CN114066257A - Electricity-gas comprehensive energy distribution robust optimization scheduling method and device - Google Patents

Electricity-gas comprehensive energy distribution robust optimization scheduling method and device Download PDF

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CN114066257A
CN114066257A CN202111365620.1A CN202111365620A CN114066257A CN 114066257 A CN114066257 A CN 114066257A CN 202111365620 A CN202111365620 A CN 202111365620A CN 114066257 A CN114066257 A CN 114066257A
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潘军
杨波
徐钦
欧绍辉
郑海光
杨怡萍
何彬彬
黄旭锐
于丰源
张行
卢彦杉
江军
詹之林
陈蔼峻
钟美玲
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Abstract

The invention discloses an electricity-gas comprehensive energy distribution robust optimization scheduling method and device, wherein the method comprises the following steps: according to historical wind power output data, a wind power output distribution fuzzy set based on empirical distribution is constructed through Wasserstein distance; preprocessing the wind power output distribution fuzzy set; establishing an optimized dispatching model of the electricity-gas integrated energy system according to the preprocessed wind power output distribution fuzzy set; and solving the optimized scheduling model through an affine rule and a dual theorem to obtain an optimized scheduling result. The method not only well correlates the real distribution with the empirical distribution, but also reduces the solving difficulty of the optimized scheduling model.

Description

Electricity-gas comprehensive energy distribution robust optimization scheduling method and device
Technical Field
The application relates to the field of optimization scheduling of an integrated energy system, in particular to a method and a device for electric-gas integrated energy distribution robust optimization scheduling.
Background
Under the large background of the energy revolution, the energy demand faces the challenges of economic benefits and clean production, and the introduction of a multi-energy complementary mode and new energy solves the problem. With the wide landing of the multi-Energy complementary mode, the power System gradually changes to an Integrated Energy System (IES), the remote transmission network of the IES is a coupling network capable of simultaneously transmitting two Energy sources of electric Energy and natural gas, and the distribution network meets the requirement conversion of various Energy sources of users, such as electricity-gas-cold-heat-hydrogen, and the like. Meanwhile, the introduction of large-scale uncertain wind power brings greater challenges to a multi-energy complementary network, and the optimization scheduling facing to the modern comprehensive energy system becomes a popular research subject.
With the deep penetration of renewable clean energy, uncertainty such as wind power and the like increases difficulty for the operation of comprehensive energy planning, wherein a random planning (SO Stochastic planning) method is a representative processing method, and the method is widely applied to problems such as unit combination, multi-energy coordination degree and power distribution network planning containing uncertain variables, but the random planning requires that unknown distribution and accurate parameters of random variables are determined in advance, SO that the solving precision is lower and the time is longer. Robust Optimization (RO) is another existing mainstream solving method for the above problem, and only the boundary range of the uncertainty set needs to be determined, and the worst uncertainty scene is screened to obtain the optimal solution that can adapt to all uncertainty conditions, but the solving result is too conservative. Recently, a Distribution Robustness Optimization (DRO) method between SO and RO has received a lot of attention, which combines the advantages of both SO and RO, and the method does not need to assume an accurate probability distribution and introduce probability information for solving an RO algorithm with good quality, SO that a solution satisfying both economy and conservation can be obtained.
In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art:
the mainstream fuzzy set construction method in the DRO algorithm mainly comprises a moment estimation method, a hypothesis test method and a clustering method, wherein the moment estimation method has the problem of non-convergence of the distribution of an indeterminate set in the process of sample set enlargement; the assumption that the detection method does not analyze the relation of true distribution in empirical distribution, and the solution has over-conservatism; at present, no better selection method for the clustering factors exists in the clustering method, and meanwhile, the clustering factors cannot be converted into the problem of direct solving, so that the problem of overlong operation time exists. In summary, in the existing method for scheduling the integrated energy system, either the real distribution cannot be well correlated with the empirical distribution, or the problem of high difficulty in solving exists.
Disclosure of Invention
The embodiment of the application aims to provide an electricity-gas comprehensive energy distribution robust optimization scheduling method and device, so as to solve the technical problems that low solving difficulty cannot be considered and real distribution and empirical distribution cannot be well associated in the related technology.
