A method and system for coordinated recovery of a post-disaster electric heating integrated system

Abstract

This invention relates to a collaborative recovery method and system for a post-disaster integrated power and heating system. The method includes the following steps: modeling the power system (EPS), district heating system (DHS), and information network as a coupled system; based on the coupled system, reconstructing faulty communication links using an information flow reconstruction model and rerouting mechanism to restore terminal equipment connections; operating control equipment according to the communication recovery results to divide the integrated power and heating system into multiple independent islands centered on cogeneration units, and scheduling the DHS using a quasi-dynamic model considering thermal inertia; and constructing a two-stage robust optimization model based on the uncertainties of energy output and ambient temperature to minimize the total recovery cost. This invention effectively improves the system's recovery resilience, combining robustness, economy, and real-time performance, adapting to the needs of complex post-disaster scenarios, and providing theoretical reference and engineering pathways for the emergency recovery of integrated energy systems.

Description

Technical Field

[0001] This invention relates to the field of integrated electric and thermal energy system restoration, and in particular to a collaborative restoration method and system for integrated electric and thermal energy systems after a disaster. Background Technology

[0002] Under the impact of natural disasters, integrated electric heating systems (IEHS) face a triple challenge: damage to power system (EPS) lines, rupture of district heating system (DHS) pipelines, and communication network paralysis. However, most current recovery optimization methods focus only on the topology reconstruction and resource scheduling of the physical system, neglecting the recovery process of the communication system and lacking comprehensive modeling of the communication-physical coupling. Furthermore, existing communication recovery methods mostly rely on a single 5G network or fiber optic network, which suffers from weak anti-interference capabilities and limited coverage, failing to guarantee reliable communication in extreme scenarios.

[0003] On the other hand, in post-disaster IEHS (Information Energy Storage and Harness) scenarios, the output of distributed power sources (such as wind power) fluctuates greatly, and outdoor temperature changes are uncertain. Traditional deterministic scheduling models or simple robust optimization models, by using fixed upper and lower bounds to cope with uncertainty, are either too conservative, leading to resource waste, or lack a fine characterization of multi-source uncertainties, resulting in unstable recovery effects. At the same time, DHS (Disaster Energy Storage and Harness) modeling often uses steady-state models that ignore thermal inertia or quasi-dynamic models that do not consider pipeline reconfiguration, which cannot accurately capture the characteristics of heat transfer delay and make it difficult to leverage the virtual energy storage function of thermal inertia; moreover, existing solutions are mostly single-stage recovery, failing to coordinate post-disaster preparation and recovery phases.

[0004] Therefore, there is an urgent need for a new recovery scheduling mechanism that can simultaneously consider communication-physical coupling characteristics, DHS quasi-dynamic reconfiguration modeling, fine characterization of multi-source uncertainties, and phased collaborative optimization capabilities, in order to adapt to the resilience recovery requirements of IEHS in complex post-disaster environments.

[0005] It should be noted that the information disclosed in the background section above is only for understanding the background of the present invention, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] This invention provides a collaborative recovery method and system for post-disaster integrated electric heating systems, which addresses the technical problem of "how to improve the overall recovery resilience, economy and reliability of post-disaster integrated electric heating systems (IEHS) under conditions of communication interruption and uncertain energy supply".

[0007] The technical solution adopted by the present invention to solve the above-mentioned technical problems is as follows.

[0008] This invention provides a method for the collaborative restoration of a post-disaster integrated electrothermal system, comprising the following steps: Coupled modeling steps: Model the power system (EPS), district heating system (DHS), and the information network that provides communication services to them as a coupled system; Information network recovery steps: Based on the coupled system, an information network recovery model is constructed using an information flow reconstruction model. By solving the information network recovery model, the recovery decision of the communication link is obtained. The rerouting mechanism in the information network is then used to reconstruct the faulty communication link in order to restore the communication connection of the terminal devices in the information network. Physical network joint reconstruction and scheduling steps: Based on the results of information network recovery, operate the control equipment in EPS and DHS, divide the integrated electric and thermal system into multiple independent islands with the cogeneration unit (CHP) as the core, and schedule the DHS using a quasi-dynamic model that considers thermal inertia. Optimization solution steps: Based on the uncertainties of energy output and ambient temperature, a two-stage robust optimization model is constructed. The total recovery cost is minimized by solving the two-stage robust optimization model. Decision execution steps: The recovery decision obtained from the solution is sent to the field equipment for execution.

[0009] In some embodiments, during the coupling modeling step, the information network includes an optical fiber network and a 5G network; the terminal equipment includes a feeder terminal unit (FTU) and a remote terminal unit (RTU). A coupled system includes physical nodes, communication nodes, and the connections between nodes; Physical nodes include power system busbars and heating system nodes; Communication nodes include FTU, RTU, router, and 5G base station; The connections between nodes include power lines, heating pipelines, and communication links.

[0010] In some embodiments, during the coupling modeling step, a communication reachability variable is introduced to constrain the recovery operation of the physical system, ensuring that electrical and thermal load recovery is allowed only at communication-reachable nodes; Communication reachability variable constraints are achieved by introducing slack variables for communication nodes. Establish, constraints are represented as:

[0011] in, For the set of FTU nodes, For the set of RTU nodes, It is a collection of information streams transmitted to the terminal FTU or RTU. The superscripts "E" and "D" represent variables in EPS and DHS, respectively. (0 represents a node) Communication restored successfully, 1 indicates node (Communication recovery failed), dummy traffic The value can be 1 or 0, where Represents information flow Through communication link transmission, Represents information flow Cannot be transmitted via communication link transmission; This represents the information flow on the communication link (j,k); For the set of information network links; this constraint ensures that the corresponding physical network node is only allowed to restore power / heat supply when the communication node is restored.

[0012] In some embodiments, the rerouting mechanism in the information network recovery step refers to using FTUs or RTUs within the 5G network coverage area as temporary routers to participate in information flow forwarding in order to reconstruct the faulty communication link; the information network recovery model aims to minimize the number of disconnected communication nodes.

