WO2025213487A1 - 一种配电系统灾后恢复源网荷储协同调度优化方法及系统 - Google Patents

一种配电系统灾后恢复源网荷储协同调度优化方法及系统

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Publication number
WO2025213487A1
WO2025213487A1 PCT/CN2024/087755 CN2024087755W WO2025213487A1 WO 2025213487 A1 WO2025213487 A1 WO 2025213487A1 CN 2024087755 W CN2024087755 W CN 2024087755W WO 2025213487 A1 WO2025213487 A1 WO 2025213487A1
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distribution system
node
network topology
time
load
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French (fr)
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叶宇剑
吴奕之
胡灏
钱思琪
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Southeast University
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Southeast University
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06313—Resource planning in a project environment
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for AC mains or AC distribution networks
    • H02J3/001—Arrangements for handling faults or abnormalities, e.g. emergencies or contingencies
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for AC mains or AC distribution networks
    • H02J3/007—Arrangements for selectively connecting one or more loads to one or more power sources or power lines
    • H02J3/0075—Arrangements for selectively connecting one or more loads to one or more power sources or power lines for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for AC mains or AC distribution networks
    • H02J3/04—Arrangements for connecting networks of the same frequency but supplied from different sources
    • H02J3/06—Controlling the transfer of power between connected networks; Controlling load sharing between connected networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for AC mains or AC distribution networks
    • H02J3/17—Demand-responsive operation of AC power transmission or distribution networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for AC mains or AC distribution networks
    • H02J3/28—Arrangements for balancing of the load in networks by storage of energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for AC mains or AC distribution networks
    • H02J3/38—Arrangements for feeding a single network from two or more generators or sources in parallel; Arrangements for feeding already energised networks from additional generators or sources in parallel
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2101/00—Supply or distribution of decentralised, dispersed or local electric power generation
    • H02J2101/20—Dispersed power generation using renewable energy sources
    • H02J2101/22—Solar energy
    • H02J2101/24—Photovoltaics
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2101/00—Supply or distribution of decentralised, dispersed or local electric power generation
    • H02J2101/20—Dispersed power generation using renewable energy sources
    • H02J2101/28—Wind energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2103/00—Details of circuit arrangements for mains or AC distribution networks
    • H02J2103/30—Simulating, planning, modelling, reliability check or computer assisted design [CAD] of electric power networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2103/00—Details of circuit arrangements for mains or AC distribution networks
    • H02J2103/30—Simulating, planning, modelling, reliability check or computer assisted design [CAD] of electric power networks
    • H02J2103/35—Grid-level management of power transmission or distribution systems, e.g. load flow analysis or active network management

