CN117787606A

CN117787606A - Network storage collaborative planning method and system for iteratively incorporating typical scene

Info

Publication number: CN117787606A
Application number: CN202311755000.8A
Authority: CN
Inventors: 王建学; 李志远; 王梓宇; 李旭霞; 王鹏; 刘红丽; 郑晓明
Original assignee: Xian Jiaotong University; Economic and Technological Research Institute of State Grid Shanxi Electric Power Co Ltd
Current assignee: Xian Jiaotong University; Economic and Technological Research Institute of State Grid Shanxi Electric Power Co Ltd
Priority date: 2023-12-19
Filing date: 2023-12-19
Publication date: 2024-03-29

Abstract

The invention discloses a grid storage collaborative planning method and a grid storage collaborative planning system for iteratively incorporating a typical scene, which comprehensively consider the complementary relation between grid rack optimization and energy storage configuration of a power grid and the possible load shedding and new energy power rejection situations in an extreme scene, take the optimal economy and reliability as targets, construct a grid-energy storage joint planning model of a system level, and iteratively incorporate the extreme scene continuously through operation simulation and adopt the typical scene generated by a time sequence clustering algorithm to carry out iterative correction on an operation part model in the planning model, so that the obtained planning scheme has strong capability of coping with the extreme scene. The scheme obtained by the method can effectively reduce investment cost while having enough extreme scene coping capability and meeting the operation constraint of the power system containing new energy.

Description

Network storage collaborative planning method and system for iteratively incorporating typical scene

Technical Field

The invention belongs to the technical field of power transmission network-energy storage collaborative planning, and particularly relates to a network-energy storage collaborative planning method and system for iteratively incorporating a typical scene.

Background

In recent years, with the increase of load and the requirement of environmental protection, renewable energy power generation has been rapidly developed. In order to improve the reliability of the power grid and the utilization level of renewable energy sources, the peak shaving capacity of the system can be improved through energy storage, and system congestion can be reduced through optimizing the system structure. Therefore, the power transmission capacity expansion planning and the energy storage planning are combined, the problem of renewable energy consumption caused by power transmission congestion and insufficient peak shaving capacity can be solved, and the economy of planning decisions can be improved.

In the joint planning problem of the power transmission line and the energy storage, the uncertainty of the load and the renewable energy sources is considered by adopting historical data or predicted data clustering time sequence curves, so that a certain number of typical scenes are obtained. To guarantee robustness of the planning result, extreme scenarios are typically chosen. The existing simple method mainly selects the maximum load period as an extreme situation or selects the maximum or minimum renewable energy output period as an extreme situation, but the simple method for selecting the extreme scheme is used for solving the planning problem, and the problem of full-period running optimization still can cause load loss or large amount of energy power-off. A more common approach is to add a relaxation variable representing the load loss to the power balance constraint and return the time period of maximum load loss as an extreme scenario to the planning problem. The method only takes the load loss period as an extreme period, and does not consider the period of insufficient peak shaving capacity of the system, so that the planning result still has difficulty in ensuring the consumption index of renewable energy sources in the planning range. Under the background of high-proportion new energy access, the peak shaving pressure is further increased due to the high uncertainty of new energy power generation, and in order to further improve the reliability of a planning result, a framework for iteratively incorporating an extreme scene is necessary to solve the problem of power transmission network-energy storage collaborative planning.

Disclosure of Invention

The technical problem to be solved by the invention is to provide a network storage collaborative planning method and a system for iteratively incorporating typical scenes aiming at the defects in the prior art, which are used for solving the technical problems that the existing network storage planning method has weaker resistance to new energy and load fluctuation and has insufficient capability of coping with polar scenes.

The invention adopts the following technical scheme:

a network storage collaborative planning method for iteratively incorporating a typical scene comprises the following steps:

s1, acquiring basic technical data of a planned horizontal year power system, and generating a power transmission line and an energy storage candidate set;

s2, modeling the basic data of the planning target year by combining the power transmission line and the energy storage candidate set obtained in the step S1, generating a modeling model and an operation stage model of the power transmission line and the energy storage, and solving to obtain a planning scheme;

s3, simulating the planning scheme generated in the step S2 by using a full-period safety constraint unit combination model to obtain annual load shedding and new energy power discarding results;

s4, determining an extreme scene set according to the annual load shedding and new energy power discarding results obtained in the step S3;

s5, generating a typical scene set by adopting a time sequence clustering algorithm;

And S6, when the set convergence condition is not reached, transmitting the extreme scene set generated in the step S4 and the typical scene set generated in the step S5 into a planning model, and outputting an updated planning scheme.

