CN116865318A

CN116865318A - Power transmission network and energy storage joint planning method and system based on two-stage random optimization

Info

Publication number: CN116865318A
Application number: CN202310799011.XA
Authority: CN
Inventors: 李志远; 丛立章; 杨钤; 王建学
Original assignee: Xian Jiaotong University
Current assignee: Xian Jiaotong University
Priority date: 2023-06-30
Filing date: 2023-06-30
Publication date: 2023-10-10

Abstract

The invention discloses a power transmission network and energy storage joint planning method and system based on two-stage random optimization, wherein the random optimization method based on a typical scene fully considers the volatility and uncertainty of load and new energy, a K-Means curve clustering and gray correlation analysis method is introduced to obtain a given number of typical days, a constraint relaxation and McCormick linearization method is adopted to process planning-operation coupling constraint, a model is converted into a mixed integer linear problem from a nonlinear problem, and then the whole problem is split into an investment main problem and an operation sub-problem by adopting a Benders decomposition framework, and the solving efficiency is improved by adopting iterative solution of the main problem and the sub-problem.

Description

Power transmission network and energy storage joint planning method and system based on two-stage random optimization

Technical Field

The invention belongs to the technical field of planning and evaluation in a power system, and particularly relates to a power transmission network and energy storage joint planning method and system based on two-stage random optimization.

Background

The high proportion of renewable energy is connected into the power grid to bring a series of uncertain factors to the power system, especially the intermittent and fluctuating characteristics of wind, light and other output forces, which can cause severe fluctuation to the net load of the power system. The annual growth in renewable energy permeability can cause grid blockage, which presents a challenge to grid planning and operation.

Therefore, in order to improve the flexibility and the renewable energy source digestion capability of the power system, the reasonable configuration of the power transmission engineering is significant for realizing the target of 30.60 carbon emission. At the same time, the energy storage system has the time migration capability of power and energy and flexible installation position, plays an important role in coping with the insufficient flexibility of the system and the transmission blockage, and is considered to be one of important technical supports of the future high-proportion renewable energy power system. For decades, both expansion planning and energy storage planning around power systems have each been largely studied. The combined planning can be started from the overall situation of power system planning, the interaction relation between energy storage and a power transmission network on the planning and operation level can be fully considered, and a unified multi-equipment planning combination scheme is determined by taking low-carbon economy as a target on the basis.

Therefore, the research on the combined planning method of the power transmission network and the energy storage has important significance for realizing the low-carbon target, improving the flexibility of the power system and reducing the investment and the operation cost of the power transmission network.

Disclosure of Invention

The invention aims to solve the technical problems that the prior art is insufficient, and provides a two-stage random optimization-based power transmission network and energy storage combined planning method and system, which are used for solving the technical problems that the conventional planning method does not consider a multi-scenario problem linearization processing and accelerating strategy and has poor capability of coping with power transmission blockage and new energy output difficulty.

The invention adopts the following technical scheme:

the combined planning method of the power transmission network and the energy storage based on the two-stage random optimization comprises the following steps:

establishing a set of daily net load curves based on basic technical data of the electric power system, and obtaining a typical scene set of reactive load and new energy uncertainty through scene reduction;

establishing a power transmission network-energy storage joint planning model based on a typical scene set of reaction load and new energy uncertainty;

constructing a construction decision of the power transmission network and the energy storage based on the first-stage constraint of the power transmission network-energy storage joint planning model to construct an upper limit of the quantity of the power transmission lines and the energy storage elements;

Linearization processing is based on bilinear terms and integer variables in second-stage constraints of the grid-energy storage joint planning model;

solving a power transmission network-energy storage joint planning model after linearization treatment by adopting a Benders decomposition frame; dividing a model into an investment main problem and a series of running sub-problems for iterative solution, taking planning decisions of a power transmission network and energy storage as the investment main problem, taking running check of a typical scene as the running sub-problem, solving the investment main problem to obtain integer parameters, solving a solution of an optimal sub-problem under the integer parameters, returning optimal cutting constraint to the main problem, and obtaining an upper bound of a joint planning problem; continuously solving and updating the investment master problem under the optimal cutting constraint to obtain new integer parameters and obtain the lower bound of the joint planning problem; and continuously and iteratively solving the investment main problem and the operation sub-problem, and realizing the combined planning of the power transmission network and the energy storage based on the obtained combined planning problem solution.

Specifically, a typical scene set for obtaining the reaction load and the uncertainty of the new energy through scene reduction is specifically:

selecting the number of required typical scenes or taking the inflection points of error squares and curves as the optimal number of typical scenes, taking the inflection points as the cluster number of clusters, clustering all the net load curves by adopting a K-Means clustering method, and dividing all the daily net load curves into curve clusters with given number;

Based on a given number of curve clusters, gray correlation degrees between all curves in the curve clusters and cluster centers thereof are calculated, and one curve with the largest correlation degree is selected as a representative daily net load curve of the curve clusters and corresponds to a representative day.

Further, the gray correlation ε (k) is calculated as follows:

wherein ,X₀ (k) The position of the kth point of the cluster center sequence curve; x is X _i (k) The position of the kth point of the ith sequence curve; ρ is the resolution factor.

