time identifier, node identifier, and certificate identifier, and signs and stores it.

[0191] In some embodiments, Figure 2 The output module 206 aligns the updated execution list with the voucher-by-voucher receipts one by one according to the voucher identifier, node, and time slice; using the constraint price of the congestion capacity voucher as a coefficient, it includes the incremental active power, reactive power, and ramp-up amount in the receipts into the accounting amount of the corresponding fulfillable capacity voucher; for entries with alternative voucher mappings, it allocates the accounting amount and constraint price according to the transfer rules; it performs deduplication and consistency checks on multiple receipts of the same resource in the same time slice, generates voucher-by-voucher settlement entries, and summarizes them to form a settlement record.

[0192] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0193] Figure 3 This is a schematic diagram of the structure of the electronic device 3 provided in an embodiment of this application. Figure 3 As shown, the electronic device 3 of this embodiment includes: a processor 301, a memory 302, and a computer program 303 stored in the memory 302 and executable on the processor 301. When the processor 301 executes the computer program 303, it implements the steps in the various method embodiments described above. Alternatively, when the processor 301 executes the computer program 303, it implements the functions of each module / unit in the various device embodiments described above.

[0194] For example, computer program 303 may be divided into one or more modules / units, which are stored in memory 302 and executed by processor 301 to complete this application. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 303 in electronic device 3.

[0195] Electronic device 3 can be a desktop computer, laptop, handheld computer, cloud server, or other electronic device. Electronic device 3 may include, but is not limited to, processor 301 and memory 302. Those skilled in the art will understand that... Figure 3 This is merely an example of electronic device 3 and does not constitute a limitation on electronic device 3. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device may also include input / output devices, network access devices, buses, etc.

[0196] Processor 301 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0197] The memory 302 can be an internal storage unit of the electronic device 3, such as a hard disk or RAM. The memory 302 can also be an external storage device of the electronic device 3, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory 302 can include both internal and external storage units of the electronic device 3. The memory 302 is used to store computer programs and other programs and data required by the electronic device. The memory 302 can also be used to temporarily store data that has been output or will be output.

[0198] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0199] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0200] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0201] In the embodiments provided in this application, it should be understood that the disclosed apparatus / computer devices and methods can be implemented in other ways. For example, the apparatus / computer device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. Multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of apparatus or units may be electrical, mechanical, or other forms.

[0202] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0203] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0204] If an integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program may include computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. A computer-readable medium may include: any entity or device capable of carrying computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0205] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although the technical solutions of this application are described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A causal settlement method for multi-service fulfillable capacity certificates, characterized in that, include: Based on the power grid topology and access nodes, network constraints and energy storage capabilities are obtained to form a set of resource capabilities and constraints. The energy storage capacity is discretized into fulfillable capacity certificates according to nodes and time slices, and congestion capacity certificates are generated according to distribution network constraints and a binding relationship is established with the fulfillable capacity certificates. In the day-ahead phase, the fulfillable capacity certificate and the congestion capacity certificate are combined and optimized, constrained by state of charge, power, energy, ramp and non-functionality, and non-overlapping relationships, to obtain a commitment list and control parameters. The control parameters are sent to the equipment to perform charging and discharging and reactive power control, and the execution information associated with the commitment list is recorded. In the real-time stage, the commitment list is rearranged with minimal disturbance and the execution list is updated according to the hard priority non-transferable and soft priority transferable rules. During the metering phase, active power, reactive power, voltage and frequency sequences are collected, time-frequency decomposition is performed, and baseline and causal attribution are combined to map incremental active power, reactive power and ramp-up quantities to the corresponding fulfillable capacity vouchers according to time slices and nodes, generating voucher-by-voucher receipts. Align the updated execution list with the voucher-by-voucher receipt, combine the constraint price corresponding to the congestion capacity voucher to complete the voucher-by-voucher accounting, and output the settlement records organized by the fulfillable capacity voucher for the settlement processing of multiple services of vehicle network load storage. The step of discretizing energy storage capacity into fulfillable capacity certificates according to nodes and time slices, and generating congestion capacity certificates based on distribution network constraints and establishing a binding relationship with the fulfillable capacity certificates includes: Capacity slices are generated using nodes and time slices as indexes. Power, energy, reactive power, ramp boundaries, mutual exclusion flags, priorities, and metering verification anchors are written in. Frequency band flags are set to distinguish between frequency modulation components and energy transfer components. Based on the power flow sensitivity of the distribution network, the branch thermal limit, the voltage deviation threshold, and the phase imbalance limit, the available capacity of the node is calculated, and a congestion capacity certificate containing capacity limits and constraint types is generated. According to the constraint mapping of the same node and the same time slice, each fulfillable capacity certificate is bound to at least one congestion capacity certificate, and linkage binding and alternative sets are set when there is coupling between adjacent nodes. The binding relationship, mutual exclusion identifier, and transfer rule are mirrored and stored in the credential metadata for day-ahead optimization and real-time reordering of references. The aforementioned day-ahead phase involves combining and optimizing the fulfillable capacity certificates and congestion capacity certificates, constrained by state of charge, power, energy, ramp and non-functionality, and non-overlapping relationships, to obtain a commitment list and control parameters, including: Construct a binding relationship graph with fulfillable capacity certificates and congestion capacity certificates as nodes, and generate a non-overlapping mutually exclusive matrix for the same energy storage unit and the same time slice; Time slice sequence constraints are set based on the charge state evolution equation, and consistency and continuity constraints are set for power, energy, reactive power and ramping. The congestion capacity limit is determined based on power flow sensitivity and node constraints, and the corresponding constraint shadow price is injected into the objective function. Hierarchical targets are set according to hard priority and soft priority. Hard priority is constrained to be non-transferable, while soft priority is characterized by a transfer limit and a penalty coefficient. Introduce scenario set robust constraints to cover the bias between load and renewable output forecasts, and retain the reserve margin field and upper and lower limits; The solution yields the commitment list and control parameters. The control parameters include charging and discharging power references, reactive power control mode parameters, ramp limits and effective time windows, as well as the distributed images corresponding to mutual exclusion flags and priorities.

