A multi-energy complementary microgrid intelligent scheduling management and control method

Abstract

The present application relates to the technical field of micro-grid, and more particularly to a multi-energy complementary micro-grid intelligent scheduling management and control method, comprising the following steps: obtaining response proposals of each micro-grid based on digital twinning generation, containing adjustable capacity, time period and response cost, summarizing and constructing a global adjustable resource pool, and matching the power grid demand to generate an intended scheduling scheme with the minimum total response cost as the target; issuing a confirmation request, receiving a re-examination voucher with a twin verification code and generating an execution instruction, the instruction and the verification code are stored in the blockchain, and the execution data is collected to generate a verification code comparison; updating the on-chain credit points according to the comparison result, and the points are used for subsequent resource combination screening. In the present application, each multi-energy complementary micro-grid independently reports the response proposal containing capacity, time period and cost based on digital twinning, and is scheduled based on the independently reported adjustable information, solving the defects of insufficient resource mining in traditional centralized scheduling mode and the inability to truly reflect the response capability of the micro-grid.

Description

Technical Field

[0001] This invention relates to the field of microgrid technology, and in particular to a method for intelligent scheduling and control of multi-energy complementary microgrids. Background Technology

[0002] With the continuous development of new energy power generation, energy storage equipment, and multi-energy conversion equipment, multi-energy complementary microgrids are increasingly being applied in industrial parks, commercial buildings, charging and battery swapping stations, and integrated energy application scenarios. Multi-energy complementary microgrids achieve comprehensive utilization and flexible control of regional energy by collaboratively managing multiple energy forms such as electricity, heat, and cooling, and combining the operational regulation capabilities of distributed energy devices, energy storage devices, and energy conversion devices. During the participation of multi-energy complementary microgrids in grid dispatch, microgrid operating status information is typically collected, and combined with energy management systems, grid dispatch platforms, and optimized dispatch algorithms, adjustable resources are analyzed and dispatched to achieve coordinated matching between multiple energy sources and grid operational needs.

[0003] Most existing multi-energy complementary microgrid scheduling methods adopt the approach of centrally acquiring microgrid operation information and uniformly formulating scheduling schemes through a scheduling platform. However, due to the lack of a mechanism for the microgrid to autonomously express its adjustable capabilities, there are problems such as insufficient mining of adjustable resources and difficulty in fully reflecting the actual response capabilities of the microgrid during the scheduling process. Summary of the Invention

[0004] To overcome the above shortcomings, this invention provides an intelligent scheduling and control method for multi-energy complementary microgrids. It aims to improve upon the existing multi-energy complementary microgrid scheduling methods, which mostly adopt the approach of centrally acquiring microgrid operation information and uniformly formulating scheduling schemes through a scheduling platform. This approach is prone to problems such as insufficient mining of microgrid adjustable resources and difficulty in fully reflecting the actual response capability of the microgrid during the scheduling process.

[0005] This invention provides the following technical solution: a multi-energy complementary microgrid intelligent scheduling and control method, comprising the following steps: S1. Obtain response capability proposals generated and reported autonomously by multiple multi-energy complementary microgrids. The response capability proposals are generated based on the digital twin models corresponding to each multi-energy complementary microgrid and include adjustable capacity information, available time period information and response cost information. S2. The multiple response capability proposals are aggregated into a global adjustable resource pool. In response to the grid dispatching needs, with the goal of minimizing the total response cost, the response capability proposals in the global adjustable resource pool are combined and selected to generate an intended dispatching scheme. S3. Send an intention confirmation request to the multi-energy complementary microgrid corresponding to the intended scheduling scheme, obtain the execution confirmation certificate returned by the multi-energy complementary microgrid after verification based on the corresponding digital twin model, the execution confirmation certificate contains a twin verification code, and generate the final execution instruction based on the execution confirmation certificate; S4. Associate the final execution instruction with the twin verification code and record it on the blockchain, obtain the actual execution data, generate the actual execution verification code based on the actual execution data, and perform a consistency comparison with the twin verification code recorded on the chain; S5. Trigger differentiated settlement and update on-chain reputation score based on the comparison result between the twin verification code and the actual execution verification code. Feed back the on-chain reputation score to the combination selection process in S2 as a scheduling evaluation parameter in the subsequent response capability proposal combination selection process.

[0006] By adopting the above technical solution, response capability proposals generated and reported autonomously by multiple multi-energy complementary microgrids are obtained. Based on the digital twin model corresponding to each multi-energy complementary microgrid, response capability proposals containing adjustable capacity information, available time period information, and response cost information are generated. This enables the scheduling process to organize resources based on the adjustable capability information actively provided by each multi-energy complementary microgrid. This improves upon the existing multi-energy complementary microgrid scheduling methods, which mostly adopt the approach of centrally obtaining microgrid operation information and uniformly formulating scheduling schemes by the scheduling platform. Due to the lack of a mechanism for microgrids to autonomously express their adjustable capabilities, there is insufficient mining of adjustable resources in microgrids and the scheduling process cannot fully reflect the actual response capability of microgrids.