According to a first aspect of embodiments of the present application, there is provided an electric-gas integrated energy distribution robust optimization scheduling method, which is applied to an electric-gas integrated energy system, and includes:
according to historical wind power output data, a wind power output distribution fuzzy set based on empirical distribution is constructed through Wasserstein distance;
preprocessing the wind power output distribution fuzzy set;
establishing an optimized dispatching model of the electricity-gas integrated energy system according to the preprocessed wind power output distribution fuzzy set;
and solving the optimized scheduling model through an affine rule and a dual theorem to obtain an optimized scheduling result.
Further, according to historical wind power output data, a wind power output distribution fuzzy set based on empirical distribution is constructed through Wasserstein distance, and the method comprises the following steps:
calculating to obtain a wind power prediction error set according to historical data of a wind power plant;
establishing experience distribution according to the wind power prediction error set;
and according to the empirical distribution, constructing a wind power output distribution fuzzy set based on the empirical distribution through the Wasserstein distance.
Further, according to the preprocessed wind power output distribution fuzzy set, establishing an optimized dispatching model of the electricity-gas integrated energy system, wherein the optimized dispatching model comprises the following steps:
establishing an optimized dispatching model of the electricity-gas integrated energy system according to the output, the start and stop, the standby cost PC, the output of the natural gas well, the output of the natural gas unit, the standby cost GC and the system operation cost IL of the power system unit as follows:
Figure BDA0003360637030000031
wherein P is the real wind power prediction error distribution,
Figure BDA0003360637030000032
(ii) is the empirical distribution;
the expressions of the power system unit output, the start-stop, the standby cost PC and the natural gas well output, the natural gas unit output, the standby cost GC and the system operation cost IL are as follows:
Figure BDA0003360637030000033
wherein NCU and NGU are respectively coal-fired and gas-fired unit sets, GS is natural gas well set, Ii,t、ui,t、vi,tThe variable refers to 0-1 variable of the running and starting and stopping states of the unit i at the moment t,
Figure RE-GDA0003415600960000034
respectively rotates up and down for standby at the time t,
Figure RE-GDA0003415600960000035
is the first stage planning output of the unit and uses the linear piecewise cost
Figure RE-GDA0003415600960000036
Is expressed in the form of SWsp,tRefers to the gas production rate of a natural gas well sp in the t period, Pi,tThe actual output of the unit in the readjustment stage, H and M respectively refer to the set of nodes of the power grid and the air grid,
Figure RE-GDA0003415600960000037
and
Figure RE-GDA0003415600960000038
respectively the more limited the power grid tide and the more limited the gas grid tide, ciFor each operating state of the unit, rhogFor natural gas production, paAnd adjusting the penalty coefficient for the unit.
Further, the optimal scheduling model is solved through an affine rule and a strong dual theorem to obtain an optimal scheduling result, which includes:
converting the optimized scheduling model into a compact form as follows:
Figure BDA00033606370300000310
s.t x∈Ω
wherein a is a cost coefficient matrix in a first-stage objective function of the optimized scheduling model, ξ is a matrix formed by uncertain variables in the optimized scheduling model, x is a first-stage variable of the optimized scheduling model, Ω is a feasible domain of the first-stage variable of the optimized scheduling model, and f (x, ξ) is a second-stage function of the optimized scheduling model;
expanding the second stage function through an affine rule:
f(x,ξ)＝inf gTω
Figure BDA0003360637030000041
Aξ+Gω≤b(x)
g is a cost coefficient matrix in a second-stage objective function of the optimized scheduling model, ω is a set of second-stage readjustment variables, l is the number of second-stage variables, η is the dimension of an uncertain vector ζ, and a, b and G are parameter matrices corresponding to constraint conditions of the second-stage function;
according to the expanded second-stage function, the optimized scheduling model in a compact form is subjected to strong dual theorem
Figure BDA0003360637030000042
Conversion is carried out to give the following formula:
Figure BDA0003360637030000043
Figure BDA0003360637030000044
Figure BDA0003360637030000045
Figure BDA0003360637030000046
Figure BDA0003360637030000047
Figure BDA0003360637030000048
Figure BDA0003360637030000049
in the formula: upsilon, pi, y and z are dual variables, K is the number of sampling samples,
Figure BDA00033606370300000410
expressing a positive one-dimensional number, ζKFor wind-electric prediction error, σηAuxiliary vector, iota, representing that only the η -th term is 1 and the other terms are all zero+And iota-Measuring parameters of the wind power output distribution fuzzy set;
and solving the optimized scheduling model processed by the affine rule and the strong dual theorem to obtain an optimized scheduling result.
