Cooperative regulation and control method for transformer area resources in fault recovery

Abstract

The invention relates to a transformer area resource collaborative regulation and control method in fault recovery, and the method comprises the steps: constructing a transformer area resource collaborative regulation and control model which comprises a prediction module and an optimization module; the prediction module predicts renewable energy sources and loads once in each time period; the optimization module optimizes a controllable resource output plan and a load regulation and control plan of the transformer area system according to a prediction result of the prediction module, and the optimization target is to realize transformer area resource optimal collaborative allocation and optimal instruction tracking in a rolling time window; in the optimization process, only the decision instruction of the latest time period in the decision instructions obtained through optimization is executed; when the next time period is reached, the time window advances backwards for one time period, and prediction and optimization are carried out again; and repeating the rolling optimization process until the fault recovery of the power distribution system is completed. According to the method, the station area resources can be accurately and cooperatively regulated and controlled in fault recovery, so that the execution of a power distribution network fault recovery strategy is matched.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network fault recovery, and specifically relates to a method for collaborative regulation of substation area resources during fault recovery. Background Art

[0002] Distribution network fault recovery refers to a series of technologies and methods to quickly detect, locate, and isolate faults in the distribution network, and at the same time restore power supply to non-fault areas to reduce power outage time and losses. During the distribution network fault recovery process, it is necessary to collaboratively regulate substation area resources to track the power command of the superior power grid and optimize the allocation of various types of controllable resources and loads in the substation area, so as to cooperate with the implementation of the distribution network fault recovery strategy. Summary of the Invention

[0003] The purpose of the present invention is to provide a method for collaborative regulation of substation area resources during fault recovery, which is beneficial to accurately collaboratively regulate substation area resources during fault recovery, so as to cooperate with the implementation of the distribution network fault recovery strategy.

[0004] To achieve the above purpose, the technical solution adopted by the present invention is: a method for collaborative regulation of substation area resources during fault recovery, which constructs a collaborative regulation model of substation area resources, including a prediction module and an optimization module; the prediction module makes a prediction of renewable energy and load once in each time period; the optimization module optimizes the output plan of controllable resources and the load regulation plan of the substation area system according to the prediction results of the prediction module, and the optimization goal is to achieve the optimal collaborative allocation of substation area resources and the optimal command tracking within a rolling time window; during the optimization process, only the decision instruction of the most recent time period in the optimized decision instructions is executed; in the next time period, the time window is advanced by one time period, and then a new prediction and optimization are carried out again; the above rolling optimization process is repeated until the distribution system fault recovery is completed.

[0005] Further, the method specifically includes the following steps:

[0006] S1. In each time period, read the updated historical data and predict the renewable energy and load for the next M time periods;

[0007] S2. According to the prediction data obtained in step S1, optimize the output plan of controllable resources and the load regulation plan of the substation area system, but only execute the decision instruction of the most recent time period;

[0008] S3. Update the time t = t + Δt, that is, update the current time period to the next time period, where Δt represents the interval time; collect data and update the historical data;

[0009] S4. Return to step S1 and continue the rolling optimization until the distribution system fault recovery is completed.

[0010] Furthermore, the optimization objective of the substation area resource collaborative regulation model is to perform optimal scheduling of substation area resources and ensure that the interactive power of the substation area can effectively track the instructions of the superior power grid, that is, to minimize the comprehensive cost of the substation area resource collaborative regulation model; the objective function of the substation area resource collaborative regulation model is shown in Equation (1):

[0011] F = F OMC + F BEC + F BFC + F RECC + F CEC + P RP (1)

[0012] Among them, F OMC is the cost of equipment operation and maintenance, F BEC is the cost of purchasing electric energy from the superior power grid, F BFC is the cost of purchasing fuel, F RECC is the cost of waste of abandoned electricity in renewable resource power generation facilities, F CEC is the cost penalty for system load shedding, P RP is the penalty for substation area instruction tracking matching degree.