[0007] Furthermore, in S1, the step of obtaining the response capability proposals autonomously generated and reported by multiple multi-energy complementary microgrids includes: Acquire operational status data for multiple multi-energy complementary microgrids; The adjustable operating range of each multi-energy complementary microgrid is determined based on the operating status data of each multi-energy complementary microgrid. Based on the adjustable operating range, corresponding response capability proposals are formed, and the corresponding identification information of the multi-energy complementary microgrid is associated with the response capability proposals; The response capability proposals containing multi-energy complementary microgrid identification information are reported for aggregation to form a globally adjustable resource pool.

[0008] Furthermore, in S1, the step of generating the response capability proposal based on the digital twin model corresponding to each multi-energy complementary microgrid includes: Acquire operational status data for multiple multi-energy complementary microgrids and update the corresponding digital twin models based on the operational status data; Simulate the operating status under different response capability adjustment conditions based on the updated digital twin model; Calculate adjustable capacity information, available time period information, and response cost information based on the simulation results corresponding to different response capability adjustment conditions; A response capability proposal is generated based on adjustable capacity information, available time period information, and response cost information.

[0009] Furthermore, in S2, the step of aggregating the multiple response capability proposals into a globally adjustable resource pool includes: Obtain response capability proposals reported by multiple multi-energy complementary micronetworks; Extract adjustable capacity information, available time period information, and response cost information from each response capability proposal; The adjustable capacity information, available time period information, and response cost information in the response capability proposal are linked and organized according to a unified data structure to form a standardized response capability record. Multiple standardized response capability records are aggregated to generate a globally adjustable resource pool.

[0010] Furthermore, in S2, the step of combining and selecting response capability proposals from the globally adjustable resource pool with the objective of minimizing the total response cost includes: Obtain power grid dispatch requirements and select candidate response capability proposals from the global adjustable resource pool based on these requirements; Candidate response capability proposals are combined to form multiple response capability proposal combinations, and the total response cost corresponding to each combination is calculated. Determine the optimal combination of response capabilities that minimizes the total response cost; Based on the response capabilities that minimize the total response cost, a proposed scheduling scheme is generated.

[0011] Furthermore, in S3, the step of sending an intention confirmation request to the multi-energy complementary microgrid corresponding to the intended scheduling scheme includes: Obtain the intended scheduling plan and determine the corresponding response capacity proposal; Based on the proposed response capabilities, the corresponding multi-energy complementary microgrid is determined; An intention confirmation request is generated based on the response capability proposal. The intention confirmation request includes adjustable capacity information, available time period information, and response cost information corresponding to the response capability proposal. Send the intention confirmation request to the corresponding multi-energy complementary microgrid.

[0012] Further, in S3, the step of obtaining the execution confirmation certificate returned by the multi-energy complementary microgrid after verification based on the corresponding digital twin model includes: Obtain the intention confirmation request and call the digital twin model corresponding to the multi-energy complementary microgrid for verification; Generate a twin verification code based on the verification results; Generate an execution confirmation credential containing twin verification codes.

[0013] Further, in S4, the step of generating an actual execution checksum based on the actual execution data and performing a consistency comparison with the twin checksum recorded on the chain includes: The actual execution data is standardized to generate verification data corresponding to the actual execution data; Generate an actual execution verification code based on the verification data corresponding to the actual execution data; Obtain the twin verification code of the on-chain record; The actual execution check code is compared with the twin check code recorded on the chain to generate a comparison result.

[0014] Furthermore, in S5, the step of triggering differentiated settlement and updating on-chain reputation points based on the comparison result between the twin verification code and the actual execution verification code includes: The degree of consistency is determined by comparing the twin check code with the actual execution check code; Differential settlement is triggered based on the degree of consistency. The credit score change value is determined based on the aforementioned differentiated settlement. Update the on-chain reputation score based on the change in reputation score value.

[0015] Furthermore, in S5, the step of feeding back the on-chain reputation score to the combination selection process in S2 as a scheduling evaluation parameter in the subsequent response capability proposal combination selection process includes: Earn on-chain reputation points; The on-chain reputation score is used as a scheduling evaluation parameter; The scheduling evaluation parameters are invoked during the selection of response capability proposal combinations; In the combination selection process in S2, the scheduling evaluation parameters are used as the evaluation basis for the combination selection of response capability proposals.

[0016] The present invention has the following beneficial effects: 1. In this invention, response capability proposals generated and reported autonomously by multiple multi-energy complementary microgrids are obtained, and response capability proposals containing adjustable capacity information, available time period information, and response cost information are generated based on the digital twin model corresponding to each multi-energy complementary microgrid. This enables the scheduling process to organize resources based on the adjustable capability information actively provided by each multi-energy complementary microgrid, thereby improving the existing multi-energy complementary microgrid scheduling methods, which mostly adopt the method of centrally obtaining microgrid operation information and uniformly formulating scheduling schemes by the scheduling platform. Due to the lack of a mechanism for the microgrid to autonomously express its adjustable capabilities, the microgrid's adjustable resources are not fully explored, and the scheduling process cannot fully reflect the actual response capability of the microgrid.

