CN112436512B - Power distribution network optimization method and device - Google Patents

Power distribution network optimization method and device Download PDF

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CN112436512B
CN112436512B CN202011313911.1A CN202011313911A CN112436512B CN 112436512 B CN112436512 B CN 112436512B CN 202011313911 A CN202011313911 A CN 202011313911A CN 112436512 B CN112436512 B CN 112436512B
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loss value
network loss
current
state group
current state
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CN112436512A (en
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霍思宇
迟永生
冯毅
蔡超
冯庆
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China United Network Communications Group Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR 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; CALCULATING OR 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—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—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 a network by storage of energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—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 parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/48—Controlling the sharing of the in-phase component
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—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 parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/50—Controlling the sharing of the out-of-phase component
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/10—The dispersed energy generation being of fossil origin, e.g. diesel generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/30—The power source being a fuel cell
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The embodiment of the invention discloses a method and a device for optimizing a power distribution network, and relates to the field of power grids. The problem of relatively poor applicability of the prior art can be solved. The method comprises the following steps: determining the optimal result of the power distribution network by using the acquired characteristic parameters in the power distribution network and the current state of the switch between at least one node by using a double-layer optimization algorithm; the current state of the switch between the nodes is an opening state or a closing state; the characteristic parameters at least comprise: each branch circuit impedance, each node load, each conventional unit output force and voltage phase angle difference; the optimal result comprises a minimum network loss value in the power distribution network, a current state group corresponding to the minimum network loss value and active power output injected by each node corresponding to the minimum network loss value; the current state group includes a current open and closed state of at least one inter-node switch in the power distribution network. The embodiment of the invention is applied to the power system.

Description

Power distribution network optimization method and device
Technical Field
The embodiment of the invention relates to the field of power grids, in particular to a method and a device for optimizing a power distribution network.
Background
With the large-scale grid connection of the distributed power supply and the large-scale access of the electric equipment under the characteristics of large bandwidth, low time delay and wide connection of 5G, the traditional passive power distribution network gradually evolves into an active power distribution network, and the power flow distribution is greatly changed, so that the stable and safe operation of the active power distribution network jointly formed by the loads of the active power distribution network is influenced, and a new requirement is provided for the coordination and optimization of the loads of the active power distribution network.
The existing optimization method of the power distribution network only establishes an optimization model according to a fixed topological structure of the power distribution network, and when the topological structure of the power distribution network changes, optimization processing cannot be carried out, so that the method is poor in applicability.
Disclosure of Invention
The invention provides a method and a device for optimizing a power distribution network, which can solve the problem of poor applicability of the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, a method for optimizing a power distribution network is provided, where the method includes: firstly, acquiring characteristic parameters in a power distribution network and the current state of a switch between at least one node; then, determining the optimal result of the power distribution network by using the characteristic parameters and the current state of the switch between at least one node by using a double-layer optimization algorithm; the current state of the switch between the nodes is an opening state or a closing state; the characteristic parameters at least comprise: each branch impedance, each node load, each conventional unit output force and voltage phase angle difference; the optimal result comprises a minimum network loss value in the power distribution network, a current state group corresponding to the minimum network loss value and active power output injected by each node corresponding to the minimum network loss value; the current state group comprises the current open-close state of at least one inter-node switch in the power distribution network.
Based on the method, aiming at the characteristics of the source network load in the active power distribution network, in order to solve the optimization problem of the active power distribution network more comprehensively, the embodiment of the application determines the optimal result of the power distribution network by utilizing the acquired characteristic parameters and the current state of the switch between at least one node by using a double-layer optimization algorithm; the topological structure of the active power distribution network is reasonably adjusted by obtaining more comprehensive optimal results of the active power distribution network aiming at the topological results of different power distribution networks, so that the applicability of the method is improved while the network loss is effectively reduced.
In a second aspect, there is provided an optimization apparatus for an electrical distribution network, the apparatus comprising:
the acquisition unit is used for acquiring characteristic parameters in the power distribution network and the current state of at least one inter-node switch; the current state of the switch between the nodes is an opening state or a closing state; the characteristic parameters at least comprise: each branch impedance, each node load, each conventional unit output and voltage phase angle difference.
The processing unit is used for determining the optimal result of the power distribution network by utilizing a double-layer optimization algorithm according to the characteristic parameters acquired by the acquisition unit and the current state of the switch between at least one node; the optimal result comprises a minimum network loss value in the power distribution network, a current state group corresponding to the minimum network loss value and active power output injected by each node corresponding to the minimum network loss value; wherein the current state group comprises a current open-close state of at least one inter-node switch in the power distribution network.
It can be understood that the optimization device for a power distribution network is used for executing the method corresponding to the first aspect, and therefore, the beneficial effects that can be achieved by the optimization device for a power distribution network may refer to the beneficial effects of the method corresponding to the first aspect and the corresponding scheme in the following detailed description, which are not described herein again.