[0013] The constraints based on the information flow reconstruction model include: the existence of information flow is determined by the terminal state; the upper and lower bounds of information flow in the communication link are constrained by bandwidth and link state; the information flow sent by the server is unique; routers, switches, and 5G base stations act as intermediate nodes to forward information flow; and the arrival state of information flow at the terminal node is represented by slack variables.

[0014] In some embodiments, during the physical network joint reconfiguration and scheduling steps, the control devices include switches in the EPS and valves in the DHS; operating the control devices in the EPS and DHS refers to operating the switches and valves; the valves in the DHS include sectional valves and interconnecting valves; each independent island contains only one CHP as the main energy supply source, and topological constraints ensure that the islands have a radial topology.

[0015] In some embodiments, the quasi-dynamic model considering thermal inertia includes a constant flow-varying temperature (CFVT) quasi-dynamic model; the constant flow-varying temperature (CFVT) quasi-dynamic model includes mass flow conservation constraints, temperature transmission and heat loss constraints of nodes within the DHS; adopting the CFVT strategy means that after the topology reconstruction of the DHS is completed, the mass flow setpoint of each pipeline is optimized and kept constant in the subsequent scheduling phase, and the DHS topology reconstruction is achieved through valve operation, using thermal inertia to alleviate the imbalance between heat load and heat production.

[0016] In some embodiments, in the optimization solution step, energy output includes wind power output, and ambient temperature includes outdoor temperature; in the optimization solution step, the nonlinear terms in the quasi-dynamic model are also linearized, and the Big-M method and auxiliary variables are used to linearize the nonlinear term of the product of mass flow rate and temperature in the quasi-dynamic model; in the optimization solution step, the main problem and sub-problems are alternately generated by the column and constraint generation (C&CG) algorithm, and the two-stage robust optimization model is solved iteratively; in the optimization solution step, when the difference between the upper bound of the total recovery cost obtained from solving the main problem and the lower bound of the total recovery cost obtained from solving the sub-problems is less than a preset convergence threshold, the two-stage robust optimization model is determined to have converged; in the optimization solution step, the first-stage decision variables of the two-stage robust optimization model include CHP fuel reserves, DHS pipeline mass flow rate, and the joint reconfiguration scheme of the integrated electric and thermal system (IEHS); the second-stage decision variables are the scheme for scheduling CHP, distributed generation (DG), and energy storage (ESS) resources under the worst uncertainty scenario.

[0017] In some embodiments, a collaborative recovery system for a post-disaster integrated electric heating system is also provided, comprising: The coupled modeling module is configured to model the power system (EPS), the district heating system (DHS), and the information network that provides communication services to them as a coupled system. The information network recovery module is configured to build an information network recovery model based on the information flow reconstruction model, and reconstruct the faulty communication link through the rerouting mechanism in the information network in order to restore the communication connection of the terminal devices in the information network. The physical network joint reconstruction and scheduling module is configured to generate control commands based on the information network recovery results to operate the control devices in EPS and DHS, divide the integrated electric and thermal system into multiple independent islands with the cogeneration unit (CHP) as the core, and schedule the DHS using a quasi-dynamic model that considers thermal inertia. The optimization solution module is configured to construct a two-stage robust optimization model based on the uncertainties of energy output and ambient temperature, and solve the two-stage robust optimization model to minimize the total recovery cost. The decision execution module is configured to send the obtained recovery decision to the field equipment for execution.

[0018] In some embodiments, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, can implement the steps of the collaborative recovery method of this application.

[0019] The present invention has the following beneficial effects: The post-disaster collaborative recovery method for integrated electric and heating systems provided by this invention, through a coupled modeling step, models the power system, district heating system, and information network as a coupled system, laying a unified data foundation and relational framework for subsequent collaborative recovery, and solving the problem of the separation between communication and physical system modeling in traditional methods; through an information network recovery step, it reconstructs communication links based on an information flow reconstruction model and rerouting mechanism, ensuring the observability of the physical system state and the reachability of control commands, providing a reliable "neural channel" for physical recovery; through a physical network joint reconstruction and scheduling step, it divides the control equipment into islands according to the communication recovery results, and uses a quasi-dynamic model considering thermal inertia to schedule the district heating system, realizing rapid reconstruction of the electric and heating network and utilizing the virtual energy storage characteristics of the heating network, enhancing the flexibility and stability of system recovery; through an optimization solution step, it constructs a two-stage robust optimization model to cope with multi-source uncertainties, achieving the optimal trade-off between the economy and robustness of the recovery strategy under multi-source uncertainty; through a decision execution step, it issues and executes the optimized decision, forming a complete closed loop from decision generation to on-site execution. This invention significantly enhances the resilience of integrated electric heating systems in complex and uncertain post-disaster environments to cope with extreme events and quickly restore core functions. It successfully solves the technical problem of "how to improve the overall recovery resilience, economy and reliability of integrated electric heating systems (IEHS) under conditions of communication interruption and uncertain energy supply".

[0020] It should be noted that the solution to the aforementioned technical problems benefits from the synergy of various technical features: the coupled modeling step is the cornerstone of the entire scheme, and its output coupled system is the common input and operational object of the information network recovery step and the physical network joint reconstruction and scheduling step, ensuring that the recovery operation is always carried out from the perspective of communication-physical coupling. The information network recovery step is a prerequisite; the restored communication connection is the premise for the realization of the constraint "based on the result of information network recovery" in the physical network joint reconstruction and scheduling step, ensuring the executability of the physical recovery strategy. The network topology and scheduling scheme generated in the physical network joint reconstruction and scheduling step are the core inputs for constructing the two-stage robust optimization model in the optimization solution step, determining the structure and constraints of the optimization problem. The optimization solution step then transforms the results of the preceding steps into optimal decisions and feeds them back to the physical world through the decision execution step, completing closed-loop control. This serial collaborative process of "modeling-communication recovery-physical reconstruction and scheduling-optimization solution-execution" and the parallel collaborative relationship of "mutual constraints between communication and physical state" systematically improve the resilience of IEHS, and can be both robust and economical. It can adapt to the characteristics of post-disaster scenarios such as communication interruption, multi-source uncertainty and electric heating operation, and provide theoretical reference and engineering path for the design of emergency recovery strategies in new integrated energy systems, and has practical promotion value.