[0017] 2. In this invention, by sending an intention confirmation request to the multi-energy complementary microgrid corresponding to the intended scheduling scheme, an execution confirmation certificate containing a twin verification code is obtained after verification based on the corresponding digital twin model. The final execution instruction is then associated with the twin verification code and recorded on the blockchain. This establishes an association and verification relationship between scheduling instruction generation, digital twin verification, and on-chain records. This improves upon the existing multi-energy complementary microgrid scheduling methods, which mostly adopt the method of directly issuing and executing scheduling instructions. Due to the lack of a pre-verification mechanism between the scheduling scheme and the actual execution capability of the microgrid, the scheduling instruction execution process lacks reliable verification.

[0018] 3. In this invention, differential settlement is triggered and on-chain reputation score is updated based on the comparison result between the twin verification code and the actual execution verification code. The on-chain reputation score is then fed back to the subsequent response capability proposal combination selection process as a scheduling evaluation parameter. This allows the response capability proposal combination selection process to be dynamically adjusted based on historical execution data. This improves upon the existing multi-energy complementary microgrid scheduling methods, which mostly adopt a one-time scheduling decision approach. Due to the lack of a feedback mechanism for historical execution consistency in the scheduling process, subsequent scheduling resource selection lacks a basis for continuous optimization. Attached Figure Description

[0019] Figure 1 A schematic diagram of the overall process of a multi-energy complementary microgrid intelligent scheduling and control method of the present invention; Figure 2 This is a schematic diagram of the response capability proposal generation process in the intelligent scheduling and control method for multi-energy complementary microgrids of the present invention; Figure 3 This is a schematic diagram of the process for constructing a globally adjustable resource pool and generating an intended scheduling scheme in a multi-energy complementary microgrid intelligent scheduling and control method of the present invention. Figure 4 This is a schematic diagram of the scheduling confirmation process based on digital twin model verification in the intelligent scheduling and control method for multi-energy complementary microgrids of the present invention. Figure 5 This is a schematic diagram of the execution evaluation feedback process based on blockchain verification in the intelligent scheduling and control method for multi-energy complementary microgrids of the present invention; Figure 6 This is a schematic diagram of the module architecture of a multi-energy complementary microgrid intelligent scheduling and control system in an embodiment of the present invention. Detailed Implementation

[0020] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Example 1: In the first embodiment of the present invention, a multi-energy complementary microgrid intelligent scheduling and control method is provided, such as... Figure 1 As shown, the process includes the following steps: S1, obtaining response capability proposals generated and reported autonomously by multiple multi-energy complementary microgrids. The response capability proposals are generated based on the digital twin models corresponding to each multi-energy complementary microgrid and include adjustable capacity information, available time period information and response cost information. Furthermore, in S1, the steps for obtaining response capability proposals autonomously generated and reported by multiple multi-energy complementary microgrids include: Acquire operational status data for multiple multi-energy complementary microgrids; The adjustable operating range of each multi-energy complementary microgrid is determined based on the operating status data of each multi-energy complementary microgrid. Based on the adjustable operating range, corresponding response capability proposals are formed, and the corresponding identification information of the multi-energy complementary microgrid is associated with the response capability proposals; The response capability proposals containing multi-energy complementary microgrid identification information are reported for aggregation to form a globally adjustable resource pool.

[0022] Specifically, the edge intelligent controller first collects operational status data of key equipment within each multi-energy complementary microgrid, including energy storage charge status, real-time photovoltaic output, power output of three types of loads (cold, heat, and electricity), and controllable unit operating points. Based on this operational status data, combined with the physical constraints and safety boundaries of each device, it determines the current adjustable operating range of each microgrid, namely, the upward adjustable capacity and the downward adjustable capacity. Based on the adjustable operating range, it forms corresponding response capability proposals, which also include response confidence levels. The response confidence level is determined based on the statistical results of the microgrid's historical prediction accuracy. The microgrid identification information is then associated with the proposals. Finally, the response capability proposals carrying the identification information are proactively reported to the virtual power plant cloud platform. Reporting methods include timed reporting according to a preset period and immediate reporting triggered when the relative change in adjustable capacity exceeds 10%, without waiting for the virtual power plant cloud platform to query. This serves as the foundational data for subsequently building a globally adjustable resource pool.

[0023] like Figure 2 As shown, further, in S1, the step of generating the response capability proposal based on the digital twin model corresponding to each multi-energy complementary microgrid includes: Acquire operational status data for multiple multi-energy complementary microgrids and update the corresponding digital twin models based on the operational status data; Simulate the operating status under different response capability adjustment conditions based on the updated digital twin model; Calculate adjustable capacity information, available time period information, and response cost information based on the simulation results corresponding to different response capability adjustment conditions; A response capability proposal is generated based on adjustable capacity information, available time period information, and response cost information.

