Campus integrated energy system multi-energy collaborative regulation method and system

Abstract

This invention proposes a multi-energy coordinated regulation method and system for a comprehensive energy system in a park, relating to the field of energy management technology. It includes: constructing energy autonomous units and local energy balance equations, energy storage state update equations, and equipment power and energy boundary constraints; solving for the elastic net exchange power range of each autonomous unit under uncertain scenarios based on the equations and constraints, symmetrically shrinking the range and reporting it to the elastic coalition scheduling layer; constructing a clearing model at the elastic coalition scheduling layer based on the reported information, solving for the target net exchange power, reserve capacity allocation, and park network energy flow of each autonomous unit; distributing the solution results to the autonomous units, which then adjust equipment output according to priority and track it in real time; monitoring equipment status and renewable energy output deviations, triggering emergency degradation control when equipment failure or deviation exceeding a threshold is detected, reconstructing the model, and introducing a graded load reduction mechanism, thereby improving the stability, reliability, and regulation accuracy of the system operation.

Description

Technical Field

[0001] This invention relates to the field of energy management technology, and in particular to a method and system for multi-energy coordinated regulation of an integrated energy system in a park. Background Technology

[0002] Currently, integrated energy systems in industrial parks typically include multiple energy sources such as electricity, heat, cooling, natural gas, and energy storage. To achieve optimal energy allocation across the park, a centralized scheduling model is commonly used. This model involves a central control system that collects data on the operating status of each energy device, forecasts load, and renewable energy output, and then calculates device output and energy flow allocation schemes. Some existing solutions set simple upper and lower power limits on the device side, using linear or mixed-integer programming to achieve energy optimization, and employing centralized scheduling commands for power tracking in real-time. While these methods are widely used in data acquisition, modeling, and optimization, enabling energy synergy at the park level, they primarily rely on a static description of the distributed device capabilities by the central system.

[0003] However, existing centralized dispatching systems generally suffer from inaccurate descriptions of the adjustability of energy autonomous units. Because the status of various energy devices is limited by renewable energy prediction errors, energy storage constraints, and load flexibility, centralized systems typically can only perform dispatching based on static power limits or single-point predictions, lacking a quantification of the actual available power range of devices under uncertain conditions. Furthermore, conventional methods do not consider the dynamic adjustment of dispatch boundaries due to real-time state changes, leading to discrepancies between reported power information and actual executable capabilities. This results in dispatching schemes becoming unreachable or frequently deviating from the plan during execution. This problem is particularly prominent in multi-energy systems because renewable energy and various types of loads have significant uncertainties, and the status of energy storage devices changes in real time, while the dispatching layer cannot obtain precise adjustability ranges. Summary of the Invention

[0004] To address the aforementioned issues, this invention proposes a multi-energy coordinated control method and system for integrated energy systems in industrial parks. This method effectively improves the matching degree between the scheduling plan and the actual execution capacity of the integrated energy system in industrial parks, avoids unattainable or frequently deviating scheduling schemes, and enhances the stability, reliability, and control accuracy of the system operation.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a multi-energy coordinated control method for an integrated energy system in a park, comprising: Construct an autonomous energy unit and its local energy balance equation, energy storage state update equation, and equipment power and energy boundary constraints; Based on the equations and constraints, the elastic net exchange power range of each autonomous unit under uncertain scenarios is solved, and after symmetrical contraction, it is reported to the elastic alliance scheduling layer. At the flexible alliance scheduling layer, based on the reported information, a collaborative optimization clearing model is constructed to solve for the target net switching power, reserve capacity allocation and campus network energy flow of each autonomous unit. The solution results are sent to the autonomous unit, which then adjusts the equipment output according to priority for real-time tracking. The system monitors equipment status and renewable energy output deviations. When equipment failure or deviation exceeding a threshold is detected, emergency degradation control is triggered, the model is reconstructed, and a tiered load reduction mechanism is introduced.

[0006] Secondly, the present invention provides a multi-energy coordinated control system for a park integrated energy system, comprising: The model building module is configured to build energy autonomous units and their local energy balance equations, energy storage state update equations, and device power and energy boundary constraints. The interval solution module is configured to solve the elastic net exchange power interval of each autonomous unit under uncertain scenarios based on the equations and constraints, perform symmetrical contraction, and then report it to the elastic alliance scheduling layer. The collaborative optimization module is configured to build a collaborative optimization clearing model based on the reported information at the elastic alliance scheduling layer, and solve for the target net switching power, reserve capacity allocation and campus network energy flow of each autonomous unit. The instruction issuing module is configured to send the solution results to the autonomous unit, which then adjusts the equipment output according to priority for real-time tracking. The emergency control module is configured to monitor equipment status and renewable energy output deviation. When equipment failure or deviation exceeding the threshold is detected, emergency degradation control is triggered to reconstruct the model and introduce a graded load reduction mechanism.