According to a second aspect of the embodiments of the present application, there is provided an electric-gas integrated energy distribution robust optimization scheduling apparatus, which is applied to an electric-gas integrated energy system, and includes:
the building module is used for building a wind power output distribution fuzzy set based on the checked distribution through Wasserstein distance according to historical wind power output data;
the preprocessing module is used for preprocessing the wind power output distribution fuzzy set;
the modeling module is used for establishing an optimized dispatching model of the electricity-gas integrated energy system according to the preprocessed wind power output distribution fuzzy set;
and the solving module is used for solving the optimized scheduling model through an affine rule and a dual theorem to obtain an optimized scheduling result.
Further, the building module comprises:
the calculation submodule is used for calculating to obtain a wind power prediction error set according to historical data of the wind power plant;
the first construction submodule is used for constructing experience distribution according to the wind power prediction error set;
and the second construction submodule is used for constructing a wind power output distribution fuzzy set based on empirical distribution through the Wasserstein distance according to the empirical distribution.
Further, the modeling module includes:
the modeling submodule is used for establishing an optimized dispatching model of the electricity-gas integrated energy system according to the output, the start and stop, the standby cost PC, the output of the natural gas well, the output of the natural gas unit, the standby cost GC and the system operation cost IL as follows:
Figure BDA0003360637030000051
wherein P is the real wind power prediction error distribution,
Figure BDA0003360637030000052
(ii) is the empirical distribution;
the expressions of the power system unit output, the start-stop, the standby cost PC and the natural gas well output, the natural gas unit output, the standby cost GC and the system operation cost IL are as follows:
Figure BDA0003360637030000053
wherein NCU and NGU are respectively coal-fired and gas-fired unit sets, GS is natural gas well set, Ii,t、ui,t、 vi,tThe variable refers to 0-1 variable of the running and starting and stopping states of the unit i at the moment t,
Figure RE-GDA0003415600960000054
respectively rotates up and down for standby at the time t,
Figure RE-GDA0003415600960000061
is the first stage planning output of the unit and uses the linear piecewise cost
Figure RE-GDA0003415600960000062
Is expressed in the form of SWsp，tRefers to the gas production rate of a natural gas well sp in the t period, Pi，tThe actual output of the unit in the readjustment stage, H and M respectively refer to the set of nodes of the power grid and the air grid,
Figure RE-GDA0003415600960000063
and
Figure RE-GDA0003415600960000064
respectively the more limited the power grid tide and the more limited the gas grid tide, ciFor each operating state of the unit, rhogFor natural gas production, paAnd adjusting the penalty coefficient for the unit.