[0024] Specifically, the digital twin model originates from a pre-built multi-business digital twin template library. This library includes templates for industrial park combined cooling, heating, and power microgrids, commercial building air conditioning loads, cold chain cold storage, charging stations, and electric heating. Each template pre-configures the core equipment model, key response characteristic parameters, and virtual energy storage methods for its corresponding business type. During instantiation, parameters are assigned to the template based on the equipment nameplate parameters of the actual microgrid. Real-time operating status data of each microgrid is acquired, and the internal state variables of the digital twin model deployed on the edge intelligent controller are updated synchronously, ensuring a high-precision mapping between the digital twin model and the physical entity's operating status. Based on the updated digital twin model, multiple sets of response capability adjustment conditions are set for simulation, simulating the evolution trajectory of key state variables within the microgrid when responding with different adjustment directions and amounts over a preset future time period. Based on the simulation results, the corresponding adjustable capacity information, available time period information, and response cost information are calculated, where the response cost... The calculation method is as follows , The increase in operating costs resulting from response adjustments, For the loss of comfort or quality due to response adjustment, To calculate the cost of equipment life loss caused by response adjustment, all three factors are extracted and quantified from the simulation output of the digital twin model. The comfort or quality loss of different business types is calculated according to the quantitative model built into the corresponding business type template. Finally, the adjustable capacity information, available time period information, response cost information and response confidence are summarized to generate the response capability proposal of the microgrid.

[0025] S2. Aggregate multiple response capability proposals into a global adjustable resource pool. In response to power grid dispatching needs, with the goal of minimizing the total response cost, combine and select response capability proposals in the global adjustable resource pool to generate an intended dispatching scheme. like Figure 3 As shown, further, in S2, the step of aggregating multiple response capability proposals into a globally adjustable resource pool includes: Obtain response capability proposals reported by multiple multi-energy complementary micronetworks; Extract adjustable capacity information, available time period information, and response cost information from each response capability proposal; The adjustable capacity information, available time period information, and response cost information in the response capability proposal are linked and organized according to a unified data structure to form a standardized response capability record. Multiple standardized response capability records are aggregated to generate a globally adjustable resource pool.

[0026] Specifically, the virtual power plant cloud platform continuously receives response capability proposals from multiple multi-energy complementary microgrids; it extracts adjustable capacity information, available time period information, response cost information, and response confidence information from each response capability proposal; it organizes the above four types of information according to a unified data structure to form a standardized response capability record for each microgrid; finally, it aggregates the standardized response capability records of all microgrids to build a global adjustable resource pool for unified use in subsequent scheduling decisions.

[0027] Furthermore, in S2, the steps for combining and selecting response capability proposals from the globally adjustable resource pool with the objective of minimizing the total response cost include: Obtain power grid dispatch requirements and select candidate response capability proposals from the global adjustable resource pool based on these requirements; Candidate response capability proposals are combined to form multiple response capability proposal combinations, and the total response cost corresponding to each combination is calculated. Determine the optimal combination of response capabilities that minimizes the total response cost; Based on the response capabilities that minimize the total response cost, a proposed scheduling scheme is generated.

[0028] Specifically, the virtual power plant cloud platform pre-stores complementary feature matrices between regions, and the complementarity coefficients in the complementary feature matrices. ;in This refers to the time difference between peak electricity consumption periods in the two regions. The Pearson correlation coefficient is the typical daily load curve of the two regions. The time difference between sunset in the two regions. , , The weighting coefficients are used as follows: When the virtual power plant cloud platform receives a dispatch request from the power grid, it first selects all response capability proposals from the global adjustable resource pool that have available time periods covering the power grid dispatch request period, sufficient adjustable capacity in the corresponding adjustment direction, and a response confidence level not lower than a preset lower limit, as a candidate proposal set. The proposals in the candidate proposal set are then combined and enumerated. During the combination and enumeration process, combinations of regional pairs with high complementarity coefficients are given priority ranking weights, and their capacity satisfaction is verified first, forming multiple response capability proposal combinations that meet the dispatch request capacity requirements. The combination selection satisfies the capacity constraints of cross-regional transmission sections. ;in For cross-regional power exchange, The available transmission capacity of the transmission section during the response period is determined. For each combination, the response costs of all proposed combinations are summed to calculate the total response cost corresponding to the combination. Among all combinations that meet the capacity requirements, the combination with the minimum total response cost is determined as the optimal combination. Based on the microgrid identifier, allocated capacity, and response period information of each proposal in the optimal combination, the final intended scheduling scheme is generated. The above combination selection mechanism also supports multi-timescale scheduling: During the day-ahead phase, each microgrid submits a sequence of response capacity proposals for the next day based on the next day's weather forecast and production plan. The virtual power plant cloud platform summarizes these proposals, performs pre-clearing, and forms a preliminary scheduling plan. After entering the operation day, each microgrid updates and reports the latest proposals based on the real-time operation status. The virtual power plant cloud platform then performs rolling revisions to the preliminary scheduling plan, realizing the coordination of day-ahead pre-clearing and intraday rolling revisions.