[0007] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the multi-energy coordinated control method for a park integrated energy system described in the first aspect.

[0008] Fourthly, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the multi-energy coordinated control method for a park integrated energy system described in the first aspect.

[0009] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) This invention introduces robust optimization modeling into the energy autonomous unit, integrates the uncertainty of energy storage, transferable load and renewable energy prediction into the energy balance constraint, and uses the safety margin coefficient to symmetrically shrink the upper and lower bounds of the obtained power, so that the reported elastic net exchange power range can truly reflect the unit's adjustability under uncertain conditions, thereby reducing the deviation between the scheduling plan and the actual execution.

[0010] (2) This invention constructs a multi-objective unified optimization model that includes segmented energy cost, carbon emission intensity and reserve capacity requirements at the scheduling layer, and applies coupling constraints to the power range reported by each autonomous unit, so as to achieve the simultaneous completion of power allocation, reserve capacity configuration and carbon emission control of multiple carriers such as electricity, heat, cold and gas in a single solution, thereby avoiding inconsistencies and iteration delays caused by multi-stage independent optimization.

[0011] (3) This invention sets the hierarchical adjustment sequence of energy storage, transferable load and energy conversion equipment in the autonomous unit, and adopts the original dual droop power allocation algorithm to realize local fast iterative adjustment, so as to quickly eliminate real-time power deviation without relying on the upper layer to solve again, while maintaining the integrity of the reserve capacity.

[0012] (4) This invention automatically reconstructs the autonomous unit energy model after detecting equipment failure, excessive renewable prediction deviation or network over-limit, removes the failure equipment constraint and introduces three levels of load reduction variables of critical, important and comfort to construct a weighted optimization problem, so as to maintain the system energy balance and prioritize the protection of critical load operation when resources are limited or network constraints cannot be met.

[0013] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0014] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute a limitation thereof.

[0015] Figure 1 A main flowchart of a multi-energy coordinated control method for a park integrated energy system provided in an embodiment of the present invention; Figure 2 A flowchart illustrating a multi-energy coordinated control method for an integrated energy system in a park, provided as an embodiment of the present invention; Figure 3 This is a schematic diagram of the internal structure of the energy autonomous unit provided in an embodiment of the present invention; Figure 4 This is a data interaction diagram for elastic alliance scheduling optimization provided in an embodiment of the present invention; Figure 5 The flowchart for emergency degradation control triggering and recovery provided in the embodiments of the present invention. Detailed Implementation

[0016] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0017] Example 1 like Figure 1 As shown in the figure, this embodiment discloses a multi-energy coordinated control method for a park's integrated energy system, including the following steps: S1: Construct the energy autonomous unit and its local energy balance equation, energy storage state update equation, and equipment power and energy boundary constraints; S2: Based on the equations and constraints, solve the elastic net exchange power range of each autonomous unit under the uncertainty scenario, perform symmetrical contraction, and report to the elastic alliance scheduling layer. S3: At the elastic alliance scheduling layer, based on the reported information, a collaborative optimization clearing model is constructed to solve for the target net switching power, reserve capacity allocation and campus network energy flow of each autonomous unit; S4: The solution results are sent to the autonomous unit, which then adjusts the equipment output according to priority for real-time tracking. S5: Monitor equipment status and renewable energy output deviation. When equipment failure or deviation exceeding the threshold is detected, trigger emergency degradation control, reconstruct the model, and introduce a graded load reduction mechanism.

[0018] Next, combined Figure 2 This embodiment provides a detailed description of a multi-energy coordinated control method for a park integrated energy system.

[0019] This invention addresses the joint scheduling of electricity, heat, cooling, gas, and energy storage. Through hierarchical interaction between energy autonomous units and the flexible coalition scheduling layer, it completes the calculation of flexible net exchange power ranges, collaborative optimization clearing, real-time tracking and control, and emergency degradation control. The method in this embodiment is executed in a rolling time sequence, with the steps repeated in each scheduling period.

[0020] (I) Local Modeling and Constraint Definition of Energy Autonomous Units To ensure consistency of notation and implementability, the following definitions of sets, indices, parameters, and variables are provided.

[0021] like Figure 3 As shown, the set of autonomous energy units in the park is as follows: ,index Energy carriers are aggregated as , representing electricity, heat, cold, and gas respectively, index .

[0022] Discrete-time set is ,index .

[0023] Autonomous Unit The energy storage device collection is ,index Autonomous Unit The energy conversion equipment assembly is ,index The load category set is as follows: ,index The set of directed edges in the park network divided by carrier is as follows: The set of incoming and outgoing edges of a node is denoted as . .

