Comprehensive energy post-disaster recovery method considering dynamic scheduling of professional maintenance personnel

Abstract

The invention discloses a comprehensive energy post-disaster recovery method considering dynamic scheduling of professional maintenance personnel, which relates to the technical field of energy recovery, and comprises the following steps: collecting equipment health state and maintenance personnel information, and constructing an optimization function with the minimum total load shedding capacity of a system as a target; and establishing a post-disaster recovery model comprising a maintenance personnel dynamic scheduling constraint, an energy supply network connectivity constraint, a load shedding power constraint and a system operation constraint, generating a recovery strategy of collaborative maintenance and system operation by solving the post-disaster recovery model, and dynamically updating and adjusting the recovery strategy according to a real-time state during execution. According to the method, the post-disaster recovery model containing the maintenance personnel dynamic scheduling constraint and the optimization function is constructed, so that collaborative optimization of human resources and equipment recovery is realized, and the overall recovery efficiency and the resource utilization rate are improved.

Description

Technical Field

[0001] This invention relates to the field of energy recovery technology, specifically to a comprehensive energy disaster recovery method that takes into account the dynamic scheduling of professional maintenance personnel. Background Technology

[0002] With the increasing frequency of extreme natural disasters caused by global climate change, power and heat energy infrastructure faces severe threats. Damage to energy facilities often leads to large-scale and long-term energy supply disruptions, causing huge socio-economic losses. Integrated energy systems combine multiple energy forms such as electricity and heat, and achieve coordinated energy supply through coupling and conversion equipment. The post-disaster recovery process is more complex than that of a single energy system. Existing recovery methods, through network reconstruction and fault equipment repair sequence optimization, generally simplify maintenance resources, such as professional maintenance personnel, to ideal conditions of static or unlimited availability. They do not fully consider actual dynamic constraints, such as limited personnel numbers, differences in skill types, work duration limits, and time consumption. Existing recovery methods are prone to causing the repair plan to become decoupled from personnel scheduling, resulting in delays in critical repair tasks due to the inability of personnel to arrive in time. This prolongs the recovery time of important electrical and thermal loads and restricts the overall recovery efficiency.

[0003] Patent CN111555280B discloses a post-disaster recovery control method for a flexible distribution network in an integrated electric-gas energy system. The patent achieves rapid and effective maximization of power supply load recovery.

[0004] The aforementioned patent employs three optimization strategies—network reconfiguration, diesel generators, and natural gas network supply—with the goal of minimizing recovery strategy costs, thereby improving the resilience of the distribution network. It also uses the amount of load loss to ensure rapid and effective maximization of power supply load restoration. However, it does not fully consider actual dynamic constraints, and there is still room for optimization in terms of dynamic scheduling of maintenance personnel.

[0005] Therefore, this application proposes a comprehensive energy disaster recovery method that can integrate constraints and dynamically schedule maintenance personnel. Summary of the Invention

[0006] The purpose of this invention is to provide a comprehensive energy disaster recovery method that takes into account the dynamic scheduling of professional maintenance personnel, in order to solve the technical problem that the existing methods mentioned in the background art do not fully consider actual dynamic constraints, which easily leads to the decoupling of repair plans and personnel scheduling, causing delays in key repair tasks, thereby lengthening the recovery time of important electrical and thermal loads and restricting the overall recovery efficiency.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a comprehensive energy disaster recovery method considering the dynamic scheduling of professional maintenance personnel, the recovery method comprising the following steps: S1. Collect the health status of equipment and the initial working hours of maintenance personnel, and analyze the power supply path of each electrical load and thermal load to form basic data for post-disaster recovery; S2. Construct an optimization function for the post-disaster recovery of the integrated energy system based on basic data. The optimization function aims to minimize the total load shedding of the system. S3. Based on the optimization function and energy supply path, establish constraint conditions including dynamic scheduling constraints for maintenance personnel, connectivity constraints for the energy supply network, load shedding power constraints, and operational constraints for the integrated energy system. S4. By optimizing the function and constraints, construct and solve the post-disaster recovery model to obtain a recovery strategy that coordinates maintenance scheduling and system operation. S5. During the execution of the recovery strategy, the parameters of the post-disaster recovery model are dynamically updated and re-solved based on the equipment status feedback, in order to adjust and continuously optimize the recovery strategy.

[0008] Preferably, the data acquisition of equipment health status includes obtaining the initial health status of equipment in the post-disaster system and representing the initial health status as a binary variable to distinguish whether the equipment is in a healthy or faulty state. The data acquisition of maintenance personnel initial working hours includes obtaining the current cumulative working hours of various professional maintenance personnel and recording them in the form of initial values ​​for subsequent dynamic scheduling. The power supply path is determined by a topology search algorithm to determine the set of all equipment on the path from the power supply energy source to the load node, which is used to determine the connectivity of the power supply network and the repair priority. The basic data includes equipment status variables, load power prediction values, maintenance personnel time data, and power supply path set. The basic data provides a data source for the construction of the optimization function.