Definitions

  • the present invention aims to provide a power distribution system
  • the post-disaster recovery source-grid-load-storage coordinated scheduling optimization method and system reduces the economic losses caused by disasters by coordinating and controlling the grid topology, power supply equipment, load equipment and energy storage equipment in the distribution system until all faults in the distribution system are repaired and the power supply capacity of the distribution system is restored to the pre-disaster level.
  • the purpose of the present invention can be achieved by the following technical solution: a method for optimizing source-grid-load-storage coordinated scheduling for post-disaster recovery of a power distribution system, the method comprising the following steps:
  • the network topology constraint of the power distribution system includes a virtual network topology constraint of the power distribution system, a geometric network topology constraint of the power distribution system, and an electrical network topology constraint of the power distribution system;
  • Distribution system equipment operating characteristic constraint data based on the fault conditions of power supply equipment, load equipment, and energy storage equipment caused by the disaster, where the distribution system equipment operating characteristic constraint data includes operating characteristic constraints of power supply equipment, load equipment, and energy storage equipment;
  • the network topology constraint data of the distribution system and the distribution system equipment operating characteristic constraint data are input into a pre-established distribution system source-grid-load-storage collaborative scheduling optimization model for post-disaster recovery scenarios, and the collaborative scheduling optimization results are output.
  • the method further includes: a process for acquiring the network topology constraints of the power distribution system is as follows: receiving connectivity constraints and radiation constraints that the network topology needs to satisfy when the power distribution system operates normally, and generating a virtual network topology constraint of the power distribution system based on the connectivity constraints and the radiation constraints;
  • the connectivity constraints of the virtual topology of the distribution system are relaxed to generate the geometric network topology constraints of the distribution system.
  • the electrical network topology constraints of the distribution system are generated.
  • the virtual network topology constraints of the distribution system, the geometric network topology constraints of the distribution system and the electrical network topology constraints of the distribution system are integrated to generate the network topology constraints of the distribution system.
  • the method further includes: constructing the connectivity constraint based on a virtual commodity flow model, wherein the connectivity constraint is formulated as follows:
  • IN is the number of nodes in the distribution system excluding the main grid substation nodes.
  • the method further includes: relaxing the connectivity constraints of the virtual topology of the power distribution system, and generating geometric network topology constraints for the power distribution system, taking into account line failures in the post-disaster power distribution system, and performing the process based on a subgraph generation principle, wherein the constraints constructed by considering line failures in the post-disaster power distribution system are as follows:
  • c i,j,t is the on/off state variable of the physical line (i,j) at time t; is the period during which the physical line (i, j) is disconnected due to a fault; is the period during which the physical line (i, j) is not faulty or has been repaired and can operate normally;
  • L U is the set of lines that are not equipped with switching devices;
  • the method further includes: constructing electrical constraints that the grid voltage, current, and power in the distribution system need to satisfy based on the DistFlow model and in combination with the second-order cone relaxation technique and the large-M relaxation technique, thereby forming the electrical network topology constraints of the distribution system, wherein the electrical constraints that the grid voltage, current, and power in the distribution system need to satisfy are as follows:
  • I is the node number set of the distribution system; p i,t is the net active power demand of the equipment connected to node i in the electrical network topology at time t; p i,j,t is the active power delivered from node i to node j by line (i,j) in the electrical network topology at time t; p j,i,t is the active power delivered from node j to node i by line (j,i) in the electrical network topology at time t.
  • the method further includes: constructing the distribution system equipment operating characteristic constraints for power supply equipment in the distribution system, represented by main grid substations, distributed fossil fuel units, and distributed renewable energy units, in combination with equipment failure conditions caused by disasters, wherein the main grid substation operating characteristic constraints are as follows:
  • IDG is the node number set of the distribution system equipped with distributed fossil fuel units; is the active power output of the distributed fossil fuel unit at node i at time t; is a binary variable representing the fault status of the distributed fossil fuel unit at node i at time t;
  • the distributed fossil fuel unit on node i can The upper limit of the output active power; is the reactive power output by the distributed fossil fuel unit at node i at time t; is the upper limit of reactive power that can be output by the distributed fossil fuel unit at node i;
  • I RES is the node number set of the distribution system equipped with distributed renewable energy units; is the active power output by the distributed renewable energy unit at node i at time t; is a binary variable representing the fault status of the distributed renewable energy unit at node i at time t; is the upper limit of the active power that the distributed renewable energy unit at node i can output at time t.
  • the method further includes: constructing operating characteristic constraints for the load devices in the power distribution system using three different types of load devices in the power distribution system: removable loads, transferable loads, and non-adjustable loads, wherein the constructed removable load operating characteristic constraints are as follows:
  • the active power and reactive power absorbed by the non-adjustable load at node i at time t are given as constants, respectively. and express.
  • the method further includes: the operating characteristic constraints of the energy storage device in the power distribution system are as follows:
  • the method further includes: constructing the pre-established distribution system source-grid-load-storage coordinated dispatch optimization model for post-disaster recovery scenarios with the optimization objective of minimizing economic dispatch costs, and constructing coupled operation constraints for the distribution system source-grid-load-storage, wherein the optimization objective of minimizing economic dispatch costs is as follows:
  • the present invention discloses a source-grid-load-storage coordinated dispatch optimization system for post-disaster recovery of a power distribution system, comprising:
  • a data acquisition module is used to acquire network topology constraint data of the power distribution system, wherein the network topology constraint of the power distribution system includes a virtual network topology constraint of the power distribution system, a geometric network topology constraint of the power distribution system, and an electrical network topology constraint of the power distribution system;
  • a data generation module is used to generate distribution system equipment operating characteristic constraint data based on the fault conditions of power supply equipment, load equipment, and energy storage equipment caused by the disaster, wherein the distribution system equipment operating characteristic constraint data includes operating characteristic constraints of power supply equipment, load equipment, and energy storage equipment;
  • the collaborative scheduling optimization module is used to input the network topology constraint data of the distribution system and the distribution system equipment operating characteristic constraint data into a pre-established distribution system source-grid-load-storage collaborative scheduling optimization model for post-disaster recovery scenarios, and output the collaborative scheduling optimization results.
  • the present invention comprehensively considers the grid topology, power supply equipment, load equipment and
  • the adjustable potential and constraint characteristics of energy storage equipment can fully mobilize all power resources in the distribution system to promote power restoration of important loads;
  • the distribution system topology control optimization model adopted can adapt to various complex post-disaster scenarios and provide the optimal scheduling plan for the corresponding scenarios, with greater robustness.
  • FIG1 is a schematic flow chart of the method of the present invention.
  • FIG2 is a schematic diagram of the workflow of the present invention.
  • FIG3 is a schematic diagram of a normal operating state of a test system used in a specific embodiment of the present invention.
  • FIG4 is a schematic diagram of the operating status of a power distribution system in a test scenario according to a specific embodiment of the present invention.
  • FIG5 is a schematic diagram of the operating status of the power distribution system under the second test scenario according to a specific embodiment of the present invention.
  • FIG6 is a schematic diagram of the operating status of the power distribution system under the third test scenario according to a specific embodiment of the present invention.
  • FIG7 is a schematic diagram of the operating status of the power distribution system under test scenario 4 according to a specific embodiment of the present invention.
  • FIG8 is a schematic diagram of the system structure of the present invention.
  • the distribution system is the section of the power system that connects the power output from the step-down distribution substation (high-voltage distribution substation) to the end user.
  • the distribution system is a power network system composed of various distribution equipment (or components) and distribution facilities that transform voltage and distribute electricity directly to end users.
  • the source-grid-load-storage system mainly consists of three parts: source, grid, and load (storage):
  • the source of "source, grid, load and storage” corresponds to energy storage on the power generation side.
  • Generation-side energy storage primarily refers to the common photovoltaic, wind, and hydropower storage systems. Its primary operating model is to collaborate with thermal power plants and power grids to regulate peak and frequency, generating revenue. This can significantly reduce local curtailment rates of solar and wind power.
  • the "source, grid, load and storage” network corresponds to grid-side energy storage.
  • Grid-side energy storage can be directly used in electrical devices such as computers, mobile phones, and refrigerators. Pumped hydro is a representative example of grid-side energy storage.
  • the grid itself can balance electricity prices. Compared to the power generation side, the grid does not need to seek additional compensation mechanisms for energy storage, such as discounts or subsidies.
  • the load (storage) of “source, grid, load and storage” corresponds to the energy storage on the user side.
  • a method for optimizing source-grid-load-storage coordinated scheduling for post-disaster recovery of a power distribution system comprises the following steps:
  • the network topology constraint of the power distribution system includes a virtual network topology constraint of the power distribution system, a geometric network topology constraint of the power distribution system, and an electrical network topology constraint of the power distribution system;
  • the process of obtaining the network topology constraints of the distribution system is as follows: receiving the connectivity constraints and radiation constraints that the network topology needs to satisfy when the distribution system operates normally, and generating the virtual network topology constraints of the distribution system according to the connectivity constraints and radiation constraints;