2. The method according to claim 1, characterized in that, The process of obtaining network constraints and energy storage capacity based on power grid topology and access nodes to form a resource capacity and constraint set includes: Based on the phase topology, the relationship between nodes and branches is established and the relationship between metering points and access points is determined, thereby obtaining node constraint parameters; The node constraint parameters are converted into congestion capacity limits based on power flow sensitivity. A multi-dimensional capability envelope containing power, energy, reactive power, and ramp boundary is generated using nodes and time slices as indexes, and a mutual exclusion identifier and priority field are built in. The congestion capacity limit is merged with the multidimensional capability envelope to form the resource capability and constraint set.

3. The method according to claim 1, characterized in that, The step of sending the control parameters to the equipment to perform charging, discharging, and reactive power control, and recording the execution information associated with the commitment list, includes: Generate a device-oriented control image, which includes a charge / discharge power reference, reactive power control parameters, ramp limits, effective time windows, and credential identifiers, mutual exclusion identifiers, and priorities corresponding to the commitment list; The control image is issued to each node and a synchronization window and execution sequence number are set. The execution receipt and commitment list are aligned item by item with the credential identifier and registered as execution information entries. When an exception occurs or network constraint changes, a local demotion strategy is triggered according to the transfer rule to generate an alternative credential mapping and incorporate it into the execution information.

4. The method according to claim 1, characterized in that, The step of rearranging the commitment list and updating the execution list with minimal disturbance in real-time according to the hard priority non-transferable and soft priority transferable rules includes: Rearrangement rules are generated based on the non-transferability of hard priority and the transfer limit and penalty coefficient of soft priority. Determine an equivalent set of substitutes for each fulfillable capacity certificate based on the mutual exclusion identifier and binding relationship; With the goal of minimizing the changes in the control image and charged state trajectory, the time slice that triggers the constraint change is replaced within the synchronization effective window; The rearrangement results are verified for feasibility based on the charge state boundary, congestion capacity limit, non-functionality and ramp continuity. If the verification is successful, an alternative credential mapping is generated and the execution list is updated. The rearrangement sequence number and causal chain are recorded and written into the runtime ledger.

5. The method according to claim 1, characterized in that, The process involves collecting active power, reactive power, voltage, and frequency sequences during the metering phase, performing time-frequency decomposition, and combining baseline and causal attribution. Incremental active power, reactive power, and ramp-up quantities are mapped to corresponding fulfillable capacity vouchers according to time slices and nodes, generating voucher-by-voucher receipts, including: Active power, reactive power, voltage and frequency sequences are collected at the grid-connected metering point according to a unified time base. A time-frequency decomposition algorithm is performed on the power sequence to obtain the high-frequency component for frequency modulation and the low-frequency component for energy transfer, and the ramp rate is extracted. A baseline model is established and corrected based on a list of non-routine events to obtain a baseline sequence; Using the frequency band identifier and metering verification anchor point of the fulfillable capacity certificate, the decomposed incremental active power, reactive power and ramp-up amount are aligned by node and time slice and causal attribution is completed. The consistency of the attribution results and the equipment receipts are verified and duplicate measurements are eliminated. A receipt for each document containing a time identifier, node identifier and voucher identifier is generated and signed and stored.