Specifically, the objective function of the preliminary model based on two-stage random optimization of the grid-energy storage joint planning is to minimize the construction cost of the grid-energy storage joint planning and the combined operation cost of the unit in each typical scene, and specifically, the objective function is as follows:

wherein ,C^Inv \C ^Op The investment cost and the operation cost; omega shape _nl \Ω _nes The method comprises the steps of collecting all lines to be selected and collecting energy storage candidate nodes in a system;the construction cost and 0-1 construction variable of the kth line are calculated; />Cost and number of construction of unit energy storage devices for the ith nodeA quantity integer variable; ζ is the fund recovery coefficient of the equipment to be built; omega shape _B \Ω _s The method comprises the steps of collecting all nodes in a system and collecting typical scenes; omega shape _WT \Ω _PV The method comprises the steps of collecting all wind turbine generators and collecting all photovoltaic turbine generators in a system; c (C) ^Load,curt Cost per unit of load loss electricity; />The power loss of the i node at t time under j scenes is obtained; />The method is characterized in that the method is the power generation cost function of the ith thermal power generating unit under j scenes, the output at the moment t, the starting cost and the stopping cost; c (C) ^WT,Curt /C ^PV,Curt Penalty cost for unit wind power generation and photovoltaic power generation is abandoned; />The method is characterized in that the maximum allowable electric quantity discarding part exceeding the wind power generation and photovoltaic power generation at the moment t under j scenes of the ith wind turbine generator system and the photovoltaic turbine generator system is adopted.

Further, the constraint conditions are as follows:

constraint of planning decision phase of power transmission network and energy storage:

the constraint conditions of the first stage comprise the upper limit of the number of the power transmission lines in the line corridor and the upper limit of the number of the candidate node energy storage devices, and the constraint conditions are as follows:

constraint of typical scenario run check phase:

the second-stage constraint comprises the running cost of the thermal power generating unit:

thermal power generating unit output limit:

shortest start-stop time constraint of thermal power generating unit:

logical constraint of thermal power generating unit operation state:

output interval of new energy unit:

new energy consumption constraint:

energy storage charge-discharge power interval:

limit energy storage and charge and discharge simultaneously:

energy storage electric quantity constraint:

network security constraints for existing lines:

Direct current power flow model of line to be selected:

phase angle of nodes at two ends of a line to be selected:

upper and lower limit constraint of line flow to be selected:

node power balancing constraints:

wherein ,Ω_lc /Ω _R The method comprises the steps of collecting all power transmission corridor sets and loads in a system;the maximum number of lines allowed in the power transmission corridor and the number of lines existing in the ith corridor are obtained; />For the single start-up cost and the single stop cost of the ith unit>The method comprises the steps of setting an operation state 0-1 variable, a starting state 0-1 variable and a shutdown state 0-1 variable of an ith unit at a moment t under an s scene; k (k) _i,j /h _i,j The slope and the intercept of a j-th section in the linear power generation cost curve of the ith unit are obtained; />The output lower limit, the output upper limit, the climbing rate, the shortest starting time and the shortest relation of the ith unit are adopted as the output lower limitTime of machine; />The method comprises the steps of setting a wind resource curve and a maximum wind abandoning rate of an ith wind turbine generator set at a moment t under a j scene; />The method comprises the steps of (1) setting an optical resource curve and a maximum light rejection rate of an ith photovoltaic unit at a moment t under a j scene;charging power and discharging power of energy storage of the ith candidate node in a j scene; />Charging efficiency and discharging efficiency of the energy storage device that is the i candidate node; />The method comprises the steps of (1) setting a charging state 0-1 variable and a discharging state 0-1 variable of energy storage of an ith candidate node at a moment t under a j scene; / >Maximum charging power, discharging power, lower limit of electric quantity and upper limit of electric quantity of the energy storage equipment for the i candidate node unit; p (P) _k,s,t /θ _k,i,s,t The phase angle value of the i-end node and the line reactance are the line power flow of the kth line at the moment t under the s scene; />The minimum value and the maximum value of the node phase angle are obtained; />Line reactance and line capacity for the kth line.

Specifically, bilinear terms and integer variables in the second-stage constraint of the linearization processing power transmission network-energy storage joint planning model are specifically:

performing relaxation treatment in energy storage operation constraint of a second stage of the power transmission network-energy storage joint planning model; in the direct current power flow of the transmission line to be built in the second stage of the model, carrying out linearization treatment through McCormick;

the second-stage constraint is converted to a mixed integer linear problem based on eliminating bilinear terms in the second-stage constraint.

Further, the relaxation treatment is as follows:

the linearization process is as follows:

wherein ,charging power in j scene for energy storage of ith candidate node, n _i For (I)>For (I)>Discharging power of energy storage of ith candidate node in j scene, +.>Maximum discharge power of energy storage device per unit of i candidate node,/ >A 0-1 variable for the charging state of energy storage of the ith candidate node at t moment in j scene, z _k Constructing a variable theta for 0-1 of the kth line _k,i,s,t Is the phase angle value of the i-end node of the kth line at the t moment in the s scene, theta _k,j,s,t Is the phase angle value of the j-end node of the kth line at the t moment in the s scene, and x _k Is the kth lineLine capacity of the road, P _k,s,t For the line flow of the kth line at the moment t under the s scene, omega _nl Omega is the set of all the candidate lines in the system _B S is the index of the operation scene, and T is the number of time sections in the scheduling period.