[0009] Preferably, the optimization function is constructed with the goal of minimizing the total load shedding of the system. The sum of the weighted electrical load shedding and the weighted thermal load shedding is used as the objective function for minimization. The weight of each load is pre-set according to the importance of the load, which is used to reflect the recovery priority of different loads in the recovery phase. The optimization function expresses the recovery decision of multiple loads in multiple time periods in a unified manner through mathematical programming, which is used to drive the post-disaster recovery model to prioritize the recovery of high-weight loads under the premise of meeting the constraints.

[0010] Preferably, the constraints include dynamic scheduling constraints for maintenance personnel, which include a lower limit constraint on the total working time of each type of maintenance personnel required for the repair of each faulty device, a single task constraint for personnel, an upper limit constraint on the total number of maintenance personnel of each type, and a working time constraint for maintenance personnel.

[0011] Preferably, the constraints include a power supply network connectivity constraint. The power supply network connectivity constraint defines binary variables for each electrical load and thermal load. The binary variables of the load are used to indicate whether the power supply path is connected, and a logical relationship is established between the binary variables of the load and the health status of each device on the path. The logical relationship is that when all devices on the power supply path of the load are in a healthy state, the load is marked as connected, allowing the load to obtain energy supply.

[0012] Preferably, the constraint conditions include load shedding power constraints. The load shedding power constraints define the difference between the predicted load and the actual load received for each load as the load shedding amount. When the load path is not connected, the actual load received is forced to be zero, and all predicted loads are counted as load shedding. When the path is connected, the actual load received takes a value within the range of zero to the predicted value, and the remaining part is the load shedding. The load shedding power constraints are used to describe the power supply and heating situation of each load in different recovery stages.

[0013] Preferably, the constraints include integrated energy system operation constraints, which include power subsystem constraints and thermal subsystem constraints. The power subsystem constraints include power balance constraints at each node, upper and lower limit constraints on generator output, and power branch operation constraints. Generators include coal-fired units, combined heat and power units, and new energy units. The thermal subsystem constraints include heat power balance constraints at each node, upper and lower limit constraints on heat source equipment output, and thermal branch operation constraints.

[0014] Preferably, the post-disaster recovery model is constructed by optimizing functions and constraints. The post-disaster recovery model integrates the optimizing functions and constraints into a mixed-integer linear programming model, which is solved by a mathematical optimization solver to generate recovery strategies.

[0015] Preferably, the post-disaster recovery model is used to solve for a recovery strategy, which includes a maintenance personnel scheduling strategy, an equipment repair sequence, a load power and heating recovery plan, and an operation plan for each energy device, to guide actual repair operations and real-time system scheduling.

[0016] Preferably, the parameters of the dynamically updated post-disaster recovery model are adjusted according to the actual repair progress and changes in equipment status. When the equipment status changes, data acquisition, model building, and solving are re-executed for online optimization of the recovery strategy.

