Distributed power grid sequential load recovery method and system based on transaction type control
By adopting a transaction-based control method in the distributed power grid, determining the power exchange clearing price and building a corresponding optimization model, the problem of lack of coordination and enhanced grid elasticity in the load recovery process is solved, and efficient load recovery and improvement of grid elasticity is achieved.
Patent Information
- Application Number
- CN202411640638.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-18
- Publication Date
- 2025-02-25
AI Technical Summary
The prior art ignores the support of active power to enhance the elasticity of the power grid during load recovery, lacks coordination of critical load recovery, and there is little research on the emergency energy trading mechanism between the power grids.
Using a distributed grid sequential load recovery method based on transaction-based control, the power exchange clearing price between distributed energy storage and microgrid is determined at each time step, and the sequential load recovery model of distributed energy storage and the microgrid are constructed to optimize the power exchange volume to minimize operating costs.
In the event of power outage, it has been realized that by coordinating emergency energy transactions between the power grids, optimizing the load recovery process, enhancing the grid elasticity, and improving the coordination and efficiency of load recovery.
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Figure CN119518748A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of electric power networks, and in particular to a distributed power grid sequential load recovery method, system and computer-readable storage medium based on transaction control. Background Art
[0002] At present, the new power system presents the characteristics of "three highs and two peaks", including a high proportion of new energy access, a high proportion of power electronic equipment access, a high degree of freedom in power generation and energy consumption, and double peaks in summer and winter. In addition, natural disasters such as hurricanes, blizzards, and floods can damage power infrastructure, causing power outages in the distribution system and causing huge social and economic losses. Therefore, improving the resilience of the power grid has become one of the keys to ensuring the safe and stable operation of the new power system.
[0003] In order to enhance the resilience of distributed power systems, distribution networks have gradually transformed from a network that simply receives and distributes electric energy to a power network that integrates source, grid, load and storage. After a power outage, it is crucial to enhance the recovery capability of distributed power grids through a series of recovery measures. A key aspect of improving the resilience of distribution systems is to manage load recovery work to quickly and intelligently restore critical loads. For traditional branch line outages, the load recovery of distributed distribution systems is mainly based on system reconstruction after the fault, and the outage load is borne by upstream transmission; for load recovery under natural disaster events, large-scale and multiple emergency events may occur simultaneously or sequentially, resulting in the unavailability of upstream utility supply.
[0004] As a new technology for integrating source, grid, load and storage on the distribution side, microgrid is an effective means to promote the local consumption of large-scale distributed new energy and realize the aggregation and interaction of multiple new elements. Microgrid has the characteristics of ensuring the power supply of key loads, improving the self-healing and recovery capabilities of the system, and enhancing the flexibility of the system. Therefore, microgrid can be used to enhance the resilience of the grid to meet this challenge.
[0005] In addition, the grid is generally regarded as a utility asset, which is directly regulated by the distribution system operator to supply the critical loads in the distribution system, so the direct control method under the private ownership of the microgrid is not applicable to most power outage situations. Transactive energy (TE) is a promising solution that enables non-utility assets such as private distributed energy resources (DERs) to provide services using economic signals instead of direct control.
[0006] However, there are few studies on the emergency energy trading mechanism between power grids during the load restoration process. The TE framework in existing research does not consider the support of active power for enhancing grid resilience, ignores the importance of MG internal operation and sequential restoration process, and lacks coordination of critical load restoration. Therefore, it is necessary to provide a new distributed power grid sequential load restoration method based on transactional control. Summary of the invention
[0007] Based on this, it is necessary for the present invention to provide a distributed power grid sequential load recovery method, system and readable storage medium based on transactional control to solve at least one of the above technical problems.