  • the connectivity constraints of the virtual topology of the distribution system are relaxed to generate the geometric network topology constraints of the distribution system.
  • the electrical network topology constraints of the distribution system are generated.
  • the virtual network topology constraints of the distribution system, the geometric network topology constraints of the distribution system and the electrical network topology constraints of the distribution system are integrated to generate the network topology constraints of the distribution system.
  • the connectivity constraint is constructed based on the virtual commodity flow model, wherein the formula of the connectivity constraint is as follows:
  • IN is the number of nodes in the distribution system excluding the main grid substation nodes.
  • the connectivity constraints of the virtual topology of the power distribution system are relaxed.
  • the line failure conditions of the post-disaster power distribution system need to be considered. This is done based on the principle of generating subgraphs.
  • the constraints constructed considering the line failure conditions of the post-disaster power distribution system are as follows:
  • c i,j,t is the on/off state variable of the physical line (i,j) at time t; is the period during which the physical line (i, j) is disconnected due to a fault; is the period during which the physical line (i, j) is not faulty or has been repaired and can operate normally;
  • L U is the set of lines that are not equipped with switching devices;
  • the electrical network topology constraints of the distribution system are constructed based on the DistFlow model and combined with the second-order cone relaxation technique and the large-M relaxation technique.
  • the electrical constraints that the grid voltage, current, and power in the distribution system must satisfy are constructed, thereby forming the electrical network topology constraints of the distribution system.
  • the electrical constraints that the grid voltage, current, and power in the distribution system must satisfy are as follows:
  • I is the node number set of the distribution system; p i,t is the net active power demand of the equipment connected to node i in the electrical network topology at time t; p i,j,t is the active power delivered from node i to node j by line (i,j) in the electrical network topology at time t; p j,i,t is the active power delivered from node j to node i by line (j,i) in the electrical network topology at time t.
  • Active power is the square of the current on line (j, i) in the electrical network topology at time t; ri ,j is the resistance of line (i, j); qi ,t is the net reactive power demand of the equipment connected to node i in the electrical network topology at time t; qi ,j,t is the reactive power delivered from line (i, j) at node i to node j in the electrical network topology at time t; qj ,i,t is the reactive power delivered from line (j, i) at node j to node i in the electrical network topology at time t; xi ,j is the reactance of line (i, j); is the square of the voltage on node i in the electrical network topology at time t; is the square of the voltage at node j in the electrical network topology at time t;
  • 2 is the two-norm operator.
  • Distribution system equipment operating characteristic constraint data based on the fault conditions of power supply equipment, load equipment, and energy storage equipment caused by the disaster, where the distribution system equipment operating characteristic constraint data includes operating characteristic constraints of power supply equipment, load equipment, and energy storage equipment;
  • the operating characteristic constraints for distribution system equipment are constructed for power equipment in the distribution system, such as main grid substations, distributed fossil fuel units, and distributed renewable energy units, taking into account equipment failures caused by disasters.
  • the operating characteristic constraints for main grid substations are as follows:
  • IDG is the node number set of the distribution system equipped with distributed fossil fuel units; is the active power output of the distributed fossil fuel unit at node i at time t; is a binary variable representing the fault status of the distributed fossil fuel unit at node i at time t. It takes 1 when the distributed fossil fuel unit can output power normally, and takes 0 otherwise. is the upper limit of the active power that can be output by the distributed fossil fuel unit at node i; is the reactive power output by the distributed fossil fuel unit at node i at time t; is the upper limit of reactive power that can be output by the distributed fossil fuel unit at node i;
  • I RES is the node number set of the distribution system equipped with distributed renewable energy units; is the active power output by the distributed renewable energy unit at node i at time t; is a binary variable representing the fault status of the distributed renewable energy unit at node i at time t. It takes 1 if the distributed renewable energy unit can output power normally, and 0 otherwise. is the upper limit of the active power that the distributed renewable energy unit at node i can output at time t.
  • the distribution system uses three different types of load devices: removable loads, transferable loads, and non-adjustable loads to construct operating characteristic constraints for the load devices in the distribution system.
  • the constructed operating characteristic constraints for removable loads are as follows:
  • the active power and reactive power absorbed by the non-adjustable load at node i at time t are given as constants, respectively. and express.
  • I ES is the node number set of the distribution system equipped with energy storage equipment; is the active power output by the energy storage device at node i at time t; is a binary variable representing the fault state of the energy storage device at node i at time t. When the energy storage device can charge and discharge normally, it takes 1, otherwise it takes 0; is a binary variable representing the charging and discharging state of the energy storage device at node i at time t. It takes 1 when the energy storage device is in the charging state and 0 when it is in the discharging state.
  • the network topology constraint data of the distribution system and the distribution system equipment operating characteristic constraint data are input into a pre-established distribution system source-grid-load-storage collaborative scheduling optimization model for post-disaster recovery scenarios, and the collaborative scheduling optimization results are output.
  • the pre-established distribution system source-grid-load-storage coordinated dispatch optimization model for post-disaster recovery scenarios is constructed by minimizing the economic dispatch cost optimization objective and constructing the coupled operation constraints of the distribution system source-grid-load-storage.
  • the minimization of the economic dispatch cost optimization objective is as follows:
  • This embodiment adopts the dual substation coupled distribution system for testing, and its normal operating state is shown in Figure 3.
  • important load nodes are equipped with distributed power sources or distributed energy storage devices, and the unadjustable load on the nodes is not zero; the unadjustable load on secondary load nodes is zero, so the formation of passive islands is allowed in extreme scenarios.
  • Test scenario 1 is shown in Figure 4.
  • the main grid substation at node 1 cannot supply power normally due to a fault.
  • the load originally supplied by the main grid substation at node 1 is transferred to the main grid substation at node 18 through the interconnection line (10, 19), ensuring sufficient power supply to half of the load nodes in the distribution system.
  • Test scenario 2 is shown in FIG5 . Based on test scenario 1, a fault also occurs on line (14, 15). After optimization by the method proposed in the present invention, in addition to connecting line (10, 19) in test scenario 1, line (7, 17) is also connected, thereby ensuring sufficient power supply to the loads on nodes 15, 16, and 17.
  • Test scenario three follows test scenario one, with the addition of faults on lines (15, 16), (6, 7), and (8, 9).
  • lines (7, 17) and (9, 26) are also connected, forming an island consisting of nodes 6, 7, 16, and 17.
  • the loads on this island are all powered by the distributed generation (DG) at node 16. Due to the small size of the DG, a small amount of load on the island is removed or shifted.
  • DG distributed generation
  • Test scenario four shown in Figure 7, follows test scenario one, but with additional faults on lines (7, 8) and (8, 9). This connects line (9, 26) in addition to line (10, 19) in test scenario one. Node 8 now forms a passive island, with its load completely removed or shifted. It's worth noting that existing technologies typically fail to properly handle scenarios where passive islands are inevitable, mistakenly assuming no feasible scheduling solution exists.
  • Embodiment 2 In the second aspect, as shown in FIG8 , in order to achieve the above-mentioned purpose, the present invention discloses a source-grid-load-storage coordinated dispatch optimization system for post-disaster recovery of a power distribution system, comprising:
  • the data acquisition module is used to acquire the network topology constraint data of the power distribution system, wherein the network topology constraint of the power distribution system includes the virtual network topology constraint of the power distribution system, the geometric network topology constraint of the power distribution system, and the network topology constraint of the power distribution system. Topology constraints and electrical network topology constraints of the power distribution system;
  • a data generation module is used to generate distribution system equipment operating characteristic constraint data based on the fault conditions of power supply equipment, load equipment, and energy storage equipment caused by the disaster, wherein the distribution system equipment operating characteristic constraint data includes operating characteristic constraints of power supply equipment, load equipment, and energy storage equipment;
  • the collaborative scheduling optimization module is used to input the network topology constraint data of the distribution system and the distribution system equipment operating characteristic constraint data into a pre-established distribution system source-grid-load-storage collaborative scheduling optimization model for post-disaster recovery scenarios, and output the collaborative scheduling optimization results.
  • the present invention also provides a computer device, which includes: one or more processors and a memory for storing one or more computer programs; the program includes program instructions, and the processor is used to execute the program instructions stored in the memory.
  • the processor may be a central processing unit (CPU), or other general-purpose processor, digital signal processor (DSP), application specific integrated circuit (ASIC), field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, etc. It is the computing core and control core of the terminal, which is used to implement one or more instructions, specifically for loading and executing one or more instructions in a computer storage medium to implement the above method.
  • the present invention also provides a computer storage medium having a computer program stored thereon, and the computer program executes the above method when executed by a processor.
  • the storage medium may adopt any combination of one or more computer-readable media.
  • the computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium.
  • the computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electrical, magnetic, infrared, or semiconductor system, apparatus or device, or any combination thereof.
  • computer-readable storage media include: an electrical connection with one or more wires, a portable computer Disk, hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above.
  • computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus or device.
  • references to terms such as "one embodiment,” “example,” or “specific example” indicate that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present disclosure.
  • schematic representations of these terms do not necessarily refer to the same embodiment or example.
  • the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

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Abstract

本发明公开了一种配电系统灾后恢复源网荷储协同调度优化方法及系统，涉及电力系统与运筹学技术领域，包括：获取配电系统的网络拓扑约束数据，其中，所述配电系统的网络拓扑约束包括配电系统的虚拟网络拓扑约束、配电系统的几何网络拓扑约束以及配电系统的电气网络拓扑约束；根据灾害引起的电源设备、负荷设备与储能设备的故障情况，生成配电系统设备运行特性约束数据，其中，配电系统设备运行特性约束数据包括电源设备、负荷设备与储能设备的运行特性约束；将配电系统的网络拓扑约束数据和配电系统设备运行特性约束数据输入至预先建立的面向灾后恢复场景的配电系统源网荷储协同调度优化模型内，输出得到协同调度优化结果。

Description

一种配电系统灾后恢复源网荷储协同调度优化方法及系统 技术领域
本发明涉及电力系统与运筹学技术领域，具体的是一种配电系统灾后恢复源网荷储协同调度优化方法及系统。
背景技术
21世纪以来，世界范围内发生的极端事件已造成多起严重的电力系统大面积停电事故。近十几年中，随着新型电力系统建设的不断推进，分布式光伏、风电等可再生能源发电设备，以及虚拟电厂负荷聚合商协调管理的空调、电动汽车等灵活性负荷在配电系统中的比例不断提高，源荷储资源在配电系统中的富集为其面向灾害场景提供了大量潜在的韧性支撑能力。
现有的配电系统灾后恢复调度方法主要存在两方面的问题：一方面，现代配电系统是一个由电网拓扑、电源设备、负荷设备与储能设备组成的有机整体，然而现有方法大多仅聚焦于其中的一类或几类可调控设备，难以充分调动配电系统中的全部电力资源以促进重要负荷的电力恢复；另一方面，传统的配电系统拓扑控制优化技术大多面向正常运行场景，当应用于具有较多设备故障的灾后恢复场景时可能产生违反安全运行要求的环路，而针对灾后恢复场景所提出的新型拓扑控制优化技术则大多假设配电系统中不存在无源孤岛，这种简化会导致一旦现实场景中由于线路断线及设备故障产生了无源孤岛，现有技术所采用的模型将无法给出可行的调度方案。
发明内容
为解决上述背景技术中提到的不足，本发明的目的在于提供一种配电系统 灾后恢复源网荷储协同调度优化方法及系统，通过协调调控配电系统中的电网拓扑、电源设备、负荷设备与储能设备，减小灾害引起的经济损失，直至配电系统中的所有故障得到修复，配电系统的供电能力恢复到灾前水平。
第一方面，本发明的目的可以通过以下技术方案实现：一种配电系统灾后恢复源网荷储协同调度优化方法，方法包括以下步骤：
获取配电系统的网络拓扑约束数据，其中，所述配电系统的网络拓扑约束包括配电系统的虚拟网络拓扑约束、配电系统的几何网络拓扑约束以及配电系统的电气网络拓扑约束；
根据灾害引起的电源设备、负荷设备与储能设备的故障情况，生成配电系统设备运行特性约束数据，其中，配电系统设备运行特性约束数据包括电源设备、负荷设备与储能设备的运行特性约束；
将配电系统的网络拓扑约束数据和配电系统设备运行特性约束数据输入至预先建立的面向灾后恢复场景的配电系统源网荷储协同调度优化模型内，输出得到协同调度优化结果。
结合第一方面，在第一方面的某些实现方式中，该方法还包括：所述配电系统的网络拓扑约束的获取过程如下：接收配电系统正常运行时网络拓扑所需满足的连通性约束与辐射性约束，根据连通性约束与辐射性约束生成配电系统的虚拟网络拓扑约束；
对配电系统虚拟拓扑的连通性约束进行松弛，生成配电系统的几何网络拓扑约束，通过接收配电系统中电网电压、电流与功率所需满足的电气约束，生成配电系统的电气网络拓扑约束，从而将配电系统的虚拟网络拓扑约束、配电系统的几何网络拓扑约束和配电系统的电气网络拓扑约束整合生成配电系统的网络拓扑约束。
结合第一方面，在第一方面的某些实现方式中，该方法还包括：所述连通性约束基于虚拟商品流模型构建，其中，所述连通性约束的公式如下：


式中，i与j为配电系统中的节点编号；IS为配电系统中主网变电站节点的集合；IN为配电系统中除主网变电站节点以外节点的集合；t为决策时刻的编号；T为决策的总时间步数；L为配电系统中的线路集合，每条线路以两端节点编号组成的二维向量形式计入L中，且两端节点编号中小的编号为二维向量的第一维、大的编号为二维向量的第二维；fi,j,t为t时刻虚拟网络拓扑中线路(i,j)上由节点i流向节点j的虚拟商品流量；fj,i,t为t时刻虚拟网络拓扑中线路(j,i)上由节点j流向节点i的虚拟商品流量；di,j,t为t时刻虚拟线路(i,j)的通断状态变量；M为线性松弛系数，数值设置为配电系统中的节点数量；
所述辐射性约束的公式如下：
式中，|IN|为配电系统中除主网变电站节点以外节点的数量。
结合第一方面，在第一方面的某些实现方式中，该方法还包括：所述对配电系统虚拟拓扑的连通性约束进行松弛，生成配电系统的几何网络拓扑约束时需考虑灾后配电系统的线路故障情况，并基于生成子图原理进行，其中，所述考虑灾后配电系统的线路故障情况所构建的约束如下：

式中，ci,j,t为t时刻物理线路(i,j)的通断状态变量；为物理线路(i,j)因故障而处于断开状态的时段；为物理线路(i,j)未故障或已被修复从而能够正常工作的时段；LU为未配有开关设备的线路集合；
所述配电系统的几何网络拓扑约束如下：
结合第一方面，在第一方面的某些实现方式中，该方法还包括：所述配电系统的电气网络拓扑约束基于DistFlow模型并结合二阶锥松弛技术与大M松弛技术，构建配电系统中电网电压、电流与功率所需满足的电气约束，从而形成配电系统的电气网络拓扑约束，其中，配电系统中电网电压、电流与功率所需满足的电气约束如下：




式中，I为配电系统的节点编号集合；pi,t为t时刻电气网络拓扑中节点i所接设备的净有功功率需求；pi,j,t为t时刻电气网络拓扑中线路(i,j)在节点i处送向节点j的有功功率；pj,i,t为t时刻电气网络拓扑中线路(j,i)在节点j处送向节点i的有功功率；为t时刻电气网络拓扑中线路(j,i)上电流的平方；ri,j为线路(i,j)的电阻；qi,t为t时刻电气网络拓扑中节点i所接设备的净无功功率需求；qi,j,t为t时刻电气网络拓扑中线路(i,j)在节点i处送向节点j的无功功率；qj,i,t为t时刻电气网络拓扑中线路(j,i)在节点j处送向节点i的无功功率；xi,j为线路(i,j)的电抗； 为t时刻电气网络拓扑中节点i上电压的平方；为t时刻电气网络拓扑中节点j上电压的平方；||·||2为二范数算符。
结合第一方面，在第一方面的某些实现方式中，该方法还包括：所述配电系统设备运行特性约束针对配电系统中以主网变电站、分布式化石燃料机组、分布式可再生能源机组为代表的电源设备，结合灾害引起的设备故障情况进行构建，其中，主网变电站运行特性约束如下：



其中，为t时刻节点i上主网变电站输出的有功功率；为表征t时刻节点i上主网变电站故障状态的二元变量；为节点i上主网变电站能够输出的有功功率上限；为t时刻节点i上主网变电站输出的无功功率；为节点i上主网变电站能够输出的无功功率上限；
所构建的分布式化石燃料机组运行特性约束如下：



其中，IDG为配有分布式化石燃料机组的配电系统节点编号集合；为t时刻节点i上分布式化石燃料机组输出的有功功率；为表征t时刻节点i上分布式化石燃料机组故障状态的二元变量；为节点i上分布式化石燃料机组能够 输出的有功功率上限；为t时刻节点i上分布式化石燃料机组输出的无功功率；为节点i上分布式化石燃料机组能够输出的无功功率上限；
所构建的分布式可再生能源机组运行特性约束如下：

其中，IRES为配有分布式可再生能源机组的配电系统节点编号集合；为t时刻节点i上分布式可再生能源机组输出的有功功率；为表征t时刻节点i上分布式可再生能源机组故障状态的二元变量；为t时刻节点i上分布式可再生能源机组能够输出的有功功率上限。
结合第一方面，在第一方面的某些实现方式中，该方法还包括：所述配电系统中通过可切除负荷、可转移负荷与不可调负荷三种不同类型的负荷设备，构建配电系统中负荷设备的运行特性约束，其中，所构建的可切除负荷运行特性约束如下：

其中，为t时刻节点i上可切除负荷吸收的有功功率；为t时刻节点i上可切除负荷吸收的有功功率上限；为t时刻节点i上可切除负荷吸收的无功功率；为t时刻节点i上可切除负荷吸收的无功功率上限；
所构建的可转移负荷运行特性约束如下：



其中，为t时刻节点i上可转移负荷的有功功率调节量；为t时刻节点i上可转移负荷的有功功率可调上限；为t时刻节点i上可转移负荷吸收的有功功率；为t时刻节点i上用户对可转移负荷有功功率的原始需求量；为t时刻节点i上可转移负荷吸收的无功功率；为t时刻节点i上用户对可转移负荷无功功率的原始需求量；
所述t时刻节点i上不可调负荷吸收的有功功率与无功功率作为常数给定，分别采用与表示。
结合第一方面，在第一方面的某些实现方式中，该方法还包括：所述配电系统中储能设备的运行特性约束如下：




其中，IES为配有储能设备的配电系统节点编号集合；为t时刻节点i上储能设备输出的有功功率；为表征t时刻节点i上储能设备故障状态的二元变量；为表征t时刻节点i上储能设备充放电状态的二元变量；为节点i上储能设备输出的有功功率上限；为t时刻节点i上储能设备吸收的有功功率；pi ch为节点i上储能设备吸收的有功功率上限；为t时刻决策后节点i上储能设备的剩余电量，0时刻节点i上储能设备的初始剩余电量作为常数给定；与分别为节点i上储能设备的放电效率与充电效率；Δt为决策时间步长；与分别为节点i上储能设备的电量存储上限与电量存储下限。
结合第一方面，在第一方面的某些实现方式中，该方法还包括：所述预先建立的面向灾后恢复场景的配电系统源网荷储协同调度优化模型通过以最小化经济调度成本优化目标进行构建，并构建配电系统源网荷储的耦合运行约束，其中，所述最小化经济调度成本优化目标如下：
其中，与分别为主网变电站、分布式化石燃料机组、可切除负荷与可转移负荷的调节价格；
配电系统源网荷储的耦合运行约束如下：

第二方面，为了达到上述目的，本发明公开了一种配电系统灾后恢复源网荷储协同调度优化系统，包括：
数据获取模块，用于获取配电系统的网络拓扑约束数据，其中，所述配电系统的网络拓扑约束包括配电系统的虚拟网络拓扑约束、配电系统的几何网络拓扑约束以及配电系统的电气网络拓扑约束；
数据生成模块，用于根据灾害引起的电源设备、负荷设备与储能设备的故障情况，生成配电系统设备运行特性约束数据，其中，配电系统设备运行特性约束数据包括电源设备、负荷设备与储能设备的运行特性约束；
协同调度优化模块，用于将配电系统的网络拓扑约束数据和配电系统设备运行特性约束数据输入至预先建立的面向灾后恢复场景的配电系统源网荷储协同调度优化模型内，输出得到协同调度优化结果。
本发明的有益效果：
本发明一方面，综合考虑了配电系统中电网拓扑、电源设备、负荷设备与 储能设备的可调潜力与约束特性，充分调动配电系统中的全部电力资源以促进重要负荷的电力恢复；另一方面，所采用的配电系统拓扑控制优化模型能够适应各类复杂的灾后复杂场景并给出对应场景下的最优调度方案，具有更强的鲁棒性。
附图说明
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图；
图1是本发明方法流程示意图；
图2是本发明工作流程示意图；
图3为本发明具体实施方式所用测试系统的正常运行状态示意图；
图4为本发明具体实施方式测试场景一下配电系统的运行状态示意图；
图5为本发明具体实施方式测试场景二下配电系统的运行状态示意图；
图6为本发明具体实施方式测试场景三下配电系统的运行状态示意图；
图7为本发明具体实施方式测试场景四下配电系统的运行状态示意图；
图8是本发明系统结构示意图。
具体实施方式
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其它实施例，都属于本发明保护的范围。
实施例一：
下面，对本申请实施例涉及的相关术语进行介绍：
配电系统：是将电力系统中从降压配电变电站(高压配电变电站)出口到用户端的这一段系统称为配电系统。配电系统是由多种配电设备(或元件)和配电设施所组成的变换电压和直接向终端用户分配电能的一个电力网络系统。
源网荷储主要由源、网、荷(储)三部分组成：
一、“源网荷储”的源，对应的是发电侧储能。
发电侧储能主要指我们常见的光伏、风电、水电配储，以配合火电厂、电网调峰调频并获收益为主要运营模式。能极大地降低当地的弃光率和弃风率等。
二、“源网荷储”的网，对应的是电网侧储能。
电网侧储能可直接用于电脑、手机、冰箱等用电设备。电网侧储能代表是抽水蓄能。电网可以自己来平衡电价，相较于发电侧，电网侧不需要为储能争取额外的补偿机制，比如电价打折、补贴等。
三、“源网荷储”的荷(储)，对应的是用户侧储能。
通常我们将户用配储、工商业配储、储能充电桩都归为用户侧，主要对象是用电方。
如图1所示，一种配电系统灾后恢复源网荷储协同调度优化方法，其特征在于，方法包括以下步骤：
获取配电系统的网络拓扑约束数据，其中，所述配电系统的网络拓扑约束包括配电系统的虚拟网络拓扑约束、配电系统的几何网络拓扑约束以及配电系统的电气网络拓扑约束；
其中，配电系统的网络拓扑约束的获取过程如下：接收配电系统正常运行时网络拓扑所需满足的连通性约束与辐射性约束，根据连通性约束与辐射性约束生成配电系统的虚拟网络拓扑约束；
对配电系统虚拟拓扑的连通性约束进行松弛，生成配电系统的几何网络拓扑约束，通过接收配电系统中电网电压、电流与功率所需满足的电气约束，生成配电系统的电气网络拓扑约束，从而将配电系统的虚拟网络拓扑约束、配电系统的几何网络拓扑约束和配电系统的电气网络拓扑约束整合生成配电系统的网络拓扑约束。
进一步地，连通性约束基于虚拟商品流模型构建，其中，所述连通性约束的公式如下：


式中，i与j为配电系统中的节点编号；IS为配电系统中主网变电站节点的集合；IN为配电系统中除主网变电站节点以外节点的集合；t为决策时刻的编号；T为决策的总时间步数；L为配电系统中的线路集合，每条线路以两端节点编号组成的二维向量形式计入L中，且两端节点编号中小的编号为二维向量的第一维、大的编号为二维向量的第二维；fi,j,t为t时刻虚拟网络拓扑中线路(i,j)上由节点i流向节点j的虚拟商品流量；fj,i,t为t时刻虚拟网络拓扑中线路(j,i)上由节点j流向节点i的虚拟商品流量；di,j,t为t时刻虚拟线路(i,j)的通断状态变量，当虚拟线路(i,j)联通时为1，当虚拟线路(i,j)断开时为0；M为线性松弛系数，其数值一般可以设置为配电系统中的节点数量；
所述辐射性约束的公式如下：
式中，|IN|为配电系统中除主网变电站节点以外节点的数量。
其中，对配电系统虚拟拓扑的连通性约束进行松弛，生成配电系统的几何网络拓扑约束时需考虑灾后配电系统的线路故障情况，并基于生成子图原理进行，其中，所述考虑灾后配电系统的线路故障情况所构建的约束如下：

式中，ci,j,t为t时刻物理线路(i,j)的通断状态变量；为物理线路(i,j)因故障而处于断开状态的时段；为物理线路(i,j)未故障或已被修复从而能够正常工作的时段；LU为未配有开关设备的线路集合；
所述配电系统的几何网络拓扑约束如下：
配电系统的电气网络拓扑约束基于DistFlow模型并结合二阶锥松弛技术与大M松弛技术，构建配电系统中电网电压、电流与功率所需满足的电气约束，从而形成配电系统的电气网络拓扑约束，其中，配电系统中电网电压、电流与功率所需满足的电气约束如下：




式中，I为配电系统的节点编号集合；pi,t为t时刻电气网络拓扑中节点i所接设备的净有功功率需求；pi,j,t为t时刻电气网络拓扑中线路(i,j)在节点i处送向节点j的有功功率；pj,i,t为t时刻电气网络拓扑中线路(j,i)在节点j处送向节点i 的有功功率；为t时刻电气网络拓扑中线路(j,i)上电流的平方；ri,j为线路(i,j)的电阻；qi,t为t时刻电气网络拓扑中节点i所接设备的净无功功率需求；qi,j,t为t时刻电气网络拓扑中线路(i,j)在节点i处送向节点j的无功功率；qj,i,t为t时刻电气网络拓扑中线路(j,i)在节点j处送向节点i的无功功率；xi,j为线路(i,j)的电抗；为t时刻电气网络拓扑中节点i上电压的平方；为t时刻电气网络拓扑中节点j上电压的平方；||·||2为二范数算符。
根据灾害引起的电源设备、负荷设备与储能设备的故障情况，生成配电系统设备运行特性约束数据，其中，配电系统设备运行特性约束数据包括电源设备、负荷设备与储能设备的运行特性约束；
配电系统设备运行特性约束针对配电系统中以主网变电站、分布式化石燃料机组、分布式可再生能源机组为代表的电源设备，结合灾害引起的设备故障情况进行构建，其中，主网变电站运行特性约束如下：



其中，为t时刻节点i上主网变电站输出的有功功率；为表征t时刻节点i上主网变电站故障状态的二元变量，当主网变电站能够正常输出电量时取1，否则取0；为节点i上主网变电站能够输出的有功功率上限；为t时刻节点i上主网变电站输出的无功功率；为节点i上主网变电站能够输出的无功功率上限；
所构建的分布式化石燃料机组运行特性约束如下：



其中，IDG为配有分布式化石燃料机组的配电系统节点编号集合；为t时刻节点i上分布式化石燃料机组输出的有功功率；为表征t时刻节点i上分布式化石燃料机组故障状态的二元变量，当分布式化石燃料机组能够正常输出电量时取1，否则取0；为节点i上分布式化石燃料机组能够输出的有功功率上限；为t时刻节点i上分布式化石燃料机组输出的无功功率；为节点i上分布式化石燃料机组能够输出的无功功率上限；
所构建的分布式可再生能源机组运行特性约束如下：

其中，IRES为配有分布式可再生能源机组的配电系统节点编号集合；为t时刻节点i上分布式可再生能源机组输出的有功功率；为表征t时刻节点i上分布式可再生能源机组故障状态的二元变量，当分布式可再生能源机组能够正常输出电量时取1，否则取0；为t时刻节点i上分布式可再生能源机组能够输出的有功功率上限。
配电系统中通过可切除负荷、可转移负荷与不可调负荷三种不同类型的负荷设备，构建配电系统中负荷设备的运行特性约束，其中，所构建的可切除负荷运行特性约束如下：

其中，为t时刻节点i上可切除负荷吸收的有功功率；为t时刻节点i上可切除负荷吸收的有功功率上限；为t时刻节点i上可切除负荷吸收的无功功率；为t时刻节点i上可切除负荷吸收的无功功率上限；
所构建的可转移负荷运行特性约束如下：



其中，为t时刻节点i上可转移负荷的有功功率调节量；为t时刻节点i上可转移负荷的有功功率可调上限；为t时刻节点i上可转移负荷吸收的有功功率；为t时刻节点i上用户对可转移负荷有功功率的原始需求量；为t时刻节点i上可转移负荷吸收的无功功率；为t时刻节点i上用户对可转移负荷无功功率的原始需求量；
所述t时刻节点i上不可调负荷吸收的有功功率与无功功率作为常数给定，分别采用与表示。
配电系统中储能设备的运行特性约束如下：




其中，IES为配有储能设备的配电系统节点编号集合；为t时刻节点i上储能设备输出的有功功率；为表征t时刻节点i上储能设备故障状态的二元变 量，当储能设备能够正常充放电时取1，否则取0；为表征t时刻节点i上储能设备充放电状态的二元变量，当储能设备处于充电状态时取1，处于放电状态时取0；为节点i上储能设备输出的有功功率上限；为t时刻节点i上储能设备吸收的有功功率；为节点i上储能设备吸收的有功功率上限；为t时刻决策后节点i上储能设备的剩余电量，0时刻节点i上储能设备的初始剩余电量作为常数给定；与分别为节点i上储能设备的放电效率与充电效率；Δt为决策时间步长；与分别为节点i上储能设备的电量存储上限与电量存储下限。
将配电系统的网络拓扑约束数据和配电系统设备运行特性约束数据输入至预先建立的面向灾后恢复场景的配电系统源网荷储协同调度优化模型内，输出得到协同调度优化结果。
预先建立的面向灾后恢复场景的配电系统源网荷储协同调度优化模型通过以最小化经济调度成本优化目标进行构建，并构建配电系统源网荷储的耦合运行约束，其中，所述最小化经济调度成本优化目标如下：
其中，与分别为主网变电站、分布式化石燃料机组、可切除负荷与可转移负荷的调节价格，分布式可再生能源机组与储能设备的调节成本忽略不计。
配电系统源网荷储的耦合运行约束如下：