In a third aspect, an optimization device for a power distribution network is provided, where the structure of the optimization device for the power distribution network includes a processor, and the processor is configured to execute program instructions, so that the optimization device for the power distribution network executes the method of the first aspect.
In a fourth aspect, a computer-readable storage medium is provided, having computer program code stored therein, which, when run on an optimization device of an electrical distribution network, causes the optimization device of the electrical distribution network to perform the method of the first aspect described above.
In a fifth aspect, there is provided a computer program product having stored thereon the above computer software instructions, which, when run on an optimization device of an electrical power distribution network, cause the optimization device of the electrical power distribution network to execute a program of the method according to the first aspect as described above.
Drawings
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Fig. 1 is a schematic structural diagram of main components of a source-network-load coordination optimization provided in an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an optimization system of a power distribution network according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a communication device according to an embodiment of the present invention;
fig. 4 is a schematic flowchart of a method for optimizing a power distribution network according to an embodiment of the present invention;
fig. 5 is a schematic flow chart of another power distribution network optimization method according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of an optimization device for a power distribution network according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
It should be noted that in the embodiments of the present invention, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" or "such as" in an embodiment of the present invention is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the word "exemplary" or "such as" is intended to present relevant concepts in a concrete fashion.
It should be noted that, in the embodiments of the present invention, "of", "corresponding" and "corresponding" may be sometimes used in combination, and it should be noted that, when the difference is not emphasized, the intended meaning is consistent.
In the embodiments of the present application, the terms "first", "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present application, the meaning of "a plurality" is two or more unless otherwise specified.
In order to more clearly understand the optimization method for the power distribution network provided in the embodiment of the present application, the following briefly describes technical elements related to the embodiment of the present application.
1. Active power distribution network
The active power distribution network refers to a power distribution network with a large number of connected distributed power supplies and power flowing in two directions, and is also called an active power distribution network. The power distribution network is an energy exchange and distribution network, the power flow and fault current of the power distribution network flow in two directions, the power flow and fault analysis, voltage reactive power control, relay protection methods and operation management measures of the traditional power distribution network are not adapted any more, and corresponding adjustment and improvement are needed. The distributed power supply is called as an active power distribution network, and aims to emphasize that the distributed power supply actively adjusts the reactive and active outputs of the distributed power supply and applies a modern communication means to carry out coordination control on the power distribution network so as to fully play the role of the distributed power supply and realize the optimized operation of the power distribution network.
2. Source network load coordination optimization
In an active power distribution network/active power distribution network, source network load coordination optimization refers to the goal of economically, efficiently and safely improving the dynamic power balance capability of a power system through various interaction forms among a power source, a load and a power grid. Referring to fig. 1, a schematic structural diagram of main components of a source-grid-load coordination optimization is provided. Wherein, the source side includes: the system comprises a controllable distributed power generation unit centralized renewable energy source, a superior power supply, a microgrid and energy storage; the net side includes: the system comprises an outgoing line switch, a segmentation/interconnection switch and a reactive voltage regulator; the load side comprises: flexible loads, public/private transformers, electric vehicle charging facilities, other controllable/adjustable loads. The source network load coordination optimization is essentially an operation mode capable of realizing the maximum utilization of energy resources.
The sources in the source network load collaborative optimization of the active power distribution network mainly refer to a distributed power supply, a superior power supply, a micro-grid, energy storage and the like. The distributed power sources mainly comprise the following types:
1) Wind power generation
Wind Generators (WG) use wind energy on the earth surface to rotate an induction motor to generate electricity. The wind power generator has the advantages of environmental protection and regeneration, global feasibility, abundant reserves, low cost and obvious scale benefit, the wind power generator is relatively simple in realization, short in construction period and mature in technology, can be used for providing power requirements of islands, remote mountainous areas and other areas, and the wind power becomes one of the new energy sources with the highest development speed at present.
Distributed wind generators mainly include three forms: the first is an off-grid wind power generation mode, which operates independently and is generally used by small users; the second is to integrate other power generation modes, mainly used for marine navigation, such as a wind-solar complementary power generation mode; the third is a grid-connected power generation mode, a plurality of fans are arranged in a wind field with rich wind power resources to form a wind power generator group to supply power to a network, and the method is a main mode for greatly utilizing wind energy at present.
2) Photovoltaic power generation
Photovoltaic power generation utilizes the photovoltaic effect and adopts a solar panel to convert solar energy into electric energy. Solar energy is the most abundant of all renewable energy sources and is not limited by regions, is flexible and convenient to install, and is an important component of a renewable energy source system. Grid-connected photovoltaic power generation equipment is the mainstream development trend of solar power generation, has already stepped into the large-scale application stage abroad, and is an important direction for photovoltaic power generation to move to a commercial power generation mode. The photovoltaic power generation equipment mainly comprises three modules, namely a battery panel, a controller and an inverter, and is simple and convenient to install and maintain, simple in device and long in service life.