[0017] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention, by designing constraints including dynamic scheduling constraints for maintenance personnel, connectivity constraints for the power supply network, load shedding constraints, and operational constraints for the integrated energy system, achieves dynamic scheduling of maintenance plans and personnel. This solves the problem that existing methods, which do not fully consider actual dynamic constraints, easily lead to a disconnect between repair plans and personnel scheduling, causing delays in critical repair tasks, thus lengthening the recovery time of important electrical and thermal loads and reducing overall recovery efficiency. The dynamic scheduling constraints for maintenance personnel ensure that each repair task is performed by qualified and available maintenance personnel, and that the workload of maintenance personnel is within a safe range. It fully maps real-world problems into a mathematical model, making the generated recovery strategy feasible in resource allocation and safe and reliable in physical operation. This solves the problem of disconnect between planning and execution, ensures the operability of the repair plan, and allows for the most rational and effective allocation of limited maintenance human resources, laying a solid foundation for the rapid recovery of critical loads. 2. This invention, by constructing an optimization function with the objective of minimizing the total system load shedding, achieves priority guidance for recovery decisions under maintenance resource constraints. It solves the problem of difficulty in making optimal choices among different types and importance of loads under maintenance resource constraints, leading to ambiguity in maintenance resource allocation. By assigning different weight coefficients to electrical and thermal loads according to their importance, the load recovery problem is transformed into a quantifiable optimization problem. Under the premise of satisfying all maintenance personnel, network, and operational constraints, it can calculate the joint repair and operation strategy that minimizes the overall social loss, establishes a scientific and transparent decision-making basis, and ensures that every investment of maintenance resources generates the maximum social benefit recovery value under extremely tight resource conditions, thereby improving the overall efficiency and fairness of the recovery process. 3. This invention, by constructing a post-disaster recovery model, transforms complex real-world recovery problems into solvable linear programming problems, solving the problem of finding the optimal recovery strategy quickly and accurately from a massive number of solutions within a limited time. By integrating dynamic personnel scheduling, network connectivity, multi-energy flow operation, and load shedding minimization objectives, a large-scale and high-dimensional mathematical programming problem is formed. The global optimal solution is obtained through a solver and transformed into an immediately executable optimal scheduling and operation scheme, improving the efficiency and scientific nature of decision-making and enhancing the efficiency and reliability of the overall recovery process. 4. This invention, through a dynamic optimization mechanism based on real-time status feedback and model re-solution, achieves online dynamic optimization of the recovery strategy. It solves the problem of static recovery plans failing due to uncertainties such as sudden failures, repair schedule deviations, or load demand fluctuations during disaster recovery. This enhances the adaptability and robustness of the post-disaster recovery model. By continuously collecting data on equipment health status, real-time location and working hours of maintenance personnel, and dynamic changes in load demand through a deployed sensor network and system, the system automatically triggers a model update when a preset status change is detected. Using the latest time as the new scheduling starting point, the initial conditions of all decision variables are updated, a post-disaster recovery model containing the latest information is reconstructed, and the solver is invoked for rapid solution. This ensures that limited maintenance resources and system operation modes can be dynamically reallocated and adjusted according to the latest situation, enabling the recovery strategy to effectively cope with multiple uncertainties in the disaster and recovery process. It avoids resource misallocation and recovery delays caused by information lag or rigid plans, improving the overall efficiency and reliability of the recovery process. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the overall recovery process of the present invention; Figure 2 This is a flowchart of the data acquisition process of the present invention; Figure 3 This is a flowchart illustrating the constraints of the present invention; Figure 4 This is a flowchart illustrating the model construction and solution process of this invention; Figure 5 This is a flowchart illustrating the dynamic update process of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Please see Figure 1 and Figure 2The present invention provides an embodiment of a comprehensive energy disaster recovery method that considers the dynamic scheduling of professional maintenance personnel. The method for collecting equipment health status includes obtaining the initial health status of equipment in the disaster system and representing the initial health status as a binary variable. The method for collecting maintenance personnel working hours includes obtaining the current working hours of various professional maintenance personnel and recording them in the form of initial values. The energy supply path is determined by a topology search algorithm to determine the set of all equipment on the path from the energy supply to each load node. The basic data includes equipment status variables, load power prediction values, maintenance personnel working hours data, and the set of energy supply paths. The basic data provides a data source for the construction of the optimization function. Furthermore, in the post-disaster period, sensors deployed at key nodes of the integrated energy system will be used to acquire the real-time operating status of all equipment. Key nodes include substations, heating stations, and distributed energy outlets. Sensors include current transformers, voltage transformers, pressure sensors, temperature sensors, and communication terminal equipment. Equipment includes coal-fired power generating units, combined heat and power units, new energy generating units, electric energy storage equipment, fossil fuel boilers, electric refrigeration and heating equipment, heat pipelines, transformers, and pumping stations. A binary health state variable Z will be defined for each piece of equipment. i，0 If the sensor data shows that the device is functioning properly, the parameters are within the normal range, and there are no fault alarms, then the device is determined to be in a healthy state and assigned the value Z. i，0 =1, if the sensor data shows that the device is offline, the parameters are abnormal, or a clear fault signal is received, such as zero current or a sudden drop in pressure, then the device is determined to be in a fault state and assigned the value Z. i，0 =0, for example, if a coal-fired unit numbered G1 in the system reports a trip and shutdown, then Z G1，0 =0, the current in a power branch numbered L12 is zero, and the switches at both ends are in the open state, then Z L12，0 =0, and simultaneously for each device in a faulty state, based on the device type, fault code, and historical maintenance records, the standard working hours (RT) required for repair by different professional maintenance personnel are retrieved from a pre-set knowledge base. j，k For example, a faulty transformer requires 4 hours of work by a high-voltage electrical maintenance professional, while a faulty thermal pipeline valve requires 2 hours of work by a pipeline welding professional. Then, information on all currently available maintenance personnel is retrieved from the maintenance personnel management information system. These personnel are categorized according to their professional skills, and the set is denoted as Ω. major For example, Ω major ={High-voltage electrical equipment, relay protection, pipeline welding, gas equipment, new energy equipment}, for each professional category K there is a corresponding maintenance team, the set of maintenance team members is denoted as Ω. k team The working hours of each maintenance worker m at the beginning of the current scheduling cycle are denoted as F. k，m，0work This is used to reflect the fatigue level and continued working ability of maintenance personnel, such as the Ω of a high-voltage electrical maintenance team. 高压电气 team ={Zhang San, Li Si}, in the high-voltage electrical maintenance team, Zhang San has already worked 2 hours today, F 高压电气，张三，0 work =2h, and simultaneously obtain the company's maximum continuous working time F for each maintenance worker on that day. k，m，0 work，max , such as F k，m，0 work，max =8h; Then, the predicted power values ​​of each electrical load and heat load in the integrated energy system are obtained, and the predicted value of any electrical load at time t is denoted as P. i，t load，pre Let H be the predicted value of any heat load at time t. i，t load，pre Let the sets of electrical load and thermal load be denoted as Ω respectively. P，load and Ω H，load Then, obtain the predicted power values ​​of the new energy generator units in the integrated energy system, and denot the predicted power value of any new energy generator unit at time t as P. i，t ren，pre ; Finally, a topology search algorithm is used to analyze the power supply path for each electrical load node and heat load node. This algorithm, based on graph theory and network analysis, automatically identifies all connection paths tracing back from each electrical or heat load point to all energy points in a comprehensive energy system network. It determines all connected devices along each power supply path, forming a device set. This set is used to establish the logical relationship between the load power supply status and the health status of each device along the path. The set of all devices along the electrical load power supply path is denoted as Ω. i P The set of all devices along the heat load supply path is denoted as Ω. i H This provides a basis for subsequent judgment on whether the load can be restored to power or heating.