Preferably, the system base technology data comprises: technical parameters of various types of power supplies in the power system include power grid and network parameters, load requirements and history information of new energy power generation.

Preferably, in step S2, the model of power transmission line and energy storage is specifically:

investment cost C of transmission line ^Line,Inv The method comprises the following steps:

wherein C is _l Annual construction cost for power transmission line l, omega _nl For the line set to be built, z _l Is a binary variable;

investment cost of energy storage C ^ESS,Inv The method comprises the following steps:

wherein Ω _nes To store energy, P is selected _j For alpha _j /β _j Representing the cost of construction per MW power capacity and capacity of the stored energy j, E _j Is that;

run phase modelThe method comprises the following steps:

wherein c ^ess For the operating costs of the energy storage,for the operating cost per unit time of the thermal power generating unit t period under the scene s, < >>For the discharge/charge power of energy store j in scene s during period t, < >>For the discharging/charging efficiency of the energy storage j to be built, U _t /U _e Omega for a typical scene/extreme scene set _g Is a thermal power unit set, and deltat is.

More preferably, constraints of the model of the transmission line and the energy storage construction are as follows:

the energy storage actual construction capacity constraint is as follows:

the relation constraint of the energy storage electric quantity and the energy storage capacity is as follows:

wherein C is ^Line,Inv The annual construction cost of the total transmission line is C _l The annual construction cost of the transmission line is calculated; omega shape _l /Ω _nl Line set already built line set to be built line set omega _nes For storing energy, the set C ^ESS,Inv To the cost of energy storage, alpha _j /β _j Representing the projected cost per MW of power capacity and charge capacity of the stored energy j,and (3) as an upper limit of energy storage power, gamma is the ratio of the capacity of electric quantity and the capacity of power of the energy storage to be built.

More preferably, the model constraints for the run phase are as follows:

the energy storage operation constraint is specifically as follows:

wherein x is _s,g,t A variable of 0-1 represents a machine set start-stop state; f (F) _s,g,t Is the output work of the thermal power generating unit under the scene s at the time tA rate; k (k) _g,n /h _g,n Slope and intercept of the piecewise linearization power generation cost of the thermal power unit g; k is a positive integer; t (T) _i ^Adi The energy storage regulation period is adopted;the initial electric quantity ratio is the energy storage; />The energy storage charge and discharge efficiency is achieved; />Store the electric quantity for energy storage, ">Charging power for energy storage->To store energy, Ω _Storage Omega, a collection of stored energy _T For a collection of typical time of day sections, +. >For the discharge power of the stored energy>For the capacity of the stored energy,for the energy storage of electricity, ">Is the upper limit of the stored electricity.

Preferably, with the aim of minimizing the operation cost, constructing a full-period safety constraint unit combination model by taking thermal power unit operation constraint, new energy unit operation constraint, energy storage operation constraint, load shedding constraint, node operation constraint and transmission line operation constraint as constraint conditions, wherein the full-period safety constraint unit combination model comprises the following specific steps:

minC ^Ope +C ^Thermal,Ope +C ^Storage,Ope +C ^Load,Ope +C ^NE,Ope

wherein C is ^Ope For running cost, C ^Thermal,Ope C is the running cost of the thermal power ^Storage,Ope For energy storage operation cost, C ^Load,Ope To cut off load penalty term, C ^NE,Ope And discarding the electricity penalty item for the new energy unit.