[0013] Furthermore, the specific form of the cost F OMC of equipment operation and maintenance is shown in Equation (2):

[0014]

[0015] Among them, Ω device represents the set of equipment installation nodes, Ω DevType represents the set of types of installed equipment, Ω T represents the set of all time period numbers within the scheduling time window, N ag represents the set of individual labels of a type of equipment in the substation area system model; represents the average operation and maintenance cost of the nth device of type Dev doing one watt of work; represents the equipment operating power, t represents the time period number; ΔT seg is the model time resolution.

[0016] Furthermore, the specific form of the cost F BEC of purchasing electric energy from the superior power grid is shown in Equation (3):

[0017]

[0018] Among them, represents the price of purchasing one degree of electricity in time period t, represents the price of selling one degree of electricity in time period t; respectively represent the active power flowing into or out of the entire distribution network from the substation area, that is, the active power obtained from the superior power grid or sold to the superior power grid; t represents the time period number; ΔT seg is the model time resolution.

[0019] Furthermore, the cost F of purchasing fuel BFC has the specific form as shown in Equation (4):

[0020]

[0021] where, Ω GT is the set of nodes equipped with gas turbines, and Ω DEG is the set of nodes equipped with diesel generators; is the gas consumption of the micro gas turbine prime mover when doing work at a power of 1 kW for 1 hour; is the fuel consumption of the diesel generator for each kWh of electricity generated; C gas and C diesel are the purchase prices of each kg of natural gas and diesel respectively.

[0022] Furthermore, the cost penalty F of waste power from renewable energy power generation facilities RECC has the specific form as shown in Equation (5):

[0023]

[0024] where, is the waste power of the photovoltaic power source at substation area i during time period t, is the penalty per kWh.

[0025] Furthermore, the cost penalty F for system load shedding CEC has the specific form as shown in Equation (6):

[0026]

[0027] where, is the load shedding power at substation area i during time period t, is the penalty per kWh of load shedding.

[0028] Furthermore, the penalty P for the matching degree of substation area command tracking RP has the specific form as shown in Equation (6):

[0029]

[0030] where, Ω DevType represents the set of types of equipment installed at substation area nodes; represents the equipment operating power, i represents the node number, t represents the time period number; ΔTseg is the single - time - period length; ρ is the instruction - tracking penalty coefficient; among them, the regulation instructions issued by the distribution - network - level regulation center to various types of controllable resources in the sub - region are known values.

[0031] Furthermore, the constraints of the sub - region resource collaborative regulation model mainly include the sub - region energy supply - demand balance constraint, the equipment operation constraint of controllable resources, and the load scheduling constraint.

[0032] Compared with the prior art, the present invention has the following beneficial effects: The present invention proposes a method for collaborative regulation of sub - region resources in fault recovery based on model predictive control. This method can optimize the output of each unit according to the latest prediction results, but only the results of the most recent time period are executed, thereby coordinating the operating states of various devices, maintaining the tracking accuracy of the interactive power instructions in the sub - region, reducing the impact of the uncertainty of renewable energy and load on operation, and strongly supporting the implementation of the distribution - network fault - recovery strategy. The feedback correction adjusts the output according to the latest collected data, reducing the impact of the uncertainty of renewable energy and load on operation. Since this method always regulates based on the latest data information and only executes the decision instructions of the most recent time period, it can achieve the optimum under uncertain disturbances faced by the system. Description of the Drawings

[0033] Figure 1 is the flowchart of the method implementation in the embodiment of the present invention;

[0034] Figure 2 is the time - window strategy diagram of the optimization module in the embodiment of the present invention;

[0035] Figure 3 is the schematic diagram of the typical sub - region system structure in the embodiment of the present invention;

[0036] Figure 4 is the diagram of the predicted and rolling - predicted sub - region load conditions during a fault in the embodiment of the present invention;

[0037] Figure 5 is the diagram of the predicted and rolling - predicted sub - region illumination conditions during a fault in the embodiment of the present invention;