Further, the problem of converting the second-stage constraint into mixed integer linearity is specifically:

all the Boolean variables in the operation constraint of the thermal power generating unit are converted into continuous variables as follows:

wherein ,for the variable of 0-1 of the running state of the ith unit at the moment t in the s scene,/for the moment t>For the variable of the starting state 0-1 of the ith unit at the moment t under the s scene,/for the moment t>The variable is the shutdown state 0-1 of the ith unit at the moment t under the s scene;

in the operation sub-problem, the charge and discharge state in the energy storage operation process is an integer variable of 0-1, the energy loss cost of the energy storage operation is added in the operation cost of the objective function to avoid the same charge and discharge of a single energy storage device, the constraint related to the charge and discharge state of the energy storage is removed by the objective function, and the objective function is as follows:

wherein ,C^Inv \C ^Op The investment cost and the operation cost; omega shape _nl \Ω _nes The method comprises the steps of collecting all lines to be selected and collecting energy storage candidate nodes in a system;the construction cost and 0-1 construction variable of the kth line are calculated; />The method comprises the steps of setting up cost and integer variable of the number of the unit energy storage devices of an ith node; ζ is the fund recovery coefficient of the equipment to be built; omega shape _B \Ω _s The method comprises the steps of collecting all nodes in a system and collecting typical scenes; omega shape _WT \Ω _PV The method comprises the steps of collecting all wind turbine generators and collecting all photovoltaic turbine generators in a system; c (C) ^Load,curt Cost per unit of load loss electricity; />The power loss of the i node at t time under j scenes is obtained; />The method is characterized in that the method is the power generation cost function of the ith thermal power generating unit under j scenes, the output at the moment t, the starting cost and the stopping cost; c (C) ^WT,Curt /C ^PV,Curt Penalty cost for unit wind power generation and photovoltaic power generation is abandoned; />The method comprises the steps that the maximum allowable electric quantity discarding part exceeding the wind power generation and photovoltaic power generation at the moment t of an ith wind turbine generator system and a photovoltaic turbine generator system under j scenes is adopted; c _L Penalty cost for energy loss during operation of the energy storage device.

Specifically, investment master problem:

operation sub-problem:

wherein ,u_s To vector the pairs of multipliers for the constraint in the constraint sub-problem, The vector y which is the integer variable already determined, y is the vector of the Boolean integer variable, eta is the auxiliary variable, and d ^T /c _s ^T Coefficient vector, x, being the objective function in the s-th scene _s Vector, A/E, being a continuous variable in the s-th scenario _s /F _s B/h as constrained coefficient matrix _s To constrain the constant column vector at the right end, Ω _s Is the set of all typical scenarios in the system.

In a second aspect, an embodiment of the present invention provides a power transmission network and energy storage joint planning system based on two-stage random optimization, including:

the scene module is used for establishing a set of daily net load curves based on the basic technical data of the power system, and obtaining a typical scene set of reactive load and new energy uncertainty through scene reduction;

the construction module is used for constructing a power transmission network-energy storage joint planning model based on a typical scene set of reaction load and new energy uncertainty;

the processing module is used for constructing a construction decision of the power transmission network and the energy storage based on the first-stage constraint of the power transmission network-energy storage joint planning model to construct an upper limit of the quantity of the power transmission line and the energy storage elements; linearization processing is based on bilinear terms and integer variables in second-stage constraints of the grid-energy storage joint planning model;

The planning module adopts a Benders decomposition frame to solve the power transmission network-energy storage joint planning model after linearization treatment; dividing a model into an investment main problem and a series of running sub-problems for iterative solution, taking planning decisions of a power transmission network and energy storage as the investment main problem, taking running check of a typical scene as the running sub-problem, solving the investment main problem to obtain integer parameters, solving a solution of an optimal sub-problem under the integer parameters, returning optimal cutting constraint to the main problem, and obtaining an upper bound of a joint planning problem; continuously solving and updating the investment master problem under the optimal cutting constraint to obtain new integer parameters and obtain the lower bound of the joint planning problem; and continuously and iteratively solving the investment main problem and the operation sub-problem, and realizing the combined planning of the power transmission network and the energy storage based on the obtained combined planning problem solution.

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

a power transmission network and energy storage joint planning method based on two-stage random optimization is characterized in that a model is established as a two-stage random optimization model, the layout of the power transmission network and the energy storage is determined in the first stage, the operation requirement of a unit combination is met in the second stage, and the structure of the model is determined. The objective function considers the economy of the planning problem and the safety of the system operation, and the constraint condition fully reflects the actual operation state of the system.

Further, clustering the original daily payload curves using the K-Means method is an essential step in the present invention to construct a typical scene set. The clustering of daily payload curves is a basis for reducing the number of scenes, the complexity of a model can be effectively reduced, the solving efficiency is improved, and the selection of one daily payload curve with the largest association degree with a clustering cluster center as a representative curve is a key step for constructing a typical scene set. The typical scene needs to reflect the fluctuation condition of the actual medium load and the new energy as far as possible, and a curve with the largest association degree with the cluster center can fully represent the characteristics of the cluster curve and reflect the fluctuation condition of the actual net load curve, so that the effectiveness of the typical scene is ensured.

Further, gray correlation epsilon (k) is used for measuring the correlation degree between the daily payload curve and the clustered cluster center curve. The day of the daily net load curve with the highest gray correlation degree with the cluster center curve is selected as a typical day, so that the common characteristics of the cluster curve as much as possible can be obtained, and the fluctuation condition of the actual net load curve can be reflected.

Furthermore, the objective function of the preliminary model based on the two-stage random optimization of the power transmission network-energy storage joint planning is to minimize the construction cost of the power transmission network-energy storage joint planning and the combined operation cost of the unit under each typical scene, and the economy of the joint planning problem and the safety of the system operation are comprehensively considered. The optimal economical efficiency of the planning problem is guaranteed, and meanwhile, the rationality of the planning result in actual operation is guaranteed.

Further, constraints reflect and characterize limits of the build process and the operational state of the system. According to the structure of the model, constraint conditions are considered in two stages, so that the rationality and the definition of the constraint conditions are guaranteed, and further simplification and decomposition of the model are facilitated.