3) Micro gas turbine
The micro gas turbine is a small heat engine, which is composed of modules such as a micro gas turbine, a high-speed alternating-current generator, a high-efficiency reflux heat exchanger power conversion controller and the like, and the fuel can be various, such as natural gas, gasoline, methane, diesel oil and the like. The micro gas turbine has the advantages of less maintenance, flexible operation control, suitability for various fuels, safety, reliability and the like, and is an ideal Distributed Generation (DG). Of all DG types, the micro gas turbine is the most mature technology with the highest reliability and has a certain commercial competitiveness.
4) Fuel cell
A fuel cell (fuel cell) is a power generation device that directly converts chemical energy present in a fuel and an oxidant into electrical energy. Fuel cells can be roughly classified into six types, depending on the type of electrolyte used, proton exchange membrane fuel cells, direct methanol fuel cells, alkaline fuel cells, phosphoric acid fuel cells, molten carbonate fuel cells, and solid oxide fuel cells.
5) Biomass power generation
The biomass energy power generation mainly utilizes agricultural, forestry and industrial wastes, even municipal refuse, as raw materials, and adopts direct combustion or gasification and other modes to generate power, including agriculture and forestry wastes direct combustion power generation, agriculture and forestry wastes gasification power generation, refuse incineration power generation, refuse landfill gas power generation and methane power generation.
Because the existing optimization method of the power distribution network only establishes an optimization model according to the fixed topological structure of the power distribution network, when the topological structure of the power distribution network changes, optimization processing cannot be carried out, and the method is poor in applicability. Thus, referring to fig. 2, the present embodiment provides an optimization system 20 for a power distribution network. The system 20 comprises an optimization device 201 of the power distribution network and an active power distribution network 202; the optimization device 201 of the power distribution network may have a server of the control center shown in fig. 2, which is all functions of the optimization device of the power distribution network, or may be set as an independent device. The embodiment of the present application does not limit the implementation form of the optimization device 201 for a power distribution network.
Here, the system architecture and the service scenario described in the embodiment of the present invention are for more clearly illustrating the technical solution of the embodiment of the present application, and do not constitute a limitation to the technical solution provided in the embodiment of the present application, and it can be known by a person skilled in the art that the technical solution provided in the embodiment of the present application is also applicable to similar technical problems along with the evolution of the network architecture and the appearance of a new service scenario.
Optionally, the apparatuses mentioned in the embodiments of the present application, such as the data acquisition apparatus, the health monitoring apparatus, and the like, may be implemented by the communication device 30 shown in fig. 3.
As shown in fig. 3, the apparatus 30 includes at least one processor 301, a communication line 302, a memory 303, and at least one communication interface 304.
The processor 301 may be a general-purpose Central Processing Unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more ics for controlling the execution of programs in accordance with the present disclosure.
The communication link 302 may include a path for transmitting information between the aforementioned components.
The communication interface 304 may be any device, such as a transceiver, for communicating with other devices or communication networks, such as an ethernet, a Radio Access Network (RAN), a Wireless Local Area Network (WLAN), etc.
The memory 303 may be, but is not limited to, a read-only memory (ROM) or other type of static storage device that may store static information and instructions, a Random Access Memory (RAM) or other type of dynamic storage device that may store information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disk read-only memory (CD-ROM) or other optical disk storage, optical disk storage (including compact disk, laser disk, optical disk, digital versatile disk, blu-ray disk, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store program code in the form of instructions or data structures and that can be accessed by a computer. The memory may be separate and coupled to the processor via a communication line 302. The memory may also be integrated with the processor.
The memory 303 is used for storing computer-executable instructions for executing the present invention, and is controlled by the processor 301. The processor 301 is configured to execute the computer executable instructions stored in the memory 303, so as to implement the optimization method for the power distribution network provided by the following embodiments of the present application.
Optionally, the computer-executable instructions in the embodiments of the present application may also be referred to as application program codes, which are not specifically limited in the embodiments of the present application.
In particular implementations, processor 301 may include one or more CPUs, such as CPU0 and CPU1 in fig. 3, as one embodiment.
In particular implementations, apparatus 300 may include multiple processors, such as processor 301 and processor 307 in FIG. 3, for example, as an example. Each of these processors may be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. A processor herein may refer to one or more devices, circuits, and/or processing cores for processing data (e.g., computer program instructions).
In one implementation, the apparatus 300 may further include an output device 305 and an input device 306, as an example. The output device 305 is in communication with the processor 301 and may display information in a variety of ways. For example, the output device 305 may be a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display device, a Cathode Ray Tube (CRT) display device, a projector (projector), or the like. The input device 306 is in communication with the processor 301 and may receive user input in a variety of ways. For example, the input device 306 may be a mouse, a keyboard, a touch screen device, or a sensing device, among others.