[0008] To achieve the above object, a distributed power grid sequential load restoration method based on transaction control includes the following steps:
[0009] Step S1: Determine the clearing price λ for power exchange between distributed energy storage DS and microgrid MGs at each time step according to the operating status of the distribution system. t ;
[0010] Step S2, with the goal of minimizing the operating cost of the microgrid MGs, solve the power exchange amount transmitted at each time step
[0011] Step S3, constructing a sequential load recovery model of distributed energy storage DS and its constraints, wherein the distributed energy storage includes a dispatchable micro-turbine MT, and the objective function of the DS sequential load recovery model is: in, The objective function representing the weighted amount of load pickup, represents the objective function of MT power generation cost, represents the objective function of the cost of renewable power curtailment, represents the objective function of the cost of purchasing emergency energy, as well as are the coefficients related to load picking, MT generation cost and renewable energy curtailment cost, respectively. is the active load shedding amount of node i, and represents the active power demand and MT active power generation of system node i at time step t, represents the active power generation of renewable energy generators at system node i at time step t, is a bilinear term;
[0012] Step S4, construct the MGs dispatch optimization model and constraint conditions of the microgrid, and its objective function is expressed as:
[0013] in and represents the cost coefficient associated with MT power generation, is the secondary cost coefficient of load reduction, is the load shedding linear cost coefficient, and represents the active load shedding in the mth MG and the active generation in the MT at time step t;
[0014] Step S5, establish a two-level dispatch model of distributed energy storage DS with microgrid MGs, and the microgrid output power expression is as follows (23):
[0015] Step S6, solving the two-level dispatch model to obtain the optimal solution as the sequential load recovery method.
[0016] Furthermore, the distributed energy storage further includes photovoltaic generators PVs, wind turbines WTs, energy storage systems ESSs and mobile emergency generators MEGs, and the constraint conditions in step S3 are one or more of the following conditions (2a)-(18e):
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[0044] in, and Indicates the active and reactive power generation of DER, P ij,t and Q ij,t represents the active and reactive power flow between nodes, x i,t is the load picking state quantity, α ij,t is a slack variable used to cancel the voltage drop relationship when the switch is turned on, y ij,t is the switch operation state quantity, and represents the lower and upper bounds of the voltage amplitude at node i, and represents the upper limit of active and reactive line flows, and Indicates the maximum MT active and reactive power generation, P i MT,ru and Indicates the MT active power generation climbing limit and descending limit. Represents the predicted active power generation of renewable energy generators, Indicates the maximum reactive power generation of renewable energy generators. Indicates the maximum reactive power generation of the capacitor bank, K t represents the maximum number of switching operations per time step, n b and n bs is the number of partitions and the number of source partitions, r z,t is the regional power-on state quantity, Indicates the maximum virtual line power, is the MEG spatiotemporal state quantity, Indicates the active power generation of MEG, Indicates ESS charging / discharging power, P i EC,max , P i ED,max Indicates the maximum charging / discharging power of ESS, Indicates the SOC level of ESS, Indicates the initial SOC level of ESS, Indicates the minimum / high SOC level of ESS, Indicates the charge / discharge efficiency.
[0045] Furthermore, the constraint conditions in step S3 also include: using the McCormick envelope to constrain the bilinear terms in the objective function Perform linearization and introduce an auxiliary variable w i,t Replace the bilinear terms and add a set of linear constraints to the model, as shown in Equation (19)
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[0047] in, Indicates the minimum and maximum emergency active power.
[0048] Furthermore, the constraint conditions in step S4 include one or more of the following:
[0049] st
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[0052] Furthermore, the step S6 specifically includes: by deriving the KKT condition, the lower-layer MGs scheduling optimization model can be replaced, thereby reconstructing the original double-layer model into a single-layer model for solving.
[0053] Furthermore, the step S6 also includes: using the big-M method to further replace the complementary relaxation conditions with a set of binary variable linear equations.
[0054] The present invention also provides a distributed power grid sequential load recovery system based on transactional control, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the distributed power grid sequential load recovery method based on transactional control as described in any one of the above items when executing the computer program.