6. The method according to claim 1, characterized in that, The step of aligning the updated execution list with the document-by-document receipt and completing document-by-document accounting in conjunction with the constraint price corresponding to the congestion capacity document includes: Align the updated execution list with the voucher-by-voucher receipts one by one based on voucher identifier, node, and time slice; Using the constraint price of the congestion capacity certificate as a coefficient, the incremental active power, reactive power and ramping amount in the receipt are included in the accounting amount of the corresponding fulfillable capacity certificate. For entries with alternative document mappings, the accounting volume and constraint price are allocated according to the transfer rules; For multiple receipts of the same resource in the same time slice, perform deduplication and consistency checks, generate settlement entries for each voucher, and summarize them to form a settlement record.

7. A multi-service fulfillable capacity certificate causal settlement device, characterized in that, include: The acquisition module is used to acquire network constraints and energy storage capacity based on the power grid topology and access nodes, forming a set of resource capabilities and constraints; The discrete module is used to discretize the energy storage capacity into fulfillable capacity certificates according to the nodes and time slices, and generate congestion capacity certificates according to the distribution network constraints and establish a binding relationship with the fulfillable capacity certificates. The optimization module is used to perform combined optimization of the fulfillable capacity certificate and the congestion capacity certificate in the day-ahead phase, constrained by the state of charge, power, energy, ramp and non-functionality, and non-overlapping relationships, to obtain the commitment list and control parameters. The distribution module is used to distribute the control parameters to the equipment to perform charging and discharging and reactive power control, and record the execution information associated with the commitment list. In the real-time stage, the commitment list is rearranged with minimal disturbance and the execution list is updated according to the hard priority non-transferable and soft priority transferable rules. The generation module is used to collect active, reactive, voltage and frequency sequences during the metering stage, perform time-frequency decomposition and combine baseline and causal attribution, map incremental active, reactive and ramp quantities to the corresponding fulfillable capacity vouchers according to time slices and nodes, and generate voucher-by-voucher receipts. The output module is used to align the updated execution list with the voucher-by-voucher receipt, combine the constraint price corresponding to the congestion capacity voucher to complete the voucher-by-voucher accounting, and output the settlement records organized by the fulfillable capacity voucher for the settlement processing of multiple services of vehicle network load storage. The discrete module is used to generate capacity slices indexed by nodes and time slices, write power, energy, reactive power and ramp boundaries, mutual exclusion identifiers, priorities and metering verification anchors, and set frequency band identifiers to distinguish between frequency modulation components and energy transfer components; calculate the available capacity of nodes based on distribution network power flow sensitivity, branch thermal limits, voltage deviation thresholds and phase imbalance limits, and generate congestion capacity certificates containing capacity limits and constraint types; establish a binding relationship between each fulfillable capacity certificate and at least one congestion capacity certificate according to the constraint mapping of the same node and the same time slice, and set linkage binding and alternative sets when there is coupling between adjacent nodes; and store the binding relationship, mutual exclusion identifiers and transfer rules in the certificate metadata for day-ahead optimization and real-time reordering of references. The optimization module is used to construct a binding relationship graph with fulfillable capacity certificates and congestion capacity certificates as nodes, and generate a non-overlapping mutually exclusive matrix for the same energy storage unit and the same time slice; it sets time slice sequence constraints based on the state of charge evolution equation, and sets consistency and continuity constraints on power, energy, reactive power and ramping; it determines the congestion capacity limit according to power flow sensitivity and node constraints, and injects the corresponding constraint shadow price into the objective function; it sets hierarchical objectives according to hard priority and soft priority, with hard priority constraints being non-transferable and soft priority being characterized by transfer upper limit and penalty coefficient; it introduces scenario set robust constraints to cover load and renewable output prediction deviations, and retains the reserve margin field and upper and lower limits; it solves to obtain the commitment list and control parameters, including charging and discharging power reference, reactive power control mode parameters, ramping limit and effective time window, and the distributed mirror corresponding to the mutual exclusion identifier and priority.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 6.

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