CN117154840A - Double-layer robust unit combination method and assembly of power system - Google Patents

Double-layer robust unit combination method and assembly of power system Download PDF

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CN117154840A
CN117154840A CN202310954740.8A CN202310954740A CN117154840A CN 117154840 A CN117154840 A CN 117154840A CN 202310954740 A CN202310954740 A CN 202310954740A CN 117154840 A CN117154840 A CN 117154840A
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model
unit
inner layer
stage
unit combination
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刘锋
康恺
张云帆
苏一帆
齐宁
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Tsinghua University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • H02J3/472—For selectively connecting the AC sources in a particular order, e.g. sequential, alternating or subsets of sources
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
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    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/10—The dispersed energy generation being of fossil origin, e.g. diesel generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously

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Abstract

The invention provides a double-layer robust unit combination method and a double-layer robust unit combination component of an electric power system, wherein an output scene set of a new energy unit of an inner layer model and a unit combination value of a thermal power unit are obtained based on an inner layer two-stage robust unit combination model; according to the output scene set of the new energy unit of the inner layer model, verifying the feasibility of the unit combination value of the thermal power unit based on the outer layer multi-period sequential decision model; when the verification of the outer layer multi-period sequential decision model is passed, taking the unit combination value of the thermal power unit as a final unit combination value; and when the verification of the outer-layer multi-period sequential decision model is not passed, updating the inner-layer two-stage robust unit combination model to repeatedly and iteratively execute the determining step and the verification step according to the updated inner-layer two-stage robust unit combination model, and deciding the unit combination of the power system, thereby obtaining a unit combination scheme capable of guaranteeing the real-time operation feasibility of the power system.

Description

Double-layer robust unit combination method and assembly of power system
Technical Field
The invention relates to the technical field of power system dispatching and operation, in particular to a double-layer robust unit combination method and a double-layer robust unit combination component of a power system.
Background
Against the pressure of resource shortage and environmental deterioration, the great development of renewable energy has become a widespread consensus in the power industry. The large-scale grid connection of renewable energy sources represented by wind power and photovoltaic promotes the green development of a power system, and meanwhile, the uncertainty of the output of the renewable energy sources brings serious challenges to the scheduling operation of the power system. To address this challenge, the power system must call more generator sets and backup resources to ensure that grid scheduling in a random renewable energy output scenario meets operational constraints.
The robust unit combination is a thermal power unit start-stop scheme decision taking the range of variation of a random variable interval of the power system into consideration, and aims to realize that the power system under the worst value of the random variable meets the operation constraint. In high-proportion renewable energy power systems, new energy output is an important type of random variable. The specificity of the robust unit combination problem is that the decision of the unit start-stop needs to meet the unexpected requirement, namely, the decision is limited by the prediction capability, the values of the new energy output are sequentially disclosed according to the time interval sequence, and the unit start-stop decision in the current time interval can only depend on the new energy output value known by the time interval and the new energy output value or range in the future limited time interval. The scheduling result without taking into account the unexpected may lead to the actual scheduling of the power system not being feasible within the day, and therefore how to take into account the unexpected decision in the robust set combination remains a key issue.
Disclosure of Invention
The invention provides a double-layer robust unit combination method and a double-layer robust unit combination component for an electric power system, which are used for solving the defect that unexpected scheduling results are not considered in the prior art, so that practical scheduling in the day of the electric power system is not feasible, fully considering the uncertainty of a renewable energy output interval and the unexpected requirements before scheduling decisions and renewable energy output, and making decisions on unit combination of the electric power system, thereby obtaining a unit combination scheme capable of guaranteeing the real-time operation feasibility of the electric power system.
The invention provides a double-layer robust unit combination method of an electric power system, wherein the electric power system at least comprises a thermal power unit and a new energy unit, and the method comprises the following steps: determining an output scene set of a new energy unit of an inner layer model and a unit combination value of the thermal power unit, which are obtained based on an inner layer two-stage robust unit combination model; according to the output scene set of the new energy unit of the inner layer model, verifying the feasibility of the unit combination value of the thermal power unit based on the outer layer multi-period sequential decision model; when the verification of the outer layer multi-period sequential decision model is passed, taking the unit combination value of the thermal power unit as a final unit combination value; and updating the inner layer two-stage robust unit combination model when the verification of the outer layer multi-period sequential decision model is not passed, so as to repeatedly and iteratively execute the determining step and the verification step according to the updated inner layer two-stage robust unit combination model until the unit combination value of the thermal power unit verified by the outer layer multi-period sequential decision model is taken as a final unit combination value when the verification of the outer layer multi-period sequential decision model is passed.
According to the double-layer robust unit combination method of the electric power system provided by the invention, the determining of the output scene set of the new energy unit of the inner layer model and the unit combination value of the thermal power unit based on the inner layer two-stage robust unit combination model comprises the following steps: acquiring parameters of an electric power system and initializing an output scene set of a new energy unit, an index set of a scene sequence of the new energy unit, an iteration index between an inner layer model and an outer layer model, an iteration index of an inner layer model stage and an unexpected cutting plane constraint set of an inner layer pre-dispatching unit combination; obtaining an output scene set and a unit combination value of the new energy unit in a pre-scheduling stage based on an inner pre-scheduling stage optimization model according to parameters of the power system, the output scene set of the new energy unit, an index set of a scene sequence of the new energy unit, an iteration index between the inner model and the outer model, the inner model stage iteration index and the inner pre-scheduling unit combination unexpected cutting plane constraint set; according to the output scene set of the new energy unit in the pre-dispatching stage, checking the unit combination value in the pre-dispatching stage based on an inner layer rescheduling stage optimization model; when the verification of the inner layer rescheduling stage optimization model is passed, taking the unit combination value of the pre-scheduling stage as the unit combination value of the thermal power unit; and updating the inner layer pre-dispatching stage optimization model when the verification of the inner layer re-dispatching stage optimization model is not passed, repeatedly and iteratively executing the step according to the parameters of the electric power system according to the updated inner layer pre-dispatching stage optimization model until the verification of the inner layer re-dispatching stage optimization model is passed, and taking the unit combination value of the pre-dispatching stage verified by the inner layer re-dispatching stage optimization model as the unit combination value of the thermal power unit.