[0021] Please see Figure 1 The present invention provides an embodiment of a comprehensive energy disaster recovery method that considers the dynamic scheduling of professional maintenance personnel. The optimization function is constructed with the goal of minimizing the total load shedding of the system. The sum of the weighted electrical load shedding and the weighted thermal load shedding is used as the objective function for minimization. The weight of each load is pre-set according to the importance of the load, which is used to reflect the recovery priority of different loads in the recovery phase. The optimization function expresses the recovery decisions of multiple loads in multiple time periods in a unified manner through mathematical programming, which is used to drive the disaster recovery model to prioritize the recovery of high-weight loads under the premise of meeting the constraints. Furthermore, the total scheduling time set for post-disaster recovery is defined as Ω. time ={1, 2, ..., T}, for example, considering the recovery process over the next 24 hours with an interval of 1 hour, then T is 24. An objective function is constructed using basic data to minimize the weighted electrical load shedding and the weighted thermal load shedding: ; P i，t shed and H i，t shed P is the decision variable. i，t shed H represents the power that is disconnected from the electrical load node at time t. i，t shed γ represents the power that is cut off at the heat load node at time t. i P γ is the weighting factor for electrical load. i H This is the weighting factor for the heat load; The optimization function is a linear weighted summation function. Integrating the optimization function into the post-disaster recovery model is used to minimize the weighted total load shedding amount of all loads in all time periods during the entire recovery period. For example, restoring power to a residential area with a weight of 0.3 and a hospital with a weight of 1, but maintenance resources can only repair the fault on the power supply path of one load first. The post-disaster recovery model will prioritize repairing the path to the hospital because reducing the load shedding of the hospital contributes more to the objective function value than reducing the load shedding of the residential area. For example, reducing the load shedding of the hospital by 1 unit can reduce the objective function value by 1, while reducing the load shedding of the residential area by 1 unit can only reduce the objective function value by 0.3. The post-disaster recovery model prioritizes making decisions that can significantly reduce the objective function value, and the final decision is to prioritize ensuring the power supply to the hospital.