More preferably, the thermal power generating unit operation constraint is specifically:

wherein,is the output power of the thermal power generating unit, omega _Thermal Omega is the collection of thermal power generating units in the system _T For a collection of typical time of day sections, +.>Maximum power for climbing up a thermal power unit, +.>The maximum power of the thermal power unit for climbing down a slope;

the operation constraint of the new energy unit is specifically as follows:

wherein,for the output power of the new energy unit, < +.>Is the output coefficient of the resource curve of the new energy unit, omega _NE Is a set of new energy units in the system, < +.>The electric power is discarded for the new energy unit;

the energy storage operation constraint is specifically as follows:

Wherein,charging power for energy storage->To store energy, Ω _Storage For the collection of stored energy in the system +.>For the stored power generation power>For the energy storage of electricity, ">Charging power for energy storage->Discharge power for energy storage, T _i ^Adi For the charge regulation period of the stored energy, < >>The initial electric quantity ratio is the energy storage;

the load shedding constraint is specifically as follows:

wherein,for switching load power, < >>For load power, Ω _Load Is a set of loads in the system;

the node operation constraint is specifically:

wherein Ω _Thermal(n) Omega is the set of thermal power generating units with n nodes _Storage(n) Aggregation of stored energy for n nodes, Ω _Load(n) For a set of n-node loads,for load power, +.>To cut off load power omega _Node Omega, a set of loads in the system _Line,s(n) Omega is a set of lines with n nodes as head end nodes _Line,e(n) Is a set of lines with n nodes as end nodes;

the operation constraint of the power transmission line is specifically as follows:

wherein,as the construction variable of the transmission line, theta _s,t Is the phase angle theta of the head end node of the power transmission line _e,t Is the phase angle of the end node of the power transmission line, omega _Line Is a candidate set of the power transmission line.

Preferably, the extreme scene set is specifically:

the day with the largest cutting load and the day with the largest new energy power discarding are respectively regarded as an extreme scene; clustering the residual daily payload curves after the extreme scenes are eliminated to obtain typical scenes; after the time series aggregation, the extreme scene set and the typical scene set update the scene set of the planning model.

Preferably, the generating a typical scene set by adopting a time sequence clustering algorithm is specifically as follows:

selecting a typical scene according to the average value of all curves in each group; weights are assigned to typical days according to the relative size of each cluster; converting the typical daily load of each cluster into a net load average value through a scaling factor; the objective function of the time sequence clustering algorithm is:

wherein c _i Is a cluster center curve after clustering, r _i,j Is a typical daily net load curve.

In a second aspect, an embodiment of the present invention provides a network storage collaborative planning system iteratively incorporating a typical scenario, including:

the data module is used for acquiring basic technical data of a planning horizontal year power system and generating a power transmission line and an energy storage candidate set;

the generation module is used for modeling the power transmission line and the energy storage candidate set obtained by combining the basic data of the planning target year with the data module, generating a projection model and an operation stage model of the power transmission line and the energy storage, and solving to obtain a planning scheme;

the simulation module is used for performing simulation operation on the planning scheme generated by the generation module by using the full-period safety constraint unit combination model to obtain annual load shedding and new energy power discarding results;

the first scene module is used for determining an extreme scene set according to the annual load shedding and new energy power discarding results obtained by the simulation module;

The second scene module is used for generating a typical scene set by adopting a time sequence clustering algorithm;

and the planning module is used for transmitting the extreme scene set generated by the first scene module and the typical scene set generated by the second scene module into the planning model when the set convergence condition is not reached, and outputting the updated planning scheme.

Compared with the prior art, the invention has at least the following beneficial effects:

the network storage collaborative planning method iteratively taking typical scenes into consideration comprehensively considers the construction cost of the power transmission line and the energy storage, and considers the new energy and the load absorption condition in extreme scenes. The method is used for reducing the load shedding and new energy power rejection situations in the operation stage in the power transmission network-energy storage planning of the power system, enhancing the coping capacity of the planning scheme to the extreme scenes, and better conforming the obtained planning scheme to practice by establishing a planning decision-operation simulation feedback mechanism, wherein the obtained planning scheme can have higher capacity of coping with the extreme scenes while having economy.

Further, the power transmission line and the energy storage candidate set are generated to ensure that the planning scheme meets the actual construction requirement.

Furthermore, the power transmission line and the energy storage building model are the basis for providing a planning scheme.

Further, the power transmission line construction constraint reflects the power transmission line construction state through 0-1 variable, and the constraint of the energy storage construction model controls the range of the actual energy storage construction capacity.

Further, the operation cost comprises energy loss cost caused by the energy storage charging and discharging process and power generation cost of the thermal power generating unit.

Furthermore, the constraint condition of the operation stage is to give out the output range, the climbing rate, the energy storage charging and discharging power range, the energy storage electric quantity storage range and the node power balance of the unit, so that the related quantity accords with the operation practice of the electric power system.