[0021] Preferably, by specifically defining the information network as including fiber optic networks and 5G networks, and specifically defining the terminal equipment as including feeder terminal units and remote terminal units, the present invention provides a redundant communication architecture that integrates wired and wireless technologies, enhances the reliability and coverage of the rerouting mechanism in the information network recovery process, and thus improves the success rate of communication recovery in extreme failure scenarios.

[0022] Preferably, by further defining the specific physical nodes, communication nodes and connection relationships included in the coupled system, the present invention makes the output of the coupling modeling step more explicit and structured, providing a clear and operable system model for subsequent steps and reducing the uncertainty of the model.

[0023] Preferably, by introducing communication reachability variables and specific mathematical constraint expressions, the present invention adds key constraints to the coupled modeling steps, ensuring that the recovery operation in the physical network joint reconstruction and scheduling steps strictly depends on the communication state, thereby realizing the synergy between communication and physical recovery at the model level and ensuring the executability of the strategy.

[0024] Preferably, the present invention refines and optimizes the execution logic of the information network recovery steps by specifically defining the implementation method of the rerouting mechanism (using a 5G terminal as a temporary router), clarifying the goal of the information network recovery model (minimizing the number of unconnected nodes), and listing the constraints of the information flow reconstruction model in detail, making the recovery process more efficient and the goal more clearly defined.

[0025] Preferably, by specifically defining the control devices as switches and valves (including sectional valves and interconnecting valves), and clarifying that each island is based on a single CHP and has a radial topology, the present invention makes the operation objects and network reconstruction goals in the physical network joint reconstruction and scheduling steps more specific, thus ensuring the stability and controllability of the island operation after reconstruction.

[0026] Preferably, the present invention refines the scheduling method for the district heating system in the joint reconstruction and scheduling steps of the physical network by specifically defining the quasi-dynamic model that considers thermal inertia as a constant flow variable temperature (CFVT) model and clarifying the constraints and strategies it includes. This enables the system to accurately utilize thermal inertia, effectively mitigate uncertain fluctuations, and improve the economy of the recovery process.

[0027] Preferably, the present invention provides a specific technical means to transform nonlinear terms in a quasi-dynamic model into linear terms by explicitly employing the Big-M method and auxiliary variables for linearization, thus ensuring the efficiency of the optimization solution steps; by explicitly employing the column and constraint generation (C&CG) algorithm and specific convergence criteria, the present invention provides a specific and efficient algorithmic implementation for solving a two-stage robust optimization model, and ensures the convergence of the solution process and the reliability of the results.

[0028] Other beneficial effects of the present invention will be further described below. Attached Figure Description

[0029] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a collaborative recovery method for a post-disaster electrothermal integrated system according to an embodiment of the present invention. Detailed Implementation

[0030] The embodiments of the present invention will be described in detail below. It should be emphasized that the following description is merely exemplary and is not intended to limit the scope and application of the present invention.

[0031] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of embodiments of the present invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0032] Based on the integration of the electrothermal integrated system and the communication system, the thermal inertia of the distributed power supply (DHS), and the multi-source uncertainty, this invention proposes a collaborative recovery scheme for the post-disaster electrothermal integrated system. This scheme is based on communication-physical coupling modeling and two-stage robust optimization, and realizes a fast, efficient and robust IEHS collaborative recovery scheduling function in post-disaster scenarios such as partial failure of the communication network, fluctuation of distributed power supply output, and changes in outdoor temperature.

[0033] In some embodiments, such as Figure 1 As shown, the present invention provides a collaborative recovery method for a post-disaster integrated electric heating system, comprising the following steps: 1) Coupled modeling: The physical system (Electric Power System EPS and District Heating System DHS) and its communication network (Fiber Optic Network and 5G Network) are modeled together; 2) Information Network Recovery Model: An information network recovery model is constructed based on the information flow reconstruction model. This model reconstructs communication links through the fiber optic network and 5G network rerouting mechanisms. FTU / RTU terminal nodes within the 5G coverage area are treated as temporary routers participating in information flow forwarding, thus reconstructing faulty communication links and maximizing the restoration of communication connections between feeder terminal units (FTUs) and remote terminal units (RTUs). It should be noted that the information flow reconstruction model is used to achieve the function of "reconstructing faulty communication links and restoring terminal device connections." This includes, but is not limited to: virtual flow models, graph theory-based connectivity optimization models (such as maximum flow and shortest path models), heuristic rule-based greedy algorithms, centralized control strategy models based on software-defined networking (SDN), and communication resource allocation models based on queuing theory, etc., which will not be elaborated further here.

[0034] 3) Physical Network Joint Reconstruction and Scheduling: Based on the results of communication network recovery, the IEHS is divided into multiple independent islands centered on cogeneration units (CHPs) through switches and valves. The district heating system adopts a quasi-dynamic model of constant flow variable temperature (CFVT) under the premise of considering reconstruction. It should be noted that if a single CHP is the main energy supplier and has an internal radial topology, it is determined to be an independent island. The division of independent islands only applies to the power / heating physical system. The communication network (fiber optic + 5G) maintains global integrated reconstruction, and cross-island communication is fully permitted. Direct or indirect interaction of power / heat energy between islands is strictly prohibited.

[0035] 4) Two-stage robust optimization model modeling and linearization: A two-stage robust optimization model is constructed based on uncertainties such as wind power output and outdoor temperature. The nonlinear term of the flow-temperature product in the quasi-dynamic model of the district heating system is linearized using the Big-M method and auxiliary variables. The column and constraint generation (C&CG) algorithm is used to solve the problem efficiently and minimize the total recovery cost. 5) Execution and feedback of scheduling results: The final recovery decisions are sent to the field equipment for execution, and the recovery status is monitored.

[0036] In some embodiments, the coupling modeling in step 1) specifically includes: modeling the integrated electrothermal system and its communication network together as a coupled system, the coupled structure including physical nodes (EPS bus, DHS heating node), communication nodes (FTU, RTU, router, 5G base station) and the connection relationships between nodes (EPS line, DHS pipe, communication link).