According to the double-layer robust unit combination method of the electric power system, the unit combination value of the pre-dispatching stage is verified based on an inner-layer rescheduling stage optimization model according to the output scene set of the new energy unit of the pre-dispatching stage, and the method comprises the following steps: obtaining an optimal solution of an auxiliary variable of the daily highest running cost of the inner layer pre-scheduling stage optimization model according to the output scene set of the new energy unit in the pre-scheduling stage; when the optimal solution of the highest operation cost auxiliary variable in the day and the objective function value of the inner layer rescheduling stage optimization model meet a first preset formula, checking the unit combination value of the pre-scheduling stage by the inner layer rescheduling stage optimization model; when the optimal solution of the highest operation cost auxiliary variable in the day and the objective function value of the inner layer rescheduling stage optimization model do not meet a first preset formula, the unit combination value of the pre-scheduling stage does not pass the verification of the inner layer rescheduling stage optimization model; the first preset formula is:
wherein,optimizing an optimal solution of an auxiliary variable eta of the daily highest running cost of the model for the inner layer pre-scheduling stage; / >Optimizing an objective function value of a model for the inner layer rescheduling stage; epsilon is a preset allowable deviation, m is an iteration index of the inner layer model stage, n is an iteration index between the inner layer model and the outer layer model, wait is a pre-scheduling stage, and see is a rescheduling stage.
According to the double-layer robust unit combination method of the electric power system, when the verification of the inner layer rescheduling stage optimization model is not passed, the inner layer rescheduling stage optimization model is updated, and the method comprises the following steps: and when the optimal solution of the highest operation cost auxiliary variable in the day and the objective function value of the inner layer rescheduling stage optimization model do not meet the first preset formula, updating the inner layer model stage iteration index, and updating the output scene set of the new energy unit and the index set of the scene sequence of the new energy unit so as to update the inner layer rescheduling stage optimization model according to the updated inner layer model stage iteration index, the output scene set of the new energy unit and the index set of the scene sequence of the new energy unit.
According to the double-layer robust unit combination method of the electric power system, provided by the invention, the feasibility of the unit combination value of the thermal power unit is verified based on an outer-layer multi-period sequential decision model according to the output scene set of the new energy unit of the inner-layer model, and the method comprises the following steps: determining a decision solution of the outer layer multi-period sequential decision model according to the output scene set of the new energy unit of the inner layer model; calculating a relaxation variable penalty cost from the decision solution; when the relaxation variable penalty cost meets a second preset formula, checking a unit combination value of the thermal power unit through the outer layer multi-period sequential decision model; when the relaxation variable penalty cost does not meet a second preset formula, the unit combination value of the thermal power unit does not pass through the verification of the outer-layer multi-period sequential decision model; the second preset formula is:
Wherein,and punish is punished for the relaxation variable punishment cost, y is an index of a new energy scene sequence, n is an iteration index between the inner layer model and the outer layer model.
According to the double-layer robust unit combination method of the power system provided by the invention, the decision solution of the outer layer multi-period sequential decision model is determined, and the method comprises the following steps: when the scheduling time period meets a preset time period threshold value, the decision solution is obtained based on the outer layer multi-time period sequential decision model; and when the scheduling period does not meet a preset period threshold, updating the scheduling period, and obtaining the decision solution based on the outer multi-period sequential decision model according to the updated scheduling period.
According to the double-layer robust unit combination method of the electric power system provided by the invention, when the verification of the outer-layer multi-period sequential decision model is not passed, the inner-layer two-stage robust unit combination model is updated, and the method comprises the following steps: and when the relaxation variable penalty cost does not meet the second preset formula, updating an iteration index between the inner layer model and the outer layer model, and updating the inner layer pre-scheduling unit combination unexpected plane constraint set so as to update the inner layer two-stage robust unit combination model according to the updated iteration index between the inner layer model and the outer layer model and the inner layer pre-scheduling unit combination unexpected plane constraint set.
The invention also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the double-layer robust unit combination method of any one of the power systems when executing the program.
The invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a double-layer robust assembly method of an electrical power system as described in any of the above.
The invention also provides a computer program product comprising a computer program which when executed by a processor implements a double-layer robust assembly method of an electrical power system as described in any of the above.
The invention provides a double-layer robust unit combination method and a double-layer robust unit combination component of an electric power system, wherein the electric power system at least comprises a thermal power unit and a new energy unit, and the method comprises the following steps: determining an output scene set of a new energy unit of an inner layer model and a unit combination value of a thermal power unit, which are obtained based on an inner layer two-stage robust unit combination model; according to the output scene set of the new energy unit of the inner layer model, verifying the feasibility of the unit combination value of the thermal power unit based on the outer layer multi-period sequential decision model; when the verification of the outer layer multi-period sequential decision model is passed, taking the unit combination value of the thermal power unit as a final unit combination value; and when the verification of the outer-layer multi-period sequential decision model is not passed, updating the inner-layer two-stage robust unit combination model, repeatedly and iteratively executing the determining step and the verification step according to the updated inner-layer two-stage robust unit combination model, and taking the unit combination value of the thermal power unit verified by the outer-layer multi-period sequential decision model as a final unit combination value until the verification of the outer-layer multi-period sequential decision model is passed, fully considering the section uncertainty of renewable energy output and the unexpected property which needs to be met before scheduling decision and renewable energy output, and deciding the unit combination of the power system, thereby obtaining a unit combination scheme capable of guaranteeing the real-time operation feasibility of the power system.
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 a schematic flow chart of a method for combining a double-layer robust unit of an electric power system;
fig. 2 is a schematic flow diagram of a method for combining a double-layer robust unit of an electric power system according to the present invention;
FIG. 3 is a topology of an IEEE39 node system provided by the present invention;
FIG. 4 is a graph of load curves, wind farm total output power prediction center values, and confidence intervals for an example provided by the present invention;
FIG. 5 is a diagram of the combined results of a unit provided by the prior art;
FIG. 6 is a graph of the additive value change of a relaxation variable provided by the prior art;
FIG. 7 is a diagram of the result of the assembly provided by the invention;
FIG. 8 is a graph of the additive value change of the relaxation variables in the inner and outer iterations provided by the present invention;
fig. 9 is a schematic structural diagram of an electronic device provided by 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.