[0029] S3. Send an intention confirmation request to the multi-energy complementary microgrid corresponding to the intended scheduling scheme, obtain the execution confirmation certificate returned by the multi-energy complementary microgrid after verification based on the corresponding digital twin model. The execution confirmation certificate contains a twin verification code. Generate the final execution instruction based on the execution confirmation certificate. like Figure 4 As shown, further, in S3, the step of sending an intention confirmation request to the multi-energy complementary microgrid corresponding to the intended scheduling scheme includes: Obtain the intended scheduling plan and determine the corresponding response capacity proposal; Based on the proposed response capabilities, the corresponding multi-energy complementary microgrid is determined; An intention confirmation request is generated based on the response capacity proposal. The intention confirmation request includes the adjustable capacity information, available time period information, and response cost information corresponding to the response capacity proposal. Send the intention confirmation request to the corresponding multi-energy complementary microgrid.

[0030] Specifically, the process involves obtaining the intended scheduling plan, parsing out the relevant response capability proposals and their corresponding microgrid identifiers; identifying the target multi-energy complementary microgrids that need to receive the intention confirmation request based on the microgrid identifiers; generating an intention confirmation request for each target microgrid based on its corresponding response capability proposal, which carries the adjustable capacity information, available time period information, expected response cost information, and the unique confirmation time limit allocated to the request by the virtual power plant cloud platform; and sending the intention confirmation request to the corresponding multi-energy complementary microgrid through the cloud-edge communication link to initiate the feasibility verification process on the microgrid side.

[0031] Furthermore, in S3, the steps for obtaining the execution confirmation certificate returned by the multi-energy complementary microgrid after verification based on the corresponding digital twin model include: Obtain the intention confirmation request and call the digital twin model corresponding to the multi-energy complementary microgrid for verification; Generate a twin verification code based on the verification results; Generate an execution confirmation credential containing twin verification codes.

[0032] Specifically, after receiving the intention confirmation request, the microgrid edge intelligent controller calls the locally maintained digital twin model. Using the current actual operating state as the initial condition, it performs a rapid simulation verification according to the adjustment amount and response time period specified in the intention confirmation request. This verifies whether executing the intended plan under the current state will trigger safety constraints or lead to unacceptable quality losses. If the verification passes, based on the predicted response curve and key state variable trajectory output by the digital twin model during the verification process, a fixed-length twin verification code is generated using the SHA-256 hash algorithm. Its generation method is ;in For instruction identification, For microgrid identification, This is a sampling sequence of the predicted power response curve with a sampling interval of 15 seconds. The trajectory sampling sequence is used to predict key state variables with a sampling interval of 15 seconds. The key state variables include at least one of the following: energy storage state of charge, cold storage temperature, and CCHP unit outlet temperature. The expected response cost for the re-evaluation This indicates a data concatenation operation; the twin verification code is encapsulated into an execution confirmation credential, which is then returned to the virtual power plant cloud platform; if the verification fails, the edge intelligent controller generates a capability change notification containing the updated adjustable capacity and returns it to the virtual power plant cloud platform, which then selects a replacement microgrid from the global adjustable resource pool accordingly; upon receiving the execution confirmation credential, the virtual power plant cloud platform treats it as an executable commitment from the microgrid to this scheduling task and generates a binding final execution instruction accordingly.

[0033] S4. Record the final execution instruction and twin verification code on the blockchain, obtain the actual execution data, generate the actual execution verification code based on the actual execution data, and perform a consistency comparison with the twin verification code recorded on the chain. like Figure 5 As shown, further, in S4, the steps of generating an actual execution checksum based on the actual execution data and comparing it with the twin checksum recorded on the chain include: The actual execution data is standardized to generate verification data corresponding to the actual execution data; Generate an actual execution verification code based on the verification data corresponding to the actual execution data; Obtain the twin verification code of the on-chain record; The actual execution check code is compared with the twin check code recorded on the chain to generate a comparison result.

[0034] Specifically, after the scheduling execution is completed, the actual execution data within the response period is obtained from the edge metering device, including the actual exchange power sequence and key state variable sequence collected at 15-second sampling intervals. The raw collected data is standardized by unifying the timestamp format, aligning the sampling time points, and using the Laida criterion to identify and remove abnormal jump data, forming standardized verification data. The SHA-256 hash algorithm, which is the same as the twin checksum generation process, is used to perform hash operations on the standardized verification data to generate the actual execution checksum. Its generation method is ;in This is a sampled sequence of the actual power response curve, acquired at 15-second sampling intervals. This is a sequence of actual trajectory samples of key state variables collected at 15-second sampling intervals. To calculate the actual response cost based on actual execution data; read the twin verification code associated with the scheduled task from the blockchain. ;Will and Perform a bitwise consistency comparison. If the two are completely identical, generate a comparison result indicating that the execution and prediction are highly consistent. If there are differences, generate a comparison result containing a deviation identifier and record the deviation type.