[0022] Please see Figure 1 and Figure 3 The present invention provides an embodiment of a comprehensive energy disaster recovery method that considers the dynamic scheduling of professional maintenance personnel. The constraints include dynamic scheduling constraints of maintenance personnel, connectivity constraints of the energy supply network, load shedding constraints, and operation constraints of the comprehensive energy system. The dynamic scheduling constraints of maintenance personnel include a lower limit constraint on the total working time of each professional maintenance personnel required for the repair of each faulty equipment, a single task constraint for personnel, an upper limit constraint on the total number of each type of professional maintenance personnel, and a working time constraint for maintenance personnel. Furthermore, the constraints are integrated into the post-disaster recovery model. The dynamic scheduling constraints for maintenance personnel include total maintenance task time constraints, maintenance continuity constraints, personnel single-task constraints, maximum number of personnel in a professional team constraints, and personnel working hours constraints. The total maintenance task time constraint applies to each faulty device j and each type of professional maintenance personnel k required; the allocated total maintenance time must meet the required working hours. ; y j，k，m，t y is a binary variable. j，k，m，t =1 indicates that professional maintenance worker m is repairing equipment j during time period t, Ω brok Let be the set of faulty devices, and Δt be the time period length, such as 1 hour. The constraints satisfy the total number of people and time required to repair one faulty device. The maintenance continuity constraint is: ; τ represents the time when the m-th maintenance worker in specialty k begins to repair the faulty equipment j. At time τ, a worker begins repairing the faulty equipment j, i.e., y j，k，m，t The maintenance continuity constraint, which changes from 0 to 1, can prevent maintenance tasks from being interrupted. The single-task constraint for personnel is: ; The maximum number of people in a professional team is limited to: ; The constraints on staff working hours are as follows: ; ; Establish Z j，t With y j，k，m，t Relationship constraints: ; Z j，t This indicates whether faulty device j has been restored to usability at time t. Z represents the state of faulty device j being restored to usability at time t. j，t The value of Z is 1, otherwise Z j，t The value of is 0; Then, the connectivity of the power supply network is constrained, and a binary variable δ is defined for the electrical load. i，t P When δ i，t P =1 indicates that the power supply path for the electrical load is unobstructed and available at time t. δ i，t P =0 indicates that there is a faulty device in the power supply path of the electrical load at time t, and power cannot be supplied. A binary variable δ is defined for the heat load. i，tH δ i，t H =1 indicates that the energy supply path for the heat load at time t is unobstructed and heat can be supplied. δ i，t H =0 indicates that at time t, there is a faulty device in the energy supply path of the heat load, and heat cannot be supplied. Then, δ is established. i，t P Constraints: ; ; Establish δ i，t H Constraints: ; ; Z e，t It is the state of device e at time t, |Ω i p |For set Ω i p The number of elements in |Ω i H |For set Ω i H The number of elements in the middle; Then, constraints on the load shedding power of the electrical load are established: ; ; Then establish constraints on the heat load shedding power: ; ; P i，t load H represents the actual electrical power received by the electrical load at time t. i，t load The actual heat power obtained by the heat load at time t; Then, constraints are applied to the operation of the integrated energy system, with the power balance constraints at each node as follows: ; Ω m G Let P be the set of all coal-fired generating units connected to power node m. i，t G The electrical power provided by the i-th coal-fired unit at time t, Ω m CHP Let P be the set of all cogeneration units connected to power node m. i，t CHPLet Ω be the electrical power of the i-th combined heat and power unit at time t. m ren Let P be the set of all new energy generator units connected to power node m. i，t ren Let Ω be the electrical power provided by the i-th new energy generator unit at time t. m ES Let P be the set of all energy storage units connected to power node m. i，t dis and P i，t char These are the discharge power and charging power of the i-th energy storage unit at time t, respectively, Ω m E，bran，in Let Ω3 be the set of all branches whose endpoints are node m. For example, in a power grid, if there are two lines that start from node 1 and node 2 respectively, and both lines are directly connected to node 3, then for node 3, this set is Ω3. E，bran，in ={1, 2}, P im，t Let Ω be the electrical power of branch im at time t. m E，bran，out Let Ω3 be the set of all endpoints of branches originating from node m. For example, if node 3 has three lines leading to nodes 4, 5, and 6 respectively, then the set of endpoints of the power branches flowing out of node 3 is Ω3. E，bran，out ={4, 5, 6}, P mi，t Let Ω be the electrical power of branch mi at time t. m EC Let P be the set of all electric air conditioners connected to power node m. i，t EC Let Ω be the electrical power consumed by the i-th air conditioner at time t. E，node It is the set of all power nodes in the system; Establish operating constraints for each coal-fired power generating unit: ; P i G，min and P i G，max Ω represents the lower and upper limits of the power generation capacity of the i-th coal-fired unit, respectively. The upper and lower limits of the equipment are determined by the equipment manufacturer during manufacturing and can be found in the technical manual. G It is the set of all coal-fired power generating units in the system; Establish operating constraints for each cogeneration unit: ; ; H i，t CHPα is the thermal power provided by the i-th cogeneration unit at time t. i The heat-to-power ratio of the i-th combined heat and power unit is provided by the integrated energy system, Ω CHP P is the set of all combined heat and power units in the system. i CHP，min and P i CHP ，max These are the lower and upper limits of the power generation capacity of the i-th cogeneration unit, respectively. The upper and lower limits of the equipment are determined by the equipment manufacturer during manufacturing and can be found in the technical manual. Establish operating constraints for new energy generator units at each node: ; Ω ren It is the set of all new energy generator sets in the system; Establish operating constraints for each energy storage unit: ; ; ; ; ; In the formula S i，t+1 and S i，t η represents the charge of the i-th energy storage unit at time t+1 and time t, respectively. i char and η i dis These are the charging efficiency and discharging efficiency of the i-th energy storage unit, respectively, Ω. ES S is the set of all energy storage units in the system. i min and S i max These are the lower and upper limits of the charge capacity of the i-th energy storage unit, respectively. i，t char This is the charging flag for the i-th energy storage device. When the i-th energy storage device is charging at time t, u... i，t char The value is 1 otherwise the value is 0. i，t dis This is the discharge flag bit for the i-th energy storage unit. When the i-th energy storage unit discharges at time t, u... i，t dis The value is 1 otherwise the value is 0. i char，min and P i char，max Let P be the lower and upper limits of the charging power of the i-th energy storage device, respectively.i dis，min and P i dis，max These are the lower and upper limits of the discharge power of the i-th energy storage device, respectively. The upper and lower limits of the device are determined by the device manufacturer during manufacturing and can be found in the technical manual. Establish operating constraints for power branch lines: ; ; ; In the formula θ i，t and θ m，t Let X be the phase angle of power node i and power node m at time t. im Z is the impedance of the power branch im. im，t For the health state of the power branch im at time t, P im min and P im max θ represents the lower and upper limits of the power transmission power of the power branch IM, respectively. i min and θ i max Ω represents the lower and upper limits of the phase angle of power node i. E，bran It is the set of all power branches in the system; The thermal power balance constraints at each node are as follows: ; ; H i，t FG Ω represents the thermal power provided by the i-th fossil fuel boiler at time t. m FG Ω is the set of all fossil fuel boilers connected to thermal node m. m CHP Ω is the set of all cogeneration units connected to thermal node m. m EG H is the set of all electric air conditioners connected to node m. i，t EC Let Ω be the heat power provided by the i-th electric air conditioner at time t. m H，bran，in H is the set of starting points of thermal branches ending at node m. im，t The thermal power of the thermal branch im is Ω m H，bran，out H is the set of thermal branches originating from node m, and H is the set of branches whose endpoints are the endpoints of the thermal branches. mi，t The thermal power of the thermal branch mi is Ω. H，nodeIt is the set of all thermal nodes in the system; The heat source equipment includes fossil fuel boilers and electric air conditioners. First, establish the operating constraints for the fossil fuel boilers: ; In the formula, H i FG，min and H i FG，max Let Ω represent the lower and upper limits of the thermal power provided by the i-th fossil fuel boiler, respectively. FG It is the set of all fossil fuels in the system; Establish operating constraints for electric air conditioners: ; ; In the formula, COP i EC Let H be the performance coefficient of the i-th electric air conditioner. i EC，min and H i EC，max Let Ω be the lower and upper limits of the heating power of the i-th electric air conditioner. EC It is the set of all electric air conditioners in the system; Establish operating constraints for thermal branch circuits: ; In the formula Z im，t For the health status of the thermal branch im at time t, H im max Ω represents the upper limit of the heat power that the thermal branch im can transfer. H，bran It is the set of all thermal branches in the system; By establishing constraints, the real and complex post-disaster recovery problem of integrated energy systems is fully mapped into a structured mathematical programming problem, ensuring that the recovery strategy generated by the post-disaster recovery model is physically feasible, resource-feasible, and operationally safe, thus making the final recovery strategy practically operable.