Further, eliminating nonlinear terms in the second-stage constraints of the model is a key step of linearizing the second-stage constraints of the model. The method converts the second-stage constraint of the model from non-linearity to mixed integer linearity, greatly reduces the complexity of the model, and directly eliminates or linearizes integer variables in the relaxed second-stage constraint, which is a key step of linearizing the second-stage constraint. And integer variables in the second-stage constraint are directly eliminated or relaxed into continuous variables, so that mixed integers are linearly converted into linearity, and the solving efficiency of the model is greatly improved. Because the adoption of the Benders solving framework needs to ensure that the sub-problems are linear problems, decision variables of the sub-problems are continuous variables, and integer variables are directly eliminated or linearized and relaxed, a solving condition is provided for the Benders solving of the model.

Furthermore, the relaxation treatment and the linearization treatment eliminate bilinear terms formed by the product of 0-1 integer variable and continuous variable in the joint planning problem, so that the complexity of the model is greatly reduced, and the decomposition of the model is facilitated.

Furthermore, the second-stage constraint is converted into a mixed integer linear problem, integer variables in the second-stage constraint are eliminated, the solving efficiency of the model is improved, and meanwhile conditions are provided for the Benders decomposition of the model.

Furthermore, the method for solving the linearized power transmission network-energy storage joint planning model by adopting the Benders algorithm is a core algorithm of the invention. Dividing a power transmission network-energy storage joint planning model into main and sub problems, taking a power transmission network-energy storage joint planning decision containing a built 0-1 integer variable as the main problem, taking operation check of a typical scene after linearization as the sub problem, and improving the solving efficiency of the model by solving the continuous iterative solution of the main and sub problems.

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 conclusion, the method is very suitable for reducing transmission blockage in the power system planning scheme, ensuring new energy delivery and improving the calculation efficiency for solving the large-scale power transmission network-energy storage joint planning problem.

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

Drawings

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

fig. 2 is a schematic diagram of an IEEE24 node test system.

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 two-stage random optimization-based power transmission network and energy storage joint planning method, which fully considers the fluctuation and uncertainty of loads and new energy sources by a random optimization method based on typical scenes, and introduces a K-Means curve clustering and gray correlation analysis method to obtain a given number of typical days. In addition, a constraint relaxation and McCormick linearization method is adopted to process programming-operation coupling constraint, and the model is converted from a nonlinear problem to a mixed integer linear problem. And furthermore, a Benders decomposition framework is adopted, the whole problem is split into an investment main problem and an operation sub-problem, and the solving efficiency is greatly improved through the iterative solution of the main problem and the sub-problem. The method provided by the invention eliminates bilinear terms and integer variables in the second-stage constraint in the power transmission network-energy storage planning model, and solves the problem by adopting a frame of Benders decomposition, and the method provided by the invention is suitable for improving the calculation efficiency of solving a large-scale power transmission network-energy storage joint planning problem by dividing the whole problem into an investment main problem and a series of operation sub-problems and solving the main problem and the sub-problem with smaller scale under the condition of ensuring the accuracy of the problem as much as possible, thereby reducing the difficulty of solving the problem and improving the solving efficiency.

Referring to fig. 1, the method for planning a power transmission network and energy storage combination based on two-stage random optimization of the invention comprises the following steps:

s1, acquiring system basic technical data from a power system planning department;

the system basic technical data comprises: technical parameters of various types of power supplies in the power system, existing power transmission grid and network parameters, technical parameters of the power transmission grid to be built, the construction cost, technical parameters and economic parameters of various types of energy storage equipment to be built, load requirements of long time scale and history information of new energy power generation.

S2, establishing a daily net load curve set, and obtaining a typical scene set of reaction load and new energy uncertainty through scene reduction;

s201, selecting the number of required typical scenes, or taking the inflection points of error squares and curves as the optimal number of typical scenes, taking the inflection points as the clustering number of the clusters, clustering all the net load curves by adopting a K-Means clustering method, and dividing all the daily net load curves into curve clusters with given number;

s2011, selecting the cluster number K of clusters, and giving K initial cluster centers;

s2012, calculating the distance between each point and each center point (usually Euclidean distance), and then grouping each point into the cluster closest to the cluster center;

S2013, calculating the gravity centers of all points in each cluster to serve as new cluster centers;

and S2014, repeating the steps S2012 and S2013, and stopping when the cluster core is not moved any more, obtaining a result.

Among the above four steps, it is important that the step S2011 selects a reasonable cluster number. Since the K-Means algorithm cannot determine the optimal cluster number, the cluster number of clusters needs to be selected at the same time of clustering.

The current common method is to select the cluster number corresponding to the inflection point of the error square sum curve as the optimal cluster number. The sum of squares (17) of errors is one of the important indexes for judging the effectiveness of clustering, and the value of the sum of squares is reduced along with the increase of the number of clusters of the clusters.

wherein ,X_i Is the set of curves in the ith cluster; c _i The clustering center of the ith clustering cluster; d (c) _i X) is the vector x to the cluster center c _i Is a euclidean distance of (c).

S202, obtaining a given number of curve clusters based on the step S201, calculating gray correlation degrees (17) between all curves in the curve clusters and cluster centers thereof, and selecting one curve with the largest correlation degree as a representative daily net load curve of the curve clusters, wherein the curve corresponds to a typical day.

wherein ,X₀ (k) The position of the kth point of the cluster center sequence curve; x is X _i (k) The position of the kth point of the ith sequence curve; ρ is a resolution factor, and is generally 0 to 0.8.

S3, establishing a power transmission network-energy storage joint planning model;

and (3) establishing a power transmission network-energy storage joint planning model based on the step (S2) obtaining a typical scene set of new energy and load. The objective function is to minimize the construction cost of the grid-energy storage joint planning and the combined running cost of the unit in each typical scene. And further considering constraint in stages, wherein the first stage is a planning decision stage of the power transmission network and the energy storage, and the construction decision of the power transmission network and the energy storage is constructed under the condition of upper limit constraint of construction cost. The second stage is a typical scene operation checking stage, and after the layout of the power transmission network and the energy storage is determined in the first stage, the second stage operation variable is adjusted so as to adapt to the fluctuation condition of new energy and load.