Further, the solving module comprises:
a transformation submodule, for transforming the optimized scheduling model into a compact form, as follows:
Figure BDA0003360637030000064
s.t x∈Ω
wherein a is a cost coefficient matrix in a first-stage objective function of the optimized scheduling model, ξ is a matrix formed by uncertain variables in the optimized scheduling model, x is a first-stage variable of the optimized scheduling model, Ω is a feasible domain of the first-stage variable of the optimized scheduling model, and f (x, ξ) is a second-stage function of the optimized scheduling model;
an expansion submodule, configured to expand the second stage function according to an affine rule:
f(x,ξ)＝inf gTω
Figure BDA0003360637030000065
Aξ+Gω≤b(x)
g is a cost coefficient matrix in a second-stage objective function of the optimized scheduling model, ω is a set of second-stage readjustment variables, l is the number of second-stage variables, η is the dimension of an uncertain vector ζ, and a, b and G are parameter matrices corresponding to constraint conditions of the second-stage function;
a conversion submodule for optimizing the scheduling model in a compact form by a strong dual theorem according to the expanded second stage function
Figure BDA0003360637030000066
Conversion is carried out to give the following formula:
Figure BDA0003360637030000071
Figure BDA0003360637030000072
Figure BDA0003360637030000073
Figure BDA0003360637030000074
Figure BDA0003360637030000075
Figure BDA0003360637030000076
Figure BDA0003360637030000077
in the formula: upsilon, pi, y and z are dual variables, K is the number of sampling samples,
Figure BDA0003360637030000078
expressing a positive one-dimensional number, ζKFor wind-electric prediction error, σηAuxiliary vector, iota, representing that only the η -th term is 1 and the other terms are all zero+And iota-Measuring parameters of the wind power output distribution fuzzy set;
and the solving submodule is used for solving the optimized scheduling model processed by the affine rule and the strong dual theorem to obtain an optimized scheduling result.
According to a third aspect of embodiments of the present application, there is provided an electronic apparatus, including:
one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a method as described in the first aspect.
According to a fourth aspect of embodiments herein, there is provided a computer-readable storage medium having stored thereon computer instructions, characterized in that the instructions, when executed by a processor, implement the steps of the method according to the first aspect.
The technical scheme provided by the embodiment of the application can have the following beneficial effects:
according to the embodiment, wind power empirical distribution is obtained through historical wind power output data, a wind power output distribution fuzzy set based on the empirical distribution is constructed through Wasserstein distance, the real distribution and the empirical distribution are well correlated, compared with an OPF (optimal power factor) model of a conventional electricity-gas comprehensive energy system, the influence of uncertain wind power on a coupling system is fully considered, and the system can still realize the unit cooperation of a power grid and a gas grid under the worst wind power scene; and establishing an optimized dispatching model of the electricity-gas integrated energy system according to the preprocessed wind power output distribution fuzzy set, and converting a complex multilayer optimization problem solved by the optimized dispatching model into an MILP problem by an affine rule and a dual method, so that the solving difficulty is reduced, and the problem can be solved by a solver directly.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
FIG. 1 is a flow diagram illustrating a method for robust optimal scheduling of electro-pneumatic integrated energy distribution in accordance with an exemplary embodiment.
Fig. 2 is a flowchart illustrating step S11 according to an exemplary embodiment.
FIG. 3 is a diagram illustrating an IEEE 6bus-6node IEGS test system topology, according to an exemplary embodiment;
FIG. 4 is a diagram illustrating the results of an algorithm runtime experiment according to an exemplary embodiment;
FIG. 5 is a block diagram illustrating an electro-pneumatic integrated energy distribution robust optimization scheduling apparatus in accordance with an exemplary embodiment.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this application and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
Fig. 1 is a flow chart illustrating a robust optimized scheduling method for electric-gas integrated energy distribution, as shown in fig. 1, applied to an electric-gas integrated energy system, which may include the following steps:
step S11: according to historical wind power output data, a wind power output distribution fuzzy set based on empirical distribution is constructed through Wasserstein distance;
step S12: preprocessing the wind power output distribution fuzzy set;
step S13: establishing an optimized dispatching model of the electricity-gas integrated energy system according to the preprocessed wind power output distribution fuzzy set;
step S14: and solving the optimized scheduling model through an affine rule and a dual theorem to obtain an optimized scheduling result.
According to the embodiment, wind power empirical distribution is obtained through historical wind power output data, a wind power output distribution fuzzy set based on the empirical distribution is constructed through Wasserstein distance, the real distribution and the empirical distribution are well correlated, compared with an OPF (optimal power factor) model of a conventional electricity-gas comprehensive energy system, the influence of uncertain wind power on a coupling system is fully considered, and the system can still realize the unit cooperation of a power grid and a gas grid under the worst wind power scene; and establishing an optimized dispatching model of the electricity-gas integrated energy system according to the preprocessed wind power output distribution fuzzy set, and converting a complex multilayer optimization problem solved by the optimized dispatching model into an MILP problem by an affine rule and a dual method, so that the solving difficulty is reduced, and the problem can be solved by a commercial solver directly.