[0024] Parameters include: autonomous units In carrier Baseline non-transferable load Upper limit of load that can be transferred or reduced Forecasted output of renewable energy Radius of uncertainty in renewable energy forecasts Energy storage capacity Upper limit of charging power Upper limit of discharge power Charging efficiency Discharge efficiency Carrier ; Input carrier of conversion equipment Output carrier set Linear efficiency of the input-to-output carrier k Upper limit of network edge capacity in the park Park-level backup requirements Safety margin coefficient Segmented cost slope With segment width Carbon emission intensity per unit of energy Alternate pricing parameters ; Penalty coefficient for breach of contract .

[0025] Decision variables include: the target net exchange power of autonomous unit i in carrier k. (Positive values ​​indicate net output to the park network, negative values ​​indicate net input from the park network): Autonomous Unit In carrier backup commitment Park network edge flow (Calculated based on the positive direction of the directed edge); Local transferable load of autonomous unit Charging power Discharge power Energy storage energy status Input power of conversion equipment To the carrier Output power The lower bound of the flexible net exchange power range publicly announced by the autonomous unit. With the upper realm .

[0026] In the integrated energy system of the park in this embodiment, the energy autonomous unit is the smallest control unit. Each energy autonomous unit contains electrical energy supply and consumption equipment, heat energy supply and consumption equipment, cold energy supply and consumption equipment, gas equipment, energy storage devices, and transferable loads, and is described and controlled based on a unified mathematical model.

[0027] Each autonomous energy unit first establishes a local energy balance model. In energy carriers The net exchange power at discrete time t is denoted as . Positive values ​​represent power output to the campus network, while negative values ​​represent power drawn from the network. Device power flow within an autonomous unit includes: transferable load power. ,in For load category, range [0, Charging power of energy storage devices Discharge power Corresponding energy state Input power of energy conversion equipment With output power The internal power balance (energy balance) equations for the carriers within the autonomous unit, including electricity, heat, cold, and gas, are as follows: ; in, Indicates the error in renewable energy forecasting. This indicates the baseline non-transferable load of autonomous unit i at carrier k and time t. It will contribute to the forecast of renewable energy within the autonomous unit.

[0028] Energy conversion equipment satisfies linear power coupling: (when ); in. The efficiency coefficient is the ratio of energy input to energy output.

[0029] Energy storage status updated to: ; It is constrained by the upper and lower bounds of power and energy. The node-edge balance of the network side for carrier k is:

[0030] Network edge capacity satisfies .

[0031] (II) Calculation of the Flexible Net Exchange Power Range After establishing energy balance and equipment constraints, for each energy autonomous unit in each carrier and time period, based on the uncertainty set [ Calculate the elastic net exchange power range. This range reflects the range of external power that can be achieved under different uncertainty conditions.

[0032] To obtain the upper realm Take the prediction error The goal is to solve a linear programming problem to maximize energy storage capacity while satisfying the following conditions: local energy balance, energy storage state update, boundary conditions between power and energy, and conversion efficiency. To obtain the lower realm Take the prediction error Solving linear programming problems under the same constraints to minimize According to the safety margin factor Symmetrical contraction of the interval:

[0033]

[0034] When a critical equipment failure is detected, the model within the energy autonomous unit will remove the power variable contract associated with the failed equipment and increase the safety margin coefficient accordingly. The above interval is recalculated and the result is reported to the upper-level scheduler.

[0035] Each energy autonomous unit periodically uploads information on the upper and lower limits of elastic net exchange power, cost parameters, carbon emission intensity, available reserve capacity, and local equipment status during the scheduling cycle. If a deviation between renewable energy output and the predicted value exceeds a preset threshold during real-time operation, the autonomous unit will temporarily shrink the exchange power range and prioritize releasing energy storage power to compensate for the difference, then recalculate the range and upload updated data.

[0036] Through the above modeling and local regulation, the energy autonomous unit can calculate the upper and lower bounds of the power available to the outside world under different prediction errors and equipment conditions, and perform step-by-step regulation after the upper-level scheduling results are issued (discussed in detail below) to maintain local energy balance and reasonable energy storage status, while providing accurate and usable regulation boundaries for upper-level collaborative scheduling.

[0037] (III) Cooperative Optimization and Emergency Control of the Flexible Alliance Scheduling Layer In the park's integrated energy system, the flexible alliance dispatch layer sits above the energy autonomous units, such as... Figure 4 As shown, this layer is responsible for receiving scheduling boundary and parameter information reported by each energy autonomous unit, establishing a unified multi-energy flow optimization model, and solving for the target net switching power, reserve capacity allocation, and campus network energy flow allocation results. This scheduling layer includes a data receiving module, a data processing module, an optimization modeling module, and a scheduling solution module. These modules transmit data via an internal bus.