[0038] Figure 6 is the diagram of the tracking situation of the active - power interactive instructions in the sub - region in Case 1 and Case 2 of the embodiment of the present invention;

[0039] Figure 7 is the diagram of the tracking situation of the reactive - power interactive instructions in the sub - region in Case 1 and Case 2 of the embodiment of the present invention;

[0040] Figure 8 is the diagram of the scheduling decision situation of the energy - storage facilities in the sub - region in Case 1 of the embodiment of the present invention;

[0041] Figure 9It is the diagram of the dispatching decision-making situation of the energy storage facilities in the power distribution area of Example 2 in the embodiments of the present invention. Detailed implementation manners

[0042] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0043] It should be noted that the following detailed descriptions are all exemplary and are intended to provide further descriptions of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present application belongs.

[0044] It should be noted that the terms used herein are only for describing specific implementation manners and are not intended to limit the exemplary implementation manners according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "include" and / or "comprise" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0045] This embodiment provides a method for coordinated regulation of power distribution area resources during fault recovery, which constructs a coordinated regulation model of power distribution area resources, including a prediction module and an optimization module; the prediction module performs a prediction of renewable energy and load once in each time period; the optimization module optimizes the output plan of controllable resources and the load regulation plan of the power distribution area system according to the prediction results of the prediction module, and the optimization goal is to achieve the optimal coordinated allocation of power distribution area resources and the optimal instruction tracking within a rolling time window; during the optimization process, only the decision instruction of the most recent time period in the optimized decision instructions is executed; at the next time period, the time window is advanced by one time period, and then a new prediction and optimization are performed again; the above rolling optimization process is repeated until the fault recovery of the power distribution system is completed. As Figure 1 shown, the method specifically includes the following steps:

[0046] S1. In each time period, read the updated historical data and predict the renewable energy and load for the next M time periods;

[0047] S2. According to the prediction data obtained in step S1, optimize the output plan of controllable resources and the load regulation plan of the power distribution area system, but only execute the decision instruction of the most recent time period;

[0048] S3. Update the time t = t + Δt, that is, update the current time period to the next time period, where Δt represents the interval time; collect data and update the historical data;

[0049] S4. Return to step S1 and continue the rolling optimization until the fault recovery of the power distribution system is completed.

[0050] Among them, the time window strategy of the optimization module is asFigure 2 As shown, according to the latest data information, it rolls and executes the regional resource collaborative regulation model within the current time window, and finally realizes the optimal regional collaborative scheduling and instruction tracking decision-making under uncertain disturbances of the system.

[0051] 1. Optimization Objectives of the Regional Resource Collaborative Regulation Model

[0052] The optimization objective of the regional resource collaborative regulation model is to perform optimal scheduling of regional resources and ensure that the interactive power of the region can effectively track the instructions of the superior power grid, that is, to minimize the comprehensive cost of the regional resource collaborative regulation model.

[0053] The objective function of the regional resource collaborative regulation model is shown in Equation (1):

[0054] F = F OMC + F BEC + F BFC + F RECC + F CEC + P RP (1)

[0055] Wherein, F OMC is the cost of equipment operation and maintenance, F BEC is the cost of purchasing electric energy from the superior power grid, F BFC is the cost of purchasing fuel, F RECC is the cost of waste of abandoned electricity from renewable energy power generation facilities, F CEC is the cost penalty for system load shedding, and P RP is the penalty for the matching degree of regional instruction tracking. The specific forms of each sub-cost item in the objective function are as follows.

[0056] The specific form of the cost F OMC of equipment operation and maintenance is shown in Equation (2):

[0057]

[0058] Equation (2) calculates the equipment operation and maintenance cost by characterizing the average operation and maintenance cost coefficient of each device doing unit work, and then calculates the equipment operation and maintenance cost for each time period and each device. Among them, Ω device represents the set of equipment installation nodes, Ω DevType represents the set of types of installed equipment, Ω T represents the set of all time period serial numbers within the scheduling time window, N ag represents the set of individual labels of a certain type of equipment in the regional system model; represents the average operation and maintenance cost of the nth device of type Dev doing one watt of work; represents the equipment operating power, and t represents the time period serial number; ΔT seg is the model time resolution.