In the specific implementation of the step S11, according to the historical wind power output data, a wind power output distribution fuzzy set based on empirical distribution is constructed through Wasserstein distance;
specifically, as shown in fig. 2, this step may include the following sub-steps:
step S21: calculating to obtain a wind power prediction error set according to historical data of a wind power plant;
because wind power has uncertainty, the predicted data of the wind power plant often has errors with the actual wind power output, the distribution of the errors is difficult to pass the statistical hypothesis, and the wind power predicted error of the actual scene is
Figure BDA0003360637030000091
The problem of constructing the distribution uncertain set is converted into the uncertain set of xi; according to historical data of the wind power plant, K groups of wind power prediction error sets zeta can be obtained1,ζ2,...,ζKWhere each group ζ ═ { ζ ═ ζ1,1,...,ζq,1,...,ζ1,T,...,ζq,T}。
Step S22: establishing experience distribution according to the wind power prediction error set;
in particular, the distribution is constructed from a set of historical prediction errors
Figure BDA0003360637030000101
Wherein
Figure BDA0003360637030000102
Is ζkDirac measure of. Defining an empirical distribution
Figure BDA0003360637030000103
To obey a series of distributions of:
Figure BDA0003360637030000104
in the formula:
Figure BDA0003360637030000105
predict error distribution for real wind power and belongs to the center of sphere
Figure BDA0003360637030000106
Radius of
Figure BDA0003360637030000107
1-Wasserstein ball set
Figure BDA0003360637030000108
Figure BDA0003360637030000109
Is a series of distribution sets satisfying the above formula between the sample set and the distribution in the random space xi,
Figure BDA00033606370300001010
1-Wasserstein distance, in the form of:
Figure BDA00033606370300001011
Figure BDA00033606370300001012
where dis (xi, β) is a continuous distance | | | xi- β | | | between xi and β in space, and its form can be defined autonomously, so that | | | xi- β | | | | | | eTmax(ι+ξ-ι+β,ι-β-ι-ξ)，ι+And iota-Is a measure parameter greater than zero, beta is a reference point of the random space; xi and zeta are compliance, respectively
Figure BDA00033606370300001013
And
Figure BDA00033606370300001014
the random variable of the distribution is varied in a way that,
Figure BDA00033606370300001015
is composed of
Figure BDA00033606370300001016
And
Figure BDA00033606370300001017
joint probability distribution of (2).
Step S23: according to the empirical distribution, a wind power output distribution fuzzy set based on the empirical distribution is constructed through the Wasserstein distance;
specifically, the radius of the fuzzy set of wind power output distribution, namely the Wasserstein sphere set
Figure BDA00033606370300001018
The selection of (a) will determine the accuracy of the solution, as determined by conventional methods
Figure BDA00033606370300001019
Generally decreases with increasing number of samples K, resulting in over-conservative distribution estimates, and the radius of the Wasserstein sphere set is defined by the following equation:
Figure BDA00033606370300001020
in the formula: σ ∈ (0,1) is a confidence level symbol, and is set as such
Figure BDA00033606370300001021
It can be guaranteed that the confidence level of the distribution estimate is not less than sigmaAnd lambda is an intermediate parameter, and the minimum value can be obtained by simple methods such as grid search and the like.