Referring to fig. 1, fig. 1 is a schematic flow chart of a method for combining a dual-layer robust unit of an electric power system according to the present invention.
Referring to fig. 2, fig. 2 is a schematic flow chart of a method for combining a dual-layer robust unit of an electric power system according to the present invention.
The invention provides a double-layer robust unit combination method of an electric power system, wherein the electric power system at least comprises a thermal power unit and a new energy unit, and the method comprises the following steps:
11: determining an output scene set of a new energy unit of an inner layer model and a unit combination value of a thermal power unit, which are obtained based on an inner layer two-stage robust unit combination model;
as a preferred embodiment, determining the set of output scenes of the new energy unit and the unit combination value of the thermal power unit based on the inner layer model obtained by the inner layer two-stage robust unit combination model includes:
101: acquiring parameters of an electric power system and initializing an output scene set of a new energy unit, an index set of a scene sequence of the new energy unit, an iteration index between an inner layer model and an outer layer model, an iteration index of an inner layer model stage and an unexpected cutting plane constraint set of an inner layer pre-dispatching unit combination;
specifically, thermal power generating unit parameters include: output range of thermal power unit iClimbing speed +.>Downhill climbing speed +.>Minimum operating time T i on Minimum downtime T i off Start-up cost->Shut-off cost->Unit power output cost->And the power generation transfer factor of the thermal power generating unit i to the line l>The set of the thermal power generating unit is +.>The number of the thermal power generating units is N G 。
The load parameters include: predicted value of load d in period tLoad d versus line l power transfer factorThe set of the load is->The number of loads is N D 。
The new energy unit parameters include: predicted output central value of new energy unit j in period tPredicted power output range of new energy unit j in period t>New energy unit j is to power generation transfer factor of circuit l +.>The new energy unit is recorded as a set of +.>The number of the new energy units is N R 。
The energy storage power station parameters include: upper limit of charge and discharge power of 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>Unit energy storage charge-discharge power cost of energy storage power station k>Power generation transfer factor of energy storage power station k to line l>The set of the energy storage power stations is->The number of the energy storage power stations is N E 。
Other parameters: the maximum power transmission capacity of 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 Note that the set of nodes is +.>Counting the number of scheduling time periods as N T The set of scheduling periods is
Initializing new energy output sceneAn index n of the iteration times of the inner layer and the outer layer and an iteration index m of two stages in the inner layer.
Specifically, the output scene set of the new energy unit used by the invention is recorded as New energy output scene +.>A marker, wherein y is the index of the new energy scene sequence,/->Is a set->Number of elements contained. Recording the index set of the scene sequence of the new energy unit as +.>There is->The initial new energy output scene +.>Set to->
In view of the limited number of English letters, the parameters are more in the whole text, for distinguishing, part of the parameters are represented by two letters, and the subscript letter part has no specific meaning, but is only convenient to distinguish from other parameters.
The index of the iteration times of the inner layer and the outer layer in the invention is set as n, and the iteration indexes of the two stages in the inner layer are set as m. Initializing n=1, and initializing m=1.
Referring to fig. 3, fig. 3 is a topology diagram of an IEEE39 node system provided in the present invention.
Referring to fig. 4, fig. 4 is a graph of a load curve, a total output power prediction center value of a wind farm, and a confidence interval according to an embodiment of the present invention. The abscissa is the scheduling period (in hours) and the ordinate is the load and the power value of the wind farm (in megawatts). The black solid line represents the power curve of the load, the black dashed line represents the predicted central value of the total output power of the wind farm, the gray area is the confidence interval of the total output power of the wind farm, and the total output power of the wind farm in the day fluctuates in the confidence interval.
102: obtaining an output scene set and a unit combination value of the new energy unit in the pre-dispatching stage based on the optimization model in the inner pre-dispatching stage according to parameters of the electric power system, the output scene set of the new energy unit, an index set of a scene sequence of the new energy unit, an iteration index between an inner layer model and an outer layer model, an inner layer model stage iteration index and an unexpected cutting plane constraint set of the inner pre-dispatching unit combination;
Specifically, the inner layer of the invention is a two-stage robust unit combination model, which has two stages of pre-scheduling and rescheduling.
In the pre-scheduling phase, the scheduling center needs to make decisions about the start-stop of the unit, which requires the construction and training of an optimization model containing constraints (1) - (17). Decision variables of the constraints (1) - (17) and the specific meaning of the constraints will be described in detail below.
Constructing inner-layer pre-dispatching thermal power generating unit start-stop constraints (1) - (3): u (u) i (t),All are zero-variable: u (u) it U is the start-stop variable of the thermal power unit it =0/1 indicates that thermal power unit i is in a shutdown/running state during period t;Indicating variable for unit start->Indicating that the thermal power generating unit i is not started in the 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;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. The constraint (1) is the association constraint of a unit start-up, shut-down variable and a state variable; constraint (2) is the minimum running time constraint of the unit; constraint (3) is a minimum shutdown time constraint of the unit.
Constructing feasible cutting constraint of inner layer pre-scheduling robust unit combination: because the inner layer uses alternating iterations of prescheduling and rescheduling to achieve convergence, for the scene set that rescheduling feeds back to prescheduling According to->Each of the scenesAnd (4) to (16) of the feasible cuts of the robust unit combination are constructed. Wherein, the variable->Representing the active output of the thermal power generating unit i under a scene y and a period t; variable->To represent the input active power of the energy storage power station k under the scene y and the period t, the variableRepresenting the output active power of the energy storage power station k under the scene y and the period t, and the variable E k,y (t) represents the stored power of the energy storage power station k in the scene y and the period t; variable->Representing the active output of the new energy power station j in a scene y and a period t; variable->Constraint relaxation variable for power balance under scenario y and period t, variable +.>A relaxation variable of the capacity constraint of the power transmission line l in the scene y; η is the highest running cost auxiliary variable in the day, which represents the system rescheduling cost of taking into account the worst renewable energy scenario in the pre-scheduling phase, where the coefficient c p Penalty cost per unit relaxation variable.