[0059] The cost F of purchasing electric energy from the superior power grid BEC has the specific form shown in Equation (3):

[0060]

[0061] Equation (3) is the cost of power interaction between the substation area and the superior power grid. Among them, represents the price of purchasing one degree of electricity during period t, represents the price of selling one degree of electricity during period t; respectively represent the active power flowing into or out of the entire distribution network from the substation area, that is, the active power obtained from the superior power grid or sold to the superior power grid; t represents the period number; ΔT seg is the time resolution of the model.

[0062] The cost F of purchasing fuel BFC has the specific form shown in Equation (4):

[0063]

[0064] Equation (4) describes the fuel purchase cost of the system. Among them, Ω GT is the set of nodes installed with gas turbines, and Ω DEG is the set of nodes installed with diesel generators; is the gas consumption of the micro gas turbine prime mover when doing work at a power of 1 kW for 1 hour, with the unit of kg / (kW·h); is the fuel consumption of the diesel generator for each kWh of electricity generated, with the unit of kg / (kW·h); C gas and C diesel are the purchase prices of each kg of natural gas and diesel respectively, with the unit of yuan / kg.

[0065] The cost of waste of abandoned electricity from renewable energy power generation facilities F RECC has the specific form shown in Equation (5):

[0066]

[0067] Equation (5) depicts the penalty cost of abandoned electricity when the renewable energy generator cannot be connected to the grid due to cost or operation safety. Among them, is the abandoned power of the photovoltaic power source at substation area i during period t, is the penalty per kilowatt-hour.

[0068] The cost penalty F for system load shedding CEC has the specific form shown in Equation (6):

[0069]

[0070] Equation (6) is the cost penalty for load shedding. When the system operation does not meet the security constraints, some non-critical loads will be discarded during operation scheduling. Among them, is the load shedding power at the i-th substation area during time period t, is the penalty per kilowatt-hour of load shedding.

[0071] The penalty term for the matching degree of substation area command tracking is described below.

[0072] The penalty P for the matching degree of substation area command tracking RP has the specific form as shown in Equation (6):

[0073]

[0074] Equation (7) is the target term for the matching degree of command tracking, and the square of the difference between the distribution network control command and the actual response of the substation area is used as the penalty. Among them, Ω DevType represents the set of types of equipment installed at the substation area nodes; represents the operating power of the equipment, i represents the node number, and t represents the time period number; ΔT seg is the single-time period length; ρ is the command tracking penalty coefficient; among them, the control command issued by the distribution network-level control center to multiple types of controllable resources in the substation area

[0075] 2. Constraints of the substation area resource collaborative control model

[0076] The constraints of the substation area resource collaborative control model mainly include the energy supply-demand balance constraint of the substation area, the equipment operation constraint of the controllable resources, and the load scheduling constraint.

[0077] The energy supply-demand balance constraint of the substation area mainly describes the energy coupling relationship within the substation area system, so as to ensure that the power exchanged by the substation area with the outside can effectively track the scheduling instructions issued by the superior power grid. The specific model is as follows:

[0078]

[0079]

[0080] Equations (8) and (11) are the expressions for the power exchanged between the substation area and the superior power grid, (9) is the load shedding constraint, (10) and (13) are the energy balance constraints of the substation area, and (12) is the load power factor constraint. In the equations, is the active power exchanged between the substation area and the superior power grid, is the active power of the actual load supplied by the power grid, is the active power reduction of the nth load at the substation area i during the time period t. The meaning of the corresponding reactive power symbol will not be elaborated here.