[0037] In some embodiments, step 2) of the information network recovery model specifically includes: constructing an information network recovery model based on an information flow reconstruction model, with the goal of minimizing the number of unconnected communication nodes; using the redundant rerouting mechanism of the fiber optic network and the 5G network, allowing FTUs / RTUs within the 5G coverage area to act as temporary routers to reconstruct faulty communication links; in this model, recovery decisions include communication link switching, terminal route reconstruction, and restoration of communication connections between FTUs and RTUs. The constraints of the information flow model include: the existence of information flow is determined by the terminal state; the upper and lower bounds of information flow in the communication link are constrained by bandwidth and link state; information flow sent by the server is unique; routers, switches, and 5G base stations act as intermediate nodes to forward information flow; and the arrival status of information flow at terminal nodes is represented by slack variables.

[0038] In some embodiments, step 3) of the physical network joint reconfiguration specifically includes: based on the recovery results of the communication network, joint reconfiguration is performed using the EPS switches and DHS valves to divide the IEHS into multiple independent islands centered on CHPs; each island contains only one CHP as the primary energy supply source, and the radial structure of the islands is ensured through topology constraints; during the reconfiguration process, faulty pipelines are isolated and backup pipelines are activated through valve operations to achieve flexible adjustment of the DHS topology. Quasi-dynamic model constraints include: pipeline mass flow conservation constraints after DHS reconfiguration, temperature transfer and heat loss constraints, DHS topology reconfiguration is achieved through valve operations, and the CFVT strategy is used to ensure that the pipeline mass flow is adjusted after DHS topology reconfiguration and remains constant in subsequent scheduling.

[0039] In some embodiments, step 4) specifically includes the two-stage robust optimization model modeling and linearization: 4.1) In the first stage, decision variables such as CHP fuel reserves, DHS mass flow rate, and IEHS joint reconfiguration scheme are determined; in the second stage, under the worst-case uncertainty scenario, resources such as CHP, distributed generation (DG), and energy storage (ESS) are scheduled to minimize the total recovery cost. 4.2) The Big-M method and auxiliary variables are used to linearize the nonlinear term of the product of mass flow rate and temperature in DHS, transforming the mixed integer nonlinear programming problem into a linear programming problem that can be solved efficiently; 4.3) The main problem and sub-problems are generated alternately by the C&CG algorithm, and the two-stage robust optimization model is solved iteratively. The convergence criterion is that the difference between the objective functions of the main problem and the sub-problems meets the preset tolerance. The solution to the recovery scheduling is then determined. The final output results include: the recovery status of the electric and heat load, the output of CHP and distributed energy, and the mass flow rate and temperature distribution of DHS.

[0040] In some embodiments, a collaborative recovery system for a post-disaster integrated electric heating system is also provided, comprising: The coupled modeling module is configured to model the power system (EPS), the district heating system (DHS), and the information network that provides communication services to them as a coupled system. The information network recovery module is configured to build an information network recovery model based on the information flow reconstruction model, and reconstruct the faulty communication link through the rerouting mechanism in the information network in order to restore the communication connection of the terminal devices in the information network. The physical network joint reconstruction and scheduling module is configured to generate control commands based on the information network recovery results to operate the control devices in EPS and DHS, divide the integrated electric and thermal system into multiple independent islands with the cogeneration unit (CHP) as the core, and schedule the DHS using a quasi-dynamic model that considers thermal inertia. The optimization solution module is configured to construct a two-stage robust optimization model based on the uncertainties of energy output and ambient temperature, and solve the two-stage robust optimization model to minimize the total recovery cost. The decision execution module is configured to send the obtained recovery decision to the field equipment for execution.

[0041] In some embodiments, a computer-readable storage medium is also provided, having a computer program stored thereon that, when executed by a processor, implements the steps of the cooperative recovery method of the present invention.

[0042] The following will further describe the specific implementation of the collaborative recovery method of the post-disaster electrothermal integrated system of the present invention. The implementation is only an illustrative example and does not mean that the present invention is limited to the following examples.

[0043] 1) Coupled modeling: Input communication network topology model set of communication nodes , containing a set of server nodes Router node set With switch node set 5G base station node set FTU node set With RTU node set Under the rerouting mechanism, the FTU / RTU is a dedicated embedded device adapted for multi-hop communication. Both can act as temporary routers. At the hardware level, it has at least two independent communication interfaces and is configured with a dedicated low-power processing chip to meet the needs of packet reception, forwarding, and simple routing calculations. At the software level, it is pre-installed with a lightweight multi-hop self-organizing routing protocol stack, supports information flow identification and forwarding rules for information flow reconstruction models, and can be remotely configured as a temporary routing node by the master server. (Communication link set) It includes fiber optic links and 5G links, forming a redundant architecture to achieve full communication link coverage. Input: Physical network model of the integrated electric and thermal energy system. , , and These represent the sets of busbars / nodes and lines / pipes, respectively.

[0044] The integrity rate of the core backbone links and core network equipment of the post-disaster fiber optic network is no less than 80%; the situation of 5G outdoor base stations being damaged is not considered, and all intact fiber optic backbone equipment is equipped with emergency backup power supplies, such as lithium batteries and diesel generators, to ensure continuous power supply after the disaster; the integrity rate of core forwarding equipment such as routers / switches is no less than 70%, providing basic hardware support for the rerouting mechanism.

[0045] 2) Information Network Recovery Model: In step 2), an optimization model is constructed using the information flow reconstruction method with the core objective of minimizing the number of disconnected communication nodes. The objective function is as follows: (1) in, It is the set of information streams transmitted to the terminal FTU or RTU, and it represents the penalty for unconnected nodes. As a slack variable, when the communication node The value is 1 when not connected.

[0046] To ensure the feasibility of the model, the following key constraints are set: Information flow transmission status constraints ensure that faulty terminals do not participate in transmission; (2) Link bandwidth constraints should be implemented to avoid link interruptions or bandwidth overload. (3) Server node information flow constraints ensure that each information flow originates uniquely from the server, while intermediate devices such as routers, switches, and 5G base stations are only used for transmitting information flows. (4) (5) Terminal node information flow constraints are used to mark unrecovered terminals using slack variables.