Further, according to the simulation result of full-period operation, the most serious days of load shedding and new energy power rejection are comprehensively selected as extreme scenes, and two extreme scenes are selected in each iteration. And adopting a time sequence clustering algorithm to the operation scene from which the extreme scene is removed, and generating four typical scenes each time of iteration.

It will be appreciated that the advantages of the second aspect may be found in the relevant description of the first aspect, and will not be described in detail herein.

In summary, the invention improves the acceptance of the power grid to clean random power sources such as wind power, solar power generation and the like, improves the supporting effect of the power grid on low carbonization of the power system, improves the operation flexibility of the system, promotes the consumption of new energy, and simultaneously realizes the optimization of economic cost, thereby being applicable to large-scale energy storage and power transmission grid joint planning.

The technical scheme of the invention is further described in detail through the drawings and the embodiments.

Drawings

FIG. 1 is a schematic flow chart of the present invention;

FIG. 2 is a diagram of a modified IEEE24 node system network architecture;

fig. 3 is a schematic diagram of a computer device according to an embodiment of the invention.

Fig. 4 is a block diagram of a chip according to an embodiment of the present invention.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

In the description of the present invention, it will be understood that the terms "comprises" and "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

It is also to be understood that the terminology used in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

It should be further understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations, e.g., a and/or B, may represent: a exists alone, A and B exist together, and B exists alone. In the present invention, the character "/" generally indicates that the front and rear related objects are an or relationship.

It should be understood that although the terms first, second, third, etc. may be used to describe the preset ranges, etc. in the embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are only used to distinguish one preset range from another. For example, a first preset range may also be referred to as a second preset range, and similarly, a second preset range may also be referred to as a first preset range without departing from the scope of embodiments of the present invention.

Depending on the context, the word "if" as used herein may be interpreted as "at … …" or "at … …" or "in response to a determination" or "in response to detection". Similarly, the phrase "if determined" or "if detected (stated condition or event)" may be interpreted as "when determined" or "in response to determination" or "when detected (stated condition or event)" or "in response to detection (stated condition or event), depending on the context.

Various structural schematic diagrams according to the disclosed embodiments of the present invention are shown in the accompanying drawings. The figures are not drawn to scale, wherein certain details are exaggerated for clarity of presentation and may have been omitted. The shapes of the various regions, layers and their relative sizes, positional relationships shown in the drawings are merely exemplary, may in practice deviate due to manufacturing tolerances or technical limitations, and one skilled in the art may additionally design regions/layers having different shapes, sizes, relative positions as actually required.

The invention provides a network storage collaborative planning method for iteratively incorporating a typical scene, which comprehensively considers the complementary relation between grid rack optimization and energy storage configuration of a power grid and the possible load shedding and new energy power rejection situations in an extreme scene, aims at optimizing economy and reliability, builds a power transmission network-energy storage joint planning model of a system level, and iteratively incorporates the extreme scene continuously through operation simulation and adopts a typical scene generated by a time sequence clustering algorithm to iteratively correct an operation part model in the planning model, so that the obtained planning scheme has strong capability of coping with the extreme scene. The scheme obtained by the method can effectively reduce investment cost while having enough extreme scene coping capability and meeting the operation constraint of the power system containing new energy.

Referring to fig. 1, the method for collaborative planning of network storage iteratively incorporated into a typical scenario of the present invention includes the following steps:

s1, acquiring basic technical data of an electric power system of a planned horizontal year, and generating a power transmission line and an energy storage candidate set;

the system basic technical data comprises: technical parameters of various types of power supplies in the power system include power grid and network parameters, load requirements and history information of new energy power generation.

s201, generating a model of the transmission line and energy storage construction;

the investment cost of the transmission line is specifically as follows:

wherein z is _l The method is characterized in that the method is a binary variable, reflects the line construction state, namely, determines whether a certain line counts the investment cost of a transmission line or not through the line construction state variable, and specifically comprises the following steps:

the investment cost of energy storage is specifically as follows:

constraint conditions:

the energy storage actual construction capacity constraint is as follows:

wherein C is ^Line,Inv The annual construction cost of the total transmission line is C _l The annual construction cost of the transmission line is calculated; omega shape _l /Ω _nl Omega for the set of lines already built/to be built _nes Energy storage candidate set, C ^ESS,Inv To the cost of energy storage, alpha _j /β _j Representing the projected cost per MW of power capacity and charge capacity of the stored energy j,and (3) as an upper limit of energy storage power, gamma is the ratio of the capacity of electric quantity and the capacity of power of the energy storage to be built.