In the specific implementation of the step S12, preprocessing the fuzzy set of wind power output distribution;
in the specific implementation of the step S13, establishing an optimized scheduling model of the electricity-gas integrated energy system according to the preprocessed fuzzy set of the wind power output distribution;
specifically, an optimized scheduling model of the electricity-gas integrated energy system is established according to the output, the start and stop, the standby cost PC, the output of the natural gas well, the output of the natural gas unit, the standby cost GC and the system operation cost IL according to the following formula:
Figure BDA0003360637030000111
wherein P is the real wind power prediction error distribution,
Figure BDA0003360637030000112
(ii) is the empirical distribution;
the expressions of the power system unit output, the start-stop, the standby cost PC and the natural gas well output, the natural gas unit output, the standby cost GC and the system operation cost IL are as follows:
Figure BDA0003360637030000113
wherein NCU and NGU are respectively coal-fired and gas-fired unit sets, GS is natural gas well set, Ii,t、ui,t、 vi,tThe variable refers to 0-1 variable of the running and starting and stopping states of the unit i at the moment t,
Figure RE-GDA0003415600960000114
respectively rotates up and down for standby at the time t,
Figure RE-GDA0003415600960000115
is the first stage gauge of the machine setPlotting force, using linear piecewise cost
Figure RE-GDA0003415600960000116
Is expressed in the form of SWsp,tRefers to the gas production rate of a natural gas well sp in the t period, Pi,tThe actual output of the unit in the readjustment stage, H and M respectively refer to the set of nodes of the power grid and the air grid,
Figure RE-GDA0003415600960000117
and
Figure RE-GDA0003415600960000118
respectively the more limited the power grid tide and the more limited the gas grid tide, ciFor each operating state of the unit, rhogFor natural gas production, paAnd adjusting the penalty coefficient for the unit.
Wherein, each constraint of the optimized scheduling model is as follows:
1. unit restraint
The method comprises the following steps of restraining the generating capacity of a unit, the up-down rotation standby of the unit, the minimum start/stop time constraint of the unit and the climbing of the unit in a readjustment stage, and specifically comprising the following steps:
Figure BDA0003360637030000121
Figure BDA0003360637030000122
Figure BDA0003360637030000123
Figure BDA0003360637030000124
Figure BDA0003360637030000125
Figure BDA0003360637030000126
Figure BDA0003360637030000127
where γ is the set of all types of units, Ton,i、Toff,iIndicates the minimum start-stop time of the unit, Hon,i、Hoff,iAnd the unit operation parameters are set.
2. Grid constraints
The direct current power flow equation and related constraints, the wind power constraint, the whole network power balance in the readjustment stage, the power grid power flow out-of-limit equation and the constraints are specifically as follows:
Figure BDA0003360637030000128
pfhk,t＝(θh,t-θk,t)/xhk
Figure BDA0003360637030000129
θj,t＝0,j∈REF
Figure BDA00033606370300001210
Figure BDA00033606370300001211
Figure BDA00033606370300001212
Figure BDA00033606370300001213
Figure BDA00033606370300001214
wherein W is a set of wind farms,
Figure BDA0003360637030000131
refers to the predicted contribution of the wind farm q at the moment t,
Figure BDA0003360637030000132
is the electrical load connected to node h, PLEh、PLFhSets pf of transmission lines, respectively node h incoming/outgoing energy flow directionhk,tFor the current of line hk at time t, θh,t、θk,tIs the phase angle, ζ, of the nodes h, k at time tq,tReferring to the actual output of the wind farm q at time t, REF is the set of balanced nodes,
Figure BDA0003360637030000133
is the set of all load nodes.
3. Natural gas net restraint
The linear gas consumption equation of the gas unit, the natural gas flow equation and related constraints, the gas production constraint of the natural gas well and the natural gas network flow out-of-limit constraint are specifically as follows. Wherein the natural gas flow direction is specified by a sign function sgn (·).
Figure BDA0003360637030000134
Figure BDA0003360637030000135
Figure BDA0003360637030000136
Figure BDA0003360637030000137
Figure BDA0003360637030000138
Figure BDA0003360637030000139
Figure BDA00033606370300001310
Figure BDA00033606370300001311
Figure BDA00033606370300001312
Wherein alpha is1,i、α2,i、α3,i、α4,iFor the gas consumption coefficient of the gas engine set in the running state, GPEm、GPFmThe natural gas pipelines are respectively collected in the gas input and output directions of the node m, gf is the natural gas flow of the line,
Figure BDA00033606370300001313
is the natural gas load connected with the node m, IP all connected natural gas pipeline set, kappa is the relation coefficient of trend and air pressure, prm,t、prn,tAnd the pressure values of the gas pipelines at the nodes m and n at the moment t are obtained.