[0081] The energy storage resource scheduling model is as follows:

[0082]

[0083] Constraint (14) is the collaborative allocation constraint of individual-level devices. (15)-(21) are, in sequence, the upper and lower limit constraints of the energy storage ratio of energy storage facilities, the constraint of the relationship between the energy storage ratio and energy storage energy of energy storage facilities, the constraint of the power-energy conversion relationship of energy storage devices, and the linear model of the charge and discharge power limit of energy storage facilities. Among them, n ∈ N EES represents the index of each individual battery energy storage device aggregated in the substation area, represents the equivalent node index of the substation area. The meanings of the remaining energy storage resource scheduling model constraints have been explained in the previous text and will not be elaborated here.

[0084] The gas generator model is as follows:

[0085]

[0086] Equations (22)-(25) represent, in sequence, the collaborative allocation constraint of individual-level devices, the active / reactive power range constraint of the gas generator, and the conversion efficiency constraint between the prime mover power of the gas turbine and the active power of electrical energy. Among them, represents the active / reactive power of the gas turbine, represents the conversion efficiency of the gas turbine from prime mover power to power generation power, represents the prime mover power of the gas turbine.

[0087] The diesel generator model is as follows:

[0088]

[0089] Equations (26)-(28) represent, in sequence, the collaborative allocation constraint of individual-level devices, the active power range constraint of the diesel generator, and the output power factor angle constraint. Among them, represents the active / reactive power of the diesel generator, represents the rated power angle of the diesel engine.

[0090] 3. Case Study

[0091] 3.1 System Configuration

[0092] During the process of distribution network fault restoration, the low-voltage substation area tracks the power command of the superior power grid and optimally allocates various types of controllable resources and loads within the substation area, and then cooperates with the distribution network fault restoration strategy to execute

[0093] The example system in this embodiment uses a typical substation area system example to verify the effectiveness of the substation area resource collaborative regulation method in fault recovery based on model predictive control proposed by the present invention, as Figure 3 shown. The system includes 4 integrated energy storage and photovoltaic devices and 4 independent distributed photovoltaic devices. The typical voltage of the system is 380V, the total scheduling period is 3 hours, the scheduling time resolution is 15 minutes, and the rolling scheduling time window is 1 hour.

[0094] Table 1 Capacity Configuration of Controllable Resources in Substation Area

[0095]

[0096]

[0097] 3.2 Example Settings

[0098] To verify the effectiveness of the substation area resource collaborative regulation method in fault recovery based on model predictive control proposed by the present invention, the following two examples are set in this embodiment for example simulation analysis and comparison:

[0099] Example 1: Apply the substation area resource collaborative regulation method in fault recovery based on model predictive control proposed by this method. During the fault recovery period (3 hours / 12 scheduling instructions), track the substation area power scheduling instructions in the fault recovery strategy of the superior power grid, and realize the rolling optimization of the collaborative scheduling of substation area resources.

[0100] Example 2: Based on the substation area resource collaborative regulation method in conventional fault recovery, the initial prediction parameters and the actual source and load output are the same as those in Scheme 1, but no rolling optimization update of the substation area resource collaborative scheduling is performed.

[0101] 3.3 Analysis of Example Results

[0102] This embodiment shows the collaborative scheduling results of substation area resources in two examples to verify the applicability and effectiveness of the substation area resource collaborative regulation method in fault recovery based on model predictive control proposed by the present invention.

[0103] Since at the time of fault occurrence, it is necessary to predict the load and renewable resource conditions at one time and formulate a distribution network fault recovery and collaborative scheduling plan within several hours, there are inaccurate predictions. In actual scheduling, large source-load fluctuations may be encountered, which tests the substation area resource coordination ability. Figure 4 And 5 shows the load prediction situation and the substation area lighting situation when the distribution network specifies a fault recovery strategy at the time of fault occurrence. It can be seen from the figure that both the photovoltaic and the load have certain fluctuations.

[0104] Table 2 Comparison of Costs for Collaborative Regulation of Controllable Resources in Substation Area

[0105]

[0106]

[0107] Table 2 shows the results of the collaborative regulation cost of controllable resources in the low-voltage area under two methods. In the results of Example 1 and Example 2, there are no situations of renewable energy curtailment and load shedding. However, the total matching degree of command tracking of the rolling regulation method proposed in this paper reaches 99.45%, while the total matching degree of command tracking of the one-time optimization method shown in Example 2 is only 97.49%.