S202, generating a model of an operation stage;

aiming at the network storage collaborative planning containing new energy, the operation cost of thermal power-containing operation cost, energy storage operation cost, load shedding penalty item for guaranteeing load supply and operation cost of new energy unit power-off penalty item for improving new energy consumption are provided. The operation phase constraint is constructed by thermal power generating unit operation constraint, energy storage operation constraint, load shedding constraint, node operation constraint and transmission line operation constraint. Specifically:

Construction of a running cost calculation model

The part considers the cost brought by the charge and discharge of the energy storage and the thermal power generating unit

The running cost of the extreme scene is specifically:

wherein c ^ess For the operating costs of the energy storage,for the operating cost per unit time of the thermal power generating unit t period under the scene s, < >>Discharging/charging power for energy store j in period t, +.>For the discharging/charging efficiency of the energy storage j to be built, U _t /U _e Omega for a typical scene/extreme scene set _g Is a thermal power generating unit set.

Construction of various operating constraints

The operation constraint comprises a thermal power generating unit operation constraint, a new energy unit operation constraint, an energy storage operation constraint, a load shedding constraint, a node operation constraint and a transmission line operation constraint:

the energy storage operation constraint is specifically as follows:

wherein x is _s,g,t A variable of 0-1 represents a machine set start-stop state; f (F) _s,g,t The output power of the thermal power unit is the output power at time t under the scene s of the thermal power unit; k (k) _g,n /h _g,n Slope and intercept of the piecewise linearization power generation cost of the thermal power unit g; k is a positive integer; t (T) _i ^Adi The energy storage regulation period is adopted;the initial electric quantity ratio is the energy storage; />The energy storage charge and discharge efficiency is achieved; />And storing electric quantity for energy storage.

S3, performing operation simulation on the planning scheme generated in the step S2 by using a full-period safety constraint unit combination model to obtain annual load shedding and new energy power discarding results;

Constructing models aimed at minimizing operating costs

The part considers the running cost of thermal power, new energy and energy storage, and simultaneously considers a load shedding penalty term introduced for ensuring load supply and a new energy unit power discarding penalty term introduced for improving new energy consumption.

minC ^Ope +C ^Thermal,Ope +C ^Storage,Ope +C ^Load,Ope +C ^NE,Ope (16)

The operation cost is calculated specifically as follows:

C ^Ope ＝C ^Thermal,Ope +C ^Storage,Ope +C ^Load,Ope +C ^NE,Ope (17)

running cost C of thermal power ^Thermal,Ope The method comprises the following steps:

energy storage running cost C ^Storage,Ope The method comprises the following steps:

load shedding penalty term C ^Load,Ope The method comprises the following steps:

new energy unit electricity discarding penalty item C ^NE,Ope The method comprises the following steps:

wherein Ω _Thermal Is a thermal power unit set; c (C) ^Thermal,Ope The running cost of the thermal power is; c (C) ^Storage,Ope The energy storage running cost; c (C) ^Load,Ope The load cost is cut; c (C) ^NE,Ope Is new energyThe source electricity discarding cost;the unit operation cost of the thermal power generating unit is;the actual output of the thermal power generating unit is obtained; />The energy storage running cost; />Charging power and discharging power for the stored energy; c ^Load Load shedding cost is unit; />For load shedding power; c ^NE The electricity discarding cost is the new energy of unit; />And discarding electric power for the new energy unit.

Construction of various operating constraints

The operation constraint comprises a thermal power unit operation constraint, a new energy unit operation constraint, an energy storage operation constraint, a load shedding constraint, a node operation constraint and a transmission line operation constraint.