(1) Constructing an expression for an objective function

The objective function of the power transmission network-energy storage joint planning model is to minimize the construction cost and the operation cost under each typical scene, wherein the construction cost comprises the equal annual value of the power transmission line and the energy storage construction cost, and the operation cost comprises the power generation cost, the load shedding penalty cost and the wind discarding and light discarding cost of the thermal power generating unit.

wherein ,C^Inv \C ^Op The investment cost and the operation cost; omega shape _nl \Ω _nes The method comprises the steps of collecting all lines to be selected and collecting energy storage candidate nodes in a system;the construction cost and 0-1 construction variable of the kth line are calculated; />The method comprises the steps of setting up cost and integer variable of the number of the unit energy storage devices of an ith node; ζ is the fund recovery coefficient of the equipment to be built; omega shape _B \Ω _s The method comprises the steps of collecting all nodes in a system and collecting typical scenes; omega shape _WT \Ω _PV For the collection and all lights of all wind turbine generator sets in the systemA collection of photovoltaic units; c (C) ^Load,curt Cost per unit of load loss electricity; />The power loss of the i node at t time under j scenes is obtained; />The method is characterized in that the method is the power generation cost function of the ith thermal power generating unit under j scenes, the output at the moment t, the starting cost and the stopping cost; c (C) ^WT,Curt /C ^PV,Curt Penalty cost for unit wind power generation and photovoltaic power generation is abandoned; />The method is characterized in that the maximum allowable electric quantity discarding part exceeding the wind power generation and photovoltaic power generation at the moment t under j scenes of the ith wind turbine generator system and the photovoltaic turbine generator system is adopted.

(2) Constructing constraints for planning decision phase of power transmission network and energy storage

Under the conditions of limitation of the grid system and upper limit constraint of the construction cost, constraint conditions of the first stage comprise an upper limit (4) of the construction quantity of the transmission lines in the line corridor and an upper limit (5) of the construction quantity of the candidate node energy storage equipment.

(3) Constructing constraints for a typical scenario run check phase

And in the second stage, after the layout of the power transmission network and the energy storage is determined in the first stage, the operation variable of the second stage is adjusted so as to adapt to the fluctuation condition of new energy and load. The second-stage constraint comprises thermal power unit operation cost (6), thermal power unit output limit (7), thermal power unit shortest start-stop time constraint (8), thermal power unit operation state logic constraint (9), new energy unit output interval (10), new energy consumption constraint (11), energy storage charging and discharging power interval (12), energy storage simultaneous charging and discharging limiting (13), energy storage electric quantity constraint (14), network safety constraint (15) of existing lines, direct current power flow model (16) of lines to be selected, phase angle (17) of nodes at two ends of the lines to be selected, upper and lower limit constraint (18) of lines to be selected and node power balance constraint (19).

/>

wherein ,Ω_lc /Ω _R The method comprises the steps of collecting all power transmission corridor sets and loads in a system;the maximum number of lines allowed in the power transmission corridor and the number of lines existing in the ith corridor are obtained; />For the single start-up cost and the single stop cost of the ith unit>The method comprises the steps of setting an operation state 0-1 variable, a starting state 0-1 variable and a shutdown state 0-1 variable of an ith unit at a moment t under an s scene; k (k) _i,j /h _i,j The slope and the intercept of a j-th section in the linear power generation cost curve of the ith unit are obtained; />The output lower limit, the output upper limit, the climbing rate, the shortest starting time and the shortest shutdown time of the ith unit; />The method comprises the steps of setting a wind resource curve and a maximum wind abandoning rate of an ith wind turbine generator set at a moment t under a j scene; />The method comprises the steps of (1) setting an optical resource curve and a maximum light rejection rate of an ith photovoltaic unit at a moment t under a j scene;charging power and discharging power of energy storage of the ith candidate node in a j scene; />Charging efficiency and discharging efficiency of the energy storage device that is the i candidate node; />The method comprises the steps of (1) setting a charging state 0-1 variable and a discharging state 0-1 variable of energy storage of an ith candidate node at a moment t under a j scene; />Maximum charging power, discharging power, lower limit of electric quantity and upper limit of electric quantity of the energy storage equipment for the i candidate node unit; p (P) _k,s,t /θ _k,i,s,t The phase angle value of the i-end node and the line reactance are the line power flow of the kth line at the moment t under the s scene; />The minimum value and the maximum value of the node phase angle are obtained; />Line reactance and line capacity for the kth line.

S4, linearizing to process bilinear terms and integer variables in second-stage constraint of the power transmission network-energy storage joint planning model;

s401, in the energy storage operation constraint of the second stage of the power transmission network-energy storage joint planning model, a bilinear term generated by multiplying the number of energy storage construction and two decision variables of charge/discharge states is subjected to relaxation treatment; in the direct current flow of the transmission line to be built in the second stage of the model, linearizing treatment is carried out through McCormick due to bilinear terms generated by multiplying the building variable and two decision variables of the node phase angle;

(1) Bilinear term generated by multiplying relaxation treatment energy storage construction quantity variable and charge and discharge state variable

The energy storage charge-discharge power interval (12) is a nonlinear inequality constraint and contains a bilinear term, and is rewritten after relaxation treatment as follows:

(2) McCormick linearization processing bilinear term generated by multiplying 0-1 construction variable of power transmission line to be constructed by node phase angle

The direct current power flow model (16) of the line to be selected is a nonlinear equation constraint and contains a bilinear term, and the model can be rewritten after McCormick linearization treatment:

s402, eliminating bilinear terms in the second-stage constraint based on the step S401, and converting the second-stage constraint into a mixed integer linearity problem. And all the integral variables of the operation states in the operation constraint of the thermal power unit of the second stage are converted into continuous variables by adopting linearization relaxation. Meanwhile, the energy loss cost of energy storage operation is added in the operation cost of the objective function, and the simultaneous charging and discharging of the energy storage is avoided, so that integer variables of charging and discharging states in the constraint of the energy storage operation of the second stage are eliminated.