[0023] Please see Figure 1 and Figure 4 The present invention provides an embodiment of a comprehensive energy disaster recovery method that considers the dynamic scheduling of professional maintenance personnel. The disaster recovery model is constructed through optimization functions and constraints. The disaster recovery model integrates the optimization functions and constraints into a mixed integer linear programming model. The disaster recovery model is solved by a mathematical optimization solver to generate a recovery strategy. The recovery strategy includes a maintenance personnel scheduling strategy, an equipment repair sequence, a load power and heating recovery plan, and an operation plan for each energy equipment, which are used to guide actual repair operations and real-time system scheduling. Furthermore, the constructed optimization function and established constraints are used to build a post-disaster recovery model within an optimization modeling platform, such as the Pyomo library in Python. The Pyomo library allows users to define variables, objective functions, and constraints, and connect a solver. The collected basic data, such as equipment sets, load sets, maintenance team sets, time period sets, power supply path topology, equipment maintenance hours, load forecasts, weights, and initial personnel states, are defined as objects in the model. Corresponding decision variables are then declared in the model. An objective function minimizing the weighted total load shedding is then constructed within the model, and constraints are added to form model rules. After the post-disaster recovery model is built, the Gurobi optimizer is selected as the solver. This tool is a high-performance commercial mathematical programming solver for large-scale linear programming, integer programming, and mixed integer programming problems. It can quickly and stably obtain high-quality solutions. For example, after a disaster, a city's integrated energy system has two fault points, A and B. Fault point A is a 110kV transmission line L1 that supplies power to the city's central hospital. Repairing fault point A requires 4 hours of high-voltage electrical work. Fault point B is a main heating pipeline H1 that supplies heating to the area. Repairing fault point B requires 3 hours of pipeline welding work. The available maintenance resources are: a high-voltage electrical team of 2 people, A and B, with A having worked for 1 hour and B for 0 hours; and a pipeline welding team of 1 person, C, with each person having a maximum continuous working time of 8 hours. The hospital's electrical load weight is 1, and the area's heat load weight is 0.7. The above parameters, predicted load, and network topology were input into the constructed post-disaster recovery model, and the Gurobi solver was used to solve it. The post-disaster recovery model needs to determine how to dispatch three maintenance personnel and how to arrange system operation within the next 12-hour recovery window to minimize the weighted total load shedding. After 2 minutes of calculation, the Gurobi solver returned the global optimal solution. The optimal strategy is to prioritize the full-scale repair of the hospital's power supply line L1. The specific recovery strategy is as follows: starting from the first hour after the disaster, two people from the high-voltage electrical team, A and B, are dispatched to the fault point A to work together. The collaboration between the two can shorten the actual time. If other high-voltage electrical faults require only one person to repair, the recovery strategy prioritizes dispatching B to repair them, allowing A, who has been working for 1 hour, to rest. Then, the pipeline welding team, C, is dispatched to the fault point B. The post-disaster recovery model calculates that A and B can jointly complete the repair of L1 by the end of the second hour. At the beginning of the third hour, L1 is restored to normal, while welder C completes the repair of H1 by the end of the third hour, and the heating network returns to normal.