[0055] The present invention also provides a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the steps of the distributed power grid sequential load recovery method based on transactional control as described in any one of the above items are implemented.
[0056] The distributed power grid sequential load restoration method based on transaction control of the present invention does not use direct coordination control signals, and determines the clearing price λ of power exchange between DS and MGs at each time step according to the operating status of the distribution system. t Based on this price signal, during the load recovery process, the MG operator optimizes its own goals according to the clearing price, determines the emergency power exchange, and solves the power exchange amount transmitted at each time step with the goal of minimizing the MG operating cost. In this way, the recovery amount of the power outage load in the distribution system is optimized; specifically, the sequential load recovery model of the distributed energy storage DS and the MGs scheduling optimization model of the microgrid are constructed, and then the MGs scheduling optimization model is embedded in the sequential load recovery model of the DS. t As the input of the lower-level MGs dispatch optimization model, the MGs dispatch optimization model is solved to obtain the power exchange amount As the input of the upper model, a two-level optimization model is obtained; and the two-level scheduling model is solved to obtain the optimal solution as the sequential load recovery method. BRIEF DESCRIPTION OF THE DRAWINGS
[0057] Other features, objectives and advantages of the present invention will become more apparent from a reading of the detailed description of a non-limiting implementation made with reference to the following accompanying drawings.
[0058] Figure 1 It is a flow chart of a distributed power grid sequential load recovery method based on transaction control provided by the present invention.
[0059] Figure 2 It is a schematic diagram of the hardware structure of a system running a distributed power grid sequential load restoration method based on transactional control in one embodiment of the present invention.
[0060] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION
[0061] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.
[0062] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.
[0063] Generally speaking, in a microgrid system, distributed storage DS (distributed storage, DS) usually consists of dispatchable micro turbines (MTs), photovoltaic generators (PVs), wind turbines (WTs), energy storage systems (ESSs) and mobile emergency generators (MEGs).
[0064] The present invention proposes a distributed power grid sequential load recovery method based on transactional control, which performs power outage load recovery by coordinating emergency energy transactions between power grids without using direct control. In the proposed transactional control framework, emergency energy is traded between distributed energy storage DS (distributed storage, DS) and networked MGs (microgrids), a two-level load recovery model is constructed, the clearing price and power exchange between power grids during the load recovery process are determined, and power exchange is performed through dynamic pricing: the price signal of the trading market is determined according to the operating status of the DS, and the MG optimizes its own goals based on the price signal, and the transmission power transmitted by the MGs to the DS is coordinated through the price signal of each time step.
[0065] To achieve this, please refer to Figure 1 and Figure 2 The present invention provides a distributed power grid sequential load recovery method based on transaction control, including the following steps S1-S6.
[0066] Step S1: Determine the clearing price λ for power exchange between distributed energy storage DS and microgrid MGs at each time step according to the operating status of the distribution system. t .
[0067] Step S2, with the goal of minimizing the operating cost of the microgrid MGs, solve the power exchange amount transmitted at each time step
[0068] Step S3, constructing a sequential load recovery model of distributed energy storage DS and its constraints, wherein the distributed energy storage includes a dispatchable micro-turbine MT, and the objective function of the DS sequential load recovery model is: in, The objective function representing the weighted amount of load pickup, represents the objective function of MT power generation cost, represents the objective function of the cost of renewable power curtailment, represents the objective function of the cost of purchasing emergency energy, as well as are the coefficients related to load picking, MT generation cost and renewable energy curtailment cost, respectively. is the active load shedding amount of node i, and represents the active power demand and MT active power generation of system node i at time step t, represents the active power generation of renewable energy generators at system node i at time step t, is a bilinear term.
[0069] because is a bilinear term, so the objective function (1) is nonlinear.
[0070] In step S3, the DS sequential load recovery model considers multiple constraints such as active power flow, which are expressed as (2)-(19) in sequence.