(1) Thermal power generating unit state relaxation

In an initial model of power transmission network-energy storage joint planning, the start-stop state variable and cross-time sequence constraint (8) of the thermal power generating unit have great influence on the solution of the model. Furthermore, the present invention employs linearized relaxation because the long-time scale planning problem does not place high demands on the accuracy of the run phase. All Boolean variables in the operation constraint of the thermal power generating unit are converted into continuous variables:

(2) Reduction and conversion of energy storage charge-discharge state

In the operation sub-problem, the charge and discharge state in the energy storage operation process is an integer variable of 0-1. To ensure linearity of the neutron problem model during the Benders decomposition solution, the constraints associated with the charge and discharge states of the stored energy should be removed. However, in order to ensure the economical efficiency of energy storage operation, a single energy storage device cannot be allowed to be charged and discharged simultaneously. Therefore, the energy loss cost of the energy storage operation is added in the operation cost of the objective function, so that the same charge and discharge of a single energy storage device are avoided:

wherein ,c_L Penalty cost for energy loss during operation of the energy storage device.

Thus, the objective function is changed to (24) while removing constraints (13) and (21) related to the state of charge and discharge of the stored energy.

S5, solving a power transmission network-energy storage joint planning model after linearization treatment by adopting a Benders decomposition frame; the model is divided into an investment main problem and a series of operation sub-problems for iterative solution, the planning decision of a power transmission network and energy storage is regarded as the investment main problem, and the operation check of a typical scene is regarded as the operation sub-problem.

After the linearization process of the models in the step S401 and the step S402, the objective function of the linearized model is (24), the constraint conditions in the first stage comprise (4) (5), and the constraint conditions in the second stage comprise (6) - (11), (14) - (15), (17) - (20) and (21) - (22); further, the overall problem of grid-energy storage planning is represented in a compact format:

wherein ,x_s Vectors that are continuous variables in the s-th scene; y is the direction of the integer variable 0-1An amount of; c _s /d _s Coefficient vectors that are objective functions in the s-th scene; A/E _s /F _s A coefficient matrix that is a constraint; b/h _s To constrain the constant column vector at the right end.

The invention constructs a two-stage power transmission network-energy storage joint planning model with investment and operation cost:

the first stage is to determine the distribution of grid and energy storage of the system for the power transmission line and energy storage planning decision problem under the constraint of line extension and energy storage equipment construction;

and the second stage is an operation simulation under the constraint condition of the unit combination in each typical scene after the decision state of the power transmission line and the energy storage is determined, and the expression is as follows:

on the basis, the whole problem is split into a main problem and a sub problem, and the expression of the main problem MP is as follows:

and the operation simulation of the unit combination in each typical scene is taken as a subproblem SP, and the expression is as follows:

wherein ,u_s Vectors of pairs of multipliers that are constraints in the constraint sub-problem;vector y, which is an integer variable that has been determined.

The feasible domain of the sub-problem is continuously changed along with the change of the value of the integer variable y, and meanwhile, the number of constraint conditions is far greater than that of decision variables due to the fact that a plurality of scenes are considered and the number of constraint conditions is too large, so that the sub-problem is converted into a dual problem to be solved, and the SDP expression of the sub-problem is as follows:

Optimal solution obtained by solving the child problem SDPThe optimal cut can be returned to the main question, with the expression form as follows:

solving the main problem MP to obtain the lower bound of the original problem, solving the sub-problem SDP to obtain the upper bound of the original problem, and continuously returning the optimal cut to the main problem by the sub-problem SDP through iteration of the main problem until the convergence gap is met.

In still another embodiment of the present invention, a combined planning system for power transmission and energy storage based on two-stage random optimization is provided, where the system can be used to implement the combined planning method for power transmission and energy storage based on two-stage random optimization, and specifically, the combined planning system for power transmission and energy storage based on two-stage random optimization includes a scenario module, a construction module, a processing module, and a planning module.

The scene module establishes a set of daily net load curves based on basic technical data of the power system, and obtains a typical scene set of reactive load and new energy uncertainty through scene reduction;

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 SpecificIntegrated 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 according to the embodiment of the invention can be used for the operation of a two-stage stochastic optimization-based power transmission network and energy storage joint planning method, and comprises the following steps:

Establishing a set of daily net load curves based on basic technical data of the electric power system, and obtaining a typical scene set of reactive load and new energy uncertainty through scene reduction; establishing a power transmission network-energy storage joint planning model based on a typical scene set of reaction load and new energy uncertainty; constructing a construction decision of the power transmission network and the energy storage based on the first-stage constraint of the power transmission network-energy storage joint planning model to construct an upper limit of the quantity of the power transmission lines and the energy storage elements; linearization processing is based on bilinear terms and integer variables in second-stage constraints of the grid-energy storage joint planning model; solving a power transmission network-energy storage joint planning model after linearization treatment by adopting a Benders decomposition frame; dividing a model into an investment main problem and a series of running sub-problems for iterative solution, taking planning decisions of a power transmission network and energy storage as the investment main problem, taking running check of a typical scene as the running sub-problem, solving the investment main problem to obtain integer parameters, solving a solution of an optimal sub-problem under the integer parameters, returning optimal cutting constraint to the main problem, and obtaining an upper bound of a joint planning problem; continuously solving and updating the investment master problem under the optimal cutting constraint to obtain new integer parameters and obtain the lower bound of the joint planning problem; and continuously and iteratively solving the investment main problem and the operation sub-problem, and realizing the combined planning of the power transmission network and the energy storage based on the obtained combined planning problem solution.