Optionally, names of the messages or names of the parameters in the messages in the following embodiments of the present application are only examples, and other names may also be used in specific implementations, and this is not specifically limited in the embodiments of the present application.
The principle of the method is as follows: considering the characteristics of distributed power output, adjustable load and a power distribution network topological structure in the source network load of an active power distribution network, a double-layer optimization algorithm of constraint conditions of distributed power generation device output constraint, transformer substation injection power constraint, power balance constraint, node voltage constraint, branch current constraint, network topology constraint and adjustable load regulation constraint is provided, and an optimal result consisting of a minimum network loss value suitable for the power distribution network, the current on-off state of at least one inter-node switch in the power distribution network corresponding to the minimum network loss value and active output injected by each node corresponding to the minimum network loss value is determined by utilizing the double-layer optimization algorithm to calculate characteristic parameters and the current state of the at least one inter-node switch in the power distribution network.
The following describes in detail an optimization method of a power distribution network according to an embodiment of the present application with reference to fig. 2 and fig. 3. Referring to fig. 4, a schematic flow chart of an optimization method for a power distribution network is provided, and the optimization method specifically includes the following contents.
S401, obtaining characteristic parameters in the power distribution network and the current state of the switch between at least one node.
The current state of the switch between the nodes is an opening state or a closing state; the characteristic parameters at least comprise: each branch impedance, each node load, each conventional unit output and voltage phase angle difference.
S402, determining the optimal result of the power distribution network by using the characteristic parameters and the current state of the switch between the at least one node through a double-layer optimization algorithm.
The optimal result comprises a minimum network loss value in the power distribution network, a current state group corresponding to the minimum network loss value and active power output injected by each node corresponding to the minimum network loss value; wherein the current state group comprises a current open-close state of at least one inter-node switch in the power distribution network.
The active output injected by each node meets the constraint conditions of DG output constraint, conventional unit power constraint, transformer substation injection power constraint, power balance constraint, node voltage constraint, branch current constraint, network topology constraint and adjustable load regulation constraint of the distributed generation device.
Alternatively, the objective function is determined according to equation (1-1). The objective function refers to a minimum network loss value in the power distribution network, the network loss power at a certain moment is equal to the total generated power minus the consumed power of the system, and the value is equal to the sum of the injected powers of all the nodes, and the expression is as follows:
Figure BDA0002790709020000081
in the formula: p i Active power out (or active power) injected for node i; n is the total number of nodes. The constraints of the model are as follows.
It should be noted that the output, i.e., power, of the present application is not described in detail below.
P in the above formula i The constraint conditions of the method are mainly divided into three layers including a source layer, a net layer and a charge layer, and specifically include:
1.1 Source layer
1.1.1 DG output constraint: neglecting the randomness of the outage and output of the DG and the uncertainty of the load, the active output and the reactive output of the DG at the node i cannot exceed the rated upper limit and cannot be lower than the lower limit, namely:
Figure BDA0002790709020000091
in the formula: p DGl Means the first DG active power output, Q DGl The first station DG reactive power output;
Figure BDA0002790709020000092
sequentially refers to the upper limit of the active output and the upper limit of the reactive output of the first station DG,
Figure BDA0002790709020000093
sequentially indicating the lower limit of the active output and the lower limit of the reactive output of the first DG; the total DG output cannot be greater than the maximum permeability.
1.1.2 Conventional unit output constraints: the active output and the reactive output of the conventional unit cannot exceed the rated upper limit and cannot be lower than the lower limit, namely:
Figure BDA0002790709020000094
in the formula: p CUs,i 、Q CUs,i Sequentially representing the active output and the reactive output of the conventional unit at the node i;
Figure BDA0002790709020000095
sequentially showing the upper limit of active power output and the upper limit of reactive power output of the s conventional unit,
Figure BDA0002790709020000096
and sequentially representing the lower limit of active power output and the lower limit of reactive power output of the s-th conventional unit.
1.2 ) mesh layer
1.2.1 Substation injection power constraints: the active and reactive power injected into the main grid of a substation (substation) node cannot exceed the rated capacity of the transformer, i.e.:
Figure BDA0002790709020000097
in the formula: p sub,y 、Q sub,y Sequentially representing active injection power and reactive injection power of the y-th transformer substation;
Figure BDA0002790709020000098
respectively the upper limit and the lower limit of active injection power;
Figure BDA0002790709020000099
respectively refer to the upper and lower limits of the reactive injection power.