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[0098] Based on the assumption that the nonlinear term representing line loss in the DistFlow equation can be ignored, the simplified DistFlow model has been widely used in the calculation of radial DS and MG. Based on this model, the approximate active power flow and reactive power flow constraint equations in DS can be expressed as Equations (2a) and (2b), where and Indicates the active and reactive power generation of DER, P ij,t and Q ij,t represents the active and reactive power flow between nodes; Formula (3) is the active load reduction constraint, where x i,t is the load pickup state; constraint (4) indicates that the load cannot trip again once it is restored; equation (5) represents the voltage drop relationship between adjacent nodes, introducing the slack variable α ij,t , used to cancel the voltage drop relationship when the switch is open, y ij, t is the switch operation state; constraint (6) limits the node voltage, and represents the lower and upper bounds of the voltage amplitude at node i; Equation (7) is the line load constraint, and Indicates the upper limit of active and reactive line flow.
[0099] Formula (8) is the MT active power generation and ramp constraint, and (9) is the MT reactive power generation constraint, where and Indicates the maximum MT active and reactive power generation, P i MT,ru and represents the climbing limit and descending limit of MT active power generation; Equation (10) calculates the active power generation of WT and PV and constrains the reactive power generation, Represents the predicted active power generation of renewable energy generators, represents the maximum reactive power generation of renewable energy generators; Formula (11) is the reactive power generation constraint of capacitor banks, Indicates the maximum reactive power generation of the capacitor bank.
[0100] Formula (12) is the switch operation times constraint, K t represents the maximum number of switch operations per time step; constraint (13) indicates that the switch cannot be opened once it is closed; constraint (14) is a radial constraint, which ensures the radial system topology of each island during the recovery process, n b and n bs is the number of partitions and the number of source partitions, r z,t is the regional power-on state quantity; constraint (15) is the fictitious line power flow balance equation, which is used to ensure that each island has an interconnected system; equation (16) is the virtual line load limit, Indicates the maximum virtual line power.
[0101] When the DS load is restored, emergency power support can be provided by dispatching MEG. The MEG shutdown or startup state transition constraint is expressed by equation (17), where is the MEG spatiotemporal state quantity, represents the active power generation of MEG. (17a) defines the initial state of MEG as stopping at each power station at the initial time step, (17b) indicates that each MEG has only one spatial state at each time step, (17c) indicates that MEG cannot stop at two different stations without moving in adjacent time steps, (17d) and (17e) represent the spatial state relationship between the previous time step and the current time step, and (17f) limits the active power generation of MEG.
[0102] Formula (18) is the ESS operation constraint, where Indicates ESS charging / discharging power, P i EC,max , P i ED,max Indicates the maximum charging / discharging power of ESS, Indicates the SOC level of ESS, Indicates the initial SOC level of ESS, Indicates the minimum / high SOC level of ESS, represents the charging / discharging efficiency. (18a) limits the ESS charging and discharging power, (18b) indicates that the ESS can only operate in one of the following working modes: charging, discharging, and idling. (18c) calculates the ESS state of charge (SoC) level and constrains it in (18d). (18e) represents the ESS reactive power generation constraint.
[0103] In the present invention, the DS sequential load restoration problem is expressed as a mixed integer nonlinear programming (MINLP) model in the above equations (1)-(18). To facilitate the calculation and solution, the McCormick envelope is used to calculate the bilinear terms in the objective function. Perform linearization:
[0104] Introduce an auxiliary variable w i,t The bilinear terms are replaced and a set of linear constraints are added to the model to ensure the convexity of the model while minimizing the new feasible region (maintaining a sufficiently tight boundary), thus converting the MINLP model into a mixed integer quadratic programming (MIQCP) model.
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[0106] in, Indicates the minimum and maximum emergency active power.