[0024] Please see Figure 1 and Figure 5The present invention provides an embodiment of a comprehensive energy disaster recovery method that considers the dynamic scheduling of professional maintenance personnel. The parameters of the disaster recovery model are dynamically updated according to the actual repair progress and changes in equipment status. When the equipment status changes, data acquisition, model building and solving are re-executed for online optimization of the recovery strategy. Furthermore, in a disaster-stricken area's power supply system, there are three faulty devices: transformer A supplying power to a hospital, line B supplying power to a residential area, and switch C supplying power to a shopping mall. The system has only one power maintenance team, which can only repair one fault at a time. After the post-disaster maintenance model optimizes the calculation based on the importance of each load, the hospital has the highest weight. The initial strategy is to first focus on repairing transformer A, which is expected to take 4 hours. After that, line B will be repaired, which will take 2 hours. Finally, switch C will be repaired, which will take 1 hour. The maintenance team starts repairing transformer A as planned. After 4 hours of continuous work, transformer A is successfully repaired, and the hospital is restored to power. At this time, the system status changes. An unplanned cable D supplying power to a regional water supply pumping station fails. The water supply pumping station is a critical load for ensuring people's livelihood, and its weight is the same as that of the hospital. The new fault information is captured in real time by sensors and fed back to the integrated energy system. The integrated energy system takes the current moment as a new starting point and re-collects data. Transformer A has been restored to a healthy state, and the maintenance team is in a usable state but has been working for 4 hours. The set of faulty devices is updated to line B, switch C, and the newly appeared high-priority cable D. With the new parameters, the integrated energy system re-executes optimization modeling and solving. In the new optimization calculation, the objective function is to minimize the total weighted load shedding over a future period. After the solver re-solves, it generates a new recovery strategy. The new recovery strategy is that after the maintenance team completes the maintenance of transformer A, it immediately moves on to the maintenance of cable D. After the maintenance of cable D is completed in 3 hours, the repair sequence of line B and switch C is re-evaluated based on the latest status at that time. This transforms the recovery decision from a static plan to a dynamic intelligent response, which can effectively cope with the uncertainty in the disaster evolution process and ensure that limited maintenance resources are always scheduled to solve the most urgent problems and the problems that contribute the most to the system recovery, thereby maximizing the overall benefits of the recovery process globally.

[0025] Working principle: First, sensors deployed at key nodes of the integrated energy system collect real-time data on equipment health status, maintenance personnel information, and load demand to build basic data reflecting the post-disaster status of the system. Based on the basic data, an optimization function is constructed with the goal of minimizing the weighted load shedding, and a post-disaster recovery model is established in combination with constraints. The post-disaster recovery model generates a recovery strategy that coordinates maintenance and operation, guiding maintenance personnel to repair faulty equipment according to priority and adjust the operation mode of the integrated energy system to quickly restore power and heating. During the execution of the strategy, the integrated energy system continuously monitors the equipment status and maintenance progress. Once a new fault occurs or the repair progress changes, the model parameters are immediately updated and the solution is re-solved, dynamically adjusting the subsequent repair sequence and resource allocation. This achieves dynamic matching between maintenance resources and system recovery, ensuring rapid restoration of critical loads under limited manpower conditions and improving the overall efficiency and reliability of the integrated energy system's post-disaster recovery.

[0026] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A comprehensive energy disaster recovery method considering the dynamic scheduling of professional maintenance personnel, characterized in that: The recovery method includes the following steps: S1. Collect the health status of equipment and the initial working hours of maintenance personnel, and analyze the power supply path of each electrical load and thermal load to form basic data for post-disaster recovery; S2. Construct an optimization function for the post-disaster recovery of the integrated energy system based on basic data. The optimization function aims to minimize the total load shedding of the system. S3. Based on the optimization function and energy supply path, establish constraint conditions including dynamic scheduling constraints for maintenance personnel, connectivity constraints for the energy supply network, load shedding power constraints, and operational constraints for the integrated energy system. S4. By optimizing the function and constraints, construct and solve the post-disaster recovery model to obtain a recovery strategy that coordinates maintenance scheduling and system operation. S5. During the execution of the recovery strategy, the parameters of the post-disaster recovery model are dynamically updated and re-solved based on the equipment status feedback, in order to adjust and continuously optimize the recovery strategy.