[0047] (6) 3) Joint reconfiguration and scheduling of physical networks: This step focuses on the joint reconfiguration of the electric and heating system centered on CHP. By quantifying node affiliation and line / pipeline relationships, intact energy supply equipment and load nodes are precisely assigned to the same energy supply island. This effectively isolates faulty areas and addresses energy gaps in isolated areas through cross-system resource integration. To address the thermal inertia problem of the heating system, the model employs a CFVT strategy to construct a quasi-dynamic model. After the DHS network reconfiguration, the pipeline mass flow rate is adjusted, and efforts are made to maintain these flow rate setpoints unchanged during subsequent scheduling periods. Based on this, temperature transmission and heat loss are dynamically calculated, accurately quantifying heat energy transmission delay and pipeline energy storage characteristics. This fully leverages the value of thermal inertia, enabling it to effectively cope with uncertainties caused by fluctuations in new energy output and changes in outdoor temperature, ensuring the stability and economy of the recovery process.

[0048] First, acquire equipment status data after the communication network is restored, collect EPS network topology, DG, wind turbine WT and CHP parameters, bus load data, energy storage (ESS) charging and discharging parameters; and DHS pipeline topology, valve status, node type, and heat load data.

[0049] Secondly, using CHP as the core, the electrothermal system is jointly reconfigured using topological constraint methods:

[0050] in, Indicates a collection of combined heat and power (CHP) units. Indicates busbar / node Combined heat and power unit Connection relationships between them (if bus / node) Connected to a cogeneration unit ,but ), binary variables Indicates busbar / node Is it an isolated island? , Indicates the line Is it an isolated island? , For the line The state. Constraint (7) ensures that each island consists of the corresponding bus / node connected to the cogeneration unit. In (8)-(9), only buses / nodes with restored communication can be included in an island. Constraint (10) ensures that each bus / node belongs to only one island. Constraints (11)-(12) indicate that when the line / pipeline Intact and both ends are isolated islands At that time, the line / pipeline Only those belonging to isolated islands Constraint (13) ensures the radial topology.

[0051]

[0052] in, Indicates from node To the node The virtual stream exists, and when The time indicates that the power supply path passes through the bus / node. Constraints (14) and (15) stipulate that the starting point of the power supply path must be connected to the CHP bus / node. Constraint (16) indicates the power supply path's connection to the line / pipeline. The number of visits must not exceed one. In (17), the bus / node Downstream buses / nodes can be accessed only if their upstream buses / nodes are accessed. Constraint (18) states that the power supply path can only pass through the lines / pipes in the island segment. Constraints (19)-(20) determine the bus / node. Can CHP restore power supply?

[0053] To accurately and efficiently describe the post-disaster operating status of EPS, this step uses the Lin-DistFlow model to linearize and construct EPS, ensuring power balance and voltage stability of the power system through simplified linear constraints.

[0054]

[0055] in, , and These represent the active power of distributed generation, wind turbine generators, and combined heat and power (CHP), respectively. and Represents the discharge power and charging power of the energy storage system. and These represent the final active and reactive power demand after load reduction, respectively. and Indicates the line Active and reactive power, , and DG, WT, and CHP represent combined heat and power systems, respectively. Constraints (21)-(22) describe the active and reactive power balance in the power system. (Line) The active and reactive power are constrained by (23)-(24). Constraints (25)-(26) specify the line Voltage boundaries between two adjacent nodes. Constraint (27) sets the upper and lower limits of the voltage.

[0056] For thermal networks, a constant flow-varying temperature (CFVT) strategy is adopted to construct a quasi-dynamic model of the DHS, which accurately captures the thermal inertia and heat transfer delay characteristics of the pipeline, balancing modeling accuracy and computational efficiency.

[0057]

[0058] In this context, the subscripts "s" and "r" represent the inlet pipe and return pipe, respectively. Indicates pipeline The mass flow rate is defined in (29). Constraint (28) is Kirchhoff's law for each node. The positive direction of the mass flow rate is defined in (29). Constraint (30) ensures that the mass flow rates of the inlet and outlet pipes are equal.

[0059]

[0060] in and These represent the temperatures at the beginning and end of the supply and return water pipes, respectively (in this article, the beginning of the supply water pipe...). Define as a node The starting end of the return water pipe is defined as a node. ), For nodes temperature, and These are the maximum and minimum temperatures at the start and end points of the pipeline, respectively. Outdoor temperature This represents the loss coefficient related to pipeline parameters. Node The temperature is determined by the first law of thermodynamics in equation (31), and the temperature at the pipe inlet is determined by the pipe inlet node in equation (32). Constraint (33) limits the pipe temperature, and constraint (34) reflects the pipe heat loss, which depends on the direction of the mass flow rate. To quantify the thermal energy, auxiliary variables are introduced in equations (35)-(38). These represent the heat energy at the end and beginning of the pipe, respectively. Where is the hydrothermal capacity constant. This refers to the heat energy produced by combined heat and power (CHP). For a distributed thermal power system, it is the set of load nodes. , and These represent the final heat load, the original heat load, and the heat load reduction, respectively.

[0061]

[0062] This step employs a back-pressure combined heat and power (CHP) system, where power output and heat generation are linearly related. For the power output of the combined heat and power system, Indicates the conversion efficiency of a combined heat and power system. and These are the minimum and maximum output power of CHP, respectively. This represents the upper limit of the power ramp rate. Indicates time Fuel reserves at CHP and These are the lower and upper limits for fuel reserves, respectively.

[0063] 4) Two-stage robust optimization model modeling and linearization: During the recovery process, multiple uncertainties exist, such as wind power output and outdoor temperature, which pose a threat to the recovery plan. Given the low probability of disaster occurrence, it is difficult to obtain the probability distribution of uncertain scenarios and solve the model using stochastic programming methods. Therefore, the Bertsimas robust budget set is used to characterize the uncertainties of wind power output and outdoor temperature. By introducing a spatiotemporal budget, a trade-off between robustness and conservatism is achieved. The constraints are as follows:

[0064] in, and These represent the actual power output and the predicted power output of the wind turbine, respectively. and They represent their upper and lower limits, respectively. and These are normalized auxiliary variables representing the degree of deviation towards maximum power output and the degree of deviation towards minimum power output, respectively. This represents the maximum number of time steps in which the output of a single wind turbine deviates from the predicted value. The maximum number of wind turbines that deviate from the predicted value at the same time is adjustable. and Balancing robustness and cost-effectiveness.