[0107] Step S4, construct the MGs dispatch optimization model and constraint conditions of the microgrid, and its objective function is expressed as:
[0108] in and represents the cost coefficient associated with MT power generation, is the secondary cost coefficient of load reduction, is the load shedding linear cost coefficient, and It represents the active load shedding in the mth MG and the active generation in the MT at time step t.
[0109] Each MG consists of dispatchable MTs and controllable loads, and operators can reserve and support emergency energy for DS by reducing non-critical loads. Based on the transaction control framework in the present invention, each MG operator can optimize its operating costs according to the clearing price and determine the amount of power exchange.
[0110] The MG dispatch optimization model in the present invention aims to minimize the MT generation cost and load shedding cost by utilizing the revenue from the sold emergency energy, and furthermore, applies a quadratic cost function to the MT operation.
[0111] Step S5, establish a two-level dispatch model of distributed energy storage DS with microgrid MGs, and the microgrid output power expression is as follows (23):
[0112] In step S5, during the load recovery process, the MG operator optimizes its own goals according to the clearing price and determines the emergency power exchange. Thus, the MGs scheduling optimization model is embedded in the sequential load recovery model of the DS, and the clearing price λ is determined. t As the input of the lower-level MGs dispatch optimization model, the MGs dispatch optimization model is solved to obtain the power exchange amount As the input of the upper model, the sequential load restoration problem of DS with MG based on transactional control is therefore formulated as a two-level optimization model.
[0113] Step S6, solve the two-level dispatch model and obtain the optimal solution as the sequential load recovery method. Further, the step S6 specifically includes: by deriving the KKT condition, the MGs dispatch optimization model of the lower layer can be replaced, so as to reconstruct the original two-layer model into a single-layer model for solution. Further, the step S6 also includes: using the big M method to further replace the complementary relaxation condition with a set of binary variable linear equations.
[0114] Specifically, by deriving the Karush-Kuhn-Tucker (KKT) condition, the lower-level MG optimization model can be replaced, thereby reconstructing the original two-layer model into a single-layer model for solution. Since the MG scheduling problem is expressed as a convex quadratic programming optimization model, its optimal solution can be guaranteed by solving its KKT condition. In addition, due to the existence of nonlinear complementary relaxation conditions, the single-layer optimization model is still difficult to solve. The complementary relaxation conditions can be further replaced by a set of binary variable linear equations using the Big M method.
[0115] The present invention also provides a distributed power grid sequential load recovery system based on transactional control. The system is established and operated on the basis of a computer system, and specifically includes a memory 61, a processor 62, and a computer program 63 stored in the memory 61 and executable on the processor 62. When the processor 62 executes the computer program 63, the steps of the distributed power grid sequential load recovery method based on transactional control as described in any one of the above items are implemented.
[0116] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the computer.
[0117] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.
[0118] The memory may be an internal storage unit, such as a hard disk or a memory; the memory may also be an external storage device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the memory may include both an internal storage unit and an external storage device. The memory is used to store the computer program and other programs and data required by the terminal device. The memory may also be used to temporarily store data that has been output or is to be output.
[0119] The present invention also provides a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the steps of the distributed power grid sequential load recovery method based on transactional control as described in any one of the above items are implemented.
[0120] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.
[0121] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.
[0122] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.
[0123] In the embodiments provided by the present invention, it should be understood that the disclosed devices / systems and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.
[0124] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
[0125] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.
[0126] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.
[0127] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.
[0128] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.
Claims
1. A distributed power grid sequential load restoration method based on transaction control, characterized in that: The following steps are involved: Step S1: Determine the clearing price λ for power exchange between distributed energy storage DS and microgrid MGs at each time step according to the operating status of the distribution system. t ; Step S2, with the goal of minimizing the operating cost of the microgrid MGs, solve the power exchange amount transmitted at each time step Step S3, constructing a sequential load recovery model of distributed energy storage DS and its constraints, wherein the distributed energy storage includes a dispatchable micro-turbine MT, and the objective function of the DS sequential load recovery model is: in, The objective function representing the weighted amount of load pickup, represents the objective function of MT power generation cost, represents the objective function of the cost of renewable power curtailment, represents the objective function of the cost of purchasing emergency energy, as well as are the coefficients related to load picking, MT generation cost and renewable energy curtailment cost, respectively. is the active load shedding amount of node i, and represents the active power demand and MT active power generation of system node i at time step t, represents the active power generation of renewable energy generators at system node i at time step t, is a bilinear term; Step S4, construct the MGs dispatch optimization model and constraint conditions of the microgrid, and its objective function is expressed as: in and represents the cost coefficient associated with MT power generation, is the secondary cost coefficient of load reduction, is the load shedding linear cost coefficient, and represents the active load shedding in the mth MG and the active generation in the MT at time step t; Step S5, establish a two-level dispatch model of distributed energy storage DS with microgrid MGs, and the microgrid output power expression is as follows (23): Step S6, solving the two-level dispatch model to obtain the optimal solution as the sequential load recovery method.
2. The distributed power grid sequential load restoration method based on transactional control according to claim 1 is characterized in that: The distributed energy storage also includes photovoltaic generators PVs, wind turbines WTs, energy storage systems ESSs and mobile emergency generators MEGs. The constraint conditions in step S3 are one or more of the following conditions (2a)-(18e): in, and Indicates the active and reactive power generation of DER, P ij,t and Q ij,t represents the active and reactive power flow between nodes, x i,t is the load picking state quantity, α ij,t is a slack variable used to cancel the voltage drop relationship when the switch is turned on, y ij,t is the switch operation state quantity, and represents the lower and upper bounds of the voltage amplitude at node i, and represents the upper limit of active and reactive line flows, and Indicates the maximum MT active and reactive power generation, P i MT,ru and Indicates the MT active power generation climbing limit and descending limit. Represents the predicted active power generation of renewable energy generators, Indicates the maximum reactive power generation of renewable energy generators. Indicates the maximum reactive power generation of the capacitor bank, K t represents the maximum number of switching operations per time step, n b and n bs is the number of partitions and the number of source partitions, r z,t is the regional power-on state quantity, Indicates the maximum virtual line power, is the MEG spatiotemporal state quantity, Indicates the active power generation of MEG, Indicates ESS charging / discharging power, P i EC,max , P i ED,max Indicates the maximum charging / discharging power of ESS, Indicates the SOC level of ESS, Indicates the initial SOC level of ESS, Indicates the minimum / high SOC level of ESS, Indicates the charge / discharge efficiency.
3. The distributed power grid sequential load restoration method based on transactional control according to claim 2, characterized in that: The constraint conditions in step S3 also include: using the McCormick envelope to constrain the bilinear terms in the objective function Perform linearization and introduce an auxiliary variable w i,t Replace the bilinear terms and add a set of linear constraints to the model, as shown in Equation (19) in, Indicates the minimum and maximum emergency active power.
4. The distributed power grid sequential load restoration method based on transactional control according to claim 1, characterized in that: The constraints in step S4 include one or more of the following: st 5. The distributed power grid sequential load restoration method based on transactional control according to claim 1, characterized in that: The step S6 specifically includes: by deriving the KKT condition, the lower-layer MGs scheduling optimization model can be replaced, thereby reconstructing the original double-layer model into a single-layer model for solving.
6. The distributed power grid sequential load restoration method based on transactional control according to claim 5, characterized in that: The step S6 also includes: using the big-M method to further replace the complementary relaxation conditions into a set of binary variable linear equations.
7. A distributed power grid sequential load restoration system based on transaction control, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the distributed power grid sequential load recovery method based on transactional control as described in any one of claims 1 to 6 when executing the computer program.
8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the distributed power grid sequential load restoration method based on transactional control are implemented as described in any one of claims 1 to 6.