[0038] Specifically, each energy autonomous unit sets the upper and lower bounds of its elastic net exchange power in each scheduling cycle. , Cost parameters defined by power segment Carbon emission intensity per unit of energy Maximum available backup capacity The current operating status of the device is uploaded to the data receiving module.

[0039] The data processing module verifies the integrity and boundary rationality of the received data, and stores the data into the input parameter set of the scheduling model according to the correspondence between energy carrier, electrical topology and thermal topology.

[0040] The optimization modeling module is used to construct a collaborative optimization clearing model. First, it establishes energy balance and transmission constraints based on the park's network topology. For energy carrier k, the network node balance equation is: ; in, Let $k$ be the energy flow of carrier $k$ on edge $e$. The capacity of each network edge is limited by:

[0041] To describe the cost of segmented energy, a segmented energy allocation variable is set. ,satisfy: ,

[0042] Among them, cost parameters Piecewise slope Segment upper limit Provided by the autonomous energy unit.

[0043] To ensure that the reported power range is consistent with the reserve capacity, range coupling constraints (network node balancing and edge capacity constraints) are applied:

[0044] in, This is a spare variable for autonomous unit i on carrier k.

[0045] The park's backup requirements are constrained as follows: ; in, This is to meet the reserve capacity requirements during the park's scheduling cycle.

[0046] The objective function consists of the segmented energy cost, carbon emission cost, and reserve capacity cost of each autonomous unit in each carrier: ; in, This is the carbon emission cost coefficient. This is the standby capacity cost factor. = .

[0047] The optimization model is solved in each scheduling cycle, and the output results include: the target net exchange power of each energy autonomous unit in each carrier. Backup capacity allocation and the energy flow of each side of the park's energy network. .

[0048] Within the scheduling solution module, upper-layer collaborative optimization scheduling solution is implemented: based on the elastic net switching power range reported by each autonomous unit, campus network constraints, reserve requirements and cost targets, a coalition-level collaborative optimization clearing model is constructed, and the following are obtained: target net switching power of each autonomous unit, reserve capacity allocation of each autonomous unit, and energy flow of each edge of the campus network.

[0049] After completing the solution, the scheduling module writes the results into the output dataset and sends them to each energy autonomous unit via the communication interface for further local real-time control. The energy autonomous units possess local real-time control capabilities. Upon receiving the old standard net exchange power from the upper layer... and reserve capacity Following the command, the autonomous unit adjusts the equipment output according to a preset sequence: first, adjusting within the energy storage charging and discharging power range. Secondly, adjust the transferable load. Finally, adjust the input and output power of the energy conversion equipment. This is to meet local energy balance, energy storage state updates, and power boundary constraints, while maintaining a margin for reserve availability. Real-time control adopts a primitive-dual drooping power allocation method to achieve rapid power point tracking without changing the overall scheduling solution.

[0050] The scheduling solution module also records the deviation between the reported intervals of each unit in the current cycle and the actual scheduling results, using this as a reference input for the next cycle. If a critical device failure is detected, the corresponding variables and constraints are removed from the local model, and the load is increased. Recalculate and And upload it. If the actual output of renewable energy is detected to be inconsistent with... The deviation exceeds (in If the threshold parameter is used, then the temporary contraction interval will be used and the energy storage regulation will be released preferentially.

[0051] When abnormal conditions occur during the scheduling period, such as the traffic on one side of the network exceeding [a certain threshold], [the situation will change]. Or the backup capacity is insufficient. The optimization modeling module will call the emergency degradation control to reconstruct the model, temporarily introduce load reduction variables and resolve the scheduling model.

[0052] Specifically, such as Figure 5 As shown, emergency degradation control is initiated when any of the following conditions are met: equipment failure exists, or there is flow rate e on either side of carrier k. The deviation exceeds the threshold. A multi-level weighted optimization model is constructed, and reduction variables are set. In energy balance Alternative And according to the preset weight relationship , , Perform hierarchical minimization. After solving, update the issued local instructions and intervals. After the anomaly is resolved, restore according to the set step size. ,recover Once the value reaches the normal level, perform conventional optimization to find the solution.

[0053] Furthermore, within the park's integrated energy system, real-time tracking and control are executed by the energy autonomous unit in each scheduling cycle. This unit receives the target net exchange power and reserve capacity allocation results from the flexible alliance scheduling layer, and achieves power tracking and reserve maintenance through tiered adjustment of various locally controllable resources. This process includes four stages: status detection, determination of adjustment sequence, power allocation calculation, and issuance of execution commands.

[0054] At the start of each time step, the state detection module of the energy autonomous unit collects local electrical power, thermal power, cold power, gas flow, energy status of energy storage equipment, current output of transferable loads, operating point of energy conversion equipment, and actual output of renewable energy, and compares them with the command values ​​issued by the dispatch layer.