Among the constraints (4) - (16), the constraint (4) is the upper limit and the lower limit of the output of the thermal power generating unit, the constraint (5) and the constraint (6) are the uphill constraint and the downhill constraint of the thermal power generating unit respectively, the constraint (7) and the constraint (8) are the energy storage charging and discharging power limit respectively, the constraint (9) is the time period coupling constraint of the stored energy, and the constraint (10) is the upper limit and the lower limit of the energy storage capacity; the constraint (11) is a numerical constraint of the energy storage electric quantity at the end of the day, the constraint (12) is a typical new energy output power constraint, the constraint (13) and the constraint (14) are a system power balance constraint and a line capacity constraint respectively, the constraint (15) is a relaxation variable non-negative constraint, and the constraint (16) is a constraint representing the highest running cost in the day under a typical scene.
Constructing and adding an unexpected cut plane constraint (17) of an inner layer pre-scheduling unit combination: in the iteration process of the inner layer and the outer layer, the outer layer can transmit the unexpected cutting plane constraint of the unit combination to the inner layer, so that the start-stop variable of the thermal power unit needs to meet the constraint (17). Wherein,is a set of unit combination unexpected cutting plane constraint sets: when n=1, _a->Represents u i (t)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 (59).
Constructing an inner layer prescheduling objective function (18): the objective function of the pre-scheduling stage is to minimize the system scheduling cost, and the expression of the objective function is shown in formula (18).
Constructing and training an inner layer pre-scheduling optimization model (19): the objective function (18) includes the thermal power unit start-up cost, the thermal power unit shut-down cost, and the typical scenario maximum operating cost. The expression of the prescheduling stage optimization model is shown in formula (19).
s.t.(1)～(17)
The total operation cost of the system in the pre-dispatching stage, the decision value of the auxiliary variable eta of the highest operation cost in the day and the unit start-stop variable can be obtained by training the inner pre-dispatching optimization model (19)Is a decision value of (a). Since η represents the system rescheduling cost of considering the worst renewable energy scenario in the prescheduling stage, then the optimal solution of the highest running cost auxiliary variable η in (19) is recorded as +. >Record->Is the decision value of (a)Here, the footnote n is an index of the iteration number of the inner layer and outer layer model of the present invention; footnote m is pre-schedule in the inner layerAnd rescheduling the index of the iteration number. Then, will->And->And transmitting to a rescheduling stage optimization model.
Training a rescheduling stage optimization model of the mth iteration of the inner layer to obtain a rescheduling objective function valueAt rescheduling cost->And rescheduling objective function value->Satisfy->When the method is used, the unit combination decision value of the inner layer is output>Namely, the combination value of the units in the pre-dispatching stage and the output scene set of the updated new energy unit +.>And index set of scene sequence of new energy unit +.>And (5) to an outer layer multi-period sequential decision model.
103: according to the output scene set of the new energy unit in the pre-dispatching stage, checking the unit combination value in the pre-dispatching stage based on the inner layer rescheduling stage optimization model;
104: when the verification of the model is optimized through the inner layer rescheduling stage, taking the unit combination value of the prescheduling stage as the unit combination value of the thermal power unit;
105: and updating the inner layer pre-dispatching stage optimization model when the verification of the inner layer re-dispatching stage optimization model is not passed, so as to repeatedly and iteratively execute the steps according to the parameters of the electric power system according to the updated inner layer pre-dispatching stage optimization model until the verification of the inner layer re-dispatching stage optimization model is passed, and taking the unit combination value of the pre-dispatching stage verified by the inner layer re-dispatching stage optimization model as the unit combination value of the thermal power unit.
As a preferred embodiment, according to the output scene set of the new energy unit in the pre-dispatching stage, the verification of the unit combination value in the pre-dispatching stage based on the inner layer rescheduling stage optimization model includes: obtaining an optimal solution of an auxiliary variable of the daily highest running cost of an inner-layer pre-dispatching stage optimization model according to the output scene set of the new energy unit in the pre-dispatching stage; when the optimal solution of the auxiliary variable of the highest running cost in the day and the objective function value of the inner layer rescheduling stage optimization model meet a first preset formula, checking the unit combination value of the pre-scheduling stage through the inner layer rescheduling stage optimization model; when the optimal solution of the highest operation cost auxiliary variable in the day and the objective function value of the inner layer rescheduling stage optimization model do not meet a first preset formula, the unit combination value of the pre-scheduling stage does not pass the verification of the inner layer rescheduling stage optimization model; the first preset formula is:
wherein,optimizing an optimal solution of an auxiliary variable eta of the daily highest running cost of the model for the inner layer pre-scheduling stage;optimizing the objective function value of the model for the inner layer rescheduling stage; epsilon is a preset allowable deviation, m is an iteration index of the inner layer model stage, and n is an inner layer model and an outer layer model The iteration index between the two, wait is the pre-scheduling stage and see is the rescheduling stage.
As a preferred embodiment, updating the inner layer prescheduling stage optimization model when the verification of the inner layer rescheduling stage optimization model is not passed, includes: when the optimal solution of the highest operation cost auxiliary variable in the day and the objective function value of the inner layer rescheduling stage optimization model do not meet the first preset formula, updating the inner layer model stage iteration index, and updating the output scene set of the new energy unit and the index set of the scene sequence of the new energy unit so as to update the inner layer pre-scheduling stage optimization model according to the updated inner layer model stage iteration index, the output scene set of the new energy unit and the index set of the scene sequence of the new energy unit.
Specifically, to facilitate model training, the rescheduling stage of the two-stage robust set assembly assumes that all of the time periods of renewable energy output values are simultaneously revealed.
During the rescheduling phase, the power system needs to build and train an optimization model containing constraints (20) - (34). Decision variables of the constraints and the specific meaning of the constraints (20) - (34) will be described in detail below.
And constructing an inner layer and rescheduling the constraints of the power generating unit on the output and climbing:
In constraints (20) - (22), the variablesThe active output of the thermal power generating unit i in the period t is shown. The constraint (20) is the upper and lower limit constraint of the thermal power unit output, and the constraint (21) and the constraint (22) are the uphill constraint and the downhill constraint of the thermal power unit respectively.
In constraints (23) - (27), the variablesRepresenting the input active power of the energy storage power station k in the period t, and the variableRepresenting the output active power of the energy storage power station k in the period t, and the variable E k (t) represents the stored charge of the energy storage power station k during a period t. The constraint (23) and the constraint (24) are respectively energy storage charging and discharging power limits, the constraint (25) is a time period coupling constraint of a stored electric quantity state, the constraint (26) is an upper limit constraint and a lower limit constraint of an energy storage capacity, and the constraint (27) is a numerical constraint of the energy storage electric quantity at the beginning and the end of an energy storage day.
Constructing inner layer rescheduling new energy power station operation constraints (28) - (31): in the constraints (28) - (31),is a new energy sourceActive output variable of power station j under scene y and period t;Zero-one variable, ++>The active output of the new energy power station j in the period t is shown to be in the upper bound of the output prediction range;And the active output of the new energy power station j in the period t is shown to be in the lower bound of the output prediction range. Γ -shaped structure s Is the space cluster coefficient of the new energy station, Γ T Is the time smoothing coefficient of the new energy station. The constraint (28) is the constraint of the active output variable of the new energy, and the new energy output of the severe scene is set to be possibly present in the prediction center value and the prediction boundary; constraint (29) indicates that new energy output cannot be in both upper and lower bounds for the same period; the constraint (30) and the constraint (31) are respectively a space cluster effect constraint of a plurality of new energy stations and a time smooth effect constraint of a single new energy station.
Constructing system power balance and transmission line capacity constraints (32) - (34) for inner layer rescheduling: in constraints (32) - (34), s 1 (t),s 2 (t) is a power balance constraint relaxation variable,is a relaxation variable of the capacity constraint of the power transmission line. Constraint (32) is a system power balancing constraint, constraint (33) is a line capacity constraint, and constraint (34) is a relaxation variable non-negative constraint.
Constructing an inner layer rescheduling objective function (35): the objective function of the rescheduling stage is to minimize the running cost of the system under the worst new energy output scene, and the expression of the objective function is shown as a formula (35).
Constructing and training an inner layer rescheduling optimization model (36): the objective function (35) comprises the running cost of the thermal power generating unit, the energy storage charging and discharging cost and the expression of the slack variable penalty cost rescheduling stage optimization model as shown in (36).
Training an inner layer rescheduling optimization model (36) to calculate a rescheduling objective function eta see Numerical values of (2)Active power output of new energy unitVariable->Is +.>
Constructing an inner layer iteration convergence criterion (37): the value of the cost to rescheduleThe value of the auxiliary variable for the highest operating costs in the day, which is obtained in the pre-scheduling phase +.>And (3) carrying into the formula (37), wherein epsilon is a preset allowable deviation.
If the formula (37) is established, the inner layer is ended, and the method is transferred to an outer layer multi-period sequential decision model; otherwise, the bad new energy output scene newly obtained in the rescheduling stage is usedInclusion set->Let m=m+1, return to the "prescheduling stage optimization model", continue iteration until equation (37) holds.
After the pre-dispatching and rescheduling iteration is finished, the inner layer outputs a decision value of starting and stopping of the thermal power generating unitAnd the output scene set of the new energy unit obtained by inner layer iteration +.>Will beAnd->To the outer layer, and may enter into the iteration of the inner and outer layers of the present invention.
12: according to the output scene set of the new energy unit of the inner layer model, verifying the feasibility of the unit combination value of the thermal power unit based on the outer layer multi-period sequential decision model;
13: when the verification of the outer layer multi-period sequential decision model is passed, taking the unit combination value of the thermal power unit as a final unit combination value;
14: and updating the inner layer two-stage robust unit combination model when the verification of the outer layer multi-period sequential decision model is not passed, repeating the steps of performing determination and verification according to the updated inner layer two-stage robust unit combination model until the verification of the outer layer multi-period sequential decision model is passed, and taking the unit combination value of the thermal power unit verified by the outer layer multi-period sequential decision model as a final unit combination value.
As a preferred embodiment, according to the set of output scenes of the new energy unit of the inner layer model, verifying the feasibility of the unit combination value of the thermal power unit based on the outer layer multi-period sequential decision model includes: determining a decision solution of an outer layer multi-period sequential decision model according to the output scene set of the new energy unit of the inner layer model; calculating a relaxation variable penalty cost according to the decision solution; when the relaxation variable penalty cost meets a second preset formula, checking a unit combination value of the thermal power unit through an outer layer multi-period sequential decision model; when the relaxation variable punishment cost does not meet a second preset formula, the unit combination value of the thermal power unit does not pass the verification of the outer layer multi-period sequential decision model; the second preset formula is:
Wherein,for relaxing variable punishment cost, y is an index of a new energy scene sequence, n is an iteration index between an inner layer model and an outer layer model, and push is punishment.
As a preferred embodiment, determining a decision solution for an outer layer multi-period sequential decision model comprises: when the scheduling time period meets a preset time period threshold value, a decision solution is obtained based on an outer layer multi-time period sequential decision model; and when the scheduling period does not meet the preset period threshold, updating the scheduling period, and obtaining a decision solution based on an outer-layer multi-period sequential decision model according to the updated scheduling period.
As a preferred embodiment, updating the inner two-stage robust set combination model when the verification of the outer multi-period sequential decision model is not passed, includes: and when the relaxation variable penalty cost does not meet a second preset formula, updating an iteration index between the inner layer model and the outer layer model, and updating an unexpected plane constraint set of the inner layer pre-scheduling unit combination, so as to update an inner layer two-stage robust unit combination model according to the updated iteration index between the inner layer model and the outer layer model and the unexpected plane constraint set of the inner layer pre-scheduling unit combination.
Specifically, the outer layer multi-period sequential decision model: inner layer of And->After being transmitted to the outer layer, the outer layer decision model of the invention can be entered. In the foregoing description, < >>The footnote m in (c) represents only the inner layer iterative process. For the sake of presentation brevity, the outer layer will be used without causing confusionRepresentation->Wherein, the footnote n is the index of the iteration times of the inner layer and the outer layer of the invention. />
In actual scheduling, the time period t is limited by the prediction technology 0 Can only predict the slave period t with a relatively high accuracy 0 By time period t 0 +Δt new energy predicted force, where Δt is the number of periods in which new energy output can be accurately predicted. For the followingIs +.>In period t 0 The predicted new energy output value of the electric power system is +.>Considering time period coupling characteristics of decision variables of thermal power generating units and energy storage power stations, and time period t 0 -1 set output value ∈ ->And stored electrical quantity of the energy storage power station +.>Will be for period t 0 Is influenced by the decision variables of (a).
t 1 ＝min(N T ,t 0 +ΔT) (38)
According to the above feature, in the scheduling period t 0 From t needs to be considered 0 To t 1 Set output decision variable of (1), wherein t 1 Satisfying equation (38), the power system needs to construct constraints (39) - (49) to establish the schedule period t 0 Is described.
Constructing an outer thermal power unit output range and climbing constraints (39) - (41): in the constraints (39) - (41), And the active output variable of the thermal power generating unit i under the scene y and the period t is represented. The constraint (39) is the upper and lower limit constraint of the thermal power unit output, and the constraint (40) and the constraint (41) are the uphill constraint and the downhill constraint of the thermal power unit respectively.
Constructing outer energy storage power station operational constraints (42) - (46): in the constraints (42) - (44),active power input variable of energy storage power station k under scene y and period t, +.>Representing the active power output variable 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 in the scenario y and the period t. The constraint (42) and the constraint (43) are respectively the charge and discharge power limits of the energy storage power station, the constraint (44) is the time period coupling constraint of the energy storage capacity, and the constraint (45) is the upper limit constraint and the 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 Constraints (46) need to be considered when.
Constructing an outer new energy prediction output constraint (47): constraint (47) is a new energy predicted output constraint, wherein,is the active output variable of the new energy power station j under the scene y and the period t. />
Constructing outer layer power balance and transmission line capacity constraints (48) - (50): in constraints (48) - (50), Andis the power balance constraint relaxation variable in scenario y, +.>And->And the relaxation variable is the capacity constraint of the power transmission line l under the scene y. Constraint (48) and constraint (49) are system power balancing constraint and line capacity constraint, respectively, and constraint (50) is a relaxed variable non-negative constraint.
Constructing an outer layer objective function (51): for new energy scene setIn (2), a scheduling period t 0 Is to minimize the target function from t 0 To t 1 The expression of the objective function is shown in the expression (51). Wherein c p Penalty cost per unit relaxation variable.
An outer layer multi-period sequential decision model (52) is constructed and trained: the objective function (51) 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 Is shown.
In period t 0 Training an outer multi-period sequential decision model (52) for an available period t 0 By time period t 1 Is selected from the variable decision values of t=t 0 As a sequential decision solution for the current time period. For all time periods t 0 ＝1,...,N T After training the outer layer multi-period sequential decision model (52), the first time can be obtainedNew energy scene->Decision value about start and stop of machine set >Multi-time-period sequential decision solution of (2)
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.
Establishing a termination criterion of inner layer and outer layer iteration:is-> The feasibility of sequential decision scheduling solutions is characterized: if it is arbitrary->Has the following componentsIf so, ending the iteration process of the inner layer and the outer layer, and outputting the current unit start-stop decision value +.>The unit combination value of the thermal power unit is used as a decision result of the method, namely a final unit combination value; conversely, according to the relaxation variable decision valueAnd constructing a set of unexpected cut plane constraints, then enabling n=n+1, updating the constraints (17) of the inner layer pre-scheduling stage, and starting the inner layer and outer layer iteration of the next round.
The following describes a method for constructing the unexpected cutting plane constraint of the unit combination.
Establishing unit combination unexpected cutting plane constraint construction methods (53) - (59): defining scenes in the nth iterationThe relaxation variable penalty cost of +.>The expression (53) thereof is shown.
In period t 0 In an optimization model (52) characterizing a scene y and containingThe constraints of (2) are as shown in (54) - (56), wherein +. >And->The dual variables of constraint (54), constraint (55) and constraint (56), respectively.
After the optimization model (52) training of all time periods is completed, recordAnd->Respectively is a dual variable->And->Is a value of (a).
Recording the combination unexpected cutting plane constraint set of the machine set asIf the relaxation cost of scene y ∈>Non-zero, constraint set of unexpected cutting plane in unit combination +.>Adding constraints (57).
In the constraint (57) of the present invention,the method is a weight coefficient, can improve the correction effect of the unit combination sequential evolution cutting plane on an upper layer optimization model, and improves the feasibility of n+1 times of sequential evolution results. In the present invention, < >>The specific expression of (2) is shown in formula (58).
Then after the nth iteration of the present invention,the expression of (2) is shown in formula (59).
The effects of the present invention are described below using an example.
Simulation system: consider an improved IEEE39 node system, the system topology being shown in fig. 2. There are 10 thermal power units in the system, and parameters of the thermal power units are shown in table 1. The distribution of the system load at the nodes is shown in fig. 2, the peak value and the valley value of the daily load are 5003.4MW and 2952MW respectively, and the proportion of the load of each node to the total load is the same as that of the standard IEEE39 node system. In the present example, at node 8, node 14 and node 29, an energy storage power station with a installed capacity of 500MW (corresponding to 250 standard fans) and an upper power limit of 200MW and an upper capacity limit of 200MWh are respectively installed. The predicted central value and the confidence interval of the total wind power output power of the three wind power stations are shown in fig. 3. The parameters of the three energy storage power stations are the same, and the parameters of each energy storage power station are shown in table 2. The system has 46 lines with the same line transmission capacity as the IEEE39 node system. Within a day, 1 time is scheduled per hour, and the number of scheduling periods N T ＝24。
TABLE 1 thermal power generating unit parameters
Table 2 energy storage cell related parameters
During initialization, a predicted central value of the total output power of the wind power plant is put into an output scene set of a new energy unitSetting the period number deltat=4 during which the new energy output can be accurately predicted. Setting ε of equation (37) to 0.01, and setting penalty cost c for unit relaxation variable p ＝5×10 4 $/MW. The actual scheduling strategy considering each time period in the day of the power system is the same as the strategy used by the outer layer of a multi-time period sequential decision model. Initializing n=1, m=1, and then performing calculationThe results are shown in comparison with the examples.
Referring to fig. 5, fig. 5 is a diagram of a combined result of a unit provided in the prior art.
Referring to fig. 6, fig. 6 is a graph showing the addition value of the relaxation variables provided in the prior art.
Referring to fig. 7, fig. 7 is a diagram of a combined result of a unit provided by the present invention.
Referring to fig. 8, fig. 8 is a graph of the addition value of the relaxation variable in the inner and outer layer iteration according to the present invention.