[0108] The specific situation of command tracking of the interactive power in the low-voltage area in Example 1 and Example 2 is as Figure 6 and 7 shown. In terms of the command tracking of the active power interactive power in the low-voltage area, in Example 1, the low-voltage area uses the collaborative regulation method of low-voltage area resources in the fault recovery based on model predictive control. Through the flexible scheduling of the charge and discharge power of the energy storage facilities as shown in Figure 8 , it can achieve complete tracking of the active power interactive power command issued by the low-voltage area to the superior power grid according to the fault recovery strategy; while the low-voltage area in Example 2 lacks the flexibility of active power scheduling because the scheduling decisions of relevant equipment have been determined in advance, so the active power command tracking rate is only 98.33%. In terms of the command tracking of the reactive power interactive power, as can be seen from Figure 7 , the main reason for the command tracking error is the existence of reactive power deviation, because there is a lack of flexible and inexpensive reactive power compensation resources, and the small battery energy storage device is set to be unable to provide reactive power support, while the operating cost and fuel cost of gas turbines and diesel engines are too expensive to be specially used to generate a small amount of reactive power.

[0109] It can be seen from the simulation results of the examples shown in this embodiment that when facing the random fluctuations or prediction errors of renewable resources and loads in the actual scheduling process, after adopting the collaborative regulation method of low-voltage area resources in the fault recovery based on model predictive control, the controllable resources in the low-voltage area can be adaptively corrected according to the real-time observation / prediction data, coordinate the operating states of various devices, so as to maintain the tracking accuracy of the interactive power command in the low-voltage area, and strongly support the implementation of the distribution network fault recovery strategy. At the same time, the method proposed by the present invention can fully absorb the generated electricity of renewable resources and maintain the stable supply of loads in the low-voltage area, and optimize the operating cost of the low-voltage area.

[0110] In summary, the method for collaborative regulation of substation area resources in fault recovery based on model predictive control proposed by the present invention can ensure that when the substation area system faces source-load fluctuations, rolling optimization is performed according to the prediction / observation results of renewable energy and load within each instruction cycle, correcting the output plan of controllable resources and the load regulation plan of the substation area system, better realizing the optimal collaborative allocation of substation area resources and the optimal instruction tracking within the fault recovery time window, and also providing flexible and efficient decision support for the successful implementation of the fault recovery strategy of the superior distribution network.

[0111] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented 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.

[0112] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate for implementing in the process Figure 1 one process or multiple processes and / or blocks Figure 1 a device for the functions specified in one block or multiple blocks.

[0113] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements in the process Figure 1 one process or multiple processes and / or blocks Figure 1 a device for the functions specified in one block or multiple blocks.

[0114] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 a device for the functions specified in one block or multiple blocks.

[0115] As described above, it is only the preferred embodiment of the present invention, and it is not a limitation to the present invention in other forms. Any person skilled in the art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution content of the present invention still belong to the protection scope of the technical solution of the present invention.

Claims

1. A method for coordinated control of regional resources in fault recovery, characterized in that: Construct a coordinated control model of substation resources, including a prediction module and an optimization module; the prediction module predicts renewable energy and load once in each time period; the optimization module optimizes the controllable resource output plan and load control plan of the substation system according to the prediction results of the prediction module, and the optimization goal is to achieve optimal coordinated allocation of substation resources and optimal instruction tracking within the rolling time window; during the optimization process, only the decision instructions of the most recent time period among the optimized decision instructions are executed; when it comes to the next time period, the time window is advanced one time period, and prediction and optimization are performed again; the above rolling optimization process is repeated until the distribution system fault recovery is completed.