[0055] The deviation is given by It means that, among them, To measure net switching power in real time, This represents the target net switching power assigned by the scheduling layer. If... Exceeding the set threshold It has entered the adjustment phase.

[0056] The adjustment sequence is as follows: energy storage device, transferable load, and energy conversion device. First, adjust the charging power within the energy storage device. Discharge power Adjustment amount Determined by the following formula: ; in, As the carrier of energy storage, Indicates will Limited to the range The energy storage state update satisfies: ; in, , .

[0057] If the deviation is still not eliminated, adjust the transferable load. Set an available adjustment margin for each type of transferable load. The loads are reduced or increased in the following order: Critical Load (CRIT), Important Load (IMP), and Adaptable Load (COMF) until a new balance is achieved. The adjusted load power is: ; If a residual deviation still exists, adjust the input power of the energy conversion equipment. and corresponding output And re-examine the local energy balance: ; Real-time power point tracking employs a primary-dual drooping distribution method. Each autonomous unit defines an equality-constrained Lagrange multiplier for each carrier k. The total power adjustment is based on the droop coefficient. calculate, Locally, updates are made iteratively to satisfy balance constraints and align with upper-level commands. In this way, autonomous units can achieve rapid local power regulation without reporting detailed internal states.

[0058] Reserved capacity is maintained by reserving it during the scheduling cycle. This is achieved by locking a portion of the energy storage power range and the available transferable load adjustment margin to respond to potential backup call commands from higher levels. During backup call, the reserved energy storage power is first released, then the locked transferable load is reduced or increased, and finally the input and output power of the energy conversion equipment is adjusted to maintain a balance. Within the safe range. The status detection module continuously monitors real-time deviations, and when a deviation is detected... Furthermore, when resources are insufficient, a status alarm is sent to the upper layer.

[0059] Furthermore, in the integrated energy system of the park, emergency degradation control is used to perform graded load reduction and reconstruct the local energy model when conventional scheduling and real-time tracking are unable to maintain system energy balance or network security, in order to maintain system stability and controllability. This control strategy is executed by the emergency degradation control device and interacts with the energy autonomous unit and the flexible coalition scheduling layer.

[0060] Emergency triggering conditions are determined based on three detection results: (1) The equipment status detection module detects that key energy equipment has malfunctioned or failed, such as the energy storage device losing its charging and discharging ability or the energy conversion equipment shutting down; (2) The renewable energy prediction deviation detection module determines the actual output. Compared with the predicted value The difference exceeds the threshold, i.e. ,in This is a preset proportional coefficient; (3) The network traffic monitoring module detected the traffic on any edge e of carrier k. Exceeding capacity limit .

[0061] When any of the above conditions are met, the emergency degradation control process is initiated. This process includes four steps: model reconstruction, tiered load reduction optimization, instruction issuance, and system recovery.

[0062] First, during the model reconstruction phase, power variables and constraints related to failed devices are removed from the energy autonomous unit model, or their maximum available power is set to zero. For failed energy storage devices, their charging / discharging power and energy state constraints are deleted. For failed energy conversion devices, the corresponding input and output power coupling relationship is removed. Simultaneously, the safety margin coefficient is updated. The original elastic net exchange power range is further narrowed to ensure that the remaining resources remain feasible even under uncontrollable conditions.

[0063] In graded load reduction optimization, a reduction variable is introduced. This indicates that autonomous unit i belongs to the load category on carrier k. The power reduction. At this point, the local energy balance is adjusted as follows: ; And maintain energy storage status updates, equipment power limits, network power balance, and capacity constraints. Reduction amount The optimization objective is to minimize the load importance-based weighted algorithm, with the following parameters: ; in, , representing the priority weights for reducing critical loads, important loads, and comfort loads, respectively.

[0064] This optimization problem maintains a linear structure, and feasible reduction solutions can be obtained in a short time using a solver.

[0065] Once the reduction results are obtained, the emergency degradation control device will send new load commands through the communication interface. and the adjusted target net exchange power The data is distributed to the relevant energy autonomous units, which then execute the adjustments according to the aforementioned real-time control sequence. The scheduling layer records the interval boundaries and actual power output after this emergency adjustment, which is used as input for subsequent rolling scheduling.

[0066] Once the triggering conditions are lifted, the system enters the recovery phase. The recovery process is divided into multiple time periods, gradually restoring the reduced load according to a preset ratio while simultaneously restoring the safety margin factor. Return to normal values, and the autonomous unit recalculates the new elastic net exchange power range. The result is reported to the scheduling layer. The scheduling layer then performs a new routine optimization solution, replacing the emergency degradation optimization result.