[0065] Accordingly, the same constraints are applied to the uncertainty of outdoor temperature:

[0066] To enhance the resilience and efficiency of IEHS in post-disaster recovery, a collaborative recovery plan was designed to be implemented in two phases.

[0067] Post-disaster phase: At this stage, the system is typically in its worst-case scenario, with damaged infrastructure and limited controllability. Therefore, preparatory measures must be implemented in advance to lay the foundation for effective recovery in subsequent phases. This phase comprises two steps: 1) Communication network restoration: The primary objective is to rebuild the entire system's communication network connectivity, ensuring the observability and controllability of the physical system. This step guarantees real-time data exchange required for physical system control during subsequent scheduling. 2) Resource preparation: Based on uncertainty predictions, determine the fuel reserves for combined heat and power (CHP) units, adjust the mass flow rate of distributed thermal system pipelines, plan the operating status of energy storage systems, and formulate joint reconfiguration strategies. These measures significantly mitigate the impact of uncertainties in the subsequent recovery phase.

[0068] Recovery Phase: This phase executes real-time physical recovery of the IEHS based on the decisions made in the post-event phase. Building upon the decisions made in the preliminary phase, a two-phase scheduling problem is solved under the worst-case scenario of uncertainties to minimize the recovery cost.

[0069] DHS reconfiguration will still result in changes to mass flow rate under the CFVT strategy. This change introduces a nonlinear term into the product of mass flow rate and temperature, causing the recovery model to transform into a mixed-integer nonlinear programming (MINLP) problem. To reduce computational complexity, the mass flow rate will be adjusted during the pre-recovery phase and kept constant in subsequent phases.

[0070] First, assume that the mass flow rate of the pipe connected to the heat load node is always equal to the unit mass flow rate when it is undamaged. Therefore, the pipeline The mass flow rate can be expressed by the general formula:

[0071]

[0072] in For a discrete set of mass flow rates, From A one-hot encoded vector of selected values. (Set) with vector The definition is as follows:

[0073]

[0074] in, Let be a positive integer, defining the range of values ​​for the flow rate multiplier. With unit mass flow rate The product of these factors does not exceed the pipe's rated maximum mass flow rate. Constraints (51)-(54) ensure that flow can only be obtained from the set. Select a flow rate value from the list.

[0075] Furthermore, the product of mass flow rate and temperature is a nonlinear term. For example, introducing continuous auxiliary variables :

[0076]

[0077] in, and These represent the upper and lower limits of the pipe temperature, respectively.

[0078] At this point, the nonlinear term can be passed... Replace with (56):

[0079] In addition, the Big M method can be used to linearize the pipe temperature equation containing heat loss in equation (34), which neither removes feasible solutions nor causes excessive relaxation of the model, thus ensuring the accuracy of linearization and the stability of the solution.

[0080] This invention employs a two-stage robust optimization (TSRO) framework, dividing recovery scheduling decisions into a first-stage post-disaster preparation decision and a second-stage real-time scheduling decision. The core optimization objective is to minimize the total cost of the entire recovery process, achieving phased collaborative optimization of "resource preparation - real-time scheduling." The core logic of the model is: in the first stage, fixed decisions that do not change with uncertain scenarios are determined; in the second stage, for the worst-case uncertainty scenario, the real-time scheduling strategy is optimized to ensure that the recovery plan is feasible under all possible scenarios. The overall objective function of the model is:

[0081] The variables and cost items are defined as follows: The first-stage decision variable set (post-disaster preparedness decision, which does not change with uncertain scenarios) includes the fuel reserves of the CHP unit, the mass flow rate of the DHS pipeline, and the on / off / valve status of the IEHS joint reconfiguration, etc. For uncertain scenarios; The second-stage decision variables include the output of CHP / DG / ESS, the reduction in electrical / thermal load, and the real-time heat generation of the CHP unit; the total cost of the recovery process includes four items: To reconstruct costs, For CHP fuel storage costs, For equipment maintenance costs, This is the cost of load shedding.

[0082] After linearization, the two-stage robust optimization model is transformed into a MILP problem. This invention uses a column and constraint generation algorithm to solve it efficiently. This algorithm gradually approaches the optimal solution of the original problem by alternately generating the main problem and sub-problems, avoiding enumeration of all uncertain scenarios and greatly reducing computational complexity.

[0083] 5) Scheduling result execution and feedback: Based on the two-stage robust optimization results obtained from the C&CG algorithm, scheduling decisions are executed in the post-disaster and recovery stages. The system operation data is collected in real time through FTU and RTU to monitor the recovery status and ensure the execution of the recovery plan.

[0084] 6) Specific application examples: To verify the effectiveness and superiority of the collaborative recovery method of the post-disaster electrothermal integrated system of the present invention, a simulation verification is carried out in conjunction with a typical engineering electrothermal integrated system example. The simulation platform uses MATLAB 2022b, the solver is GUROBI11.0, and the hardware environment is Intel(R) Core(TM) i7-14650 CPU @ 2.30GHz, 16GB memory.

[0085] This embodiment employs an integrated electrothermal system (IEHS) that couples an IEEE 33-node power distribution system with a 20-node district heating system. The system configuration is as follows: a) Power side: Includes 3 combined heat and power (CHP) units, 1 distributed generation (DG) power source, 3 energy storage (ESS) units, 2 wind turbine units (WT), and 5 switches; b) Heating side: includes 3 combined heat and power (CHP) units, 8 heating load nodes, and 2 valves; c) Communication side: Each terminal unit (FTU / RTU) is connected to the control center via fiber optic network links and five 5G base stations, forming a fiber-optic-5G redundant communication architecture to provide communication support for the FTU / RTU.

[0086] The simulation of a fault scenario for an integrated electrothermal system following a natural disaster is as follows: a) Physical system failure: 4 power lines were broken and 3 sections of heating pipes were ruptured; b) Communication system failure: Three fiber optic links failed, and initial communication was interrupted for some FTU / RTU nodes.