The operation constraint of the thermal power generating unit is specifically as follows:

The operation constraint of the new energy unit is specifically as follows:

the energy storage operation constraint is specifically as follows:

the load shedding constraint is specifically as follows:

the node operation constraint is specifically:

the day with the largest cutting load and the day with the largest new energy waste are respectively regarded as one extreme scene. And clustering the residual daily payload curves after the extreme scenes are eliminated, and obtaining the typical scenes. After the time series aggregation, the extreme scene set and the typical scene set will update the scene set of the planning model.

the objective function of the clustering algorithm generated by the typical scene set is:

s501, selecting a typical scene according to the average value of all curves in each group;

s502, weight is distributed for typical days according to the relative size of each cluster;

the typical day allocation weights are specifically:

s503, converting the typical daily load of each cluster into a net load average value through a scaling factor.

The scaling factor is specifically:

wherein r is _i,j A timing curve j for cluster r; c _i /nc _j The number of the curves is the cluster center curve and the cluster; n (N) ^c Is the total number of time sequence curves; sigma (sigma) _s Scaling factor for scene s; v _j,t Is the value of the timing curve j during period t.

And S6, if the set convergence condition is not met, transmitting the extreme scene set generated in the step S4 and the typical scene set generated in the step S5 into a planning model, updating the scene set of the planning model, and outputting the updated planning scheme.

In still another embodiment of the present invention, a system for collaborative planning of a network storage of an iterative inclusion typical scenario is provided, where the system can be used to implement the method for collaborative planning of a network storage of an iterative inclusion typical scenario described above, and specifically, the system for collaborative planning of a network storage of an iterative inclusion typical scenario includes a data module, a generating module, a simulation module, a first scenario module, a second scenario module, and a planning module.

The data module acquires basic technical data of a planning horizontal year power system and generates a power transmission line and an energy storage candidate set;

In yet another embodiment of the present invention, a terminal device is provided, the terminal device including a processor and a memory, the memory for storing a computer program, the computer program including program instructions, the processor for executing the program instructions stored by the computer storage medium. The processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processor, digital signal processor (Digital Signal Processor, DSP), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, etc., which are the computational core and control core of the terminal adapted to implement one or more instructions, in particular to load and execute one or more instructions to implement the corresponding method flow or corresponding functions; the processor of the embodiment of the invention can be used for iterating the operation of the network storage collaborative planning method which is incorporated into a typical scene, and comprises the following steps:

Basic technical data of a planned horizontal year power system is obtained, and a power transmission line and an energy storage candidate set are generated; modeling basic data of a planning target year by combining the power transmission line and the energy storage to-be-selected set, generating a modeling model and an operation stage model of the power transmission line and the energy storage, and solving to obtain a planning scheme; simulating the generated planning scheme by using a full-period safety constraint unit combination model to obtain a full-year load shedding and new energy power discarding result; determining an extreme scene set according to annual load shedding and new energy power discarding results; generating a typical scene set by adopting a time sequence clustering algorithm; and when the set convergence condition is not reached, transmitting the generated extreme scene set and the generated typical scene set into a planning model, and outputting an updated planning scheme.

Referring to fig. 3, the terminal device is a computer device, and the computer device 60 of this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61, the computer program 63 when executed by the processor 61 implements the reservoir inversion wellbore fluid composition calculation method of the embodiment, and is not described in detail herein to avoid repetition. Alternatively, the computer program 63, when executed by the processor 61, implements the functions of iteratively incorporating the models/units in the network storage collaborative planning system of the exemplary scenario, and is not described herein in detail to avoid repetition.

The computer device 60 may be a desktop computer, a notebook computer, a palm top computer, a cloud server, or the like. Computer device 60 may include, but is not limited to, a processor 61, a memory 62. It will be appreciated by those skilled in the art that fig. 3 is merely an example of a computer device 60 and is not intended to limit the computer device 60, and may include more or fewer components than shown, or may combine certain components, or different components, e.g., a computer device may also include an input-output device, a network access device, a bus, etc.

The processor 61 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory 62 may be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 may also be an external storage device of the computer device 60, such as a plug-in hard disk provided on the computer device 60, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like.

Further, the memory 62 may also include both internal storage units and external storage devices of the computer device 60. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is to be output.

Referring to fig. 4, the terminal device is a chip, and the chip 600 of this embodiment includes a processor 622, which may be one or more in number, and a memory 632 for storing a computer program executable by the processor 622. The computer program stored in memory 632 may include one or more modules each corresponding to a set of instructions. Further, the processor 622 may be configured to execute the computer program to perform the above-described iterative inclusion of a network collaborative planning method for a typical scenario.