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 corresponding steps of the above-described embodiments with respect to a grid and energy storage joint planning method based on two-stage stochastic optimization; 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 validity of the method of the present invention, a test system of IEEE24 nodes is selected for calculation and analysis. The grid structure of the system is shown in fig. 1, and totally comprises 24 nodes, 5 transformers, 34 power transmission lines, 14 thermal power units, 6 wind power units and 4 photovoltaic units. Thermal power, wind power and photovoltaic installation are 3405MW, 1200MW and 1550MW respectively. On the basis of the existing system network frame, the maximum allowable number of lines in each loop corridor is set to be 3, so that a candidate set of the system network frame is obtained, and the number of typical scenes is set to be 30.

Table 1 cost comparison of three different models

Table 2 comparison of the load loss, wind and light rejection for three different models

TABLE 3 comparison of Benders decomposition and Integrated solution time

The specific calculation results are shown in tables 1, 2 and 3. The cost comparison in table 1 shows that the combined grid-energy storage planning is lower than the cost of the grid and energy storage alone planning, verifying the economy of the combined planning cost being lower than the cost of the alone planning. The comparison of the results of table 2 shows that the load loss electric quantity and the wind-abandoning and light-abandoning electric quantity of the combined planning of the power transmission network and the energy storage are both 0, so that the combined planning is verified to be more capable of reducing the load loss problem caused by power transmission blockage and the problem of difficult new energy delivery caused by high power generation ratio of the new energy, and the new energy is ensured to be consumed. Comparison of the results in Table 3 shows that the Benders solution is more efficient, and compared with the integrated solution, the efficiency is improved by 69%, and the solution efficiency is improved to a great extent.

In summary, the two-stage stochastic optimization-based power transmission network and energy storage joint planning method and system can solve the problem of poor power transmission blockage and new energy output difficulty coping capability caused by load increase, and improve the calculation efficiency of a power transmission network and energy storage joint planning model under the condition of ensuring that the uncertainty of the load and the new energy is fully considered. Compared with the traditional power system planning problem, the planning object selected by the method is the power transmission network and the energy storage, and the combined planning of the power transmission network and the energy storage can fully solve the problems of power transmission blockage and difficult new energy delivery caused by load increase and high-proportion new energy power generation duty ratio in the power system. The method provided by the invention eliminates bilinear terms and integer variables in the second-stage constraint in the power transmission network-energy storage planning model, and solves the problem by adopting a frame of Benders decomposition, and the method provided by the invention is suitable for improving the calculation efficiency of solving a large-scale power transmission network-energy storage joint planning problem by dividing the whole problem into an investment main problem and a series of operation sub-problems and solving the main problem and the sub-problem with smaller scale under the condition of ensuring the accuracy of the problem as much as possible, thereby reducing the difficulty of solving the problem and improving the solving efficiency.

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, the specific names of the functional units and modules are only for 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 U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RandomAccess Memory, RAM), an electrical carrier wave signal, a telecommunications signal, a software distribution medium, etc., it should be noted that the computer readable medium may contain content that is appropriately increased or decreased according to the requirements of jurisdictions and patent practices, such as in certain jurisdictions, according to the jurisdictions and patent practices, the computer readable medium does not contain 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 power transmission network and energy storage joint planning method based on two-stage random optimization is characterized by comprising the following steps of:

2. The two-stage stochastic optimization-based power transmission network and energy storage joint planning method according to claim 1, wherein the typical scene set for obtaining the reaction load and the uncertainty of the new energy through scene reduction is specifically:

3. The two-stage stochastic optimization-based power transmission network and energy storage joint planning method according to claim 2, wherein the gray correlation epsilon (k) is calculated as follows:

4. The two-stage stochastic optimization-based power transmission network and energy storage joint planning method according to claim 1, wherein the objective function of the two-stage stochastic optimization preliminary model based on power transmission network-energy storage joint planning is to minimize the construction cost of the power transmission network-energy storage joint planning and the combined operation cost of the unit in each typical scene, specifically:

wherein ,C^Inv \C ^Op The investment cost and the operation cost; omega shape _nl \Ω _nes The method comprises the steps of collecting all lines to be selected and collecting energy storage candidate nodes in a system;the construction cost and 0-1 construction variable of the kth line are calculated; />The method comprises the steps of setting up cost and integer variable of the number of the unit energy storage devices of an ith node; ζ is the fund recovery coefficient of the equipment to be built; omega shape _B \Ω _s The method comprises the steps of collecting all nodes in a system and collecting typical scenes; omega shape _WT \Ω _PV The method comprises the steps of collecting all wind turbine generators and collecting all photovoltaic turbine generators in a system; c (C) ^Load,curt Cost per unit of load loss electricity; />The power loss of the i node at t time under j scenes is obtained; />The method is characterized in that the method is the power generation cost function of the ith thermal power generating unit under j scenes, the output at the moment t, the starting cost and the stopping cost; c (C) ^WT,Curt /C ^PV,Curt Penalty cost for unit wind power generation and photovoltaic power generation is abandoned; />The method is characterized in that the maximum allowable electric quantity discarding part exceeding the wind power generation and photovoltaic power generation at the moment t under j scenes of the ith wind turbine generator system and the photovoltaic turbine generator system is adopted.