[0067] In this embodiment, the emergency degradation control strategy enhances the operational resilience and fault response capabilities of the park's integrated energy system through a triple triggering mechanism of equipment failure, prediction bias, and network overrun, combined with model reconstruction and tiered load reduction. Its tiered weighted optimization prioritizes power supply to critical loads, reducing losses for important users; simultaneously, through dynamic shrinkage of safety margins and gradual recovery mechanisms, it ensures a smooth transition to normal scheduling after disturbances, effectively improving operational reliability and recovery efficiency under extreme conditions.

[0068] This invention accurately characterizes the real adjustment capabilities of multi-energy devices under renewable energy fluctuations, energy storage constraints, and load flexibility by constructing energy balance, energy storage status update, and equipment power constraint models for energy autonomous units. It solves the elastic net exchange power range under uncertain scenarios, realizing a quantitative expression of the available adjustment range. After symmetrical contraction, the data is reported to the alliance scheduling layer, providing reliable and executable boundary information to the upper layer, solving the distortion problems caused by static power limits and single-point predictions from the source. The alliance scheduling layer constructs a collaborative optimization clearing model based on reliable intervals to obtain target power, reserve allocation, and energy flow, and then distributes it to units for real-time tracking according to priority, ensuring that the scheduling plan can be implemented. When equipment fails or output deviation exceeds the threshold, emergency degradation control and graded load reduction are triggered, the model is dynamically reconstructed, and the scheduling boundary is corrected to avoid unachievable plans or frequent deviations, effectively improving the robustness, accuracy, and execution stability of multi-energy collaborative scheduling.

[0069] Example 2 This embodiment provides a multi-energy coordinated control system for a park's integrated energy system, including: The model building module is configured to build energy autonomous units and their local energy balance equations, energy storage state update equations, and device power and energy boundary constraints. The interval solution module is configured to solve the elastic net exchange power interval of each autonomous unit under uncertain scenarios based on the equations and constraints, perform symmetrical contraction, and then report it to the elastic alliance scheduling layer. The collaborative optimization module is configured to build a collaborative optimization clearing model based on the reported information at the elastic alliance scheduling layer, and solve for the target net switching power, reserve capacity allocation and campus network energy flow of each autonomous unit. The instruction issuing module is configured to send the solution results to the autonomous unit, which then adjusts the equipment output according to priority for real-time tracking. The emergency control module is configured to monitor equipment status and renewable energy output deviation. When equipment failure or deviation exceeding the threshold is detected, emergency degradation control is triggered to reconstruct the model and introduce a graded load reduction mechanism.

[0070] In one implementation approach, the model building module is deployed at each energy autonomous node and is implemented collaboratively by the local data acquisition unit and the computing and processing unit. This module collects real-time information on power, energy, flow rate, equipment status, and load status through hardware such as electricity meters, heat meters, flow meters, energy storage monitoring interfaces, generator control interfaces, and load control interfaces.

[0071] Based on the collected data, the model building module embeds energy balance calculation logic, energy storage state update logic, and power constraint checking logic to construct: the local energy balance equation of the energy autonomous unit; the state update equation of the energy storage device; and the power and energy boundary constraints of various devices (energy storage, transferable loads, and energy conversion devices).

[0072] This module stores the completed local model parameters for use by the interval solution module and provides basic model support for subsequent real-time control.

[0073] In one implementation approach, the interval solution module and the model building module are deployed on the same autonomous node, reusing their computational processing units. Based on the equations and constraints established by the model building module, this module solves for the elastic net exchange power interval of each autonomous unit under uncertain scenarios.

[0074] Specifically, the interval solution module introduces uncertainty parameters and safety margin coefficients to perform robust optimization or scenario analysis on the local model, obtaining the upper and lower bounds of the net exchange power. After the solution is completed, the interval is symmetrically shrunk to reduce scheduling redundancy caused by interval asymmetry. The processed elastic net exchange power interval, along with segmented cost parameters, carbon emission intensity per unit energy, maximum available standby capacity, and equipment operating status, is reported to the collaborative optimization module via the communication interface.

[0075] As one implementation method, the collaborative optimization module is centrally deployed in the park's main control center and consists of a data receiving unit, a data storage unit, an optimization modeling unit, and a scheduling and solving unit.

[0076] The data receiving unit receives information from the solution modules of each interval, including the upper and lower bounds of the elastic net switching power, segmented cost parameters, carbon emission intensity, reserve capacity, and equipment operating status, via a communication interface, and performs integrity and boundary rationality checks. The data storage unit stores the campus network topology, historical scheduling data, and long-term operating parameters of each unit, providing data support for optimization modeling. The optimization modeling unit constructs a multi-energy flow network balance model and constraints based on the campus network topology, including energy balance equations, network capacity constraints, segmented energy allocation variables, and reserve capacity coupling constraints, forming a collaborative optimization clearing model. The scheduling solution unit executes the solution of the above optimization model in each scheduling cycle, outputting the target net switching power, reserve capacity allocation, and energy flow of each edge of the campus network for each autonomous unit. The solution results are transmitted to the instruction issuing module via an internal bus.