[0035] S5. Trigger differentiated settlement and update on-chain reputation score based on the comparison result between twin verification code and actual execution verification code. Feed back the on-chain reputation score to the combination selection process in S2 as a scheduling evaluation parameter in the subsequent response capability proposal combination selection process. Furthermore, in S5, the steps for triggering differentiated settlement and updating on-chain reputation scores based on the comparison between the twin checksum and the actual execution checksum include: The degree of consistency is determined by comparing the twin check code with the actual execution check code; Differential settlement is triggered based on the degree of consistency; The change in credit score is determined based on differentiated settlement. Update the on-chain reputation score based on the change in reputation score value.

[0036] Specifically, after obtaining the comparison results, the smart contract determines the consistency assessment level according to the following rules: If If it is determined to be completely identical; and If it is determined to be a benign deviation; and If the actual response cost exceeds the expected value by 15%, it is judged as a malicious deviation; if the execution data is not submitted on time or the data chain is interrupted, it is judged as data missing; differentiated settlement is triggered according to the consistency assessment level. Full consistency and benign deviation trigger full settlement, malicious deviation calculates the penalty ratio according to the preset deduction rules and triggers deduction settlement, and data missing temporarily withholds compensation and triggers an alarm; after each settlement is completed, the on-chain reputation score of the micronet is updated. The update method is ;in This refers to the microgrid's reputation score prior to this scheduling. The initial value is set to 100. The lower limit is 0. The upper limit is 200. The change in credit score determined based on the consistency assessment level is considered when all aspects are completely consistent. When there is a benign deviation When malign deviations occur When data is missing ;Will Write the information to the blockchain to complete the on-chain update of the reputation score.

[0037] Furthermore, in S5, the step of feeding back on-chain reputation scores to the combination selection process in S2 as scheduling evaluation parameters in the subsequent response capability proposal combination selection process includes: Earn on-chain reputation points; Use on-chain reputation points as a scheduling evaluation parameter; The scheduling evaluation parameters are invoked during the selection of response capability proposal combinations. In the combination selection process in S2, the scheduling evaluation parameters are used as the evaluation basis for the combination selection of response capability proposals.

[0038] Specifically, before initiating the combination selection in each scheduling cycle, the latest on-chain reputation score of each micronet is queried and read from the blockchain; the read on-chain reputation score is directly used as the scheduling evaluation parameter corresponding to the micronet; the scheduling evaluation parameter is associated and bound with the response capability proposal of the micronet, so that the scheduling evaluation parameter enters the combination selection process along with the response capability proposal; in the combination selection process of S2, among multiple candidate combinations with the same total response cost or a difference within a preset range, the combination containing the micronet with the higher scheduling evaluation parameter is selected first, and the response capability proposal of the micronet with the scheduling evaluation parameter lower than the preset threshold is given lower priority in the combination selection.

[0039] Example 2: In the second embodiment of the present invention, the present invention provides a multi-energy complementary microgrid intelligent scheduling and control system, such as... Figure 6 As shown, it includes the following modules: The response capability acquisition and processing module is used to collect the operation status information of multiple multi-energy complementary microgrids, generate response capability proposals based on the digital twin models corresponding to each multi-energy complementary microgrid, and send the response capability proposals to the resource aggregation and processing module. The response capability proposals include adjustable capacity information, available time period information, and response cost information. The resource aggregation and processing module is used to receive multiple response capability proposals, aggregate the multiple response capability proposals to form a global adjustable resource pool, determine the response capability proposal combination from the global adjustable resource pool according to the power grid dispatching requirements, generate an intentional dispatching scheme according to the response capability proposal combination, and send it to the dispatching confirmation processing module. The scheduling confirmation processing module is used to receive the intended scheduling scheme, determine the multi-energy complementary microgrid corresponding to the intended scheduling scheme, send an intention confirmation request to the corresponding multi-energy complementary microgrid, receive the execution confirmation certificate generated by the multi-energy complementary microgrid after verification based on the corresponding digital twin model, and generate the final execution instruction based on the execution confirmation certificate. The execution verification processing module is used to receive the final execution instruction, associate the final execution instruction with the twin verification code in the execution confirmation certificate and record it on the blockchain, collect the actual execution data corresponding to the final execution instruction, generate an actual execution verification code based on the actual execution data, compare the actual execution verification code with the twin verification code recorded on the blockchain, and generate a comparison result. The scheduling evaluation update module is used to receive the comparison results, determine the differentiated settlement information based on the comparison results, update the on-chain reputation score, and send the on-chain reputation score as a scheduling evaluation parameter to the resource aggregation processing module, so that the resource aggregation processing module can call the scheduling evaluation parameter in the subsequent response capability proposal combination selection process.