1.2.2 Power balance constraint: the incoming and outgoing power of each node is to satisfy the power balance equation, i.e.:
Figure BDA0002790709020000101
Figure BDA0002790709020000102
in the formula: p i 、Q i Sequentially representing active power injected by a node i and reactive power injected by the node i; k is ij Is a variable of switch communication between nodes i, j, where K ij =1 denotes that the line between nodes i, j remains connected, K ij =0 represents a line disconnection between nodes i, j; g ij 、B ij 、δ ij Sequentially representing conductance, susceptance and voltage phase angle difference between the nodes i and j; n is the total number of nodes of the power distribution network; u shape i 、U j Sequentially representing the voltage amplitude of the node i and the voltage amplitude of the node j; p DGl,i 、Q DGl,i Sequentially representing the active power output of the distributed power supply at the node i and the reactive power output of the distributed power supply at the node i; p CUs,i 、Q CUs,i Sequentially representing the active power output of the conventional power supply at the node i and the reactive power output of the conventional power supply at the node i, and processing according to a constant value during calculation; p is Load,i 、Q Load,i Sequentially representing the active load at the node i and the reactive load at the node i; p K,i 、Q K,i And sequentially representing the active regulating variable of the controllable load at the node i and the reactive regulating variable of the controllable load at the node i.
1.2.3 Node voltage constraint: the voltage at each node cannot exceed its rated upper limit nor fall below the lower limit, i.e.:
U min ≤U i ≤U max ,i∈Ω (1-7)
in the formula: u shape min 、U max Sequentially representing an upper limit and a lower limit of the node voltage; Ω is the set of system nodes.
1.2.4 Branch current constraint: in order to guarantee the safety of the network operation, the current flowing between the nodes i and j cannot exceed the rated upper limit and cannot be lower than the lower limit, namely:
Figure BDA0002790709020000103
in the formula:
Figure BDA0002790709020000104
represents the current flowing between nodes i, j;
Figure BDA0002790709020000105
which in turn represent the upper and lower limits of the current flowing between nodes i, j.
1.2.5 Network topology constraints: the reconstructed network is still a radial connected network, and no isolated island looped network exists, namely:
Figure BDA0002790709020000111
in the formula: y is a transformer substation node set in the power distribution network; the formula satisfies the constraint that the system topology is radial.
Figure BDA0002790709020000112
In the formula: variables with a 'A' at the upper right corner represent all non-substation nodes, are intermediate variables, and are not stored in an optimization result; p i 、Q i Sequentially representing active power injected by a node i and reactive power injected by the node i; k ij Is a variable of switch communication between nodes i, j, where K ij =1 denotes that the line between nodes i, j remains connected, K ij =0 denotes line disconnection between nodes i, j; g ij 、B ij 、δ ij Sequentially representing conductance, susceptance and voltage phase angle difference between the nodes i and j; n is the total number of nodes of the power distribution network; u shape i 、U j The voltage amplitude of the node i and the voltage amplitude of the node j are sequentially represented. The formula meets the constraint of a system topological structure without an island node.
1.3 "charge" layer model)
1.3.1 Adjustable load) adjustment constraint: the active and reactive adjustment quantity of the adjustable load must not be lower than the lower adjustment limit and higher than the upper adjustment limit, namely:
Figure BDA0002790709020000113
in the formula: p is K 、Q K Sequentially representing the active regulating variable and the reactive regulating variable of the adjustable load;
Figure BDA0002790709020000114
Figure BDA0002790709020000115
sequentially representing the upper limit and the lower limit of the active regulating quantity of the adjustable load;
Figure BDA0002790709020000116
and sequentially representing the upper limit and the lower limit of the adjustable load reactive power adjustment quantity.
In an implementation manner, in the embodiment of the present application, determining an optimal result of a power distribution network by using a two-layer optimization algorithm according to a characteristic parameter and a current state of a switch between at least one node specifically includes:
and determining the current network loss value by utilizing a nonlinear programming algorithm according to the characteristic parameters and the current state group. Then, how to determine the intermediate network loss value is judged according to the following three conditions:
and judging whether the current network loss value is determined for the first time or not under the condition of one.
And judging whether the current network loss value is smaller than the middle network loss value or not under the second condition.
And thirdly, judging whether the last opening and closing state in the intermediate state group is consistent with the updated current state group after being inverted.
The manner of determining the intermediate network loss value by combining the above judgment conditions is specifically as follows:
in the first mode, when the current network loss value is determined for the first time, the current state group is assigned to the intermediate state group; negating a first open-close state in the current state group to update the current state group; then, determining an updated current network loss value by utilizing a nonlinear programming algorithm according to the characteristic parameters and the updated current state group; and selecting the minimum value from the current network loss value and the updated current network loss value, and assigning the minimum value to the middle network loss value.
In the second mode, when the current network loss value is determined to be not determined for the first time and is smaller than the middle network loss value, assigning the current state group to the middle state group; negating a first open-close state in the current state group to update the current state group; then, determining an updated current network loss value by utilizing a nonlinear programming algorithm according to the characteristic parameters and the updated current state group; and selecting the minimum value from the current network loss value and the updated current network loss value, and assigning the minimum value to the middle network loss value.