[0077] This module supports periodic rolling solutions and records the deviation between the reported interval of each unit and the actual scheduling result within the scheduling cycle, which serves as a reference input for subsequent optimization.

[0078] In one implementation, the instruction issuance module and the collaborative optimization module are jointly deployed in the main control center, and the output results of the scheduling solution unit are sent to the interval solution module and real-time control unit of each autonomous node through the communication interface.

[0079] The issued content includes: the target net exchange power of each energy autonomous unit in each carrier; the allocation of reserve capacity of each energy autonomous unit; and the energy flow of each side of the park's energy network.

[0080] Upon receiving the instruction, the autonomous node adjusts the equipment output according to the preset adjustment sequence to achieve real-time power tracking. The specific adjustment process employs a primary-dual drooping power allocation method, using local Lagrange multiplier iterations to achieve rapid power response without altering the overall scheduling solution. Simultaneously, the autonomous node reserves adjustment margins corresponding to standby capacity to handle standby call demands.

[0081] As one implementation method, the emergency control module and the collaborative optimization module are deployed in parallel, consisting of a status monitoring unit and a degradation optimization unit.

[0082] The status monitoring unit receives real-time data from each autonomous node, including equipment status, actual output of renewable energy versus predicted deviation, and network traffic monitoring data, to determine emergency triggering conditions.

[0083] The degradation optimization unit performs model reconstruction and graded load reduction optimization after emergency triggering: Model reconstruction: Remove power variables and constraints related to failed devices from the models of each autonomous node, update the safety margin coefficient, and shrink the elastic net exchange power range again to ensure that the remaining resources are still feasible under uncontrollable conditions.

[0084] Tiered load reduction optimization: Introduce reduction variables and construct a weighted minimization objective according to the priority of critical load, important load and comfort load, and solve for the reduction scheme.

[0085] The adjusted load command and target net exchange power are sent to the relevant autonomous nodes, and the collaborative optimization module is notified to update the scheduling boundary synchronously.

[0086] Once the emergency triggering conditions are lifted, the emergency control module and the collaborative optimization module work together to gradually restore the safety margin coefficient and load command to normal values, and the system smoothly returns to the normal optimized scheduling mode.

[0087] As one implementation method, the system consists of a data upload interface and a command issuance interface, supporting data interaction based on industrial Ethernet, fiber optic communication, or other real-time communication protocols. All interfaces support time synchronization signal input to ensure consistency in data acquisition and command execution across different units within the same scheduling cycle. The communication interfaces employ encrypted transmission to ensure data integrity and security during transmission.

[0088] In terms of deployment, the system can be applied to scenarios such as industrial parks, integrated commercial areas, or research parks. During deployment, the model building module and the interval solution module are installed at the corresponding energy subsystem sites according to their physical locations, and are connected to the field controllers and metering devices via wired buses or industrial communication protocols. Other modules are centrally deployed in the park's main control room and connected to each energy autonomous unit via a fiber optic network. The operating cycles of all modules are coordinated by a unified time synchronization signal to ensure the consistency of scheduling and control commands throughout the entire park.

[0089] Example 3 This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the multi-energy coordinated control method for a park integrated energy system as described in Embodiment 1 above.

[0090] Example 4 This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the multi-energy coordinated control method of a park integrated energy system as described in Embodiment 1 above.

[0091] The steps or modules involved in Embodiments 2 to 4 above correspond to those in Embodiment 1. For specific implementation details, please refer to the relevant description section of Embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood as including any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and enabling the processor to perform any of the methods in this invention.

[0092] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for multi-energy coordinated regulation of an integrated energy system in a park, characterized in that, include: Construct an autonomous energy unit and its local energy balance equation, energy storage state update equation, and equipment power and energy boundary constraints; Based on the equations and constraints, the elastic net exchange power range of each autonomous unit under uncertain scenarios is solved, and after symmetrical contraction, it is reported to the elastic alliance scheduling layer. At the flexible alliance scheduling layer, based on the reported information, a collaborative optimization clearing model is constructed to solve for the target net switching power, reserve capacity allocation and campus network energy flow of each autonomous unit. The solution results are sent to the autonomous unit, which then adjusts the equipment output according to priority for real-time tracking. The system monitors equipment status and renewable energy output deviations. When equipment failure or deviation exceeding a threshold is detected, emergency degradation control is triggered, the model is reconstructed, and a tiered load reduction mechanism is introduced.