2. The comprehensive energy disaster recovery method considering the dynamic scheduling of professional maintenance personnel according to claim 1, characterized in that: The data acquisition of equipment health status includes obtaining the initial health status of equipment in the post-disaster system and representing the initial health status as a binary variable to distinguish whether the equipment is in a healthy or faulty state. The data acquisition of maintenance personnel initial working hours includes obtaining the current cumulative working hours of various professional maintenance personnel and recording them in the form of initial values ​​for subsequent dynamic scheduling. The power supply path is determined by a topology search algorithm to determine the set of all equipment on the path from the power supply energy source to the load node, which is used to determine the connectivity of the power supply network and the repair priority. The basic data includes equipment status variables, load power prediction values, maintenance personnel time data, and power supply path set. The basic data provides a data source for the construction of the optimization function.

3. The comprehensive energy disaster recovery method considering the dynamic scheduling of professional maintenance personnel according to claim 2, characterized in that: The optimization function is constructed with the goal of minimizing the total load shedding of the system. The sum of the weighted electrical load shedding and the weighted thermal load shedding is used as the objective function for minimization. The weight of each load is pre-set according to the importance of the load, which is used to reflect the recovery priority of different loads in the recovery phase. The optimization function expresses the recovery decision of multiple loads in multiple time periods in a unified manner through mathematical programming, which is used to drive the post-disaster recovery model to prioritize the recovery of high-weight loads under the premise of meeting the constraints.

4. The comprehensive energy disaster recovery method considering the dynamic scheduling of professional maintenance personnel as described in claim 1, characterized in that: The constraints include dynamic scheduling constraints for maintenance personnel, which include a lower limit constraint on the total working hours of each type of maintenance personnel required to repair each faulty piece of equipment, a single task constraint for personnel, an upper limit constraint on the total number of maintenance personnel of each type, and a working hours constraint for maintenance personnel.

5. A comprehensive energy disaster recovery method considering the dynamic scheduling of professional maintenance personnel according to claim 4, characterized in that: The constraints include a power supply network connectivity constraint. The power supply network connectivity constraint defines binary variables for each electrical load and thermal load. The binary variables of the load are used to indicate whether the power supply path is connected. A logical relationship is established between the binary variables of the load and the health status of each device on the path. The logical relationship is that when all devices on the power supply path of the load are in a healthy state, the load is marked as connected and the load is allowed to obtain energy supply.

6. A comprehensive energy disaster recovery method considering the dynamic scheduling of professional maintenance personnel, as described in claim 5, is characterized in that: The constraints include load shedding power constraints, which define the difference between the predicted load and the actual load received for each load as the load shedding amount. When the load path is not connected, the actual load received is forced to be zero, and all predicted loads are counted as load shedding. When the path is connected, the actual load received takes a value from zero to the predicted value, and the remaining part is the load shedding. Load shedding power constraints are used to describe the power supply and heating situation of each load in different recovery stages.

7. A comprehensive energy disaster recovery method considering the dynamic scheduling of professional maintenance personnel as described in claim 6, characterized in that: The constraints include integrated energy system operation constraints, which include power subsystem constraints and thermal subsystem constraints. Power subsystem constraints include power balance constraints at each node, upper and lower limits of generator output, and power branch operation constraints. Generators include coal-fired units, combined heat and power units, and new energy units. Thermal subsystem constraints include heat power balance constraints at each node, upper and lower limits of heat source equipment output, and thermal branch operation constraints.

8. A comprehensive energy disaster recovery method considering the dynamic scheduling of professional maintenance personnel according to claim 1, characterized in that: The post-disaster recovery model is constructed by optimizing functions and constraints. The post-disaster recovery model integrates the optimization functions and constraints into a mixed-integer linear programming model, which is solved by a mathematical optimization solver to generate recovery strategies.

9. A comprehensive energy disaster recovery method considering the dynamic scheduling of professional maintenance personnel as described in claim 8, characterized in that: The post-disaster recovery model solves for a recovery strategy, which includes a maintenance personnel scheduling strategy, an equipment repair sequence, a load power and heating recovery plan, and an operation plan for each energy device. This strategy is used to guide actual repair operations and real-time system scheduling.

10. A comprehensive energy disaster recovery method considering the dynamic scheduling of professional maintenance personnel according to claim 1, characterized in that: The parameters of the dynamically updated post-disaster recovery model are adjusted according to the actual repair progress and changes in equipment status. When the equipment status changes, data acquisition, model building, and solving are re-executed for online optimization of the recovery strategy.

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