5. The two-stage stochastic optimization-based power transmission network and energy storage joint planning method according to claim 4, wherein the constraint conditions are as follows:

constraint of typical scenario run check phase:

thermal power generating unit output limit:

shortest start-stop time constraint of thermal power generating unit:

logical constraint of thermal power generating unit operation state:

output interval of new energy unit:

new energy consumption constraint:

energy storage charge-discharge power interval:

limit energy storage and charge and discharge simultaneously:

energy storage electric quantity constraint:

network security constraints for existing lines:

direct current power flow model of line to be selected:

phase angle of nodes at two ends of a line to be selected:

upper and lower limit constraint of line flow to be selected:

node power balancing constraints:

wherein ,Ω_lc /Ω _R For the collection and negation of all transmission corridors in the systemA collection of charges;the maximum number of lines allowed in the power transmission corridor and the number of lines existing in the ith corridor are obtained; />For the single start-up cost and the single stop cost of the ith unit>The method comprises the steps of setting an operation state 0-1 variable, a starting state 0-1 variable and a shutdown state 0-1 variable of an ith unit at a moment t under an s scene; k (k) _i,j /h _i,j The slope and the intercept of a j-th section in the linear power generation cost curve of the ith unit are obtained; / >The output lower limit, the output upper limit, the climbing rate, the shortest starting time and the shortest shutdown time of the ith unit; />The method comprises the steps of setting a wind resource curve and a maximum wind abandoning rate of an ith wind turbine generator set at a moment t under a j scene; />The method comprises the steps of (1) setting an optical resource curve and a maximum light rejection rate of an ith photovoltaic unit at a moment t under a j scene;charging power and discharging power of energy storage of the ith candidate node in a j scene; />Charging efficiency and discharging efficiency of the energy storage device that is the i candidate node; />The method comprises the steps of (1) setting a charging state 0-1 variable and a discharging state 0-1 variable of energy storage of an ith candidate node at a moment t under a j scene; />Maximum charging power, discharging power, lower limit of electric quantity and upper limit of electric quantity of the energy storage equipment for the i candidate node unit; p (P) _k,s,t /θ _k,i,s,t The phase angle value of the i-end node and the line reactance are the line power flow of the kth line at the moment t under the s scene; />The minimum value and the maximum value of the node phase angle are obtained; />Line reactance and line capacity for the kth line.

6. The two-phase stochastic optimization-based power transmission network and energy storage joint planning method according to claim 1, wherein bilinear terms and integer variables in the second phase constraint of the linearization processing power transmission network-energy storage joint planning model are specifically:

7. The two-stage stochastic optimization-based power transmission network and energy storage joint planning method according to claim 6, wherein the relaxation process is as follows:

the linearization process is as follows:

-(1-z _k )π≤(θ _k,i,s,t -θ _k,j,s,t )-x _k P _k,s,t ≤(1-z _k )π

wherein ,charging power in j scene for energy storage of ith candidate node, n _i For (I)>For (I)>Discharging power of energy storage of ith candidate node in j scene, +.>Maximum discharge power of energy storage device per unit of i candidate node,/>A 0-1 variable for the charging state of energy storage of the ith candidate node at t moment in j scene, z _k Constructing a variable theta for 0-1 of the kth line _k,i,s,t Is the phase angle value of the i-end node of the kth line at the t moment in the s scene, theta _k,j,s,t Is the phase angle value of the j-end node of the kth line at the t moment in the s scene, and x _k Line capacity of the kth line, P _k,s,t For the line flow of the kth line at the moment t under the s scene, omega _nl Omega is the set of all the candidate lines in the system _B S is the index of the operation scene, and T is the number of time sections in the scheduling period.

8. The two-phase stochastic optimization-based power transmission network and energy storage joint planning method according to claim 6, wherein the converting of the second-phase constraint into a mixed integer linear problem is specifically:

wherein ,C^Inv \C ^Op The investment cost and the operation cost; omega shape _nl \Ω _nes The method comprises the steps of collecting all lines to be selected and collecting energy storage candidate nodes in a system; The construction cost and 0-1 construction variable of the kth line are calculated; />The method comprises the steps of setting up cost and integer variable of the number of the unit energy storage devices of an ith node; ζ is the fund recovery coefficient of the equipment to be built; omega shape _B \Ω _s The method comprises the steps of collecting all nodes in a system and collecting typical scenes; omega shape _WT \Ω _PV The method comprises the steps of collecting all wind turbine generators and collecting all photovoltaic turbine generators in a system; c (C) ^Load,curt Cost per unit of load loss electricity; />The power loss of the i node at t time under j scenes is obtained; />The method is characterized in that the method is the power generation cost function of the ith thermal power generating unit under j scenes, the output at the moment t, the starting cost and the stopping cost; c (C) ^WT,Curt /C ^PV,Curt Penalty cost for unit wind power generation and photovoltaic power generation is abandoned; />The method comprises the steps that the maximum allowable electric quantity discarding part exceeding the wind power generation and photovoltaic power generation at the moment t of an ith wind turbine generator system and a photovoltaic turbine generator system under j scenes is adopted; c _L Penalty cost for energy loss during operation of the energy storage device.

9. The two-stage stochastic optimization-based power transmission network and energy storage joint planning method according to claim 1, wherein the investment master problem:

minz＝d ^T y+η

s.t.Ay≥b

y∈{0,1},η≥0

operation sub-problem:

wherein ,u_s To vector the pairs of multipliers for the constraint in the constraint sub-problem,the vector y which is the integer variable already determined, y is the vector of the Boolean integer variable, eta is the auxiliary variable, and d ^T /c _s ^T Coefficient vector, x, being the objective function in the s-th scene _s Vector, A/E, being a continuous variable in the s-th scenario _s /F _s B/h as constrained coefficient matrix _s To constrain the constant column vector at the right end, Ω _s Is the set of all typical scenarios in the system.

10. A two-stage stochastic optimization-based power transmission network and energy storage joint planning system, comprising: