CN117394446A

CN117394446A - Multi-stage robust unit combination method and device based on sequential evolution of batch scenes

Info

Publication number: CN117394446A
Application number: CN202311159532.5A
Authority: CN
Inventors: 韩子娇; 贾祺; 那广宇; 苏禹泽; 屈超; 康恺; 齐宁; 刘锋; 窦文雷; 佟永吉; 芦思晨; 刘凯; 朱洪波; 张强; 李欣蔚
Original assignee: Tsinghua University; State Grid Liaoning Electric Power Co Ltd
Current assignee: Tsinghua University; State Grid Liaoning Electric Power Co Ltd
Priority date: 2023-09-08
Filing date: 2023-09-08
Publication date: 2024-01-12

Abstract

The invention provides a multi-stage robust unit combination method and device based on sequential evolution of batch scenes, wherein in the nth iteration process in the method: aiming at a new energy scene in a new energy typical scene set, solving a mixed integer linear programming problem to obtain a thermal power unit combined solution; generating new energy scenes in batches to obtain a new energy batch scene set; combining the new energy typical scene set and the new energy batch scene set to obtain a first new energy scene set; through sequential evolution, calculating a multi-time-period sequential decision solution of a new energy scene about a thermal power unit combination solution; determining the feasibility of a new energy scene corresponding to each multi-period sequential decision solution; determining whether to stop iteration according to the feasibility of the new energy scene in the first new energy scene set; under the condition of continuing iteration, updating is carried out; and under the condition of stopping iteration, taking the thermal power unit combination solution as a unit combination decision result.

Description

Multi-stage robust unit combination method and device based on sequential evolution of batch scenes

Technical Field

The invention relates to the technical field of power system scheduling, in particular to a multi-stage robust unit combination method and device based on sequential evolution of batch scenes.

Background

New energy sources (also known as renewable energy sources) have evolved rapidly. The related data show that by 2021, the global new energy installed capacity reaches 3064GW, and compared with 1829GW in 2014, the increase rate exceeds 67%; meanwhile, in consideration of industrial development and technical progress, the electricity cost of new energy is remarkably reduced, and the installed capacity and grid-connected scale of new energy will continuously increase, which will promote the development of clean power.

Most new energy sources deliver electrical energy to consumers through electrical power networks. However, the access of a high proportion of new energy sources presents a great challenge for the consumption of electrical energy. Compared with a thermal power generating unit, the novel energy source has the characteristics of low schedulability, strong randomness and volatility, and is unfavorable for the real-time power balance of the power system. As high-controllability power regulating equipment, the thermal power generating unit is an important resource for stabilizing new energy power fluctuation and guaranteeing power supply and demand balance. However, since a long time is required for starting and stopping the thermal power plant, a combined solution of the thermal power plant at each time period within a day-ahead decision day is required.

Model predictive control (model predictive control, MPC) is a widely used intra-day scheduling strategy. The intra-day MPC scheduling strategy can make an optimal decision of the current period according to the latest information currently mastered. However, whether the combination solution of the thermal power generating unit before the day can guarantee the feasibility of the MPC scheduling strategy in the day is still a pending problem.

Disclosure of Invention

The invention provides a multi-stage robust unit combination method and device based on sequential evolution of batch scenes, which are used for solving the problem that the feasibility of an intra-day MPC scheduling strategy is difficult to be better ensured by a daily unit combination result in the prior art.

In a first aspect, the present invention provides a multi-stage robust set combining method based on sequential evolution of batch scenes, including:

the method includes at least 1 iteration process, in an nth iteration process:

aiming at a new energy scene in a new energy typical scene set, solving a mixed integer linear programming problem for deciding a thermal power unit combination to obtain a thermal power unit combination solution;

generating new energy scenes in batches to obtain a new energy batch scene set;

combining the new energy typical scene set and the new energy batch scene set to obtain a first new energy scene set;

calculating a multi-period sequential decision solution of each new energy scene in the first new energy scene set with respect to the thermal power generating unit combined solution through sequential evolution;

determining the feasibility of a new energy scene corresponding to each multi-time-period sequential decision solution on the combined solution of the thermal power generating unit according to the multi-time-period sequential decision solutions;

Determining whether to stop iteration according to the feasibility of new energy scenes in the first new energy scene set about the combined solution of the thermal power generating unit;

under the condition of continuing iteration, updating a new energy typical scene set in the n+1th iteration process, and updating constraint conditions of a mixed integer linear programming problem in the n+1th iteration process;

under the condition of stopping iteration, taking the thermal power generating unit combination solution as a unit combination decision result;

wherein n is an integer of 1 or more.

Optionally, the mixed integer linear programming problem for deciding a thermal power generating unit combination includes a first objective function and a first constraint;

the first objective function is used for minimizing the dispatching cost of the power system;

the first constraint includes: thermal power generating unit start-stop constraint, thermal power generating unit output range and climbing constraint, energy storage power station operation constraint, new energy output scene constraint, system power balance and line capacity constraint, worst new energy output scene operation cost constraint and unit combination sequential evolution cutting plane set constraint.

Optionally, the calculating, through sequential evolution, a multi-time-period sequential decision solution of each new energy scene in the first new energy scene set with respect to the thermal power generating unit combined solution includes:

Solving a sequential evolution optimization problem, namely obtaining a multi-period sequential decision solution of each new energy scene in the first new energy scene set relative to a thermal power generating unit combination solution, wherein the sequential evolution optimization problem comprises a second objective function and a second constraint;

the second objective function is used for minimizing the running cost of the power system from the first period to the second period;

the second constraint includes: thermal power unit output upper and lower bound constraint, thermal power unit climbing power constraint, energy storage power station operation constraint, new energy output scene constraint, power balance constraint and transmission line capacity constraint.

Optionally, updating the constraint condition of the mixed integer linear programming problem in the n+1th iteration process at least includes: updating the unit combination sequential evolution cutting plane set constraint in the n+1th iteration process;

the expression form of the unit combination sequential evolution cutting plane set constraint in the n+1th iteration process is as follows:

representing thermal power unit i in scene z and period t ₀ Lower active force variable,/->Representing thermal power unit i in scene z and period t ₀ Active force value at-1, < ->Start-stop variable u representing thermal power unit i _i (t ₀ ) Is a decision value of (a).

Optionally, the batch generation of the new energy scene includes:

And generating new energy scenes in batches according to probability distribution of new energy output.

Optionally, the determining, according to the multi-period sequential decision solutions, feasibility of a new energy scenario corresponding to each multi-period sequential decision solution with respect to the thermal power generating unit combined solution includes:

for a z new energy scene, under the condition that a feasibility judging formula is met, the z new energy scene is feasible with respect to the thermal power generating unit combination solution;

otherwise, the z new energy scene is not feasible with respect to the thermal power generating unit combination solution;

the feasibility judging formula is as follows:

t represents a scheduling period of time,representing a set of scheduling periods->Representing the forward relaxation variable of the power balance in the z-th new energy scene +.>Is a sequential decision solution of->Represents the power balance reverse relaxation variable in the z new energy scene>Is a sequential decision solution of->Representing the capacity forward relaxation variable of the transmission line l in the z-th new energy sceneIs a sequential decision solution of->Representing the capacity reverse relaxation variable ++of the transmission line l under the z new energy scene>Sequential blocks of (a)And (3) strategy, wherein z is an integer greater than or equal to 1.

Optionally, the determining whether to stop iteration according to the feasibility of the new energy scene in the first new energy scene set with respect to the thermal power generating unit combined solution includes:

Stopping iteration under the condition that the number of feasible new energy scenes accounts for more than or equal to a preset threshold value in the total number of the new energy scenes in the first new energy scene set;

otherwise, the iteration is continued.

In a second aspect, the invention further provides a multi-stage robust unit combination device based on sequential evolution of batch scenes, which comprises a first optimization unit, a scene generation unit, a scene merging unit, a second optimization unit, a feasibility judgment unit, an iteration decision unit, an iteration update unit and a decision output unit:

the apparatus performs at least 1 iteration process, in an nth iteration process:

the first optimizing unit is used for solving a mixed integer linear programming problem for deciding a thermal power unit combination aiming at a new energy scene in a new energy typical scene set to obtain a thermal power unit combination solution;

the scene generation unit is used for generating new energy scenes in batches and obtaining a new energy batch scene set;

the scene merging unit is used for merging the new energy typical scene set and the new energy batch scene set to obtain a first new energy scene set;

the second optimizing unit is used for calculating a multi-time-period sequential decision solution of each new energy scene in the first new energy scene set relative to the thermal power generating unit combined solution through sequential evolution;

The feasibility judging unit is used for determining the feasibility of the new energy scene corresponding to each multi-time-period sequential decision solution on the combined solution of the thermal power generating unit according to the multi-time-period sequential decision solution;

the iteration decision unit is used for determining whether to stop iteration according to the feasibility of new energy scenes in the first new energy scene set about the thermal power generating unit combination solution;

the iteration updating unit is used for updating the new energy typical scene set in the n+1th iteration process and updating the constraint condition of the mixed integer linear programming problem in the n+1th iteration process under the condition of continuing iteration;

the decision output unit is used for taking the thermal power generating unit combination solution as a unit combination decision result under the condition of stopping iteration;

wherein n is an integer of 1 or more.

In a third aspect, the present invention also provides an electronic device, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the multi-stage robust set assembly method based on sequential evolution of batch scenes according to the first aspect when executing the program.

In a fourth aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a multi-stage robust set combining method based on sequential evolution of batch scenarios according to the first aspect.

According to the multi-stage robust unit combination method and device based on sequential evolution of batch scenes, provided by the embodiment of the invention, the problem of Mixed Integer Linear Programming (MILP) can be solved rapidly, so that a thermal power unit combination solution is obtained; calculating a multi-time-period sequential decision solution of each new energy scene about the thermal power unit combined solution under a large batch of new energy scenes, and quantifying the probability that the thermal power unit combined solution meets the daily MPC scheduling feasibility according to the multi-time-period sequential decision solution; if the probability of the daily MPC scheduling feasibility is not met by the thermal power unit combination solution, iteration is continued, and the thermal power unit combination solution is used as a unit combination decision result under the condition that the probability of the daily MPC scheduling feasibility is met by the thermal power unit combination solution, so that the feasibility of the thermal power unit combination solution is ensured.

Drawings

In order to more clearly illustrate the invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.

Fig. 1 is one of flow diagrams of a multi-stage robust unit combining method based on sequential evolution of batch scenes according to an embodiment of the present invention;

FIG. 2 is a second flow chart of a multi-stage robust set assembly method based on sequential evolution of batch scene according to an embodiment of the present invention;

fig. 3 is a schematic structural diagram of a multi-stage robust unit assembly device based on sequential evolution of batch scenes according to an embodiment of the present invention;

fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. 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.

The power fluctuation of new energy (also called as renewable energy) can cause that the real-time power demand of the load is difficult to ensure, and further adverse effects are brought to the operation of the economy and society, so that in the field of solving the combination of the thermal power generating unit and the future unit, a widely used scheduling method is a two-stage future robust unit combination method. The idea of the robust unit combination method is to construct random new energy output into an uncertain set form, and then find a group of thermal power unit combination solutions, so that all scenes in the uncertain set meet the scheduling feasibility under the condition of not considering the time sequence characteristics of the new energy, and the scheduling cost of unit combination is minimized under the worst scene. However, the uncertain set used by the two-stage robust unit combination model is difficult to characterize the probability distribution of new energy output, so that the probability that the unit combination result meets the daily scheduling feasibility is difficult to quantify; in addition, as the problem is simplified and modeled by the two-stage robust unit combination method to form two stages of pre-dispatching and re-dispatching, time sequence information of unit combination is not considered in each stage, so that new energy output disclosed by time sequence of a day is faced, and the feasibility of a day-ahead unit combination result in a day MPC dispatching strategy is difficult to be well ensured.

The multi-stage robust unit combination method based on sequential evolution of batch scenes provided by the embodiment of the invention is described below with reference to fig. 1-2.

Fig. 1 is one of flow diagrams of a multi-stage robust unit combination method based on sequential evolution of batch scenes according to an embodiment of the present invention, where, as shown in fig. 1, the multi-stage robust unit combination method based on sequential evolution of batch scenes according to an embodiment of the present invention includes at least one iteration process, and in an nth iteration process, the method includes:

step 110, solving a mixed integer linear programming problem for deciding a thermal power unit combination aiming at a new energy scene in a new energy typical scene set to obtain a thermal power unit combination solution;

specifically, a thermal power unit combination solution (also simply referred to as a unit combination solution) characterizes an operation state of the thermal power unit, a value of 1 of the thermal power unit combination solution indicates normal operation of the thermal power unit, and a value of 0 of the thermal power unit combination solution indicates shutdown of the thermal power unit.

Step 120, generating new energy scenes in batches, and obtaining a new energy batch scene set;

specifically, the new energy scenario may also be referred to as a new energy output scenario.

Step 130, merging the new energy typical scene set and the new energy batch scene set to obtain a first new energy scene set;

Step 140, calculating a multi-period sequential decision solution of each new energy scene in the first new energy scene set with respect to the thermal power generating unit combined solution through sequential evolution;

step 150, determining feasibility of a new energy scene corresponding to each multi-time-period sequential decision solution with respect to the thermal power generating unit combination solution according to the multi-time-period sequential decision solution;

step 160, determining whether to stop iteration according to the feasibility of the new energy scene in the first new energy scene set with respect to the thermal power generating unit combination solution;

for example, the iteration is stopped in case a feasible new energy scenario for the thermal power plant combined solution satisfies a preset condition. The preset condition may be a preset number or a preset ratio, etc.

Step 170, under the condition of continuing iteration, updating the new energy typical scene set in the n+1th iteration process, and updating the constraint condition of the mixed integer linear programming problem in the n+1th iteration process;

specifically, the first new energy scene set obtained in the step 130 is used as a new energy typical scene set in the n+1th iteration process, the constraint conditions related to the new energy typical scene set in the n+1th iteration process are updated, and the constraint conditions related to the n+1th iteration process are updated according to the calculation result of the step 140.

Step 180, under the condition of stopping iteration, taking the thermal power unit combination solution as a unit combination decision result;

wherein n is an integer of 1 or more.

Specifically, under the condition that the decision value can ensure the feasibility of the scene, stopping iteration, taking the thermal power unit combination solution obtained in the step 110 as a unit combination decision result, and outputting a final decision result.

According to the multi-stage robust unit combination method based on batch scene sequential evolution, provided by the embodiment of the invention, the problem of Mixed Integer Linear Programming (MILP) can be rapidly solved, so that a thermal power unit combination solution is obtained; calculating a multi-time-period sequential decision solution of each new energy scene about the thermal power unit combined solution under a large batch of new energy scenes, and quantifying the probability that the thermal power unit combined solution meets the daily MPC scheduling feasibility according to the multi-time-period sequential decision solution; under the condition that the thermal power unit combination solution meets the probability of the scheduling feasibility of the MPC in the day, the thermal power unit combination solution is used as a unit combination decision result, so that the feasibility of the thermal power unit combination solution is ensured. In addition, the new energy scenes are generated in batches, and the output condition of new energy in the day can be more truly depicted through the large batches of new energy scenes.

Fig. 2 is a second flow chart of a multi-stage robust unit combining method based on sequential evolution of batch scene according to an embodiment of the present invention, and a possible implementation of the above steps in a specific embodiment is further described below with reference to fig. 2.

Firstly, the structure of the multi-stage robust unit combination method based on sequential evolution of batch scenes provided by the embodiment of the invention is introduced:

the multi-stage robust unit combination method based on batch scene sequential evolution provided by the embodiment of the invention has an upper layer, a middle layer and a lower layer three-layer structure, and the unit combination result meeting the feasibility of the MPC scheduling strategy in the day can be obtained through successive iteration of the upper layer, the middle layer and the lower layer three layers. The upper layer of the invention considers the problem of Mixed Integer Linear Programming (MILP) of the scheduling feasibility in the day, and the problem can be rapidly solved to obtain a thermal power unit combination solution; the middle layer is a batch scene generation link constructed by the invention, and compared with an uncertain set used by a robust unit, the link can generate new energy output scenes in batches according to new energy output probability distribution of each time period in the day; the lower layer is based on a daily MPC scheduling strategy and aims at solving the problem of sequential evolution optimization of new energy prediction errors, and after the new energy scene generated in batch is input into the lower layer of sequential evolution optimization problem, a scheduling result based on the daily MPC scheduling strategy can be output.

Optionally, the setting of the scheduling parameters and the grid parameters may be performed before the decision is made:

scheduling parameter setting: the degree parameter mainly includes information of a scheduling period: counting the number of scheduling time periods as N _T The set of scheduling periods is

And (3) setting power grid parameters:

thermal power unit parameters: power output upper bound of thermal power generating unit iLower power output bound->Maximum uphill power of adjacent scheduling periods +.>Maximum downhill power of adjacent scheduling periods +.>Length T of minimum start-up period of unit _i ^on Minimum shutdown period length T of unit _i ^off Unit single start-up cost ∈>Unit single shut-down cost->Unit power output cost ∈>The number of the thermal power generating units is N _G The set of the thermal power generating unit is +.>

New energy unit parameters: the present invention contemplates a new energy output probability distribution represented using a mixed gaussian distribution (GMM). Recording the active GMM parameter set of the new energy station j in the period t asWherein M represents the total number of Gaussian components, ω _j,t,m Is the weight coefficient of the m-th Gaussian component, 0 <ω _j,t,m < 1 and-> Is the mean value of the mth gaussian component, +.>Is the variance of the mth gaussian component. The number of new energy units is recorded as N _R The new energy unit is assembled by +.>

Load parameters: predicted value of load d in period t The number of the recorded loads is N _D The set of loads is +.>

Energy storage power station parameters: upper limit of charge and discharge power including energy storage power station kCapacity upper limit +.>Capacity lower limit value->Charging efficiency->Discharge efficiency->Initial electric quantity value of energy storage power station k>End-of-day electrical quantity value of energy storage power station kUnit energy storage charge-discharge power cost of energy storage power station k>The number of the energy storage power stations is recorded as N _E The collection of the energy storage power stations is->

Transmission line and node parameters: the maximum power transmission capacity of the transmission line l is recorded asThe number of the transmission lines is recorded as N _L The transmission line set is->The number of the nodes is recorded as N _B The set of nodes is->

The power generation transfer factor of the thermal power generating unit i to the line l is recorded asNew energy unit j is to power generation transfer factor of circuit l +.>Load d vs. line l power generation transfer factor->Power generation transfer factor of energy storage power station k to line l>

Setting and initializing iteration parameters: the index of the iteration number of the embodiment of the present invention is set to n, and n=1 is initialized.

Setting and initializing an upper-layer new energy typical scene set: recording the n-th iteration, wherein the upper-layer new energy typical scene set is as followsNew energy output scene +.>A marker, wherein y is the index of the new energy scene sequence,/->Is a set- >Number of elements contained.

When n=1, initializeAnd will->New energy output scene +.>Set to->

It should be appreciated that the new energy scenario may also be referred to as a new energy output scenario, where one new energy scenario is a new energy power curve made up of power values of the new energy during all scheduling periods. The "new energy scenario" is the power curve of the new energy.

The parameters of the thermal power unit, the parameters of the energy storage power station and the parameters of the transmission line are set according to the parameters of the actual power network and the parameters of the power equipment. The new energy unit parameters are obtained through historical data and forecast data. The system and network parameters are fixed values.

Step 110, solving a mixed integer linear programming problem for deciding a thermal power unit combination aiming at a new energy scene in a new energy typical scene set to obtain a thermal power unit combination solution.

Step 110 of the embodiment of the present invention (i.e., the upper layer in the embodiment of the present invention may also be referred to as an upper layer structure or an upper layer link) is a mixed integer linear programming problem for deciding a thermal power generating unit combination.

Optionally, the mixed integer linear programming problem for deciding on thermal power generating unit combinations comprises first constraints (1) to (17) and a first objective function (18); after solving the upper mixed integer linear programming problem in the nth iteration, the combined solution of the thermal power generating unit can be calculated

The first objective function is used to minimize power system scheduling costs.

Optionally, the thermal power generating unit start-stop constraint is as shown in constraints (1) to (3):

constraint (1) is the association constraint of a unit start variable, a shut-down variable and a state variable:

constraint (2) is a minimum operation duration constraint of the unit:

constraint (3) is a minimum shutdown duration constraint of the unit:

wherein,is a 0-1 variable: u (u) _i (t) is a start-stop variable of the thermal power unit, u _i (t) =0/1 indicates that thermal power unit i is in a shutdown/operation state during period t, +.>Indicating variable for unit start->Indicating that thermal power unit i is not started/started in period t,/->Indicating variable for unit shut down, < >>Indicating that the thermal power generating unit i is not shut down/shut down in the period t; />And->Is an integer variable: />The operation time length variable of the thermal power unit is represented as the operation time length of the thermal power unit i in the period t; />And the shutdown time variable of the thermal power unit is represented as the shutdown time of the thermal power unit i in the period t.

Optionally, in the nth iteration, for the new energy sourceTypical scene setIs +.>It is necessary to construct constraints (4) to (16).

Optionally, the thermal power generating unit output range and the climbing constraint are as shown in constraints (4) to (6):

constraint (4) is the upper and lower limit constraint of thermal power unit output:

constraint (5) is climbing power constraint of the thermal power generating unit:

constraint (6) is a climbing power constraint of the thermal power generating unit:

wherein u is _i (t) represents a start-stop variable of the thermal power generating unit i,represents the lower power output boundary of the thermal power generating unit i,representing the active output of the thermal power generating unit i under the scene y and the period t, +.>Represents the upper power output limit of the thermal power generating unit i,represents the set of thermal power generating units->Representing a set of scheduling periods->Maximum climbing power representing adjacent scheduling periods of thermal power unit i +.>And the maximum downhill climbing power of the adjacent scheduling period of the thermal power generating unit i is represented.

The energy storage power station operation constraints are shown as constraints (7) to (11):

constraint (7) and constraint (8) are respectively energy storage charging and discharging power limits:

constraint (9) is a time period coupling constraint of stored energy:

the constraint (10) is an energy storage capacity upper and lower limit constraint:

the constraint (11) is a numerical constraint of the energy storage electric quantity at the end of the day:

Wherein,representing the input active power of the energy storage power station k in the scene y and the period t, +.>Representing the output active power of the energy storage power station k under the scene y and the period t, +.>Is a 0-1 variable, which indicates that the energy storage power station k can not charge and discharge at the same time under the conditions of a scene y and a period t, and a variable E _k,y (t) represents the stored power of the energy storage power station k under the scene y and the period t, +.>Representing the upper limit of the charge and discharge power of the energy storage power station k, < >>Representing the upper limit value of the capacity of the energy storage power station k, +.>Representing the lower limit value of the capacity of the energy storage power station k, +.>Representing the charging efficiency of the energy storage station k +.>Representing the discharge efficiency of the energy storage station k +.>Representing an initial electrical quantity value of the energy storage plant k, +.>And the end-of-day electricity value of the energy storage power station k is represented.

The new energy output scene constraint is shown as constraint (12):

wherein,representing the active output of the new energy power station j in a scene y and a period t; constraint (12) is a scene set +.>And the related new energy output power constraint.

The system power balance and line capacity constraint new energy output scene constraint is as shown in constraints (13) to (15):

constraint (13) is a system power balancing constraint:

constraint (14) is a line capacity constraint:

constraint (15) is a relaxation variable non-negative constraint:

wherein, Represents the collection of thermal power generating units, and the collection of new energy units is +.>Representing a set of loads, representing->Aggregation of energy storage power stations->Representing a set of transmission lines>Representing a set of scheduling periods->Representing the active output of the thermal power generating unit i under the scene y and the period t, +.>Representing the active power of the new energy power station j in the scene y and the period t, +.>Representing the input active power of the energy storage power station k in the scene y and the period t, +.>Representing the output active power of the energy storage power station k under the scene y and the period t, +.>Predictive value representing load d in period t, < >>Representing the forward relaxation variable of the power balance in the (y) th new energy scene,/for>Represents the power balance reverse relaxation variable in the y new energy scene,representing the capacity forward relaxation variable of the power transmission line l in the (y) th new energy scene,/and (y)>Representing the capacity reverse relaxation variable of the power transmission line l in the (y) th new energy scene, and (y)>Represents the power generation transfer factor of the thermal power generating unit i to the line l,/, and>represents the power generation transfer factor of the new energy unit j to the line l, < ->Represents the power generation transfer factor of load d to line l, < >>The power generation transfer factor of the energy storage power station k to the line l is represented.

The worst new energy output scene operation cost constraint new energy output scene constraint is shown as constraint (16):

Wherein eta is an auxiliary variable and represents the system running cost of the worst new energy output scene; coefficient c ^p Penalty cost for a unit relaxation variable,represents the set of thermal power generating units, represents +.>Aggregation of energy storage power stations->Representing a set of transmission lines>Representing a set of scheduling periods->Representing the active output of the thermal power generating unit i under the scene y and the period t,representing the input active power of the energy storage power station k in the scene y and the period t, +.>Representing the output active power of the energy storage power station k under the scene y and the period t, +.>Represents the unit power output cost of the thermal power generating unit, < + >>Representing the unit energy storage charge-discharge power cost of the energy storage power station k, < >>Representing the forward relaxation variable of the power balance in the (y) th new energy scene,/for>Represents the reverse relaxation variable of the power balance in the (y) th new energy scene,/and (y)>Representing the capacity forward relaxation variable of the power transmission line l in the (y) th new energy scene,/and (y)>And the capacity reverse relaxation variable of the power transmission line l in the y new energy scene is represented.

And the set combination sequential evolution cutting plane sets constraint new energy output scene constraint:

in a successive iteration process of an embodiment of the present invention,the lower layer can transmit and update the unit combination sequential evolution cutting plane set to the upper layer Start-stop variable u of thermal power generating unit _i (t) need to satisfy constraint (17).

Wherein,is a set of unit combination unexpected cutting plane constraint sets: n=1, < >>Is->Is a feasible region of the whole number; when n is greater than or equal to 2, the formula is->The expression of (2) is shown in the formula (44).

First objective function:

the first objective function is an objective function of a pre-scheduling stage, the first objective function is used for minimizing the scheduling cost of the power system, and the expression of the first objective function is shown as a formula (18):

wherein u is _i (t) represents a start-stop variable of the thermal power generating unit i,indicating variable for unit start->Indicating that thermal power unit i is in time periodt is not activated/activated->Indicating variable for unit shut down, < >>Indicating that thermal power unit i is not shut down/shut down +.>Representing the active power of the new energy power station j in the scene y and the period t, +.>Representing the active output of the thermal power generating unit i under the scene y and the period t, +.>Representing the input active power of the energy storage power station k in the scene y and the period t, +.>Representing the output active power of the energy storage power station k under the scene y and the period t, E _k,y (t) represents the stored power of the energy storage power station k under the scene y and the period t, +.>Represents the power balance forward relaxation variable in the y new energy scene,represents the reverse relaxation variable of the power balance in the (y) th new energy scene,/and (y) >Representing the capacity forward relaxation variable of the power transmission line l in the (y) th new energy scene,/and (y)>Representing power transmission line in the (y) th new energy sceneWay l capacity reverse relaxation variable,/>Represents the single start-up cost of the thermal power unit i, < ->Represents the single shutdown cost of the thermal power unit i, eta is an auxiliary variable, eta represents the system operation cost of the worst new energy output scene, and +.>Represents the collection of thermal power generating units, and the collection of new energy units is +.>Representation->Aggregation of energy storage power stations->Representing a set of scheduling periods.

Optionally, solving the first objective function (18) under the constraints of equations (1) to (17):

the first objective function (18) includes thermal power unit start-up costs, thermal power unit shut-down costs, and typical scenario maximum operating costs η.

The expression of the MILP problem in the upper layer, namely the mixed integer linear programming problem for deciding the thermal power unit combination, is shown in a formula (19).

Solving the optimization problem (19) can obtain the total running cost of the power system of the upper-layer problem and the start-stop variable of the unitThermal power generating unit groupCombination of-> The footnote n here is an index of the number of iterations of an embodiment of the present invention. Then, the thermal power generating unit combination obtained from the upper layer is de-added>And new energy batch scene set used by upper layer +. >To the lower layer.

Step 120 is a middle layer in the embodiment of the present invention: and generating a middle layer batch scene.

parameter set-up and initialization in step 120:

in the nth iteration, the middle layer will newly generate N _SO New energy scene of the new energy batch scene set formed by the new energy batch scenes is recorded asThe upper right hand corner is the number of iterations. The integer h is noted (h=1, N _SO ) Is a set->Index of each scene in the database.

Optionally, the batch generation of the new energy scene includes:

Specifically, a new energy output probability distribution represented by a mixed gaussian distribution (GMM) is used. Recording the active GMM parameter set of the new energy station j in the period t asWherein M represents the total number of Gaussian components, ω _j,t,m Is the weight coefficient of the mth Gaussian component, with 0 < omega _j,t,m < 1 and-> Is the mean value of the mth gaussian component, +.>Is the variance of the mth gaussian component. The number of new energy units is recorded as N _R The new energy unit is assembled by +.>

Since new energy station j (j=1,., N _R ) The active power GMM parameter set at time period t isRecord omega _j,t,m | _m＝0 =0, then, the set can be generated by>Scene h of (2):

for the new energy station j of the period t, the new energy station j is firstly distributed uniformly in intervals [0,1 ]]Random number generation in a computerThen there must be an integer M ₀ (1≤M ₀ And M) is not more than one, so that the formula (20) is established.

The active output of the new energy station j in the period t can be changed from the average value to the average valueSum of variances of->Random sampling in normal distribution of (2) and recording the generated new energy output as +.>Traversing the new energy station j in the traversing period t to obtain a set +.>New energy output of middle scene h>

With the aid of the above-described manner, all scenes h can be generated (h=1, N _SO ) New energy output of (a)These scenes constitute the set->Then, the collection ++>To the lower layer.

According to the batch scene generation link, the new energy scene is generated in batches based on the GMM, and the new energy scene is generated in batches according to the probability distribution of the predicted output of the new energy in the day-ahead stage, so that the output condition of the new energy in the day can be more truly described.

Steps 130 to 180 are lower layers according to the embodiment of the present invention, which are based on the intra-day MPC scheduling strategy and account for the sequential evolution optimization problem of the new energy prediction error.

Constructing a first new set of energy scenes that need to be used for sequential evolution(first new energy scene set), the footnote n in the upper right corner is the number of iterations. />Is used for new energy scene-> Represented, wherein z is the set +.>Index of new energy scene sequence, +.>Is a set->Number of elements contained.

Order theIs->And->The union of (a) is:

thus there isAnd-> Can be connected withAnd-> One-to-one correspondence.

specifically, the upper layer Middle layer->After the data is transmitted to the lower layer, the problem of sequential evolution optimization of the lower layer based on the intra-day MPC scheduling strategy can be entered.

In the lower layer sequential evolution optimization problem, due to incomplete prediction information, at period t ₀ Only slave period t ₀ By time period t ₀ The new energy sources of +DeltaT predict the forces, and certain errors exist in the predicted forces. Wherein Δt is the number of new energy output periods predicted backward.

For the followingIs +.>In period t ₀ The new energy output value predicted by the electric power system is +.>Wherein->Respectively expressed in period t ₀ Predict 0,1 backward. In the application process, there are generally The error value of (2) may be estimated from historical data.

Considering time period coupling characteristics of decision variables of thermal power generating units and energy storage power stations, and time period t ₀ -1 unit output valueAnd stored electrical quantity of the energy storage power station +.>Will be for period t ₀ Is influenced by the decision variables of (a).

According to the characteristics, the first period t of the MPC scheduling strategy in the day ₀ Consideration is given to the time from the first period t ₀ To a second period t ₁ System operation constraint of (2), second period t ₁ The expression of (2) is as shown in (22):

t ₁ ＝min(N _T ,t ₀ +ΔT) (22)

thus, in period t of the sequential evolution optimization problem ₀ It is necessary to construct second constraints (23) to (34) and to establish a second objective function (35).

Optionally, the thermal power generating unit output upper and lower bounds and the climbing power constraint are as shown in (23) to (25):

the constraint (23) is the upper and lower limit constraint of the thermal power unit output:

the constraint (24) is a climbing constraint of the thermal power generating unit:

constraint (25) is a thermal power generating unit downhill climbing constraint:

wherein,start-stop variable u representing thermal power unit i _i Decision value of (t)/(t)>Represents the upper power output limit of the thermal power generating unit i, < ->Representing thermal powerLower power output limit of unit i +.>Representing the active output variable of the thermal power generating unit i in the scene z and the period t, +.>Represents the set of thermal power generating units->Representing thermal power unit i in scene z and period t ₀ Active force value at-1, < ->Maximum climbing power representing adjacent scheduling periods of thermal power unit i +.>And the maximum downhill climbing power of the adjacent scheduling period of the thermal power generating unit i is represented.

Optionally, the energy storage power plant operating constraints are as shown in (26) to (30):

constraint (26) and constraint (27) are respectively the charge-discharge power limits of the energy storage power station:

constraint (28) is a time period coupling constraint of energy storage capacity:

The constraint (29) is an upper and lower limit constraint of the energy storage capacity.

/>

In addition, during the scheduling period t ₀ When=1, there areIn the end-of-day scheduling period, the numerical constraint of the stored energy needs to be considered, namely: when t ₀ +ΔT≥N _T Constraint (30) needs to be considered when:

wherein,active power input variable of energy storage power station k in scene z and period t, +.>Representing the active power output variable of the energy storage power station k in the scene z and the period t, the variable +.>Is 0-1 variable, which indicates that the energy storage power station k can not charge and discharge at the same time under the scene z and the period t, < >>Represents the upper limit of the charge and discharge power of the energy storage power station k, E _k,z (t) represents the stored power of the energy storage power station k in the scenario z and the period t, +.>Representing the energy storage power station k in the scene z and the period t ₀ -value of the stored electrical quantity under-1, < >>Representing the charging efficiency of the energy storage station k +.>Representing the discharge efficiency of the energy storage station k +.>Representing the upper limit value of the capacity of the energy storage power station k, +.>Representing the lower limit value of the capacity of the energy storage power station k, +.>Representing an initial electrical quantity value of the energy storage plant k, +.>Represents the end-of-day electrical quantity value of the energy storage power station k, < >>Representing a collection of energy storage power stations.

The new energy output scene constraint is as shown in (31):

constraint (31) is a new energy predicted output constraint, wherein,is the active output variable of the new energy power station j in the scene z and the period t, ++ >Represents the set of new energy units +.>Representation set->The z-th new energy scene in the system,indicated at time period t ₀ Backward prediction of t-t ₀ Random error values for each period.

The power balance constraint and the transmission line capacity constraint are as shown in (32) to (34):

constraint (32) is a system power balancing constraint:

constraint (33) is a line capacity constraint:

constraint (34) is a relaxation variable non-negative constraint:

wherein,represents the set of thermal power generating units->Represents the set of new energy units +.>Representing a collection of energy storage power stations->Representing a set of transmission lines>Representing a set of loads, +.>Representing the active output variable of the new energy power station j in the scene z and the period t, ++>Representing the active output variable of the thermal power generating unit i in the scene z and the period t, +.>Representing the input active power of the energy storage power station k in the scene z and the period t, +.>Representing the output active power of the energy storage power station k in the scene z and the period t, +.>Predictive value representing load d in period t, < >>Represents the power generation transfer factor of the thermal power generating unit i to the line l,/, and>represents the power generation transfer factor of the new energy unit j to the line l, < ->Represents the power generation transfer factor of load d to line l, < >>Representing the power generation transfer factor of the energy storage power station k to the line l,/, and >Is a forward relaxation variable of power balance in a new energy scene z, < >>Representing the z-th new energy sceneLower power balance reverse relaxation variable, +.>Representing the capacity forward relaxation variable of the transmission line l in the z-th new energy scene,/and (B)>And the capacity reverse relaxation variable of the power transmission line l in the z-th new energy scene is represented.

Second objective function:

for the followingIn (2), a scheduling period t ₀ Is to minimize the time period t from the first time period t ₀ To a second period t ₁ The expression of the second objective function is shown in the formula (35):

the meaning of the parameters in the second objective function (35) is described above and will not be described in detail here.

Constructing an underlying period optimization problem:

the second objective function (35) includes a slave period t ₀ To t ₁ The active output cost of the thermal power generating unit, the energy storage charge-discharge cost and the relaxation variable penalty cost. Thus, the scheduling period t ₀ The optimization problem expression of (3) is as shown in (36):

in period t ₀ Solving the optimization problem (36) to obtain a first time period t ₀ To a second period t ₁ Is selected from the variable decision values of t=t ₀ As a sequential decision solution for the current time period. Solving all time periods t ₀ ＝1,...,N _T Optimizing question of (2)After the problem (36), the first step can be obtained New energy scene->Combined solution for thermal power generating unit>Multiple time-segment sequential decision solution +.>

At->In (I)>Is a decision variable +.>Is a sequential decision solution of->Decision variables +.>Is a sequential decision solution of->Decision variables +.>And the footnote n is an index of the number of inner and outer layer iterations of the present invention.

The embodiment of the invention is matched with the MPC scheduling strategy widely used in reality, constructs a lower layer sequential evolution link based on the intra-day MPC scheduling strategy, and fully considers the prediction error of new energy output.

The feasibility judging formula is as follows:

t represents a scheduling period of time,representing a set of scheduling periods->Representing the forward relaxation variable of the power balance in the z-th new energy scene +.>Is a sequential decision solution of->Represents the power balance reverse relaxation variable in the z new energy scene>Is a sequential decision solution of->Representing the capacity forward relaxation variable of the transmission line l in the z-th new energy sceneIs a sequential decision solution of->Representing the capacity reverse relaxation variable ++of the transmission line l under the z new energy scene>And z is an integer of 1 or more.

In particular, the method comprises the steps of,is->The feasibility of sequential decision scheduling solutions is characterized: if you are right->There is->If true, then indicate that-> Under the MPC scheduling strategy in the day, scene +.>The scheduling is feasible; if->Then indicate scene +.>Scheduling is not feasible.

Otherwise, the iteration is continued.

Specifically, the convergence criterion in the embodiment of the invention is as follows: the ratio of the number of lower-layer scheduling feasible scenes to the number of all scenes (the total number of new energy scenes in the first new energy scene set) is greater than or equal to a preset threshold epsilon _threshold 。

Set in the nth iteration, setIs->The number of new energy scenes which are feasible to schedule in the individual scenes is->In case the number of viable new energy scenes is equal to or larger than the preset threshold value in relation to the total number of new energy scenes in the first set of new energy scenes, i.e. equation (37) is established, the iterative process ends,

formula (37):

the convergence criterion of the embodiment of the invention is as follows: the ratio of the number of lower layer scheduling feasible scenes to the number of all scenes exceeds a certain threshold. On the one hand, the convergence criterion can better quantify the probability of ensuring the feasibility of the MPC scheduling strategy in the day by the combination result of the unit under the condition that the output probability distribution of new energy is known; on the other hand, the convergence criterion has clear physical significance, and after reasonably selecting the threshold value of the convergence criterion in combination with actual demands, the robustness and the economy of the unit combination can be well considered.

Specifically, if equation (37) does not hold, then the first new energy scene set is usedDecision value of all scheduling infeasible scenarios +.>Constructing a set combination sequential evolution cutting plane set, and adding a first new energy scene set +.>Assigning value to new energy typical scene set in n+1th iteration process>Let n=n+1 then update the constraints of the upper layer optimization problem (17) and go through the next iteration.

Optionally, updating the constraint condition of the mixed integer linear programming problem in the n+1th iteration process at least includes: and updating the unit combination sequential evolution cutting plane set constraint in the n+1th iteration process.

The method for constructing the set of unit combination sequential evolution cutting planes is described below.

In the lower layer sequential evolution optimization problem of the nth iteration, definition is definedPenalty cost for scene z at time period t isThe expression is shown as (38), and the full-time penalty cost of the scene z is defined as +.>The expression is shown in (39).

In period t ₀ In the optimization problem (36) of (1), a scene z is characterized and containsThe constraints of (2) are detailed in constraints (40) to (42). Wherein (1)>And->The dual variables of constraint (40), constraint (41) and constraint (42), respectively.

Optimization problem for all time periods (36) After the completion of the solution, record And->Respectively is a dual variable->And->Is a decision value of (a). />

In the nth iteration, constructing a set combination sequential evolution cutting plane set of the (n+1) th iterationIf the relaxation cost of scene z ∈>Then in the collection->Adding constraint (43):

then after n iterations of the process,the expression of (2) is shown in the formula (44):

wherein,represents the set constraint of the set combination sequential evolution cutting plane in the n+1th iteration process, u _i (t) represents the start-stop variable of the thermal power generating unit i,/-)>Represents the set of thermal power generating units->Representing a set of scheduling periods->Represents the upper power output limit of the thermal power generating unit i, < ->Represents the lower power output limit of the thermal power generating unit i, < ->Representing the maximum downhill climbing power of adjacent scheduling periods of the thermal power unit i +.>Represents the nth ₀ Full-time penalty cost of the z-th new energy scene in a new energy typical scene set in the iterative process +.>Representing the dual variable +.>Decision value of->Representing the dual variable +.>Decision value of->Representing the dual variable +.>Decision value of->Representation->Is>Representation ofIs>Representation ofIs>Representing thermal power unit i in scene z and period t ₀ Lower active force variable,/->Representing thermal power unit i in scene z and period t ₀ Active force value at-1, < ->Start-stop variable u representing thermal power unit i _i (t ₀ ) Is a decision value of (a).

It should be appreciated that the generation occurs during the nth iterationIn the process of (1) the iteration is required to be traversedRelated parameters in a new energy typical scene set, such as full-time penalty cost of traversing the z-th new energy scene in the new energy typical scene set from iteration 1 to iteration n ₀ =1..n is used to denote the course of the traversal.

The embodiment of the invention constructs a lower layer sequential evolution link based on an intra-day MPC scheduling strategy and taking new energy output prediction errors into account, and provides a method for constructing a unit combination sequential evolution cutting plane set according to the feasibility of a lower layer scheduling result. The constraint set is fed back to an upper layer, corresponding unit combination constraints are added, an improved unit combination result can be obtained, and the feasibility of the intra-day MPC scheduling strategy can be remarkably improved.

wherein n is an integer of 1 or more.

Specifically, a thermal power generating unit combined solution is output As a result of the decision.

The invention establishes a multi-stage robust unit combination method based on sequential evolution of batch scenes for the problem of power system day-ahead unit combination. Compared with a new energy uncertain set represented by intervals, the method can better consider probability distribution of new energy output in a daily time period, and constructs a sequential evolution optimization problem based on the daily MPC scheduling strategy and considering new energy prediction errors aiming at the MPC scheduling strategy widely used in the daily, the optimization problem can verify thermal power unit combined solutions of each iteration, and a cutting plane is constructed by using the feasibility of the results so as to correct the thermal power unit combined solutions of the next iteration, and finally the obtained thermal power unit combined solutions can obviously improve the feasibility of the daily MPC scheduling strategy.

The multi-stage robust unit combination device based on the sequential evolution of the batch scene is described below, and the multi-stage robust unit combination device based on the sequential evolution of the batch scene and the multi-stage robust unit combination method based on the sequential evolution of the batch scene described below can be correspondingly referred to each other.

Fig. 3 is a schematic structural diagram of a multi-stage robust unit assembly device based on sequential evolution of a batch scene according to an embodiment of the present invention, and as shown in fig. 3, the multi-stage robust unit assembly device based on sequential evolution of a batch scene according to an embodiment of the present invention includes: a first optimizing unit 310, a scene generating unit 320, a scene merging unit 330, a second optimizing unit 340, a feasibility judging unit 350, an iterative decision unit 360, an iterative updating unit 370, and a decision output unit 380:

the first optimizing unit 310 is configured to solve a mixed integer linear programming problem for deciding a thermal power unit combination according to a new energy scene in the new energy typical scene set, so as to obtain a thermal power unit combination solution;

the scene generating unit 320 is configured to generate new energy scenes in batches, and obtain a new energy batch scene set;

the scene merging unit 330 is configured to merge the new energy typical scene set and the new energy batch scene set to obtain a first new energy scene set;

the second optimizing unit 340 is configured to calculate, through sequential evolution, a multi-period sequential decision solution of each new energy scene in the first new energy scene set with respect to the thermal power generating unit combined solution;

the feasibility determining unit 350 is configured to determine, according to the multi-period sequential decision solutions, feasibility of a new energy scenario corresponding to each multi-period sequential decision solution with respect to the thermal power generating unit combination solution;

the iteration decision unit 360 is configured to determine whether to stop iteration according to feasibility of a new energy scene in the first new energy scene set with respect to the thermal power generating unit combined solution;

The iteration updating unit 370 is configured to update the new energy typical scene set in the n+1st iteration process and update the constraint condition of the mixed integer linear programming problem in the n+1st iteration process under the condition of continuing iteration;

the decision output unit 380 is configured to use the thermal power generating unit combination solution as a unit combination decision result under the condition that iteration is stopped;

wherein n is an integer of 1 or more.

wherein,representing thermal power unit i in scene z and period t ₀ Lower active force variable,/->Representing thermal power unit i in scene z and period t ₀ Active force value at-1, < ->Start-stop variable u representing thermal power unit i _i (t ₀ ) Is a decision value of (a).

Optionally, the batch generation of the new energy scene includes:

the feasibility judging formula is as follows:

otherwise, the iteration is continued.

It should be noted that, the above device provided in the embodiment of the present invention can implement all the method steps implemented in the method embodiment and achieve the same technical effects, and detailed descriptions of the same parts and beneficial effects as those in the method embodiment in this embodiment are omitted.

Fig. 4 illustrates a physical schematic diagram of an electronic device, as shown in fig. 4, which may include: processor 410, communication interface (Communications Interface) 420, memory 430 and communication bus 440, wherein processor 410, communication interface 420 and memory 430 communicate with each other via communication bus 440. Processor 410 may invoke logic instructions in memory 430 to perform a multi-stage robust set combining method based on sequential evolution of batch scenarios, the method comprising:

the method includes at least 1 iteration process, in an nth iteration process:

Generating new energy scenes in batches to obtain a new energy batch scene set;

wherein n is an integer of 1 or more.

Further, the logic instructions in the memory 430 described above may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.

In another aspect, the present invention also provides a computer program product, where the computer program product includes a computer program, where the computer program can be stored on a non-transitory computer readable storage medium, where the computer program, when executed by a processor, can perform a multi-stage robust set combining method based on sequential evolution of batch scenes provided by the above methods, where the method includes:

the method includes at least 1 iteration process, in an nth iteration process:

generating new energy scenes in batches to obtain a new energy batch scene set;

wherein n is an integer of 1 or more.

In yet another aspect, the present invention further provides a non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor is implemented to perform a multi-stage robust set assembly method based on sequential evolution of batch scenarios provided by the methods above, the method comprising:

the method includes at least 1 iteration process, in an nth iteration process:

generating new energy scenes in batches to obtain a new energy batch scene set;

wherein n is an integer of 1 or more.

The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.

From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.

Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. The multi-stage robust unit combination method based on sequential evolution of batch scenes is characterized by comprising the following steps of:

the method includes at least 1 iteration process, in an nth iteration process:

generating new energy scenes in batches to obtain a new energy batch scene set;

wherein n is an integer of 1 or more.

2. The multi-stage robust thermal power generation assembly method based on sequential evolution of batch scenarios of claim 1, wherein the mixed integer linear programming problem for deciding thermal power generation assembly includes a first objective function and a first constraint;

3. The multi-stage robust set of combining methods based on sequential evolution of batch scenes of claim 2, wherein the computing of a multi-time-period sequential decision solution for each new energy scene in the first set of new energy scenes with respect to the thermal power set of combining solutions by sequential evolution comprises:

4. A multi-stage robust set assembly method based on sequential evolution of batch scenarios according to claim 3, characterized in that said updating constraints of mixed integer linear programming problem in n+1th iteration process at least comprises: updating the unit combination sequential evolution cutting plane set constraint in the n+1th iteration process;

wherein,represents the set constraint of the set combination sequential evolution cutting plane in the n+1th iteration process, u _i (t) represents the start-stop variable of the thermal power generating unit i,/-)>Represents the set of thermal power generating units->Representing a set of scheduling periods->Represents the upper power output limit of the thermal power generating unit i, < ->Represents the lower power output limit of the thermal power generating unit i, < ->Representing the maximum downhill climbing power of adjacent scheduling periods of the thermal power unit i +. >Represents the nth ₀ Full z new energy scene in new energy typical scene set in secondary iteration processTime period punishment costs->Representing the dual variable +.>Decision value of->Representing the dual variable +.>Decision value of->Representing the dual variable +.>Decision value of->Representation ofIs>Representation ofIs>Representation ofIs>Representing thermal power unit i in scene z and period t ₀ Lower active force variable,/->Representing thermal power unit i in scene z and period t ₀ Active force value at-1, < ->Start-stop variable u representing thermal power unit i _i (t ₀ ) Is a decision value of (a).

5. The multi-stage robust set assembly method based on sequential evolution of batch scenarios of any one of claims 1 to 4, wherein the batch generation of new energy scenarios comprises:

6. The multi-stage robust thermal power generation set combining method based on batch scene sequential evolution of any of claims 1 to 4, wherein the determining the feasibility of each new energy scene corresponding to the multi-time period sequential decision solution with respect to the thermal power generation set combining solution according to the multi-time period sequential decision solution comprises:

the feasibility judging formula is as follows:

t represents a scheduling period of time,representing a set of scheduling periods->Representing the forward relaxation variable of the power balance in the z-th new energy scene +.>Is a sequential decision solution of->Representing a power balance reverse relaxation variable in a z-th new energy sceneIs a sequential decision solution of->Representing the capacity forward relaxation variable ++of the transmission line l under the z-th new energy scene>Is a sequential decision solution of->Representing the capacity reverse relaxation variable ++of the transmission line l under the z new energy scene>And z is an integer of 1 or more.

7. The multi-stage robust thermal power generation assembly method based on sequential evolution of batch scenes according to any of claims 1 to 4, wherein said determining whether to stop iteration based on feasibility of new energy scenes in the first set of new energy scenes with respect to the thermal power generation assembly solution comprises:

Otherwise, the iteration is continued.

8. The multi-stage robust unit combination device based on batch scene sequential evolution is characterized by comprising a first optimization unit, a scene generation unit, a scene merging unit, a second optimization unit, a feasibility judgment unit, an iteration decision unit, an iteration update unit and a decision output unit:

wherein n is an integer of 1 or more.

9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements a multi-stage robust assembly method based on sequential evolution of batch scenarios according to any one of claims 1 to 7 when executing the program.

10. A non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor implements a multi-stage robust set assembly method based on sequential evolution of batch scenes according to any of claims 1 to 7.