In specific implementation, the natural gas flow equation contains multiple nonlinear terms such as root numbers, so that the optimized model is non-convex integrally, and the model cannot be solved directly. In the application, an Incremental Piecewise Linearization (IPL) method is adopted to perform linearization approximation on the linear image.
Firstly, carrying out equivalent replacement on a natural gas flow equation by using the following formula:
Figure BDA0003360637030000141
Figure BDA0003360637030000142
in the upper type, by pim,tInstead of the square term pr of air pressurem,t 2Then, it is necessary to continue the quadratic term gfmn,t|gfmn,tIs converted into linear terms, order
Figure BDA0003360637030000143
The number of linear segments is μ, gfmn,tIs divided equally into mu parts in the definition domain
Figure BDA0003360637030000144
While corresponding can be calculated
Figure BDA0003360637030000145
The specific expression of IPL is as follows:
Figure BDA0003360637030000146
Figure BDA0003360637030000147
Figure BDA0003360637030000148
Figure BDA0003360637030000149
by the above operation, gf can be adjustedmn,tIs replaced by
Figure BDA00033606370300001410
Wherein:
Figure BDA00033606370300001411
is a (0,1) continuous variable for determining the composition gfmn,tThe ratio value of each section of the table,
Figure BDA00033606370300001412
is an integer variable of 0-1, and is used for determining cutoff segment and limiting
Figure BDA00033606370300001413
Are taken in a segment order.
4. Affine constraints
In order to solve the problem simply and conveniently, the method adopts an imitation decision rule simplified model based on the AGC relation between the unit and the power grid power variation, considers the difference of the adjustment participation degrees of the second stage coal-fired unit and the gas unit, and establishes the following affine relation between the wind power prediction error and the second stage readjustment quantity of the unit:
Figure BDA00033606370300001414
Figure BDA00033606370300001415
wherein the content of the first and second substances,
Figure BDA00033606370300001416
are respectively the regulating factors of coal-fired and gas-fired units and satisfy the relationship
Figure BDA0003360637030000151
To control the affine approximation from excessively violating limits.
In the specific implementation of step S14, the optimized scheduling model is solved through affine rules and dual theorem to obtain an optimized scheduling result;
specifically, this step includes the following substeps:
step S31: converting the optimized scheduling model into a compact form as follows:
Figure BDA0003360637030000152
s.t x∈Ω
wherein a is a cost coefficient matrix in a first-stage objective function of the optimized scheduling model, ξ is a matrix formed by uncertain variables in the optimized scheduling model, x is a first-stage variable of the optimized scheduling model, Ω is a feasible domain of the first-stage variable of the optimized scheduling model, and f (x, ξ) is a second-stage function of the optimized scheduling model;
step S32: expanding the second stage function through an affine rule:
f(x,ξ)＝inf gTω
Figure BDA0003360637030000153
Aξ+Gω≤b(x)
g is a cost coefficient matrix in a second-stage objective function of the optimized scheduling model, ω is a set of second-stage readjustment variables, l is the number of second-stage variables, η is the dimension of an uncertain vector ζ, and a, b and G are parameter matrices corresponding to constraint conditions of the second-stage function;
step S33: according to the expanded second-stage function, the optimized scheduling model in a compact form is subjected to strong dual theorem
Figure BDA0003360637030000154
Conversion is carried out to give the following formula:
Figure BDA0003360637030000161
Figure BDA0003360637030000162
Figure BDA0003360637030000163
Figure BDA0003360637030000164
Figure BDA0003360637030000165
Figure BDA0003360637030000166
Figure BDA0003360637030000167
in the formula: upsilon, pi, y and z are dual variables, K is the number of sampling samples,
Figure BDA0003360637030000168
expressing a positive one-dimensional number, ζKFor wind-electric prediction error, σηAuxiliary vector, iota, representing that only the η -th term is 1 and the other terms are all zero+And iota-Measuring parameters of the wind power output distribution fuzzy set;
step S34: solving the optimized scheduling model processed by the affine rule and the strong dual theorem to obtain an optimized scheduling result;
the solving problem of the optimized scheduling model processed in the steps S31-S33 is converted from a complex multilayer optimization problem to an MILP problem, the solving difficulty is reduced, and the problem can be solved directly by a Cplex solver.