[0040] In large industrial parks with integrated energy applications, the parks typically contain various energy units, including photovoltaic power generation equipment, energy storage equipment, gas-fired combined cooling, heating and power (CCHP) equipment, and adjustable load equipment. Each energy unit has different operating states, regulation capabilities, and response costs. During grid demand response, because the operating information of each energy unit is dispersed across different microgrids, the grid dispatching side struggles to obtain the actual available regulation capabilities of each microgrid in a timely manner. Furthermore, the lack of a capability verification process before execution after the dispatching plan is generated easily leads to a mismatch between the dispatching plan and the actual operating state of the microgrid. To address these issues, this invention employs a multi-energy complementary microgrid intelligent dispatching and control system, such as... Figure 6 As shown, the specific implementation process of this system is as follows: First, the operational status information of multiple multi-energy complementary microgrids within the industrial park is collected by the response capability acquisition and processing module. Based on the digital twin model corresponding to each multi-energy complementary microgrid, the operational status of energy equipment is mapped and analyzed to generate response capability proposals containing adjustable capacity information, available time period information, and response cost information. These proposals are then sent to the resource aggregation and processing module. Through this process, each multi-energy complementary microgrid can proactively form response capability information that can participate in scheduling based on its own operational status, reducing the reliance of the scheduling process on direct interaction of detailed operational data within the microgrid. Then, the resource aggregation and processing module receives response capability proposals reported by multiple multi-energy complementary microgrids, aggregates the response capability proposals corresponding to different microgrids to form a global adjustable resource pool, and performs combination analysis on the response capability proposals in the global adjustable resource pool according to the current grid dispatch requirements to determine the combination of response capability proposals that meet the dispatch requirements and generate the corresponding intended dispatch scheme. By uniformly organizing and combining the response capabilities of multiple microgrids, the coordinated utilization of the regulation capabilities of different energy units is realized, improving the problem that it is difficult for single microgrid resources to form overall coordination in the existing dispatch process. Then, the scheduling confirmation processing module receives the intended scheduling scheme, determines the corresponding multi-energy complementary microgrid based on the intended scheduling scheme, and sends an intention confirmation request to the corresponding multi-energy complementary microgrid. After receiving the intention confirmation request, the multi-energy complementary microgrid calls the corresponding digital twin model to verify the adjustment process under the current operating state, generates an execution confirmation certificate containing a twin verification code based on the verification result, and returns it to the scheduling confirmation processing module. By adding a verification process based on the digital twin model before the scheduling scheme is executed, the scheduling scheme can be confirmed in combination with the actual operating state of the microgrid, which improves the problem of the lack of execution capability verification after the existing scheduling scheme is generated. Then, the scheduling confirmation processing module generates the final execution instruction based on the execution confirmation certificate and sends the final execution instruction to the corresponding multi-energy complementary microgrid for execution. At the same time, the execution verification processing module associates the final execution instruction with the twin verification code in the execution confirmation certificate and records it on the blockchain. It also collects the actual execution data during the execution process of the corresponding microgrid, generates an actual execution verification code based on the actual execution data, and compares the actual execution verification code with the twin verification code recorded on the blockchain. By associating and verifying the execution instruction, the predicted execution status, and the actual execution status, the execution status verification during the scheduling process is realized, which improves the problem of the lack of reliable execution status verification in the existing scheduling process. Finally, the scheduling evaluation and update module determines the corresponding differentiated settlement based on the consistency comparison between the twin checksum and the actual execution checksum, and updates the on-chain reputation score. At the same time, the on-chain reputation score is sent as a scheduling evaluation parameter to the resource aggregation and processing module, so that the resource aggregation and processing module can call the scheduling evaluation parameter in the subsequent response capability proposal combination selection process to evaluate the response capability proposals participating in the combination selection. By feeding back the historical execution status to the subsequent scheduling process, the microgrid response capability evaluation and subsequent scheduling decision form a closed loop, which improves the problem in the existing scheduling method that it is difficult to feed back the execution results to the subsequent resource selection process. Through the above system, in the scenario of multi-energy collaborative dispatch in industrial parks, a complete dispatch and control process is realized, including autonomous generation of response capability of multi-energy complementary microgrids, collaborative selection of dispatch schemes, verification of execution capability, and feedback evaluation of execution results. This enables the grid dispatch demand to be dynamically matched with the adjustable resources of multiple microgrids.

[0041] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent scheduling and control of multi-energy complementary microgrids, characterized in that, Includes the following steps: S1. Obtain response capability proposals generated and reported autonomously by multiple multi-energy complementary microgrids. The response capability proposals are generated based on the digital twin models corresponding to each multi-energy complementary microgrid and include adjustable capacity information, available time period information and response cost information. S2. The multiple response capability proposals are aggregated into a global adjustable resource pool. In response to the grid dispatching needs, with the goal of minimizing the total response cost, the response capability proposals in the global adjustable resource pool are combined and selected to generate an intended dispatching scheme. S3. Send an intention confirmation request to the multi-energy complementary microgrid corresponding to the intended scheduling scheme, obtain the execution confirmation certificate returned by the multi-energy complementary microgrid after verification based on the corresponding digital twin model, the execution confirmation certificate contains a twin verification code, and generate the final execution instruction based on the execution confirmation certificate; S4. Associate the final execution instruction with the twin verification code and record it on the blockchain, obtain the actual execution data, generate the actual execution verification code based on the actual execution data, and perform a consistency comparison with the twin verification code recorded on the chain; S5. Trigger differentiated settlement and update on-chain reputation score based on the comparison result of the twin verification code and the actual execution verification code. Feed back the on-chain reputation score to the combination selection process in S2 as a scheduling evaluation parameter in the subsequent response capability proposal combination selection process.