In addition, chip 600 may further include a power supply component 626 and a communication component 650, where power supply component 626 may be configured to perform power management of chip 600, and communication component 650 may be configured to enable communication of chip 600, e.g., wired or wireless communication. In addition, the chip 600 may also include an input/output (I/O) interface 658. Chip 600 may operate based on an operating system stored in memory 632.

In a further embodiment of the present invention, the present invention also provides a storage medium, in particular, a computer readable storage medium (Memory), which is a Memory device in a terminal device, for storing programs and data. It will be appreciated that the computer readable storage medium herein may include both a built-in storage medium in the terminal device and an extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also stored in the memory space are one or more instructions, which may be one or more computer programs (including program code), adapted to be loaded and executed by the processor. The computer readable storage medium may be a high-speed RAM Memory or a Non-Volatile Memory (Non-Volatile Memory), such as at least one magnetic disk Memory.

One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the respective steps of the networked collaborative planning method for iterative inclusion in a typical scenario in the above-described embodiments; one or more instructions in a computer-readable storage medium are loaded by a processor and perform the steps of:

For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

Referring to fig. 2, to verify the effectiveness of the method of the present invention, a modified IEEE 24 node system was selected to verify the effectiveness of the proposed method, the system comprising 14 3765 megawatt thermal generators, 6 600 megawatt wind farms and 4 2000 megawatt solar farms. In order to effectively verify the proposed method, 14 transmission lines are respectively built on BUS2-BUS 4 (multiplied by 2), BUS 3-BUS9 (multiplied by 2), BUS6-BUS10 (multiplied by 2), BUS 7-BUS 8 (multiplied by 2), BUS12-BUS23 (multiplied by 2), BUS13-BUS23 (multiplied by 2) and BUS15-BUS16 (multiplied by 2), and 7 groups of energy storage are respectively built on BUS6, BUS9, BUS10, BUS13, BUS15, BUS19 and BUS 22.

After 4 iterations, the power transmission line and the energy storage are jointly planned by using the extreme scene selected by the method, and the result is shown in table 1.

Table 1 detailed investment results after four iterations

In order to reduce the system cut load, both the energy storage and the power transmission line have certain investment. The load rate change conditions of the original power transmission line in the power transmission corridor before and after investment are shown in table 2.

Table 2 comparison of transmission line load ratios before and after investment

Table 3 transmission line and energy storage investment results for two methods in planning model

With the input of a new transmission line, the load rate of the original line is gradually reduced. Therefore, the investment of power transmission can relieve the pressure of the power transmission of the original line. The investment of the invention on the transmission line and the energy storage can effectively reduce the cut load caused by high-proportion new energy permeation and transmission blockage. The investment results of the two methods are shown in table 3. The simple extreme scene selection method has significantly smaller calculation results of investment cost and capacity of the transmission line and the energy storage than the iterative inclusion extreme scene method. The method is very suitable for improving the coping capability of the network storage planning scheme to the polar scene.

In summary, the network storage collaborative planning method and system for iteratively incorporating the typical scene can effectively solve the problems of excessive load and new energy consumption caused by high-proportion new energy and power transmission blockage, the uncertainty of representing the new energy and the load through the typical scene and the extreme scene reduces the difficulty of solving the planning problem to a certain extent, meanwhile, the planning scheme is evaluated through long-term operation simulation, and further the scene set is iteratively fed back and expanded, so that the reliability of the planning scheme is ensured. Therefore, the invention can fully coordinate the contradiction between the solving efficiency of the planning problem and the reliability of the planning scheme, and ensures the effectiveness of the planning scheme on the basis of fully ensuring the economy of the network storage planning scheme. Therefore, the method provided by the invention is suitable for efficiently and reliably solving the large-scale network storage planning problem.

It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.

In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal and method may be implemented in other manners. For example, the apparatus/terminal embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a usb disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a Random-Access Memory (RAM), an electrical carrier wave signal, a telecommunications signal, a software distribution medium, etc., it should be noted that the content of the computer readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in jurisdictions, such as in some jurisdictions, according to the legislation and patent practice, the computer readable medium does not include electrical carrier wave signals and telecommunications signals.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

The above is only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited by this, and any modification made on the basis of the technical scheme according to the technical idea of the present invention falls within the protection scope of the claims of the present invention.