2. According to the method of coordinated control of regional resources in fault recovery in claim 1, it is characterized in that: The following steps are involved: S1. In each time period, read the updated historical data and predict the renewable energy and load for the next M time periods; S2. According to the forecast data obtained in step S1, the controllable resource output plan and load regulation plan of the substation system are optimized, but only the decision instructions of the most recent period are executed; S3, update time t=t+Δt, that is, update the current period to the next period, Δt represents the interval time; Collect data and update historical data; S4. Return to step S1 and continue rolling optimization until the power distribution system fault recovery is complete.

3. A method for coordinated control of regional resources in fault recovery according to claim 1, characterized in that: The optimization goal of the coordinated control model of the substation resources is to optimally dispatch the substation resources and ensure that the interactive power of the substation can effectively track the instructions of the upper power grid, that is, to minimize the comprehensive cost of the coordinated control model of the substation resources. The objective function of the coordinated control model of the substation resources is shown in formula (1): F=F OMC +F BEC +F BFC +F RECC +F CEC +P RP (1) Among them, F OMC is the cost of equipment operation and maintenance, F BEC is the cost of purchasing electricity from the upper grid, F BFC is the cost of purchasing fuel, F RECC The cost of abandoning electricity from renewable energy generation facilities is wasted, F CEC is the cost penalty for system load shedding, P RP Tracking matching penalty for area commands.

4. A method for coordinated control of regional resources in fault recovery according to claim 3, characterized in that: The cost of operating and maintaining the equipment F OMC The specific form is shown in formula (2): Among them, Ω device Represents the set of device installation nodes, Ω DevType A collection of installed device types, Ω T Represents the set of all time period sequence numbers in the scheduling time window, N ag It represents a set of individual labels of a type of equipment in the substation system model; Indicates the average operation and maintenance cost of the nth device of type Dev to produce one watt of work; Indicates the operating power of the equipment, t indicates the time period number; ΔT seg is the model time resolution.

5. A method for coordinated control of regional resources in fault recovery according to claim 3, characterized in that: The cost of purchasing electricity from the upper grid is F BEC The specific form is shown in formula (3): in, represents the price of purchasing one kilowatt-hour of electricity in time period t, It represents the price of one kilowatt-hour of electricity sold in time period t; They represent the active power flowing into or out of the entire distribution network from the substation, that is, the active power obtained from the upper grid or the active power sold to the upper grid; t represents the time period number; ΔT seg is the model time resolution.

6. A method for coordinated control of regional resources in fault recovery according to claim 3, characterized in that: The cost of purchasing fuel F BFC The specific form of is shown in formula (4): Among them, Ω GT is the set of nodes with gas turbines installed, Ω DEG A collection of nodes with diesel generators installed; The gas consumption of the micro gas turbine prime mover when it performs work at 1kW for 1 hour; The fuel consumption per kWh of electricity generated by the diesel generator; C gas and C diesel are the purchase prices per kg of natural gas and diesel respectively.

7. A method for coordinated control of regional resources in fault recovery according to claim 3, characterized in that: The cost of wasted electricity from renewable energy power generation facilities is F RECC The specific form of is shown in formula (5): in, is the abandoned power of the photovoltaic power source at the station area i in the time period t, Penalty per kWh.

8. A method for coordinated control of regional resources in fault recovery according to claim 3, characterized in that: The system load shedding cost penalty F CEC The specific form of is shown in formula (6): in, is the load shedding power at station i in time period t, Penalty for each kWh of load shedding.

9. A method for coordinated control of regional resources in fault recovery according to claim 3, characterized in that: The station area instruction tracks the matching penalty P RP The specific form of is shown in formula (6): Among them, Ω DevType A collection representing the types of equipment installed at the substation node; represents the operating power of the equipment, i represents the node number, and t represents the time period number; ΔT seg is the length of a single period; ρ is the instruction tracking penalty coefficient; where the control instructions issued by the distribution network-level control center to the multi-type controllable resources in the substation is a known value.

10. A method for coordinated control of regional resources in fault recovery according to claim 1, characterized in that: The constraints of the coordinated control model of substation resources mainly include substation energy supply and demand balance constraints, controllable resource equipment operation constraints and load scheduling constraints.

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