Under the real-time scheduling strategy of each time period in the day, the unit combination result obtained by the prior art can generate the condition that the sum of the relaxation variables is larger than 0 in each of the time period 22, the time period 23 and the time period 24, which represents the phenomenon of power imbalance of the power system. During these three periods, the feasibility of actual scheduling within the power system day is difficult to meet.
In the invention, after 6 'inner layer-outer layer' iterations, the unit combination result of the invention is obtained. From the addition value of the relaxation variables of the unit combination results of the 1 st to 6 th iterations, which are scheduled in real time in each time period in the day, the unit combination result of the 6 th iteration can enable the real-time scheduling strategy of each time period in the day to be feasible.
Compared with the unit combination result in the prior art, the unit combination result obtained by the invention increases the unit G5 from the period 20 to the period 23, and closes the unit G8 from the period 23 and the period 24, and the adjustment ensures that the feasibility of actual scheduling in the day of the power system is satisfied by the unit combination result of the invention.
In summary, the invention designs a double-layer robust unit combination method of an electric power system aiming at the problem of the combination scheduling of the electric power system day-ahead units, and the method fully considers the interval uncertainty of renewable energy output and the unexpected property which needs to be met before the scheduling decision and the renewable energy output, and makes a decision on the unit combination of the electric power system, thereby obtaining a unit combination scheme capable of guaranteeing the real-time operation feasibility of the electric power system.
The beneficial effects of the invention are as follows:
firstly, the invention establishes a multi-period sequential decision model which can carry out multi-period sequential decisions on a given new energy scene set, and the model is closer to the actual scheduling of a power system;
secondly, based on a scheduling solution of a multi-period sequential decision model, the invention provides a method for constructing unexpected cutting plane constraint of unit combination;
thirdly, the unexpected cutting plane constraint of the unit combination is added into the two layers of robust unit combination models, so that the unit combination result can be corrected, the unexpected property of the unit combination result is improved, and the feasibility of real-time scheduling of the unit combination result is further improved obviously;
fourth, the method provided by the invention is general and is suitable for various types of integer variable optimization decisions with decision unexpected requirements. Meanwhile, the method has good compatibility with a widely used two-stage robust unit combination method, high calculation efficiency and strong engineering practicability.
Fig. 9 illustrates a physical schematic diagram of an electronic device, as shown in fig. 9, which may include: processor 901, communication interface (Communications Interface) 902, memory 903 and communication bus 904, wherein processor 901, communication interface 902 and memory 903 communicate with each other via communication bus 904. The processor 901 may invoke logic instructions in the memory 903 to perform a dual-layer robust unit combination method of an electrical power system, the electrical power system at least including a thermal power unit and a new energy unit, the method comprising: determining an output scene set of a new energy unit of an inner layer model and a unit combination value of a thermal power unit, which are obtained based on an inner layer two-stage robust unit combination model; according to the output scene set of the new energy unit of the inner layer model, verifying the feasibility of the unit combination value of the thermal power unit based on the outer layer multi-period sequential decision model; when the verification of the outer layer multi-period sequential decision model is passed, taking the unit combination value of the thermal power unit as a final unit combination value; and updating the inner layer two-stage robust unit combination model when the verification of the outer layer multi-period sequential decision model is not passed, repeating the steps of performing determination and verification according to the updated inner layer two-stage robust unit combination model until the verification of the outer layer multi-period sequential decision model is passed, and taking the unit combination value of the thermal power unit verified by the outer layer multi-period sequential decision model as a final unit combination value.
Further, the logic instructions in the memory 903 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 of 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, and when the computer program is executed by a processor, the computer can execute the method for combining a double-layer robust unit of an electric power system provided by the above methods, where the electric power system at least includes a thermal power unit and a new energy unit, and the method includes: determining an output scene set of a new energy unit of an inner layer model and a unit combination value of a thermal power unit, which are obtained based on an inner layer two-stage robust unit combination model; according to the output scene set of the new energy unit of the inner layer model, verifying the feasibility of the unit combination value of the thermal power unit based on the outer layer multi-period sequential decision model; when the verification of the outer layer multi-period sequential decision model is passed, taking the unit combination value of the thermal power unit as a final unit combination value; and updating the inner layer two-stage robust unit combination model when the verification of the outer layer multi-period sequential decision model is not passed, repeating the steps of performing determination and verification according to the updated inner layer two-stage robust unit combination model until the verification of the outer layer multi-period sequential decision model is passed, and taking the unit combination value of the thermal power unit verified by the outer layer multi-period sequential decision model as a final unit combination value.
In still 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 method for combining two-layer robust units of an electric power system provided by the above methods, the electric power system at least including a thermal power unit and a new energy unit, the method comprising: determining an output scene set of a new energy unit of an inner layer model and a unit combination value of a thermal power unit, which are obtained based on an inner layer two-stage robust unit combination model; according to the output scene set of the new energy unit of the inner layer model, verifying the feasibility of the unit combination value of the thermal power unit based on the outer layer multi-period sequential decision model; when the verification of the outer layer multi-period sequential decision model is passed, taking the unit combination value of the thermal power unit as a final unit combination value; and updating the inner layer two-stage robust unit combination model when the verification of the outer layer multi-period sequential decision model is not passed, repeating the steps of performing determination and verification according to the updated inner layer two-stage robust unit combination model until the verification of the outer layer multi-period sequential decision model is passed, and taking the unit combination value of the thermal power unit verified by the outer layer multi-period sequential decision model as a final unit combination value.
The apparatus embodiments described above are merely illustrative, wherein elements illustrated as separate elements may or may not be physically separate, and 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 (10)

1. A double-layer robust unit combination method of an electric power system, wherein the electric power system at least comprises a thermal power unit and a new energy unit, the method comprising:
determining an output scene set of a new energy unit of an inner layer model and a unit combination value of the thermal power unit, which are obtained based on an inner layer two-stage robust unit combination model;
according to the output scene set of the new energy unit of the inner layer model, verifying the feasibility of the unit combination value of the thermal power unit based on the outer layer multi-period sequential decision model;
when the verification of the outer layer multi-period sequential decision model is passed, taking the unit combination value of the thermal power unit as a final unit combination value;
And updating the inner layer two-stage robust unit combination model when the verification of the outer layer multi-period sequential decision model is not passed, so as to repeatedly and iteratively execute the determining step and the verification step according to the updated inner layer two-stage robust unit combination model until the unit combination value of the thermal power unit verified by the outer layer multi-period sequential decision model is taken as a final unit combination value when the verification of the outer layer multi-period sequential decision model is passed.
2. The method for combining the double-layer robust units of the power system according to claim 1, wherein the determining obtains the set of output scenes of the new energy units of the inner layer model and the set combination value of the thermal power unit based on the inner layer two-stage robust unit combination model, and comprises the following steps:
acquiring parameters of an electric power system and initializing an output scene set of a new energy unit, an index set of a scene sequence of the new energy unit, an iteration index between an inner layer model and an outer layer model, an iteration index of an inner layer model stage and an unexpected cutting plane constraint set of an inner layer pre-dispatching unit combination;
obtaining an output scene set and a unit combination value of the new energy unit in a pre-scheduling stage based on an inner pre-scheduling stage optimization model according to parameters of the power system, the output scene set of the new energy unit, an index set of a scene sequence of the new energy unit, an iteration index between the inner model and the outer model, the inner model stage iteration index and the inner pre-scheduling unit combination unexpected cutting plane constraint set;
According to the output scene set of the new energy unit in the pre-dispatching stage, checking the unit combination value in the pre-dispatching stage based on an inner layer rescheduling stage optimization model;
when the verification of the inner layer rescheduling stage optimization model is passed, taking the unit combination value of the pre-scheduling stage as the unit combination value of the thermal power unit;
and updating the inner layer pre-dispatching stage optimization model when the verification of the inner layer re-dispatching stage optimization model is not passed, repeatedly and iteratively executing the step according to the parameters of the electric power system according to the updated inner layer pre-dispatching stage optimization model until the verification of the inner layer re-dispatching stage optimization model is passed, and taking the unit combination value of the pre-dispatching stage verified by the inner layer re-dispatching stage optimization model as the unit combination value of the thermal power unit.
3. The method for combining double-layer robust units of an electric power system according to claim 2, wherein the verifying the unit combination value of the pre-dispatching stage based on the inner-layer rescheduling stage optimization model according to the output scene set of the new energy unit of the pre-dispatching stage comprises:
Obtaining an optimal solution of an auxiliary variable of the daily highest running cost of the inner layer pre-scheduling stage optimization model according to the output scene set of the new energy unit in the pre-scheduling stage;
when the optimal solution of the highest operation cost auxiliary variable in the day and the objective function value of the inner layer rescheduling stage optimization model meet a first preset formula, checking the unit combination value of the pre-scheduling stage by the inner layer rescheduling stage optimization model;
when the optimal solution of the highest operation cost auxiliary variable in the day and the objective function value of the inner layer rescheduling stage optimization model do not meet a first preset formula, the unit combination value of the pre-scheduling stage does not pass the verification of the inner layer rescheduling stage optimization model;
the first preset formula is:
wherein,optimizing an optimal solution of an auxiliary variable eta of the daily highest running cost of the model for the inner layer pre-scheduling stage;optimizing an objective function value of a model for the inner layer rescheduling stage; epsilon is a preset allowable deviation, m is an iteration index of the inner layer model stage, n is an iteration index between the inner layer model and the outer layer model, wait is a pre-scheduling stage, and see is a rescheduling stage.
4. The method of claim 3, wherein updating the inner layer pre-dispatch stage optimization model when the verification of the inner layer re-dispatch stage optimization model is not passed comprises:
and when the optimal solution of the highest operation cost auxiliary variable in the day and the objective function value of the inner layer rescheduling stage optimization model do not meet the first preset formula, updating the inner layer model stage iteration index, and updating the output scene set of the new energy unit and the index set of the scene sequence of the new energy unit so as to update the inner layer rescheduling stage optimization model according to the updated inner layer model stage iteration index, the output scene set of the new energy unit and the index set of the scene sequence of the new energy unit.
5. The method of combining double-layer robust units of an electric power system according to any one of claims 2 to 4, wherein the verifying the feasibility of the unit combination value of the thermal power unit based on an outer multi-period sequential decision model according to the output scene set of the new energy unit of the inner model comprises:
Determining a decision solution of the outer layer multi-period sequential decision model according to the output scene set of the new energy unit of the inner layer model;
calculating a relaxation variable penalty cost from the decision solution;
when the relaxation variable penalty cost meets a second preset formula, checking a unit combination value of the thermal power unit through the outer layer multi-period sequential decision model;
when the relaxation variable penalty cost does not meet a second preset formula, the unit combination value of the thermal power unit does not pass through the verification of the outer-layer multi-period sequential decision model;
the second preset formula is:
wherein,and punish is punished for the relaxation variable punishment cost, y is an index of a new energy scene sequence, n is an iteration index between the inner layer model and the outer layer model.
6. The method of claim 5, wherein determining a decision solution for the outer layer multi-period sequential decision model comprises:
when the scheduling time period meets a preset time period threshold value, the decision solution is obtained based on the outer layer multi-time period sequential decision model;
and when the scheduling period does not meet a preset period threshold, updating the scheduling period, and obtaining the decision solution based on the outer multi-period sequential decision model according to the updated scheduling period.
7. The method of claim 5, wherein updating the inner two-stage robust set combination model when the outer multi-stage sequential decision model fails to check comprises:
and when the relaxation variable penalty cost does not meet the second preset formula, updating an iteration index between the inner layer model and the outer layer model, and updating the inner layer pre-scheduling unit combination unexpected cut plane constraint set so as to update the inner layer two-stage robust unit combination model according to the updated iteration index between the inner layer model and the outer layer model and the inner layer pre-scheduling unit combination unexpected cut plane constraint set.
8. 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 double-layer robust assembly method of the power system according to any of claims 1 to 7 when executing the program.
9. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements a double-layer robust assembly method of a power system according to any of claims 1 to 7.
10. A computer program product comprising a computer program, characterized in that the computer program, when executed by a processor, implements a double-layer robust assembly method of a power system according to any of claims 1 to 7.
CN202310954740.8A 2023-07-31 2023-07-31 Double-layer robust unit combination method and assembly of power system Pending CN117154840A (en)

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