Claims

1. The network storage collaborative planning method for iteratively incorporating the typical scene is characterized by comprising the following steps of:

2. The method for collaborative planning of network storage for iterative inclusion in a typical scenario of claim 1, wherein the system base technology data comprises: technical parameters of various types of power supplies in the power system include power grid and network parameters, load requirements and history information of new energy power generation.

3. The method for collaborative planning of network storage for iterative inclusion in a typical scenario according to claim 1, wherein in step S2, the model for modeling the transmission line and the stored energy is specifically:

run phase modelThe method comprises the following steps:

wherein c ^ess For the operating costs of the energy storage,is the running cost per unit time of the thermal power generating unit t period under the scene s,for the discharge/charge power of energy store j in scene s during period t, < >>For the discharging/charging efficiency of the energy storage j to be built, U _t /U _e Omega for a typical scene/extreme scene set _g Is a thermal power unit set, and deltat is.

4. A network storage collaborative planning method according to claim 3 iteratively incorporating a typical scenario, characterized in that constraints of a model of transmission line and energy storage construction are as follows:

the energy storage actual construction capacity constraint is as follows:

wherein C is ^Line,Inv The annual construction cost of the total transmission line is C _l The annual construction cost of the transmission line is calculated; omega shape _l /Ω _nl Line set already built line set to be built line set omega _nes For storing energy, the set C ^ESS,Inv To the cost of energy storage, alpha _j /β _j Representing the projected cost per MW of power capacity and charge capacity of the stored energy j,as the upper limit of energy storage power, gamma is the capacity and power of the electric quantity of energy storage to be built Capacity ratio.

5. A method for collaborative planning of network storage for iterative inclusion in a typical scenario according to claim 3 wherein the model constraints at run-time are as follows:

the energy storage operation constraint is specifically as follows:

wherein x is _s,g,t A variable of 0-1 represents a machine set start-stop state; f (F) _s,g,t The output power of the thermal power unit is the output power at time t under the scene s of the thermal power unit; k (k) _g,n /h _g,n Slope and intercept of the piecewise linearization power generation cost of the thermal power unit g; k is a positive integer; t (T) _i ^Adi The energy storage regulation period is adopted;the initial electric quantity ratio is the energy storage; />The energy storage charge and discharge efficiency is achieved; />Store the electric quantity for energy storage, ">Charging power for energy storage->To store energy, Ω _Storage Omega, a collection of stored energy _T For a collection of typical time of day sections, +.>For the discharge power of the stored energy>For the capacity of energy storage->For the energy storage of electricity, ">Is the upper limit of the stored electricity.

6. The network storage collaborative planning method for iteratively incorporating a typical scenario according to claim 1, wherein a full-period safety constraint unit combination model is constructed by taking thermal power unit operation constraint, new energy unit operation constraint, energy storage operation constraint, load shedding constraint, node operation constraint and transmission line operation constraint as constraint conditions with the aim of minimizing operation cost, and specifically comprises the following steps:

min C ^Ope +C ^Thermal,Ope +C ^Storage,Ope +C ^Load,Ope +C ^NE,Ope

7. The method for collaborative planning of network storage for iterative inclusion in a typical scenario of claim 6, wherein thermal power generating unit operating constraints are specifically:

the operation constraint of the new energy unit is specifically as follows:

the energy storage operation constraint is specifically as follows:

wherein,charging power for energy storage->To store energy, Ω _Storage For the collection of stored energy in the system,for the stored power generation power>For the energy storage of electricity, ">Charging power for energy storage->Discharge power for energy storage, T _i ^Adi Power regulation for energy storagePeriod of->The initial electric quantity ratio is the energy storage;

the load shedding constraint is specifically as follows:

the node operation constraint is specifically:

8. The method for collaborative planning of network storage for iterative inclusion of a canonical scene according to claim 1, wherein the set of extreme scenes is specifically:

9. The method for collaborative planning of network storage for iterative inclusion of a canonical scene according to claim 1, wherein generating a canonical scene set using a temporal clustering algorithm is specifically:

wherein c _i Is a cluster center curve after clustering, r _i,j Is a typical dayA payload curve.

10. A networked collaborative planning system for iteratively incorporating a representative scenario, comprising: