CN115759589A - Power distribution network data-driven robust extension planning method containing high-proportion new energy - Google Patents
Power distribution network data-driven robust extension planning method containing high-proportion new energy Download PDFInfo
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Abstract
The invention relates to a power system scheduling technology, in particular to a power distribution network data-driven robust extension planning method containing high-proportion new energy, which comprises the following steps: establishing a double-layer data driving robust extension planning frame; proposing an uncertain set of data driving ellipsoids; providing an urban distribution network expansion planning model containing storage battery and super capacitor hybrid energy storage; and (3) solving the urban distribution network expansion planning model by improving the CCG method, decomposing the planning problem of the min-max-min structure into a main problem and a sub problem, and solving to obtain a final planning scheme. The planning method can well describe the correlation of uncertain variables such as wind power, photovoltaic and load, and obtain a high-precision uncertain set, so that the planning decision result is better in economy and lower in conservation; by combining the characteristics of different charging and discharging characteristics of the storage battery and the super capacitor, the proposed model and the planning result can better absorb new energy and reduce carbon emission.
Description
Technical Field
The invention belongs to the technical field of power system scheduling, and particularly relates to a power distribution network data-driven robust expansion planning method containing high-proportion new energy.
Background
With the access of a large number of novel loads such as distributed power sources and electric vehicles to a power distribution network, the load types and characteristics of the novel loads become more complex and more variable. The power distribution network is used as an important carrier for energy production, transmission, distribution and storage, and a novel power system is built to promote a main battlefield for realizing a double-carbon target. In the traditional urban distribution network planning research, the influence on the distributed new energy access is not considered, and deterministic planning is mostly adopted; and most of the planning objects are only limited to the positioning and sizing of the grid structure and the gas turbine, and the consideration of energy storage is rarely introduced. From the perspective of uncertain planning theory methods, the current random optimization method has the defects of overlarge calculated amount and overlarge influence of probability distribution data on the result accuracy; the traditional robust optimization method is too high in conservative property, the mutual relation among uncertain factors tends to be decoupled in the existing data-driven robust method, and the influence of uncertain variable correlation on the accuracy of an uncertain set is ignored.
Disclosure of Invention
Aiming at the problems in the background art, the invention provides a data-driven robust expansion planning method for an urban distribution network, which considers the access of high-proportion new energy.
In order to solve the technical problems, the invention adopts the following technical scheme: a power distribution network data-driven robust extension planning method containing high-proportion new energy comprises the following steps:
step 1, establishing a data-driven robust expansion planning framework of an urban distribution network;
step 2, historical data of wind power, photovoltaic and energy utilization load are placed in a set, a data driving ellipsoid set is constructed, and a minimum volume problem formula is provided; enhancing the dimension of the ellipsoid set, and solving the minimum volume of the ellipsoid set; translating the obtained coordinates to obtain vertex coordinates, and obtaining extreme scene coordinates and a data driving uncertain set through inverse transformation;
step 3, establishing an urban distribution network expansion planning model containing storage batteries and super-capacitor hybrid energy storage SEC + SC, wherein the urban distribution network expansion planning model comprises a planning layer and a scheduling layer;
and 4, solving the urban distribution network expansion planning model, decomposing the planning problem of the min-max-min structure into a main problem and a sub problem, and solving to obtain a final planning scheme.
In the method for the power distribution network data-driven robust expansion planning containing the high-proportion new energy, the implementation of the step 1 comprises the following steps of dividing the planning problem into two aspects of annual planning and extreme scene daily operation:
1, layer 1: planning the whole life cycle year; considering the life cycle and the breakage rate of the equipment, converting the cost of the full life cycle into the planning construction cost of each year in the life cycle, and carrying out the lowest cost and the minimum optimization of energy consumption carbon emission of a planning scheme according to the feedback of an operation level and the wind power, photovoltaic and load characteristic constraints in the data driving uncertain set; the planning object includes: determining the site selection, the number, the capacity and the model of the gas turbine and the energy storage equipment, and expanding and planning strategies for the energy transmission power limit value of a superior power grid and the grid structure of the power distribution network;
and 2, layer 2: carrying out daily operation scheduling in an extreme scene; importing the extreme scenes in the data-driven ellipsoid uncertain set into a daily operation scheduling model, and scheduling multiple targets of economy, carbon emission and energy utilization rate by combining equipment operation, energy balance and power flow constraint; the decision content comprises the following steps: the power generation strategy comprises a gas turbine, photovoltaic and wind power output strategy, a load shedding amount, an energy storage charging and discharging strategy and an upper-level power grid electricity purchasing and selling strategy.
In the power distribution network data-driven robust expansion planning method containing the high-proportion new energy, the implementation of the step 2 comprises the following steps:
step 2.1, constructing a data drive ellipsoid uncertain set considering the relevance of uncertain variables;
putting photovoltaic output and load demand historical data into a set:
wherein, ω is i For column vectors,' is used as the transpose symbol; n is a radical of hydrogen p 、N L 、N s Respectively the photovoltaic quantity, the load quantity and the sampling days;
step 2.2, constructing a high-dimensional ellipsoid set:
a full-dimensional ellipsoid represented by a symmetric positive definite matrix A and a central fixed point c is described by the following mathematical method:
E(A,c)＝{ω∈R n |(ω-c) T A(ω-c)≤1}；
step 2.3, when the size of the set is quantified, the volume of a feasible domain is used as a calculation object; by volume of unit sphere ρ n Multiplying by a transform to calculate an ellipsoidThe volume of E;
solving the ellipsoid minimum volume by the following problem equation, and determining the values of A and c:
step 2.4, solving the above formula by using a lift-and-project KY-1 method to obtain an ellipsoid set:
by two N P +N L The smallest volume ellipsoid of the dimension wraps all scene sets for a certain time period, one is N P +N L Scene set of dimensions
The other is a deviation set
The entire problem is described as an optimization model as follows:
wherein p is a data point in the set; solving using lift-and-project KY-1, we will get (N) P +N L )×N s The set of dimensions ω is lifted to (N) P +N L +1)×2N s Set of dimensions ω':
ω'＝{±q 1 ,±q 2 ,…,±q m }
wherein, the central point of omega ' is the same as the central point of omega, and the solution of new problem MVEE (omega ') composed of omega ' obtains the solution of MVEE (omega);
pri:
(P-MVEE(ω'))min M -log(detM)
dual:
(D-MVEE(ω'))min u -log(detV(u))
s.t.e T u＝1,u≥0
the lift-and-project KY-1 method is used for solving conveniently;
then, the original problem can be solved by the following formula through the obtained u and ω':
and (3) calculating vertex coordinates:
by transformation, translating the ellipse into an axial ellipse E', finding the vertex coordinates:
ω i '＝P×(ω i -c)
E'(D)＝{ω'∈R n |ω' T Dω'≤1}
wherein, ω is i 、ω i ' are an original ellipsoid scene and a transformed axial ellipsoid scene respectively; p is for orthogonal decomposition A = P T An orthogonal matrix of DPs; d is a characteristic value diagonal matrix of A; e' is an axial ellipsoid obtained after transformation;
obtaining the axial ellipsoid vertex coordinate omega after transformation e,i ' obtaining the vertex coordinate omega of the original ellipse by coordinate transformation e,i ：
ω i ＝c+P -1 ω i '。
In the method for the data-driven robust extension planning of the power distribution network containing the high-proportion new energy, the step 3 is realized by constructing a city power distribution network data-driven robust extension planning model containing mixed energy storage and considering the access of the high-proportion new energy with the goals of lowest cost, energy conservation and low carbon as follows:
3.1, the objective function of the urban distribution network data-driven robust extension planning model considering the high-proportion new energy access, which contains hybrid energy storage, comprises planning construction cost, operation maintenance cost and energy-consuming carbon emission penalty cost; dividing the extension plan into an annual planning stage and a daily operation scheduling stage;
step 3.2, taking the lowest construction cost as a target function in an annual planning stage, considering the maximum power requirement of equipment operation fed back in the operation stage and photovoltaic, wind power and load data related constraints of the worst operation scene, and obtaining the decision of equipment capacity, quantity, model selection results and a power distribution network structure;
the constraints of the annual planning phase include: equipment investment construction constraints and network radialization and connectivity constraints;
3.3, the objective function of the daily operation scheduling stage is the lowest cost, the lowest carbon emission and the best new energy consumption effect;
the constraint conditions of the daily operation scheduling phase comprise: power flow constraint of a power distribution network, node voltage and current upper and lower limit safety constraint, distributed power supply output constraint, loss load constraint, energy storage equipment charge and discharge constraint, generator operation constraint, electric energy power balance constraint and tie line transmission power constraint;
step 3.4, investment and construction constraints:
x wind 、x pv 、x line 、x PG respectively representing the variables of 0-1 whether the wind power, the photovoltaic, the newly-built line and the generator are put into construction or not; n is a radical of wind 、N pv 、N line 、N PG The maximum allowable number of constructions;
step 3.5, power distribution network flow constraint:
1) The node power balance constraints are as follows:
wherein the content of the first and second substances,
is the square of the current value of line ij; delta. For the preparation of a coating j Is a line set with j as the head end; phi is a j Is a line set with j as the terminal; omega wind 、Ω pv 、Ω SP 、Ω load Respectively numbering sets of wind power, photovoltaic, a superior power grid and load; p is ij，t 、Q ij，t Respectively the active and reactive power of line ij;
the active load demand and the reactive load demand of the node j are respectively; r is ij Is the resistance value of line ij; x is a radical of a fluorine atom ij Is the reactance value of line ij;
the main network active power, the fan active power output and the photovoltaic active power output are respectively;
the main network reactive power, the fan reactive power output and the photovoltaic reactive power output are respectively;
2) The capacity planning constraints of the energy storage device are as follows:
wherein E is k,min And E k,max Maximum and minimum capacity limits for the device; m min And M max The maximum charge-discharge power limit value of the energy storage equipment is obtained;
3) The constraints of the generator are:
4) The charge-discharge constraints and state constraints of the energy storage device are as follows:
wherein, P max For maximum charge-discharge power, S is the energy stored by the energy storage device at a certain time, η 1 Is the self-loss coefficient, eta, of the energy storage device 2 The charge-discharge efficiency of the energy storage device is obtained;
5) Energy purchase constraints; defining the maximum transmission power, P, of the network buymax Maximum purchase per unit of electricity time;
6) The constraints on power balance are as follows:
wherein, the variable symbols of photovoltaic, wind power and load are represented by wave numbers as uncertain variables; and the coefficient is the generating efficiency coefficient of the generator set.
In the above power distribution network data-driven robust extension planning method with high-proportion new energy, the implementation of step 4 includes:
step 4.1, splitting a two-stage robust optimization problem of a min-max-min structure into a main problem and a sub problem by an improved CCG method to solve;
step 4.2, solving the problems of network architecture of the power distribution network, equipment capacity determination and site selection, type selection and transmission line upper limit by the main problem, fixing the solved result and transmitting the solved result to the sub-problems;
4.3, solving the subproblems by an enumeration method;
the subproblems obtain the worst scene with the highest cost by traversing the extreme scenes in the uncertain set, and transmit the serial number of the worst scene back to the main problem;
adding related wind power, photovoltaic and load constraints in the main problem to obtain a new decision, and entering next iteration;
through continuous iteration, when the final main problem and the sub problem are consistent in decision, the optimal solution of the robust planning problem is obtained;
and 4.4, the main problem is an annual planning problem, and the mathematical description is as follows:
wherein X and Y are feasible domains of decision variables and uncertain variables, and n is the current iteration times of the column and constraint generation method; sigma is the maximum scheduling cost in the last iteration;
respectively extreme scenes w h D and e are coefficient variables; obtaining a preliminary planning cost value through solving a main problem, fixing a decision variable and transmitting the decision variable to a sub-problem;
step 4.5, the subproblem is a daily operation scheduling problem, and the mathematical description is as follows:
wherein, the first and the second end of the pipe are connected with each other,
the method comprises the following steps of (1) obtaining a planning decision variable of an nth iteration main problem, wherein Ne is the number of extreme scenes;
and 4.6, iteratively and alternately solving the main problem and the sub-problems based on a column and constraint generation method, wherein the flow is as follows:
(1) Initialization:
setting an initialization scene omega 1 The convergence flag δ; the iteration number n =1 and the upper bound value U are set B = + ∞, lower bound value L B ＝-∞；
(2) Solving a main problem:
solving the annual planning problem to obtain the best solution in the day ahead
And maximum value of real-time scheduling cost in main problem limit scene
Updating the lower bound according to the solved value:
(3) Solving the sub-problem:
decision variables in a problem planning year of a main problem
Under the condition of invariance, all limit scenes h =1,2, \ 8230;, N are solved e ；
If a feasible strategy can be found in all scenes, selecting the scene with the highest cost, recording the cost number, and updating the upper bound:
U B ＝min(U B ,F sub )
wherein, F sub Is the maximum cost value for the subproblem; if a feasible solution cannot be found in a certain limit scene, recording the number of the scene to become the worst scene;
(4) Confirming convergence:
the basis for judging convergence is as follows:
U B -L B <δ
if the conditions are met, the iteration is finished to obtain the optimal solution of the problem
And if not, updating the wind power, photovoltaic and load constraint conditions of the worst scene of the annual planning of the main problem, adding the constraint conditions into the main problem, fixing the operation and maintenance cost, and jumping back to the second step for continuous iteration.
Compared with the prior art, the invention has the beneficial effects that:
in order to achieve the low-carbon target, a distribution network structure combining carbon capture and multi-component hybrid energy storage is constructed, and the effects of improving the energy utilization rate and saving energy and low carbon are achieved through the reutilization of carbon rows, the flexible energy conversion of electric-heat multi-component storage and the complementary advantages of a storage battery-super capacitor combination.
The data-driven robust optimization method based on the extreme scene ellipsoid set is provided, and aiming at the problem that the robust optimization result is too conservative, the ellipsoid set is used for describing the correlation among three uncertainties of wind power, photovoltaic and a user side, so that the uncertain set is more accurate, and the economy of the result is improved.
Aiming at the difficulty that the traditional data driving method still needs to calculate the problem of complex probability, an ellipsoid set endpoint is utilized to extract a representative extreme scene, so that the calculation process is simplified, the problem of complex probability calculation is avoided, and the dependence on data accuracy is reduced;
aiming at the problem that the conventional CCG algorithm needs to perform complex dual processing on the subproblems, an improved method based on an ellipsoid extreme scene is adopted, so that the difficulty of model and solution is greatly reduced.
The method can well describe the correlation of uncertain variables such as wind power, photovoltaic and load, obtain a higher-precision uncertain set, and enable the planning decision result to be better in economy and lower in conservation. By combining the characteristics of different charging and discharging characteristics of the storage battery and the super capacitor, the model and the planning result provided by the invention can better absorb new energy and reduce carbon emission.
Drawings
Fig. 1 is a flowchart of a data-driven robust extension planning method for a power distribution network containing high-proportion new energy according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an urban distribution network expansion planning framework including high-proportion new energy and stored energy according to an embodiment of the present invention;
fig. 3 is a flow chart of solving an extended planning model of an urban distribution network containing high-proportion new energy and stored energy according to the embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the following embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive efforts based on the embodiments of the present invention, shall fall within the scope of protection of the present invention.
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The present invention is further illustrated by the following examples, which are not to be construed as limiting the invention.
The data-driven ellipsoid method based on the extreme scene proposed by the embodiment is a result of improving a data-driven box-type uncertainty set method commonly used in the field, and has the following advantages:
1. aiming at the problem that the robust optimization result is too conservative, an ellipsoid set is used for describing the correlation among three uncertainties of wind power, photovoltaic and a user side, so that the uncertain set is more accurate, and the economy of the result is improved;
2. aiming at the difficulty that the traditional data driving method still needs to calculate the problem of complex probability, an ellipsoid set endpoint is used for extracting a representative extreme scene;
3. aiming at the problem that the conventional CCG algorithm needs to perform complex dual processing on the subproblems, an improved method based on an ellipsoid extreme scene is adopted, so that the difficulty of model and solution is greatly reduced.
In the model that this embodiment provided, ingenious utilized electric heat many first energy storage and "BES + SC" hybrid energy storage, not only had the advantage in traditional energy storage space-time, still had the advantage in energy type, equipment characteristic two aspects, specifically as follows:
1. the advantage of multi-energy complementary utilization in peak-valley period is enhanced by utilizing electric heating multi-energy storage;
2. the hybrid electric energy storage integrates the advantages that the storage battery can stably discharge for a long time and the super capacitor can be rapidly charged and discharged to deal with sudden change, so that the energy utilization and the fault strain are more flexible in time.
The method for the data-driven robust expansion planning of the power distribution network containing the high-proportion new energy is used for establishing a double-layer data-driven robust expansion planning framework in order to enable the urban power distribution network to have bearing capacity for the high-proportion new energy and fully utilize the space-time allocation capacity of energy storage for electric power; aiming at the uncertainty of wind power, photovoltaic and energy load, a data driving ellipsoid uncertainty set is provided, the number of extreme scenes is reduced, and the complexity of calculation and a model is simplified; aiming at the problem that the existing power distribution network is insufficient in energy storage utilization, an urban power distribution network expansion planning model containing storage battery and super capacitor hybrid energy storage (SEC + SC) is provided and divided into a planning layer and a scheduling layer, the planning layer aims to obtain a device capacity and a site selection scheme with the minimum cost, the minimum wind and light abandonment and the minimum load shedding, and the scheduling layer aims to ensure the safe and stable operation of a power distribution network system under the worst condition and properly ensure the economical efficiency of the power distribution network system. And improving the CCG method, solving the urban distribution network expansion planning model in a more simplified manner, decomposing the planning problem of the min-max-min structure into a main problem and a sub-problem, and solving to obtain a final planning scheme.
The embodiment is realized by the following technical scheme, as shown in fig. 1, a power distribution network data-driven robust expansion planning method containing high-proportion new energy comprises the following steps:
s1: in order to enable the urban distribution network to have bearing capacity for high-proportion new energy, the space-time allocation capacity of energy storage to electric power is fully utilized, and a double-layer data-driven robust expansion planning framework is established.
S2: researching the output characteristics of the wind power, photovoltaic and energy utilization load uncertainty, and putting the historical data of the wind power, photovoltaic and energy utilization load uncertainty into a set; constructing a data driving ellipsoid set and providing a minimum volume problem formula; enhancing the dimension of the ellipsoid set, and solving the minimum volume of the ellipsoid set; and translating the obtained coordinates to obtain vertex coordinates, so as to obtain extreme scene coordinates through inverse transformation and a data driving uncertain set.
S3: aiming at the problem that the existing power distribution network is insufficient in energy storage utilization, an urban power distribution network expansion planning model containing storage battery and super capacitor hybrid energy storage (SEC + SC) is provided, the model is divided into a planning layer and a scheduling layer, and the planning layer is used for obtaining an equipment capacity and site selection scheme with the minimum cost, wind and light abandonment and load shedding; the dispatching layer aims to ensure the safe and stable operation of the power distribution network system under the worst condition and properly ensure the economy of the power distribution network system.
S4: and solving the urban distribution network expansion planning model, decomposing the planning problem of the min-max-min structure into a main problem and a sub problem, and solving to obtain a final planning scheme.
And S1, firstly, establishing a data-driven robust extension planning framework of the urban distribution network, dividing a planning problem into two layers of annual planning and extreme scene daily operation, wherein the specific steps and related formula description are shown in figure 2, and the steps are divided into two aspects of a planning layer and an operation layer:
1, layer 1: and (4) planning the whole life cycle year. And considering the life cycle and the breakage rate of the equipment, converting the cost of the full life cycle into the planning construction cost of each year in the life cycle, and carrying out the lowest cost and the minimum optimization of energy consumption carbon emission of the planning scheme according to the feedback of the operation level and the wind power, photovoltaic and load characteristic constraints in the data driving uncertain set. The planning objects of the present layer are: the method comprises the steps of determining the site selection, the quantity, the capacity and the model of the gas turbine and the energy storage equipment, and expanding and planning strategies with the energy transmission power limit value of a superior power grid and the grid structure of the power distribution network.
And (2) layer: and (5) carrying out daily operation scheduling in an extreme scene. And importing the extreme scenes in the uncertain set of the data driving ellipsoids into a daily operation scheduling model, and scheduling the extreme scenes in multiple targets of economy, carbon emission and energy utilization rate by combining constraints such as equipment operation, energy balance and power flow. The decision content in the layer is as follows: the power output strategies of the gas turbine, the photovoltaic and the wind power, the load shedding amount, the energy storage charging and discharging strategies, the power purchasing and selling strategies to the upper-level power grid and the like. According to the scheme, the power distribution network extension planning scheme obtained by decision can be ensured to ensure that the power system can operate safely, stably and economically with low carbon under the worst condition.
S2, constructing a data drive ellipsoid uncertain set considering the relevance of uncertain variables;
putting photovoltaic output and load demand historical data into a set:
wherein, ω is i For column vectors,' is used as the transpose symbol; n is a radical of p 、N L 、N s Respectively the photovoltaic quantity, the load quantity and the sampling days;
constructing a high-dimensional ellipsoid set:
a full-dimensional ellipsoid represented by a symmetric positive definite matrix A and a central fixed point c is described by the following mathematical method:
E(A,c)＝{ω∈R n |(ω-c) T A(ω-c)≤1}；
in quantifying the "size" of a set, the volume of the feasible domain is used as a computational object; by volume of unit sphere ρ n Multiplying by the transform to calculate the volume of ellipsoid E; since it is formed by R n The sphere of the space is linearly transformed.
Solving the ellipsoid minimum volume by the following problem equation, and determining the values of A and c:
solving the above equation by using lift-and-project KY-1 method to obtain an ellipsoid set:
by two N P +N L The smallest volume ellipsoid of the dimension wraps all scene sets for a certain time period, one is N P +N L Scene set of dimensions
The other is a deviation set
The entire problem is described as an optimization model as follows:
wherein p is a data point in the set; solving using lift-and-project KY-1, the original (N) P +N L )×N s The set of dimensions ω is lifted to (N) P +N L +1)×2N s Set of dimensions ω':
wherein, the central point of omega ' is the same as the central point of omega, and the solution of new problem MVEE (omega ') composed of omega ' obtains the solution of MVEE (omega);
pri:
(P-MVEE(ω'))min M -log(detM)
dual:
(D-MVEE(ω'))min u -log(detV(u))
s.t.e T u＝1,u≥0
because the dual mode is a convex optimization model with only one equality constraint and non-negative dual variables, the lift-and-project KY-1 method can be used for solving conveniently;
then, the original problem can be solved by the following formula through the obtained u and ω':
finding vertex coordinates (extreme scenes):
by transformation, translating the ellipse into an axial ellipse E', finding the vertex coordinates:
ω i '＝P×(ω i -c)
E'(D)＝{ω'∈R n |ω' T Dω'≤1}
wherein, ω is i 、ω i ' are an original ellipsoid scene and a transformed axial ellipsoid scene respectively; p is for orthogonal decomposition A = P T An orthogonal matrix of DPs; d is a characteristic value diagonal matrix of A; e' is the axial ellipsoid obtained after transformation.
The axial ellipsoid vertex coordinate omega obtained by transformation e,i ' obtaining the vertex coordinate omega of the original ellipse by coordinate transformation e,i (i.e., extreme scenarios):
ω i ＝c+P -1 ω i '。
and S3, aiming at the lowest cost, energy conservation and low carbon, constructing a city power distribution network data-driven robust expansion planning model which takes mixed energy storage into consideration and high-proportion new energy access, and specifically comprising the following steps:
the objective function of the urban distribution network data-driven robust expansion planning model with mixed energy storage and high-proportion new energy access consideration mainly comprises planning construction cost, operation maintenance cost and energy-consuming carbon emission punishment cost. The extension planning is divided into two stages of annual planning and daily operation scheduling. The two stages are mutually influenced and constrained, annual planning takes the lowest construction cost as a target function, the maximum power requirement of equipment operation fed back in the operation stage and the photovoltaic, wind power and load data related constraints of the worst operation scene are considered, and the scheduling decision of the daily operation stage can be influenced by the decisions of the capacity, the quantity, the model selection result, the power distribution network structure and the like of the equipment obtained in the stage.
The constraint conditions of the annual planning stage mainly comprise: equipment investment construction constraints and network spoke and connectivity constraints. The daily operation stage objective function is the lowest cost, the lowest carbon emission and the best new energy consumption effect. The constraint conditions of the daily scheduling operation stage mainly comprise: the method comprises the following steps of power distribution network power flow constraint, node voltage and current upper and lower limit safety constraint, distributed power supply output constraint, loss load constraint, energy storage equipment charging and discharging constraint, generator operation constraint, electric energy and power balance constraint and tie line transmission power constraint.
Investment and construction constraints:
x wind 、x pv 、x line 、x PG respectively wind power, photovoltaic, newly-built circuit and power generationA variable of 0-1 for whether the machine is in construction or not; n is a radical of wind 、N pv 、N line 、N PG The maximum allowed number of builds.
In the power flow constraint, the node power balance constraint is as follows:
wherein the content of the first and second substances,
is the square of the current value of line ij; delta j Is a line set with j as the head end; phi is a j Is a line set with j as the terminal; omega wind 、Ω pv 、Ω SP 、Ω load Respectively numbering sets of wind power, photovoltaic, a superior power grid and load; p is ij，t 、Q ij，t Respectively the active and reactive power of line ij;
the active load demand and the reactive load demand of the node j are respectively; r is ij Is the resistance value of line ij; x is the number of ij Is the reactance value of line ij;
the main network active power, the fan active power output and the photovoltaic active power output are respectively;
the main network reactive power, the fan reactive power and the photovoltaic reactive power are respectively.
Taking the energy storage device as an example, the capacity planning constraint is as follows:
wherein, E k,min And E k,max Maximum and minimum capacity limits for the device; m min And M max And the maximum charging and discharging power limit value of the energy storage equipment.
Plant operating constraints take the generator as an example:
the charge and discharge constraints and state constraints of the energy storage device are as follows:
wherein P is max For maximum charge-discharge power, S is the energy stored by the energy storage device at a certain time, η 1 Is the self-loss coefficient, eta, of the energy storage device 2 The charge-discharge efficiency of the energy storage device.
The energy purchase constraint is defined as follows, specifying the maximum transmission power, P, of the grid buymax Is the maximum purchase per unit of electricity time.
The constraints on power balance are as follows:
wherein, the variable symbols of photovoltaic, wind power, load and the like are represented by wave numbers as uncertain variables; the coefficient of the generating efficiency of the generator set.
And S4, solving an expansion planning model of the high-proportion new energy urban distribution network.
The two-stage robust optimization problem of the min-max-min structure is divided into a main problem and a sub problem to be solved through an improved CCG method. The main problem solves the problems of network architecture of the power distribution network, equipment capacity and location selection, type selection and transmission line upper limit, and the solved result is fixed and transmitted to the sub-problems. Due to the special advantages of the ellipsoid set, the subproblems do not need to be subjected to dual processing like the conventional CCG method, and can be directly solved by an enumeration method. The sub-problem obtains the worst scene with the highest cost by traversing the extreme scenes in the uncertain set, and transmits the serial number of the worst scene back to the main problem. And adding related wind power, photovoltaic and load constraints in the main problem to obtain a new decision, and entering the next iteration. And obtaining the optimal solution of the robust planning problem by continuous iteration when the final main problem and the sub-problem are consistent in decision.
In S4, the main question is a year planning question, and the mathematical description thereof is as follows:
wherein X and Y are feasible domains of decision variables and uncertain variables, and n is the current iteration times of the column and constraint generation method; σ is the scheduling maximum cost (i.e., upper bound value) in the last iteration;
respectively extreme scenes w h D and e are coefficient variables. And (4) obtaining a preliminary planning cost value (namely a lower bound value) through solving the main problem, fixing the decision variable and transmitting the decision variable to the sub-problem.
The subproblem is a daily run scheduling problem, and is mathematically described as follows:
wherein, the first and the second end of the pipe are connected with each other,
is the planning decision variable (fixed) solved by the nth iteration main problem, and Ne is the number of extreme scenes.
S4, iteratively and alternately solving the main problem and the sub-problem based on a column and constraint generation method, wherein the flow is as follows:
(1) Initializing;
setting initialization scene omega 1 And a convergence flag δ. Set the number of iterations n =1, upper bound value U B = + ∞, lower bound value L B ＝-∞。
(2) Solving a main problem;
solving the annual planning problem to obtain the best solution before the day
And maximum value of real-time scheduling cost in main problem limit scene
Then, based on the solved value, the lower bound is updated:
(3) Solving the subproblems;
decision variables in a problem planning year of a main problem
Under the unchanged condition, all limit scenes are solved (h =1,2, \8230;, N) e ). If a feasible strategy can be found in all the scenes, selecting the scene with the highest cost,note down this cost number, while updating the upper bound:
U B ＝min(U B ,F sub )
wherein, F sub Is the maximum cost value that the sub-problem derives. If a feasible solution cannot be found in a certain limit scene, the number of the scene is recorded, and the worst scene is obtained.
(4) Confirming convergence;
the basis for judging convergence is as follows:
U B -L B <δ
if the conditions are met, the iteration is ended to obtain the optimal solution of the problem
And if not, updating the wind power, photovoltaic and load constraint conditions of the worst scene planned in the main problem year, adding the constraint conditions into the main problem, fixing the operation and maintenance cost, and jumping back to the second step for continuous iteration.
In specific implementation, the data-driven robust extension planning method for the power distribution network containing high-proportion new energy comprises the following steps:
s1: in order to enable the urban distribution network to have bearing capacity for high-proportion new energy, the space-time allocation capacity of energy storage to electric power is fully utilized, and a double-layer data-driven robust extension planning framework is established.
Firstly, establishing a data-driven robust extension planning framework of an urban power distribution network, and dividing a planning problem into two aspects of annual planning and extreme scene daily operation, which are specifically as follows:
layer 1: and (4) planning the whole life cycle year. And considering the life cycle and the breakage rate of the equipment, converting the cost of the full life cycle into the planning construction cost of each year in the life cycle, and carrying out the lowest cost and the minimum optimization of energy consumption carbon emission of the planning scheme according to the feedback of the operation level and the wind power, photovoltaic and load characteristic constraints in the data driving uncertain set. The planning objects of the present layer are: the method comprises the steps of determining the site selection, the number, the capacity and the model of the gas turbine and the energy storage equipment, and expanding and planning strategies with the energy transmission power limit value of a superior power grid and the grid structure of the power distribution network.
And 2, layer 2: and (5) carrying out daily operation scheduling in an extreme scene. And importing the extreme scenes in the uncertain set of the data driving ellipsoids into a daily operation scheduling model, and scheduling the extreme scenes in multiple targets of economy, carbon emission and energy utilization rate by combining constraints such as equipment operation, energy balance, power flow and the like. The decision content in the layer is as follows: the power output strategies of the gas turbine, the photovoltaic and the wind power, the load shedding amount, the energy storage charging and discharging strategies, the power purchasing and selling strategies to the upper-level power grid and the like. According to the scheme, the power distribution network expansion planning scheme obtained by decision-making can ensure safe and stable low-carbon economic operation of the power system under the worst condition.
S2: researching the output characteristics of the wind power, photovoltaic and energy utilization load uncertainty, and putting the historical data of the wind power, photovoltaic and energy utilization load uncertainty into a set; constructing a data driving ellipsoid set and providing a minimum volume problem formula; enhancing the dimension of the ellipsoid set, and solving the minimum volume of the ellipsoid set; and translating the obtained coordinates to obtain vertex coordinates, so as to obtain extreme scene coordinates through inverse transformation and a data driving uncertain set.
S2, constructing a data drive ellipsoid uncertain set considering the relevance of uncertain variables;
putting photovoltaic output and load demand historical data into a set:
wherein, ω is i For column vectors, "'" is used as the transpose symbol; n is a radical of hydrogen p 、N L 、N s Respectively the photovoltaic quantity, the load quantity and the sampling days;
constructing a high-dimensional ellipsoid set:
a full-dimensional ellipsoid represented by a symmetric positive definite matrix A and a central fixed point c is described by the following mathematical method:
E(A,c)＝{ω∈R n |(ω-c) T A(ω-c)≤1}；
in quantifying the "size" of a set, the volume of the feasible domain is used as a computational object; by volume of unit sphere ρ n Multiplying by the transform to calculate the volume of ellipsoid E; because it is formed by R n The sphere of the space is linearly transformed.
Solving the ellipsoid minimum volume by the following problem equation, and determining the values of A and c:
solving the above formula by using a lift-and-project KY-1 method to obtain an ellipsoid set:
by two of N P +N L The minimum volume ellipsoid of the dimension wraps all scene sets for a certain time period, one is N P +N L Scene set of dimensions
The other is a deviation set
The entire problem is described as an optimization model as follows:
wherein p is a data point in the set; solving using lift-and-project KY-1, we will get (N) P +N L )×N s The set of dimensions ω is lifted to (N) P +N L +1)×2N s Set of dimensions ω':
wherein, the central point of omega ' is the same as the central point of omega, and the solution of new problem MVEE (omega ') composed of omega ' obtains the solution of MVEE (omega);
pri:
(P-MVEE(ω'))min M -log(detM)
dual:
(D-MVEE(ω'))min u -log(detV(u))
s.t.e T u＝1,u≥0
because the dual mode is a convex optimization model with only one equality constraint and non-negative dual variables, the lift-and-project KY-1 method can be used for solving conveniently;
then, the original problem can be solved by the following formula through the obtained u and ω':
finding vertex coordinates (extreme scenes):
translating the ellipse into an axial ellipse E' by conversion, and solving vertex coordinates:
ω i '＝P×(ω i -c)
E'(D)＝{ω'∈R n |ω' T Dω'≤1}
wherein, ω is i 、ω i ' are an original ellipsoid scene and a transformed axial ellipsoid scene respectively; p is for orthogonal decomposition A = P T An orthogonal matrix of DP; d is a characteristic value diagonal matrix of A; e' is the axial ellipsoid obtained after transformation.
The axial ellipsoid vertex coordinate omega obtained by transformation e,i ' obtaining the vertex coordinate omega of the original ellipse by coordinate transformation e,i (i.e., extreme scenarios):
ω i ＝c+P -1 ω i '。
s3: aiming at the problem that the existing power distribution network is insufficient in energy storage utilization, an urban power distribution network expansion planning model containing storage battery and super capacitor hybrid energy storage (SEC + SC) is provided, the model is divided into a planning layer and a scheduling layer, and the planning layer is used for obtaining an equipment capacity and site selection scheme with the minimum cost, wind and light abandonment and load shedding; the dispatching layer aims to ensure the safe and stable operation of the power distribution network system under the worst condition and properly ensure the economy of the power distribution network system.
S3, aiming at the lowest cost, energy conservation and low carbon, constructing a city power distribution network data-driven robust expansion planning model which contains mixed energy storage and considers the access of high-proportion new energy sources, and specifically comprising the following steps:
the objective function of the urban distribution network data-driven robust expansion planning model with mixed energy storage and high-proportion new energy access consideration mainly comprises planning construction cost, operation maintenance cost and energy-consuming carbon emission punishment cost. The extension planning is divided into two stages of annual planning and daily operation scheduling. The two stages are mutually influenced and constrained, annual planning takes the lowest construction cost as an objective function, the maximum power requirement of equipment operation fed back in the operation stage and the related constraints of photovoltaic, wind power and load data in the worst operation scene are considered, and the scheduling decision of the daily operation stage can be influenced by the decision of the capacity, the quantity, the model selection result, the power distribution network structure and the like of the equipment obtained in the stage.
The constraint conditions of the annual planning stage mainly comprise: equipment investment construction constraints and network spoke and connectivity constraints. The daily operation stage objective function is the lowest cost, the lowest carbon emission and the best new energy consumption effect. The constraint conditions of the daily scheduling operation stage mainly comprise: the method comprises the following steps of power distribution network power flow constraint, node voltage and current upper and lower limit safety constraint, distributed power output constraint, loss load constraint, energy storage equipment charging and discharging constraint, generator operation constraint, electric energy power balance constraint and tie line transmission power constraint.
Investment and construction constraints:
x wind 、x pv 、x line 、x PG respectively representing the variables of 0-1 whether the wind power, the photovoltaic, the newly-built line and the generator are put into construction or not; n is a radical of wind 、N pv 、N line 、N PG The maximum allowed number of builds.
In the power flow constraint, the node power balance constraint is as follows:
wherein the content of the first and second substances,
is the square of the current value of line ij; delta j Is a line set with j as the head end; phi is a j Is a line set with j as the terminal; omega wind 、Ω pv 、Ω SP 、Ω load Respectively numbering sets of wind power, photovoltaic, a superior power grid and load; p ij，t 、Q ij，t Respectively the active and reactive power of line ij;
the active load demand and the reactive load demand of the node j are respectively; r is ij Is the resistance value of line ij; x is the number of ij Is the reactance value of line ij;
are respectively asMain network active power, fan active power output and photovoltaic active power output;
the main network reactive power, the fan reactive power output and the photovoltaic reactive power output are respectively.
Taking the energy storage device as an example, the capacity planning constraint is as follows:
wherein, E k,min And E k,max Maximum and minimum capacity limits for the device; m min And M max And the maximum charge-discharge power limit value of the energy storage device.
Plant operating constraints take the generator as an example:
the charging and discharging constraints and the state constraints of the energy storage device are as follows:
wherein P is max For maximum charge-discharge power, S is the energy stored by the energy storage device at a certain time, η 1 Is the self-loss coefficient, eta, of the energy storage device 2 The charge and discharge efficiency of the energy storage device.
The energy purchase constraint is as follows, specifying the maximum transmission power, P, of the grid buymax Is the maximum purchase per unit of electricity time.
The constraints on power balance are as follows:
wherein, the variable symbols of photovoltaic, wind power, load and the like are represented by wave numbers as uncertain variables; and the coefficient is the generating efficiency coefficient of the generator set.
S4: and solving the urban distribution network expansion planning model, decomposing the planning problem of the min-max-min structure into a main problem and a sub problem, and solving to obtain a final planning scheme.
As shown in fig. 3, S4 is to solve the high-proportion new energy urban distribution network expansion planning model.
The two-stage robust optimization problem of the min-max-min structure is divided into a main problem and a sub problem by an improved CCG method to be solved. The main problem solves the problems of network architecture of the power distribution network, equipment capacity and location selection, type selection and transmission line upper limit, and the solved result is fixed and transmitted to the sub-problems. Due to the special advantages of the ellipsoid set, the subproblems do not need to be subjected to dual processing like the conventional CCG method, and can be directly solved by an enumeration method. The sub-problem obtains the worst scene with the highest cost by traversing the extreme scenes in the uncertain set, and transmits the serial number of the worst scene back to the main problem. And adding related wind power, photovoltaic and load constraints in the main problem to obtain a new decision, and entering the next iteration. And obtaining the optimal solution of the robust planning problem by continuous iteration when the final main problem and the sub-problem are consistent in decision.
In S4, the main question is a year planning question, and the mathematical description thereof is as follows:
wherein X and Y are feasible domains of decision variables and uncertain variables, and n is the current iteration times of the column and constraint generation method; σ is the scheduling maximum cost (i.e., upper bound value) in the last iteration;
respectively extreme scenes w h D and e are coefficient variables. And (4) obtaining a preliminary planning cost value (namely a lower bound value) through the solution of the main problem, fixing the decision variable and transmitting the decision variable to the sub-problem.
The subproblem is a daily run scheduling problem, and is mathematically described as follows:
wherein the content of the first and second substances,
is the planning decision variable (fixed) solved by the nth iteration main problem, and Ne is the number of extreme scenes.
S4, iteratively and alternately solving the main problem and the sub-problem based on the column and constraint generation method, where the flow is shown in fig. 3, and specifically as follows:
(1) Initializing;
setting an initialization scene omega 1 And a convergence flag δ. Set the number of iterations n =1, upper bound value U B = + ∞, lower bound value L B ＝-∞。
(2) Solving a main problem;
solving the annual planning problem to obtain the best solution before the day
And maximum value of real-time scheduling cost in main problem limit scene
Then, based on the solved value, the lower bound is updated:
(3) Solving the sub-problem;
decision variables in a problem planning year of a main problem
Under the unchanged condition, all limit scenes are solved (h =1,2, \8230;, N) e ). If a feasible strategy can be found in all scenes, selecting the scene with the highest cost, recording the cost number, and updating the upper bound:
U B ＝min(U B ,F sub )
wherein, F sub Is the maximum cost value that the sub-problem derives. If a feasible solution cannot be found in a certain limit scene, the number of the scene is recorded, and the worst scene is obtained.
(4) Confirming convergence;
the basis for judging convergence is as follows:
U B -L B <δ
if the conditions are met, the iteration is finished to obtain the optimal solution of the problem
And if not, updating the wind power, photovoltaic and load constraint conditions of the worst scene planned in the main problem year, adding the constraint conditions into the main problem, fixing the operation and maintenance cost, and jumping back to the second step for continuous iteration.
While the invention has been described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made without departing from the spirit and scope of the invention.
Claims (5)
1. A power distribution network data-driven robust expansion planning method containing high-proportion new energy is characterized by comprising the following steps:
step 1, establishing an urban distribution network data-driven robust extension planning framework;
step 2, historical data of wind power, photovoltaic and energy utilization load are placed in a set, a data driving ellipsoid set is constructed, and a minimum volume problem formula is provided; promoting the ellipsoid set dimension, and solving the ellipsoid set dimension by the minimum volume; translating the obtained coordinates to obtain vertex coordinates, and obtaining extreme scene coordinates and a data driving uncertain set through inverse transformation;
step 3, establishing an urban distribution network expansion planning model containing storage batteries and super-capacitor hybrid energy storage SEC + SC, wherein the urban distribution network expansion planning model comprises a planning layer and a scheduling layer;
and 4, solving the urban distribution network expansion planning model, decomposing the planning problem of the min-max-min structure into a main problem and a sub problem, and solving to obtain a final planning scheme.
2. The data-driven robust expansion planning method for the power distribution network containing the high-proportion new energy sources as claimed in claim 1, wherein the implementation of step 1 includes dividing planning problems into two aspects of annual planning and extreme scene daily operation, specifically as follows:
layer 1: planning the whole life cycle year; considering the life cycle and the breakage rate of the equipment, converting the cost of the full life cycle into the planning construction cost of each year in the life cycle, and carrying out the lowest cost and the minimum optimization of energy consumption carbon emission of a planning scheme according to the feedback of an operation level and the wind power, photovoltaic and load characteristic constraints in the data driving uncertain set; the planning objects include: determining the site selection, the number, the capacity and the model of the gas turbine and the energy storage equipment, and expanding and planning strategies for the energy transmission power limit value of a superior power grid and the grid structure of the power distribution network;
and 2, layer 2: carrying out daily operation scheduling in an extreme scene; importing the extreme scenes in the data-driven ellipsoid uncertain set into a daily operation scheduling model, and scheduling multiple targets of economy, carbon emission and energy utilization rate by combining equipment operation, energy balance and power flow constraint; the decision content comprises the following steps: the power generation strategy comprises a gas turbine, photovoltaic and wind power output strategy, a load shedding amount, an energy storage charging and discharging strategy and an upper-level power grid electricity purchasing and selling strategy.
3. The data-driven robust expansion planning method for the power distribution network containing the high-proportion new energy according to claim 1, wherein the step 2 is realized by the following steps:
step 2.1, constructing a data drive ellipsoid uncertainty set considering the relevance of the uncertainty variables;
putting photovoltaic output and load demand historical data into a set:
wherein, ω is i For column vectors,' is used as the transpose symbol; n is a radical of p 、N L 、N s Respectively the photovoltaic quantity, the load quantity and the sampling days;
step 2.2, constructing a high-dimensional ellipsoid set:
a full-dimensional ellipsoid represented by a symmetric positive definite matrix A and a central fixed point c is described by the following mathematical method:
E(A,c)＝{ω∈R n |(ω-c) T A(ω-c)≤1}；
step 2.3, when the size of the set is quantified, the volume of a feasible domain is used as a calculation object; by volume of unit sphere ρ n Multiplying by the transform to calculate the volume of ellipsoid E;
solving the ellipsoid minimum volume by the following problem equation, and determining the values of A and c:
s.t.(ω h,1 -c) T A(ω h,1 -c)≤1
(ω h,2 -c) T A(ω h,2 -c)≤1
…
step 2.4, solving the above formula by using a lift-and-project KY-1 method to obtain an ellipsoid set:
by two N P +N L The minimum volume ellipsoid of the dimension wraps all scene sets for a certain time period, one is N P +N L Scene set of dimensions
The other is a deviation set
The entire problem is described as an optimization model as follows:
s.t.(p i -c) T A(p i -c)≤1,i＝1,…,N s
A＝A T ,A＞0
wherein p is a data point in the set; using liftSolving for-and-project KY-1 to obtain the original (N) P +N L )×N s The set of dimensions ω is lifted to (N) P +N L +1)×2N s Set of dimensions ω':
ω'＝{±q 1 ,±q 2 ,…,±q m }
wherein, the central point of omega ' is the same as the central point of omega, and the solution of new problem MVEE (omega ') composed of omega ' obtains the solution of MVEE (omega);
pri:
(P-MVEE(ω'))min M -log(detM)
s.t.(q i ) T Mq i ≤1,i＝1,2,…,N s
dual:
(D-MVEE(ω'))min u -log(detV(u))
s.t.e T u＝1,u≥0
the lift-and-project KY-1 method is used for solving conveniently;
then, the original problem can be solved by the following formula through the obtained u and ω':
MVEE(ω)＝{(ω-c) T A(ω-c)≤1}
and (3) solving vertex coordinates:
translating the ellipse into an axial ellipse E' by conversion, and solving vertex coordinates:
ω i '＝P×(ω i -c)
E'(D)＝{ω'∈R n |ω' T Dω'≤1}
wherein, ω is i 、ω i ' are an original ellipsoid scene and a transformed axial ellipsoid scene respectively; p is for orthogonal decomposition A = P T An orthogonal matrix of DP; d is a characteristic value diagonal matrix of A; e' is obtained after transformationAn axial ellipsoid;
axial ellipsoid vertex coordinates omega obtained through transformation e,i ' obtaining the vertex coordinate omega of the original ellipse by coordinate transformation e,i ：
ω i ＝c+P -1 ω i '。
4. The method for data-driven robust extension planning of the power distribution network containing the high-proportion new energy according to claim 1, wherein the implementation of the step 3 comprises the steps of constructing a city power distribution network data-driven robust extension planning model containing hybrid energy storage and considering the access of the high-proportion new energy with the goals of lowest cost, energy conservation and low carbon as follows:
3.1, the objective function of the urban distribution network data-driven robust expansion planning model considering the high-proportion new energy access comprises planning construction cost, operation maintenance cost and energy-consuming carbon emission punishment cost; dividing the extension plan into a year planning stage and a daily operation scheduling stage;
step 3.2, taking the lowest construction cost as a target function in an annual planning stage, considering the maximum power requirement of equipment operation fed back in the operation stage and photovoltaic, wind power and load data related constraints of the worst operation scene, and obtaining the decision of equipment capacity, quantity, model selection results and a power distribution network structure;
the constraints of the annual planning phase include: equipment investment construction constraints and network radialization and connectivity constraints;
3.3, the objective function of the daily operation scheduling stage is the lowest cost, the lowest carbon emission and the best new energy consumption effect;
the constraint conditions of the daily operation scheduling phase comprise: power flow constraint of a power distribution network, node voltage and current upper and lower limit safety constraint, distributed power supply output constraint, loss load constraint, energy storage equipment charge and discharge constraint, generator operation constraint, electric energy power balance constraint and tie line transmission power constraint;
step 3.4, investment and construction constraints:
x wind 、x pv 、x line 、x PG respectively representing the variables of 0-1 whether the wind power, the photovoltaic, the newly-built line and the generator are put into construction or not; n is a radical of wind 、N pv 、N line 、N PG The maximum allowable number of constructions;
step 3.5, power distribution network flow constraint:
1) The node power balance constraints are as follows:
wherein, I sqr ij，t Squared as the current value of line ij; delta j Is a line set with j as the head end; phi is a j Is a line set with j as the terminal; omega wind 、Ω pv 、Ω SP 、Ω load Respectively a serial number set of wind power, photovoltaic, a superior power grid and load; p ij，t 、Q ij ， t Respectively the active and reactive power of line ij; p load j，t 、Q load j，t The active load demand and the reactive load demand of the node j are respectively; r is a radical of hydrogen ij Is the resistance value of line ij; x is the number of ij Is the reactance value of line ij; p SP j，t 、P wind j，t 、P pv j，t The main network active power, the fan active power output and the photovoltaic active power output are respectively; q SP j，t 、Q wind j，t 、Q pv j，t The main network reactive power, the fan reactive power output and the photovoltaic reactive power output are respectively;
2) The capacity planning constraints of the energy storage device are as follows:
wherein E is k,min And E k,max Maximum and minimum capacity limits for the device; m min And M max The maximum charging and discharging power limit value of the energy storage equipment;
3) The constraints of the generator are:
4) The charge-discharge constraints and state constraints of the energy storage device are as follows:
wherein, P max For maximum charge and discharge power, S is the energy storage device at a certain timeStored energy, η 1 Is the self-loss coefficient, eta, of the energy storage device 2 The charge-discharge efficiency of the energy storage device is obtained;
5) Energy purchase constraints; defining the maximum transmission power, P, of the network buymax Maximum purchase per unit of electricity time;
6) The constraints of power balancing are as follows:
wherein, the variable symbols of photovoltaic, wind power and load are represented by wave numbers as uncertain variables; and the coefficient is the generating efficiency coefficient of the generator set.
5. The data-driven robust extension planning method for the power distribution network containing the high-proportion new energy sources as claimed in claim 1, wherein the implementation of step 4 comprises:
step 4.1, splitting a two-stage robust optimization problem of a min-max-min structure into a main problem and a sub problem by an improved CCG method to solve;
step 4.2, solving the problems of network architecture of the power distribution network, equipment capacity and location determination, model selection and transmission line upper limit by the main problem, fixing the solved result and transmitting the solved result to the sub-problems;
4.3, solving the subproblems by an enumeration method;
the sub-problem obtains the worst scene with the highest cost by traversing the extreme scenes in the uncertain set, and transmits the serial number of the worst scene back to the main problem;
adding related wind power, photovoltaic and load constraints in the main problem to obtain a new decision, and entering next iteration;
obtaining the optimal solution of the robust planning problem when the final main problem and the sub-problem are consistent in decision through continuous iteration;
and 4.4, the main problem is an annual planning problem, and the mathematical description is as follows:
wherein X and Y are feasible domains of decision variables and uncertain variables, and n is the current iteration times of the column and constraint generation method; sigma is the maximum scheduling cost in the last iteration;
respectively extreme scenes w h D and e are coefficient variables; obtaining a preliminary planning cost value through solving a main problem, fixing a decision variable and transmitting the decision variable to a sub-problem;
step 4.5, the subproblem is a daily operation scheduling problem, and the mathematical description is as follows:
wherein, the first and the second end of the pipe are connected with each other,
the method comprises the following steps of (1) obtaining a planning decision variable of an nth iteration main problem, wherein Ne is the number of extreme scenes;
step 4.6, iteratively and alternately solving the main problem and the sub-problem based on a column and constraint generation method, wherein the flow is as follows:
(1) Initialization:
setup initializationScene omega 1 The convergence flag δ; the iteration number n =1 and the upper bound value U are set B = + ∞, lower bound value L B ＝-∞；
(2) Solving a main problem:
solving the annual planning problem to obtain the best solution before the day
And maximum value of real-time scheduling cost in main problem limit scene
Updating the lower bound according to the value obtained by solving:
(3) Solving the sub-problem:
decision variables in a problem planning year of a main problem
Under the condition of invariance, all limit scenes h =1,2, \ 8230;, N are solved e ；
If a feasible strategy can be found in all scenes, selecting the scene with the highest cost, recording the cost number, and updating the upper bound:
U B ＝min(U B ,F sub )
wherein, F sub Is the maximum cost value for the sub-problem; if a feasible solution cannot be found in a certain limit scene, recording the number of the scene to become the worst scene;
(4) And (3) confirming convergence:
the basis for judging convergence is as follows:
U B -L B <δ
if the conditions are met, the iteration is ended to obtain the optimal solution of the problem
And if not, updating the wind power, photovoltaic and load constraint conditions of the worst scene of the annual planning of the main problem, adding the constraint conditions into the main problem, fixing the operation and maintenance cost, and jumping back to the second step for continuous iteration.
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