In a third mode, when the current network loss value is determined to be not determined for the first time and the last opening and closing state in the intermediate state group is not consistent with the updated current state group after being negated, the current state group is assigned to the intermediate state group; negating the (n + 1) th switch state of the current state group to update the current state group, and determining an updated current network loss value according to the characteristic parameters and the updated current state group by using a nonlinear programming algorithm; the last update of the current state group is used for negating the nth switch state; and selecting the minimum value from the current network loss value and the updated current network loss value, and assigning the minimum value to the middle network loss value.
And finally, when the updated current network loss value is determined to be not less than the middle network loss value and the last on-off state in the middle state group is inverted and is consistent with the updated current state group, the middle network loss value, the current state group corresponding to the middle network loss value and the active power output injected by each node corresponding to the middle network loss value are the optimal results.
For better understanding, referring to fig. 5, an embodiment of the present application provides a flowchart of another power distribution network optimization method, where a current state group is set to K [ m [ ]]Iteration parameter N A =1, intermediate state set is A [ m ]]The current network loss value is Z, and the middle network loss value is Z1; the method comprises the following specific steps:
s501, acquiring characteristic parameters in the power distribution network and the current state of a switch between at least one node; jump to S502.
S502, calculating Z by utilizing a nonlinear programming algorithm according to the characteristic parameters and K [ m ] in the power distribution network; it jumps to step S503.
It should be noted that K [ m ] is generated by statistics of the current state of at least one inter-node switch.
S503 determines whether Z is the first solution? If so, go to step S504, otherwise, go to step S505.
S504, let K [ m ]]＝A[m]To K [ m ]]Negation of the element at the middle head (i.e. N) A = 1). To update K [ m ]]Generating an updated K [ m ]](ii) a Return to step S502.
S505, determine whether Z is not less than Z1? If yes, go to step S506, otherwise go to step S507.
S506, let K [ m ]]＝A[m]For K [ m ]]Negation of the element at the middle head (i.e. N) A = 1) to update K [ m ]]Generating an updated K [ m ]](ii) a Return to step S502.
S507, judging whether the last opening and closing state in A [ m ] is consistent with K [ m ] after being inverted? If so, go to step S509, otherwise, go to step S508.
S508, let K [ m ]]＝A[m]，N A ＝N A +1; for K [ m ]]N in (1) A The elements are negated to update K [ m ]]Generating an updated K [ m ]]. It jumps to step S502.
In addition, for K [ m ]]N in (1) A N in the negation of individual elements A N as described before for this sentence A +1。
And S509, enabling K [ m ] = A [ m ], and outputting K [ m ] and a corresponding network loss value. And (6) ending.
In addition, in the embodiment of the present application, the optimization method of the power distribution network is exemplarily described in conjunction with table 1 below. Let K m]= {1,1,1}, intermediate state set is a [ m [ ]]N th A The state of each switch is inverted; and taking parameters such as the switching value array, the impedance of each branch, the load of each node, the output of each conventional unit, the voltage phase angle difference and the like as initial input conditions. Here, "1" indicates that a certain switch between nodes is in an off state, and "0" indicates that a certain switch between nodes is in an on state. The algorithm flow is shown as follows:
TABLE 1
Figure BDA0002790709020000131
Figure BDA0002790709020000141
Then, let the array K [ m ] = a [ m ] = {0,0,1}, and the network loss value Z4 is the minimum network loss value in the calculated distribution network.
In summary, in the embodiments of the present application, in order to solve the optimization problem of the active power distribution network more comprehensively for the characteristics of the source network load in the active power distribution network, the embodiments of the present application determine the optimal result of the power distribution network by using a double-layer optimization algorithm on the acquired characteristic parameters and the current state of the switch between at least one node; by aiming at the topological results of different power distribution networks, a more comprehensive optimal result of the active power distribution network is obtained, the topological structure of the active power distribution network is reasonably adjusted, and the method can improve the applicability while effectively reducing the network loss. In addition, the accuracy of the optimization result of the power distribution network is improved by adding the constraint conditions of the source network load, the source network load and the grid load.
In the embodiment of the present invention, the optimization device 201 of the power distribution network may be divided into functional modules according to the method embodiment, for example, each functional module may be divided corresponding to each function, or two or more functions may be integrated into one processing module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. It should be noted that, the division of the modules in the embodiment of the present invention is schematic, and is only a logic function division, and there may be another division manner in actual implementation.
As shown in fig. 6, which is a schematic structural diagram of an optimization apparatus 201 for a power distribution network according to an embodiment of the present invention, the optimization apparatus 201 for a power distribution network includes an obtaining unit 601 and a processing unit 602.
Specifically, the obtaining unit 601 is configured to obtain a characteristic parameter in the power distribution network and a current state of a switch between at least one node; the current state of the switch between the nodes is an opening state or a closing state; the characteristic parameters at least comprise: each branch impedance, each node load, each conventional unit output force and voltage phase angle difference. For example, in conjunction with fig. 4, the acquisition unit 601 may be configured to perform step S401.
The processing unit 602 is configured to determine an optimal result of the power distribution network by using a double-layer optimization algorithm for the characteristic parameters acquired by the acquiring unit 601 and the current state of the switch between the at least one node; the optimal result comprises a minimum network loss value in the power distribution network, a current state group corresponding to the minimum network loss value and active power output injected by each node corresponding to the minimum network loss value; wherein the current state group comprises a current open-close state of at least one inter-node switch in the power distribution network. The processing unit 602 may be configured to perform step S402.
Optionally, the processing unit 602 is specifically configured to determine the current network loss value by using a nonlinear programming algorithm according to the characteristic parameters and the current state group.
The processing unit 602 is further configured to assign the current state group to the intermediate state group when the current network loss value is determined for the first time.
The processing unit 602 is further configured to negate a first open-close state in the current state group to update the current state group, and determine an updated current network loss value according to the characteristic parameter and the updated current state group by using a nonlinear programming algorithm.
The processing unit 602 is further configured to select a minimum value from the current network loss value and the updated current network loss value, and assign the minimum value to the intermediate network loss value.
The processing unit 602 is further configured to determine that the updated current network loss value is not less than the intermediate network loss value, and when the last open-close state in the intermediate state group is inverted and is consistent with the updated current state group, the intermediate network loss value, the current state group corresponding to the intermediate network loss value, and the active power injected by each node corresponding to the intermediate network loss value are the optimal results.
Optionally, the processing unit 602 is further configured to assign the current state group to the intermediate state group when it is determined that the current network loss value is not the first determination and the current network loss value is smaller than the intermediate network loss value.
The processing unit 602 is further configured to negate a first open-close state in the current state group to update the current state group, and determine an updated current network loss value according to the characteristic parameter and the updated current state group by using a nonlinear programming algorithm.
The processing unit 602 is further configured to select a minimum value from the current network loss value and the updated current network loss value, and assign the minimum value to the intermediate network loss value.
Optionally, the processing unit 602 is further configured to assign the current state group to the intermediate state group when it is determined that the current network loss value is not determined for the first time, and the last open/close state in the intermediate state group is inconsistent with the updated current state group after being inverted.
The processing unit 602 is further configured to negate an (n + 1) th switching state of the current state group, so as to update the current state group, and determine an updated current network loss value according to the characteristic parameter and the updated current state group by using a nonlinear programming algorithm; wherein the last update of the current state group is used for negating the nth switch state.
The processing unit 602 is further configured to select a minimum value from the current network loss value and the updated current network loss value, and assign the minimum value to the intermediate network loss value.
Optionally, the active power output injected by each node satisfies constraint conditions of output constraint of the distributed power generation device, injection power constraint of the substation, power constraint of a conventional unit, power balance constraint, node voltage constraint, branch current constraint, network topology constraint, and adjustment quantity constraint of an adjustable load.
Of course, the optimization device 201 of the power distribution network provided in the embodiment of the present invention includes, but is not limited to, the above modules, and for example, the optimization device 201 of the power distribution network may further include a sending unit 603 and a storage unit 604. The sending unit 603 may be configured to send relevant data in the optimization apparatus 201 of the power distribution network to other devices, so as to implement data interaction with the other devices. The storage unit 604 may be configured to store program codes of the optimization device 201 of the power distribution network, and may also be configured to store data generated by the optimization device 201 of the power distribution network during operation, such as data in a write request.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions within the technical scope of the present invention are intended to be covered by the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.

Claims (11)

1. A method for optimizing a power distribution network, comprising:
acquiring characteristic parameters in the power distribution network and the current state of at least one inter-node switch; the current state of the switch between the nodes is an opening state or a closing state; the characteristic parameters at least comprise: each branch circuit impedance, each node load, each conventional unit output force and voltage phase angle difference;
determining a current network loss value by utilizing a nonlinear programming algorithm according to the characteristic parameters and the current state group; wherein the current state group comprises the current on-off state of at least one inter-node switch in the power distribution network;
when the current network loss value is determined for the first time, assigning the current state group to an intermediate state group;
negating a first open-close state in the current state group to update the current state group, and determining an updated current network loss value by using a nonlinear programming algorithm according to the characteristic parameters and the updated current state group;
selecting a minimum value from the current network loss value and the updated current network loss value, and assigning the minimum value to a middle network loss value;
when it is determined that the updated current network loss value is not less than the intermediate network loss value and the last on-off state in the intermediate state group is inverted and is consistent with the updated current state group, the active power injected by the intermediate network loss value, the current state group corresponding to the intermediate network loss value and each node corresponding to the intermediate network loss value are optimal; the optimal result comprises a minimum network loss value in the power distribution network, a current state group corresponding to the minimum network loss value and active power output injected by each node corresponding to the minimum network loss value.
2. The method of optimizing an electrical distribution network of claim 1, further comprising:
when the current network loss value is determined to be not determined for the first time and is smaller than the middle network loss value, assigning the current state group to the middle state group;
negating a first open-close state in the current state group to update the current state group, and determining an updated current network loss value by using a nonlinear programming algorithm according to the characteristic parameters and the updated current state group;
and selecting the minimum value from the current network loss value and the updated current network loss value, and assigning the minimum value to the intermediate network loss value.
3. The method of optimizing an electrical distribution network of claim 1, further comprising:
when the current network loss value is determined to be not determined for the first time and the last opening and closing state in the intermediate state group is determined to be not consistent with the updated current state group after being negated, assigning the current state group to the intermediate state group;
negating the (n + 1) th switch state of the current state group to update the current state group, and determining an updated current network loss value according to the characteristic parameters and the updated current state group by using a nonlinear programming algorithm; wherein the last update of the current state group is used for negating the nth switch state;
and selecting the minimum value from the current network loss value and the updated current network loss value, and assigning the minimum value to the intermediate network loss value.
4. The method of optimizing an electrical distribution network according to claim 1, further comprising:
the active power output injected by each node meets the constraint conditions of the output constraint of the distributed power generation device, the injection power constraint of the transformer substation, the power constraint of the conventional unit, the power balance constraint, the node voltage constraint, the branch current constraint, the network topology constraint and the regulation quantity constraint of the adjustable load.
5. An optimization device for a power distribution network, comprising:
the acquisition unit is used for acquiring characteristic parameters in the power distribution network and the current state of at least one inter-node switch; the current state of the switch between the nodes is an opening state or a closing state; the characteristic parameters at least comprise: each branch impedance, each node load, each conventional unit output force and voltage phase angle difference;
the processing unit is specifically used for determining a current network loss value by utilizing a nonlinear programming algorithm according to the characteristic parameters and the current state group; wherein the current state group comprises a current on-off state of at least one inter-node switch in the power distribution network;
the processing unit is further configured to assign the current state group to an intermediate state group when the current network loss value is determined for the first time;
the processing unit is further configured to negate a first open-close state in the current state group to update the current state group, and determine an updated current network loss value according to the characteristic parameter and the updated current state group by using a nonlinear programming algorithm;
the processing unit is further configured to select a minimum value from the current network loss value and the updated current network loss value, and assign the minimum value to an intermediate network loss value;
the processing unit is further configured to determine that the updated current network loss value is not smaller than the intermediate network loss value, and when the latest open-close state in the intermediate state group is inverted and is consistent with the updated current state group, the intermediate network loss value, the current state group corresponding to the intermediate network loss value, and the active power injected by each node corresponding to the intermediate network loss value are optimal results; the optimal result comprises a minimum network loss value in the power distribution network, a current state group corresponding to the minimum network loss value and active power output injected by each node corresponding to the minimum network loss value.
6. The optimization device for the power distribution network according to claim 5, comprising:
the processing unit is further configured to assign the current state group to the intermediate state group when it is determined that the current network loss value is not determined for the first time and the current network loss value is smaller than the intermediate network loss value;
the processing unit is further configured to negate a first open-close state in the current state group to update the current state group, and determine an updated current network loss value by using a nonlinear programming algorithm according to the characteristic parameter and the updated current state group;
and the processing unit is further configured to select a minimum value from the current network loss value and the updated current network loss value, and assign the minimum value to the intermediate network loss value.
7. The optimization device for the power distribution network according to claim 5, comprising:
the processing unit is further configured to assign the current state group to the intermediate state group when it is determined that the current network loss value is not first determined and the last open-close state in the intermediate state group is inconsistent with the updated current state group after being negated;
the processing unit is further configured to invert an n +1 th switching state of the current state group to update the current state group, and determine an updated current network loss value according to the characteristic parameter and the updated current state group by using a nonlinear programming algorithm; wherein the last update of the current state group is used for negating the nth switch state;
and the processing unit is further configured to select a minimum value from the current network loss value and the updated current network loss value, and assign the minimum value to the intermediate network loss value.
8. The optimization device for the power distribution network according to claim 5, further comprising:
the active power output injected by each node meets the constraint conditions of the output constraint of the distributed power generation device, the injection power constraint of the transformer substation, the power constraint of the conventional unit, the power balance constraint, the node voltage constraint, the branch current constraint, the network topology constraint and the regulation quantity constraint of the adjustable load.
9. An optimization device for a power distribution network, characterized in that the structure of the optimization device for the power distribution network comprises a processor for executing program instructions to cause the optimization device for the power distribution network to execute the optimization method for the power distribution network according to any one of claims 1 to 4.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored therein a computer program code which, when run on an optimization device of a power distribution network, causes the optimization device of the power distribution network to execute the optimization method of the power distribution network according to any one of claims 1-4.
11. A computer program product, characterized in that it stores computer software instructions which, when run on an optimization device of a power distribution network, cause the optimization device of the power distribution network to perform the optimization method of the power distribution network according to any one of claims 1-4.
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