2. The multi-energy coordinated control method for a comprehensive energy system in a park as described in claim 1, characterized in that, The energy autonomous unit is the smallest control unit, which includes electrical energy, thermal energy, cold energy, gas supply and consumption equipment, energy storage devices, energy conversion equipment, and transferable loads.

3. The multi-energy coordinated control method for a comprehensive energy system in a park as described in claim 1, characterized in that, The local energy balance equation is constructed based on the power balance relationship between net exchange power on each energy carrier, output of conversion equipment, energy storage discharge, local load, input of conversion equipment, energy storage charging and renewable energy output. The energy storage state update equation is constructed based on the relationship between the influence of charge / discharge power and charge / discharge efficiency on the energy storage energy. The power and energy boundary constraints of the equipment are constructed based on the upper and lower limits of the rated power, the upper and lower limits of the energy storage capacity, and the state of charge range of each equipment.

4. The multi-energy coordinated control method for a comprehensive energy system in a park as described in claim 1, characterized in that, The process of solving the elastic net exchange power range of each autonomous unit under uncertain scenarios based on the equations and constraints, performing symmetrical contraction, and then reporting it to the elastic alliance scheduling layer specifically includes: To address the uncertainty of renewable energy forecasting errors, a linear programming problem is solved under the most unfavorable overestimation and underestimation scenarios, with the objective of maximizing or minimizing net exchange power, to obtain initial upper and lower bounds. The interval is symmetrically shrunk according to a preset safety margin coefficient; When a critical equipment failure is detected, remove the relevant constraints of the failed equipment from the model, increase the safety margin coefficient, recalculate the interval, and report it. If the actual output of renewable energy deviates from the predicted value by more than a threshold during real-time operation, the range will be temporarily narrowed and energy storage will be adjusted first to compensate, and then the reported data will be updated.

5. The multi-energy coordinated control method for a comprehensive energy system in a park as described in claim 1, characterized in that, In the elastic alliance scheduling layer, based on the reported information, a collaborative optimization clearing model is constructed to solve for the target net switching power, reserve capacity allocation, and campus network energy flow of each autonomous unit. Specifically, this includes: Receive reports from each autonomous unit on the flexibility range, segmented costs, carbon emission intensity, and maximum reserve capacity; Based on the park's network topology, construct the node balance equations and edge capacity constraints for each energy carrier; Set segmented energy allocation variables to describe segmented costs; Establish coupling constraints between reserve capacity and elasticity range; Construct an objective function that minimizes the sum of total energy cost, carbon emission cost, and backup cost; The solution model yields the target net switching power, reserve capacity allocation, and energy flow of each side of the campus network for each autonomous unit.

6. The multi-energy coordinated control method for a comprehensive energy system in a park as described in claim 1, characterized in that, The monitoring of equipment status and renewable energy output deviation specifically includes: real-time monitoring of the operating status of each device within the autonomous unit to determine whether a failure has occurred; monitoring the deviation between the actual renewable energy output and the predicted value, and triggering adjustments when the deviation exceeds a preset threshold.

7. The multi-energy coordinated control method for a park integrated energy system as described in claim 1, characterized in that, When a device failure or deviation exceeding a threshold is detected, emergency degradation control is triggered, the model is reconstructed, and a tiered load reduction mechanism is introduced, specifically including: When equipment failure, network flow exceeding limits, or insufficient backup capacity are detected, a multi-level weighted optimization model is constructed, and load reduction variables are introduced. The reduction amount is minimized in a tiered manner according to the priority order of critical load, important load, and adjustable load; After solving, update and issue instructions to the autonomous units; After the anomaly is resolved, the safety margin coefficient and load limit are gradually restored to normal values, and conventional optimization solutions are performed again.

8. A multi-energy coordinated control system for an integrated energy system in a park, characterized in that, include: The model building module is configured to build energy autonomous units and their local energy balance equations, energy storage state update equations, and device power and energy boundary constraints. The interval solution module is configured to solve the elastic net exchange power interval of each autonomous unit under uncertain scenarios based on the equations and constraints, perform symmetrical contraction, and then report it to the elastic alliance scheduling layer. The collaborative optimization module is configured to build a collaborative optimization clearing model based on the reported information at the elastic alliance scheduling layer, and solve for the target net switching power, reserve capacity allocation and campus network energy flow of each autonomous unit. The instruction issuing module is configured to send the solution results to the autonomous unit, which then adjusts the equipment output according to priority for real-time tracking. The emergency control module is configured to monitor equipment status and renewable energy output deviation. When equipment failure or deviation exceeding the threshold is detected, emergency degradation control is triggered to reconstruct the model and introduce a graded load reduction mechanism.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the multi-energy coordinated control method for a park integrated energy system as described in any one of claims 1-7.

10. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the multi-energy coordinated control method for a park integrated energy system as described in any one of claims 1-7.

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