具体的，下面通过实施例对本发明方案作进一步阐述：
本实施方式采用双变电站耦合配电系统进行测试，其正常运行状态如图3 所示。其中，重要负荷节点均配有分布式电源或分布式储能设备，节点上的不可调负荷不为0；次要负荷节点上的不可调负荷为0，因此在极端场景下允许形成无源孤岛。
测试场景一如图4所示，节点1的主网变电站因故障而无法正常供电，经本发明所提方法优化后，通过联通线路(10,19)将原先由节点1主网变电站供电的负荷转移至节点18主网变电站下，保障了配电系统中半数负荷节点的充足供电。
测试场景二如图5所示，在测试场景一的基础上，还发生了线路(14,15)的故障，经本发明所提方法优化后，除联通测试场景一中的线路(10,19)外，还联通了线路(7,17)，从而保障了节点15、16、17上负荷的充足供电。
测试场景三如图6所示，在测试场景一的基础上，还发生了线路(15,16)、(6,7)、(8,9)的故障，经本发明所提方法优化后，除联通测试场景一中的线路(10,19)外，还联通了线路(7,17)与线路(9,26)，从而形成了一个由节点6、7、16、17组成的孤岛。该孤岛上的负荷均由节点16上的分布式电源进行供电，由于分布式电源体量较小，因此孤岛的负荷被少量切除或平移。
测试场景四如图7所示，在测试场景一的基础上，还发生了线路(7,8)、(8,9)的故障，除联通测试场景一中的线路(10,19)外，还联通了线路(9,26)，此时节点8单独形成了一个无源孤岛，其负荷被全部切除或平移。值得一提的是，现有技术通常无法正常处理此类必定形成无源孤岛的场景时，将错认为该场景下不存在可行的调度方案。
实施例二：第二方面，如图8所示，为了达到上述目的，本发明公开了一种配电系统灾后恢复源网荷储协同调度优化系统，包括：
数据获取模块，用于获取配电系统的网络拓扑约束数据，其中，所述配电系统的网络拓扑约束包括配电系统的虚拟网络拓扑约束、配电系统的几何网络 拓扑约束以及配电系统的电气网络拓扑约束；
数据生成模块，用于根据灾害引起的电源设备、负荷设备与储能设备的故障情况，生成配电系统设备运行特性约束数据，其中，配电系统设备运行特性约束数据包括电源设备、负荷设备与储能设备的运行特性约束；
协同调度优化模块，用于将配电系统的网络拓扑约束数据和配电系统设备运行特性约束数据输入至预先建立的面向灾后恢复场景的配电系统源网荷储协同调度优化模型内，输出得到协同调度优化结果。
基于同一种发明构思，本发明还提供一种计算机设备，该计算机设备包括：一个或多个处理器，以及存储器，用于存储一个或多个计算机程序；程序包括程序指令，处理器用于执行存储器存储的程序指令。处理器可能是中央处理单元(Central Processing Unit，CPU)，还可以是其他通用处理器、数字信号处理器(Digital Signal Processor、DSP)、专用集成电路(Application SpecificIntegrated Circuit，ASIC)、现场可编程门阵列(Field-Programmable GateArray，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等，其是终端的计算核心以及控制核心，其用于实现一条或一条以上指令，具体用于加载并执行计算机存储介质内一条或一条以上指令从而实现上述方法。
需要进一步进行说明的是，基于同一种发明构思，本发明还提供一种计算机存储介质，该存储介质上存储有计算机程序，所述计算机程序被处理器运行时执行上述方法。该存储介质可以采用一个或多个计算机可读的介质的任意组合。计算机可读介质可以是计算机可读信号介质或者计算机可读存储介质。计算机可读存储介质例如可以是但不限于电、磁、光、电、磁、红外线、或半导体的系统、装置或器件，或者任意以上的组合。计算机可读存储介质的更具体的例子(非穷举的列表)包括：具有一个或多个导线的电连接、便携式计算机 磁盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(EPROM或闪存)、光纤、便携式紧凑磁盘只读存储器(CD-ROM)、光存储器件、磁存储器件、或者上述的任意合适的组合。在本发明中，计算机可读存储介质可以是任何包含或存储程序的有形介质，该程序可以被指令执行系统、装置或者器件使用或者与其结合使用。
在本说明书的描述中，参考术语“一个实施例”、“示例”、“具体示例”等的描述意指结合该实施例或示例描述的具体特征、结构、材料或者特点包含于本公开的至少一个实施例或示例中。在本说明书中，对上述术语的示意性表述不一定指的是相同的实施例或示例。而且，描述的具体特征、结构、材料或者特点可以在任何的一个或多个实施例或示例中以合适的方式结合。
以上显示和描述了本公开的基本原理、主要特征和本公开的优点。本行业的技术人员应该了解，本公开不受上述实施例的限制，上述实施例和说明书中描述的只是说明本公开的原理，在不脱离本公开精神和范围的前提下，本公开还会有各种变化和改进，这些变化和改进都落入要求保护的本公开范围内容。

Claims (10)

  1. 一种配电系统灾后恢复源网荷储协同调度优化方法，其特征在于，方法包括以下步骤：
    获取配电系统的网络拓扑约束数据，其中，所述配电系统的网络拓扑约束包括配电系统的虚拟网络拓扑约束、配电系统的几何网络拓扑约束以及配电系统的电气网络拓扑约束；
    根据灾害引起的电源设备、负荷设备与储能设备的故障情况，生成配电系统设备运行特性约束数据，其中，配电系统设备运行特性约束数据包括电源设备、负荷设备与储能设备的运行特性约束；
    将配电系统的网络拓扑约束数据和配电系统设备运行特性约束数据输入至预先建立的面向灾后恢复场景的配电系统源网荷储协同调度优化模型内，输出得到协同调度优化结果。
  2. 根据权利要求1所述的一种配电系统灾后恢复源网荷储协同调度优化方法，其特征在于，所述配电系统的网络拓扑约束的获取过程如下：接收配电系统正常运行时网络拓扑所需满足的连通性约束与辐射性约束，根据连通性约束与辐射性约束生成配电系统的虚拟网络拓扑约束；
    对配电系统虚拟拓扑的连通性约束进行松弛，生成配电系统的几何网络拓扑约束，通过接收配电系统中电网电压、电流与功率所需满足的电气约束，生成配电系统的电气网络拓扑约束，从而将配电系统的虚拟网络拓扑约束、配电系统的几何网络拓扑约束和配电系统的电气网络拓扑约束整合生成配电系统的网络拓扑约束。
  3. 根据权利要求2所述的一种配电系统灾后恢复源网荷储协同调度优化方法，其特征在于，所述连通性约束基于虚拟商品流模型构建，其中，所述连通 性约束的公式如下：


    式中，i与j为配电系统中的节点编号；IS为配电系统中主网变电站节点的集合；IN为配电系统中除主网变电站节点以外节点的集合；t为决策时刻的编号；T为决策的总时间步数；L为配电系统中的线路集合，每条线路以两端节点编号组成的二维向量形式计入L中，且两端节点编号中小的编号为二维向量的第一维、大的编号为二维向量的第二维；fi,j,t为t时刻虚拟网络拓扑中线路(i,j)上由节点i流向节点j的虚拟商品流量；fj,i,t为t时刻虚拟网络拓扑中线路(j,i)上由节点j流向节点i的虚拟商品流量；di,j,t为t时刻虚拟线路(i,j)的通断状态变量；M为线性松弛系数；
    所述辐射性约束的公式如下：
    式中，|IN|为配电系统中除主网变电站节点以外节点的数量。
  4. 根据权利要求3所述的一种配电系统灾后恢复源网荷储协同调度优化方法，其特征在于，所述对配电系统虚拟拓扑的连通性约束进行松弛，生成配电系统的几何网络拓扑约束时需考虑灾后配电系统的线路故障情况，并基于生成子图原理进行，其中，所述考虑灾后配电系统的线路故障情况所构建的约束如下：

    式中，ci,j,t为t时刻物理线路(i,j)的通断状态变量；为物理线路(i,j)因故障而处于断开状态的时段；为物理线路(i,j)未故障或已被修复从而能够正常工作的时段；LU为未配有开关设备的线路集合；
    所述配电系统的几何网络拓扑约束如下：
  5. 根据权利要求2所述的一种配电系统灾后恢复源网荷储协同调度优化方法，其特征在于，所述配电系统的电气网络拓扑约束基于DistFlow模型并结合二阶锥松弛技术与大M松弛技术，构建配电系统中电网电压、电流与功率所需满足的电气约束，从而形成配电系统的电气网络拓扑约束，其中，配电系统中电网电压、电流与功率所需满足的电气约束如下：




    式中，I为配电系统的节点编号集合；pi,t为t时刻电气网络拓扑中节点i所接设备的净有功功率需求；pi,j,t为t时刻电气网络拓扑中线路(i,j)在节点i处送向节点j的有功功率；pj,i,t为t时刻电气网络拓扑中线路(j,i)在节点j处送向节点i的有功功率；为t时刻电气网络拓扑中线路(j,i)上电流的平方；ri,j为线路(i,j)的电阻；qi,t为t时刻电气网络拓扑中节点i所接设备的净无功功率需求；qi,j,t为t时刻电气网络拓扑中线路(i,j)在节点i处送向节点j的无功功率；qj,i,t为t时刻电气网络拓扑中线路(j,i)在节点j处送向节点i的无功功率；xi,j为线路(i,j)的电抗； 为t时刻电气网络拓扑中节点i上电压的平方；为t时刻电气网络拓扑中节点j上电压的平方；||·||2为二范数算符。
  6. 根据权利要求1所述的一种配电系统灾后恢复源网荷储协同调度优化方法，其特征在于，所述配电系统设备运行特性约束针对配电系统中以主网变电站、分布式化石燃料机组、分布式可再生能源机组为代表的电源设备，结合灾害引起的设备故障情况进行构建，其中，主网变电站运行特性约束如下：



    其中，为t时刻节点i上主网变电站输出的有功功率；为表征t时刻节点i上主网变电站故障状态的二元变量；为节点i上主网变电站能够输出的有功功率上限；为t时刻节点i上主网变电站输出的无功功率；为节点i上主网变电站能够输出的无功功率上限；
    所构建的分布式化石燃料机组运行特性约束如下：



    其中，IDG为配有分布式化石燃料机组的配电系统节点编号集合；为t时刻节点i上分布式化石燃料机组输出的有功功率；为表征t时刻节点i上分布式化石燃料机组故障状态的二元变量；为节点i上分布式化石燃料机组能够 输出的有功功率上限；为t时刻节点i上分布式化石燃料机组输出的无功功率；为节点i上分布式化石燃料机组能够输出的无功功率上限；
    所构建的分布式可再生能源机组运行特性约束如下：

    其中，IRES为配有分布式可再生能源机组的配电系统节点编号集合；为t时刻节点i上分布式可再生能源机组输出的有功功率；为表征t时刻节点i上分布式可再生能源机组故障状态的二元变量；为t时刻节点i上分布式可再生能源机组能够输出的有功功率上限。
  7. 根据权利要求6所述的一种配电系统灾后恢复源网荷储协同调度优化方法，其特征在于，所述配电系统中通过可切除负荷、可转移负荷与不可调负荷三种不同类型的负荷设备，构建配电系统中负荷设备的运行特性约束，其中，所构建的可切除负荷运行特性约束如下：

    其中，为t时刻节点i上可切除负荷吸收的有功功率；为t时刻节点i上可切除负荷吸收的有功功率上限；为t时刻节点i上可切除负荷吸收的无功功率；为t时刻节点i上可切除负荷吸收的无功功率上限；
    所构建的可转移负荷运行特性约束如下：



    其中，为t时刻节点i上可转移负荷的有功功率调节量；为t时刻节点i上可转移负荷的有功功率可调上限；为t时刻节点i上可转移负荷吸收的有功功率；为t时刻节点i上用户对可转移负荷有功功率的原始需求量；为t时刻节点i上可转移负荷吸收的无功功率；为t时刻节点i上用户对可转移负荷无功功率的原始需求量；
    所述t时刻节点i上不可调负荷吸收的有功功率与无功功率作为常数给定，分别采用与表示。
  8. 根据权利要求7所述的一种配电系统灾后恢复源网荷储协同调度优化方法，其特征在于，所述配电系统中储能设备的运行特性约束如下：




    其中，IES为配有储能设备的配电系统节点编号集合；为t时刻节点i上储能设备输出的有功功率；为表征t时刻节点i上储能设备故障状态的二元变量；为表征t时刻节点i上储能设备充放电状态的二元变量；为节点i上储能设备输出的有功功率上限；为t时刻节点i上储能设备吸收的有功功率；为节点i上储能设备吸收的有功功率上限；为t时刻决策后节点i上储能设备的剩余电量，0时刻节点i上储能设备的初始剩余电量作为常数给定；与分别为节点i上储能设备的放电效率与充电效率；Δt为决策时间步长；与分别为节点i上储能设备的电量存储上限与电量存储下限。
  9. 根据权利要求1所述的一种配电系统灾后恢复源网荷储协同调度优化方法，其特征在于，所述预先建立的面向灾后恢复场景的配电系统源网荷储协同调度优化模型通过以最小化经济调度成本优化目标进行构建，并构建配电系统源网荷储的耦合运行约束，其中，所述最小化经济调度成本优化目标如下：
    其中，与分别为主网变电站、分布式化石燃料机组、可切除负荷与可转移负荷的调节价格；
    配电系统源网荷储的耦合运行约束如下：

  10. 一种配电系统灾后恢复源网荷储协同调度优化系统，其特征在于，包括：
    数据获取模块，用于获取配电系统的网络拓扑约束数据，其中，所述配电系统的网络拓扑约束包括配电系统的虚拟网络拓扑约束、配电系统的几何网络拓扑约束以及配电系统的电气网络拓扑约束；
    数据生成模块，用于根据灾害引起的电源设备、负荷设备与储能设备的故障情况，生成配电系统设备运行特性约束数据，其中，配电系统设备运行特性约束数据包括电源设备、负荷设备与储能设备的运行特性约束；
    协同调度优化模块，用于将配电系统的网络拓扑约束数据和配电系统设备运行特性约束数据输入至预先建立的面向灾后恢复场景的配电系统源网荷储协同调度优化模型内，输出得到协同调度优化结果。
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