[0087] In this embodiment, the core parameters for two-stage robust optimization, C&CG algorithm, and operation of the electrothermal system are set as follows: a) Uncertainty parameters: Wind power output prediction error ±15%, outdoor temperature prediction error ±5℃, and uncertainty in spatiotemporal budget. ; b) Algorithm parameters: The entire process lasts for 12 time steps, and the convergence error of the C&CG algorithm is 1×10⁻⁶. -4 The maximum number of iterations is 50.

[0088] Based on the method of this invention, the collaborative recovery solution for the post-disaster integrated electric heating system is completed according to the following steps: a) Coupled modeling: Construct a coupled topology model of EPS-DHS and fiber-optic-5G communication network, and define physical nodes, communication nodes and the connection relationships between each node; b) Information network recovery: Based on the information flow reconstruction model, the communication link is reconstructed, and the 5G rerouting mechanism is used to use the FTU / RTU within the coverage area as temporary routers to restore the connection of the faulty communication node; c) Joint reconstruction of physical network: Based on the communication recovery results, the IEHS is divided into 3 independent islands with CHP as the core through switch / valve operation, and each island satisfies the radial topology; d) Two-stage robust optimization: Construct a two-stage robust optimization model, use the Big-M method and auxiliary variables to linearize the nonlinear terms, and use the C&CG algorithm to alternately generate the main problem and sub-problems for iterative solution. After 3 iterations, the convergence condition is met. e) Dispatch execution and feedback: The recovery decision is sent to the field equipment, the system operation status is monitored in real time, and the power / heat load is restored in stages.

[0089] Results: After solving the problem using the method of this invention, the C&CG algorithm reached convergence after two iterations, with an overall solution time of 9 minutes and a total recovery cost of $83,933.33. The communication node connectivity rate was 100%, the electrical load recovery rate was 86.44%, and the thermal load recovery rate was 92.90%. Compared to a single fiber optic communication recovery method, the total recovery cost was reduced by $167,737.33, and the communication node connectivity rate was increased by 36.36%; compared to a steady-state model method that ignores thermal inertia, the total recovery cost was reduced by $5,026.38.

[0090] The beneficial effects of the technical solution of this invention are reflected in: This invention constructs a collaborative recovery model for a communication-physical coupled electrothermal integrated system, combining fiber optic and 5G network communication restoration, physical system joint reconstruction, and a two-stage robust optimization mechanism to achieve rapid, robust, and collaborative recovery of IEHS (Integrated Electrothermal System) after disasters. This significantly improves the reliability, efficiency, and economy of large-scale IEHS recovery scheduling in complex post-disaster environments. The recovery strategy ensures its executability by explicitly modeling the communication-physical coupling relationship; it utilizes a quasi-dynamic model to capture the thermal inertia of the DHS (Disrupted Electrothermal System) and leverages virtual energy storage; it addresses multi-source uncertainties through two-stage robust optimization to avoid overly conservative approaches or recovery failures; and it employs linearization and C&CG algorithms for efficient solution, effectively addressing problems such as post-disaster communication interruptions, difficulties in collaborative recovery of electrothermal systems, significant uncertainties, and high computational complexity. It boasts advantages such as realistic modeling, strong robustness, and wide adaptability, making it suitable for post-disaster recovery scheduling of large-scale complex IEHS.

[0091] In a further technical solution of the present invention, a redundant communication network recovery mechanism based on optical fiber network and 5G network is used to quickly repair faulty links through information flow reconstruction model and terminal routing reconstruction. Compared with a single communication network, it significantly improves the anti-interference capability and coverage of the communication system in extreme scenarios, and provides reliable "communication-control-execution" linkage guarantee for physical system recovery.

[0092] In a further technical solution of the present invention, a quasi-dynamic CFVT model considering DHS reconstruction is adopted, which not only accurately captures the heat transfer delay and thermal inertia characteristics, but also realizes flexible adjustment of DHS topology through valve operation. Compared with the steady-state model, it reduces recovery costs while improving the flexibility and stability of heat load recovery.

[0093] In a further technical solution of this invention, the two-stage robust optimization model coordinates resource preparation and real-time scheduling in stages, and combines a space-time budget mechanism to characterize uncertainties. This avoids the problem of poor resource integration in single-stage recovery, and reduces resource waste while ensuring system robustness. By using the Big-M method and introducing auxiliary variables to linearize the nonlinear terms in the Disaster Recovery Hypothesis (DHS), combined with the C&CG algorithm, the two-stage robust optimization model is solved efficiently, significantly reducing computational complexity and ensuring that large-scale IEHS recovery problems can converge quickly, meeting the real-time requirements of post-disaster recovery.

[0094] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0095] This invention is described with reference to flowchart illustrations and / or block diagrams of collaborative recovery methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing device, generate instructions for implementing the collaborative recovery method, apparatus (system), and computer program product according to embodiments of the invention. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0096] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0097] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0098] The background section of this invention may include background information about the problems or environment in which the invention is being developed, and is not necessarily a description of prior art. Therefore, the content included in the background section does not constitute an admission of prior art by the applicant.

[0099] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Those skilled in the art, based on their understanding of the core ideas of the present invention (i.e., communication-physical collaborative recovery modeling and two-stage robust optimization), can easily conceive of using other types of power system models (such as BFS, BFM), heating system models, communication network architectures (such as 4G, Wi-Fi Mesh), linearization methods, or optimization algorithms to implement the present invention. In the description of this specification, the references to terms such as "an embodiment," "some embodiments," "preferred embodiment," "example," "specific example," or "some examples," etc., indicate that the specific features, structures, materials, or characteristics described in connection with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any suitable manner in one or more embodiments or examples. Without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification and the features of different embodiments or examples. Although embodiments of the invention and their advantages have been described in detail, it should be understood that various changes, substitutions and alterations may be made herein without departing from the scope of protection of the patent application.