2. The intelligent scheduling and control method for a multi-energy complementary microgrid according to claim 1, characterized in that, In S1, the step of obtaining the response capability proposals autonomously generated and reported by multiple multi-energy complementary microgrids includes: Acquire operational status data for multiple multi-energy complementary microgrids; The adjustable operating range of each multi-energy complementary microgrid is determined based on the operating status data of each multi-energy complementary microgrid. Based on the adjustable operating range, corresponding response capability proposals are formed, and the corresponding identification information of the multi-energy complementary microgrid is associated with the response capability proposals; The response capability proposals containing multi-energy complementary microgrid identification information are reported for aggregation to form a globally adjustable resource pool.

3. The intelligent scheduling and control method for a multi-energy complementary microgrid according to claim 1, characterized in that, In S1, the step of generating the response capability proposal based on the digital twin model corresponding to each multi-energy complementary microgrid includes: Acquire operational status data for multiple multi-energy complementary microgrids and update the corresponding digital twin models based on the operational status data; Simulate the operating status under different response capability adjustment conditions based on the updated digital twin model; Calculate adjustable capacity information, available time period information, and response cost information based on the simulation results corresponding to different response capability adjustment conditions; A response capability proposal is generated based on adjustable capacity information, available time period information, and response cost information.

4. The intelligent scheduling and control method for a multi-energy complementary microgrid according to claim 1, characterized in that, In S2, the step of aggregating the multiple response capability proposals into a globally adjustable resource pool includes: Obtain response capability proposals reported by multiple multi-energy complementary micronetworks; Extract adjustable capacity information, available time period information, and response cost information from each response capability proposal; The adjustable capacity information, available time period information, and response cost information in the response capability proposal are linked and organized according to a unified data structure to form a standardized response capability record. Multiple standardized response capability records are aggregated to generate a globally adjustable resource pool.

5. The intelligent scheduling and control method for a multi-energy complementary microgrid according to claim 1, characterized in that, In S2, the step of combining and selecting response capability proposals from the globally adjustable resource pool with the objective of minimizing the total response cost includes: Obtain power grid dispatch requirements and select candidate response capability proposals from the global adjustable resource pool based on these requirements; Candidate response capability proposals are combined to form multiple response capability proposal combinations, and the total response cost corresponding to each combination is calculated. Determine the optimal combination of response capabilities that minimizes the total response cost; Based on the response capabilities that minimize the total response cost, a proposed scheduling scheme is generated.

6. The intelligent scheduling and control method for a multi-energy complementary microgrid according to claim 1, characterized in that, In S3, the step of sending an intention confirmation request to the multi-energy complementary microgrid corresponding to the intended scheduling scheme includes: Obtain the intended scheduling plan and determine the corresponding response capacity proposal; Based on the proposed response capabilities, the corresponding multi-energy complementary microgrid is determined; An intention confirmation request is generated based on the response capability proposal. The intention confirmation request includes adjustable capacity information, available time period information, and response cost information corresponding to the response capability proposal. Send the intention confirmation request to the corresponding multi-energy complementary microgrid.

7. The intelligent scheduling and control method for a multi-energy complementary microgrid according to claim 1, characterized in that, In S3, the step of obtaining the execution confirmation certificate returned by the multi-energy complementary microgrid after verification based on the corresponding digital twin model includes: Obtain the intention confirmation request and call the digital twin model corresponding to the multi-energy complementary microgrid for verification; Generate a twin verification code based on the verification results; Generate an execution confirmation credential containing twin verification codes.

8. The intelligent scheduling and control method for a multi-energy complementary microgrid according to claim 1, characterized in that, In S4, the step of generating an actual execution checksum based on the actual execution data and performing a consistency comparison with the twin checksum recorded on the chain includes: The actual execution data is standardized to generate verification data corresponding to the actual execution data; Generate an actual execution verification code based on the verification data corresponding to the actual execution data; Obtain the twin verification code of the on-chain record; The actual execution check code is compared with the twin check code recorded on the chain to generate a comparison result.

9. The intelligent scheduling and control method for a multi-energy complementary microgrid according to claim 1, characterized in that, In S5, the step of triggering differentiated settlement and updating on-chain reputation points based on the comparison result between the twin verification code and the actual execution verification code includes: The degree of consistency is determined by comparing the twin check code with the actual execution check code; Differential settlement is triggered based on the degree of consistency. The credit score change value is determined based on the aforementioned differentiated settlement. Update the on-chain reputation score based on the change in reputation score value.

10. The intelligent scheduling and control method for a multi-energy complementary microgrid according to claim 1, characterized in that, In S5, the step of feeding back the on-chain reputation score to the combination selection process in S2 as a scheduling evaluation parameter in the subsequent response capability proposal combination selection process includes: Earn on-chain reputation points; The on-chain reputation score is used as a scheduling evaluation parameter; The scheduling evaluation parameters are invoked during the selection of response capability proposal combinations; In the combination selection process in S2, the scheduling evaluation parameters are used as the evaluation basis for the combination selection of response capability proposals.

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