Claims

1. A method for the collaborative recovery of a post-disaster integrated electrothermal system, characterized in that, Includes the following steps: Coupled modeling steps: Model the power system (EPS), district heating system (DHS), and the information network that provides communication services to them as a coupled system; Information network recovery steps: Based on the coupled system, an information network recovery model is constructed using an information flow reconstruction model. By solving the information network recovery model, the recovery decision of the communication link is obtained. The rerouting mechanism in the information network is then used to reconstruct the faulty communication link in order to restore the communication connection of the terminal devices in the information network. Physical network joint reconstruction and scheduling steps: Based on the results of the information network recovery, operate the control equipment in the EPS and the DHS to divide the integrated electric and thermal system into multiple independent islands with the cogeneration unit (CHP) as the core, and schedule the DHS using a quasi-dynamic model that considers thermal inertia. Optimization solution steps: Based on the uncertainties of energy output and ambient temperature, a two-stage robust optimization model is constructed, and the total recovery cost is minimized by solving the two-stage robust optimization model. Decision execution steps: The recovery decision obtained from the solution is sent to the field equipment for execution.

2. The collaborative recovery method as described in claim 1, characterized in that, In the coupling modeling step, the information network includes an optical fiber network and a 5G network; the terminal equipment includes a feeder terminal unit (FTU) and a remote terminal unit (RTU). The coupling system includes physical nodes, communication nodes, and the connections between nodes; The physical nodes include power system busbars and heating system nodes; The communication nodes include FTU, RTU, router, and 5G base station; The connections between the nodes include power lines, heating pipelines, and communication links.

3. The collaborative recovery method as described in claim 2, characterized in that, In the coupling modeling step, a communication reachability variable is introduced to constrain the physical system's recovery operation, ensuring that electrical and thermal load recovery is only allowed at communication-reachable nodes; The communication reachability variable constraint is achieved by introducing communication node relaxation variables. Establish, constraints are represented as: in, For the set of FTU nodes, For the set of RTU nodes, It is a collection of information streams transmitted to the terminal FTU or RTU. The superscripts "E" and "D" represent variables in EPS and DHS, respectively. (0 represents a node) Communication restored successfully, 1 indicates node (Communication recovery failed), dummy traffic The value can be 1 or 0, where Represents information flow Through communication link Transmission, 0 indicates information flow Cannot be transmitted via communication link transmission; This represents the information flow on the communication link (j,k); For the set of information network links; this constraint ensures that the corresponding physical network node is only allowed to restore power / heat supply when the communication node is restored.

4. The collaborative recovery method as described in claim 1, characterized in that, In the information network recovery step, the rerouting mechanism refers to using FTUs or RTUs within the 5G network coverage area as temporary routers to participate in information flow forwarding in order to reconstruct the faulty communication link. The information network recovery model aims to minimize the number of disconnected communication nodes. The constraints of the information flow reconstruction model include: the existence of information flow is determined by the terminal state; the upper and lower bounds of information flow in the communication link are constrained by bandwidth and link state; the information flow sent by the server is unique; routers, switches, and 5G base stations act as intermediate nodes to forward information flow; and the arrival status of information flow at the terminal node is represented by slack variables.

5. The collaborative recovery method as described in claim 1, characterized in that, In the physical network joint reconfiguration and scheduling steps, the control devices include switches in the EPS and valves in the DHS; operating the control devices in the EPS and DHS refers to operating the switches and valves; the valves in the DHS include sectional valves and interconnecting valves; each independent island contains only one CHP as the main energy supply source, and topological constraints ensure that the island has a radial topology structure.

6. The collaborative recovery method as described in claim 1, characterized in that, The quasi-dynamic model considering thermal inertia includes the constant flow-varying temperature (CFVT) quasi-dynamic model; the constant flow-varying temperature (CFVT) quasi-dynamic model includes mass flow conservation constraints, temperature transmission and heat loss constraints of nodes within the DHS; the CFVT strategy refers to optimizing the set mass flow rate of each pipeline after the DHS topology reconstruction is completed, and keeping it constant in the subsequent scheduling phase, realizing DHS topology reconstruction through valve operation, and using thermal inertia to alleviate the imbalance between heat load and heat production.

7. The collaborative recovery method as described in claim 1, characterized in that, In the optimization solution step, the energy output includes wind power output, and the ambient temperature includes outdoor temperature. The optimization solution step also linearizes the nonlinear terms in the quasi-dynamic model by using the Big-M method and auxiliary variables to linearize the nonlinear term of the product of mass flow rate and temperature in the quasi-dynamic model. In the optimization solution step, the main problem and sub-problems are alternately generated using the Column and Constraint Generation (C&CG) algorithm, and the two-stage robust optimization model is solved iteratively. In the optimization solution step, when the difference between the upper bound of the total recovery cost obtained from solving the main problem and the lower bound of the total recovery cost obtained from solving the sub-problems is less than a preset convergence threshold, the two-stage robust optimization model is determined to have converged. In the optimization solution step, the first-stage decision variables of the two-stage robust optimization model include CHP fuel reserves, DHS pipeline mass flow rate, and the integrated electrothermal system (IEHS) joint reconfiguration scheme. The second-stage decision variable is the scheme for scheduling CHP, distributed generation (DG), and energy storage (ESS) resources under the worst-case uncertainty scenario.

8. A collaborative recovery system for a post-disaster integrated electric heating system, characterized in that, include: The coupled modeling module is configured to model the power system (EPS), the district heating system (DHS), and the information network that provides communication services to them as a coupled system. The information network recovery module is configured to construct an information network recovery model based on an information flow reconstruction model, and reconstruct the faulty communication link through the rerouting mechanism in the information network to restore the communication connection of the terminal devices in the information network. The physical network joint reconstruction and scheduling module is configured to generate control commands based on the results of the information network recovery to operate the control devices in the EPS and the DHS, divide the integrated electric and thermal system into multiple independent islands with the cogeneration unit (CHP) as the core, and schedule the DHS using a quasi-dynamic model that considers thermal inertia. The optimization solution module is configured to construct a two-stage robust optimization model based on the uncertainties of energy output and ambient temperature, and solve the two-stage robust optimization model to minimize the total recovery cost. The decision execution module is configured to send the obtained recovery decision to the field equipment for execution.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program can implement the steps of the collaborative recovery method as described in any one of claims 1 to 7.

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