The electricity-gas comprehensive energy distribution robust optimization scheduling method provided by the application is tested on an improved IEEE 6bus-6node IEGS test system, a topological diagram is shown in figure 3, and in a power system: g1 and G4 are coal-fired units, G2 and G3 are gas-fired units, and W refers to a wind farm. In the natural gas system, D1 and D2 represent natural gas loads, and S1 and S2 represent natural gas well intake air amounts.
And analyzing the optimized scheduling result, namely the unit combination, obtained in the step S34, wherein the optimized scheduling result comprises unit starting and stopping conditions, unit output and the like, and meanwhile, carrying out comparative analysis. Firstly, wind power access is not considered, the combination condition of the EGTN system unit is directly analyzed, and a table 1 is a unit running state table in each time period.
TABLE 1
Figure BDA0003360637030000169
Figure BDA0003360637030000171
Table 1 indicates, from the peak-to-valley trend of the daily load curve, that the combined output of the EGTN units is: the coal-fired unit with low operation cost and long start-stop period is preferentially used, meanwhile, in order to ensure the reliability of power supply, the gas unit G3 with large operation capacity and relatively slow start-stop speed compared with G4 is put into use, in the sudden rise stage of power consumption, in order to ensure the reliability of the system, the unit G3 with quick response is also put into use, the good peak clipping and valley filling functions are successfully achieved, and the unit operation table also reflects the multi-energy complementary advantages of the electricity-gas comprehensive energy system.
The wind power uncertainty is considered, the model is solved by applying the electricity-gas comprehensive energy distribution robust optimization scheduling method, more possibilities are provided for collocation of the unit combination due to wind power access, meanwhile, the wind power uncertainty is fully considered by the design model, only the wind power prediction data are used as optimization reference constraints of the wind power, the up-and-down rotation standby of the unit, the starting and stopping conditions in uncertain time space and the like are more complex: under the thinking of robustness, consider the worst wind-powered electricity generation scene promptly for the system power consumption is absolutely reliable, reserves absolute adjustment space for the unit simultaneously, and table 2 is for considering wind-powered electricity generation's each period unit running state table:
TABLE 2
Figure BDA0003360637030000172
According to analysis of a table 2, wind power access is considered, loads are in a low-ebb period within 1-4h, meanwhile, wind capacity is sufficient, load requirements can be completely met by means of power supply of thermal power generating units G1 and G2, then the loads gradually enter a daily power utilization peak, wind power is gradually reduced according to reference of wind power prediction data, the reliability of the electricity utilization of the wind power is considered by a robust algorithm, the gas power generating units G3 and G4 are put into use together on the premise of minimum cost, and reliable preparation of power supply is carried out at the load peak of the reduction of the wind power. After the wind power prediction trend is stable, the G1 and G2 systems which are accessed by wind power can provide complete power supply reliability for the worst condition of the system, and G3 and G4 are cut out for use.
As shown in table 3, the present application further provides comparison of the start-stop conditions of the test system units of the robust optimization scheduling (WRO) method for the power-gas integrated energy distribution, the stochastic programming (SO) method based on multiple sets of wind power historical prediction data, and the deterministic programming (DO) method based on the wind power prediction data before the calculation day in an embodiment.
TABLE 3
Figure BDA0003360637030000181
According to the table 3, WRO and the SO, under the condition that the load is considered to rise, the wind power is not determined to show a descending trend after 5h, the quick start-stop units G3 and G4 with high early start cost provide storage for system functions, and the DO only needs to meet the lowest cost energy supply of the load for determining the wind power situation, SO that the form of thermal power unit combination is still adopted between 5h and 7h, however, although the lowest unit combination cost can be achieved and the load in the current time period is met, the start-stop limit of the unit is received, and the wind abandon or load cut cost in the later time period is high. Compared with WRO, it can be seen that WRO directly starts all fast response gas turbine units as back spares at 5h, whereas SO only starts G3 with relatively low cost compared with G4, which can prove that WRO of the algorithm considers the worst situation, the situation in 12h period is similar, and also considers the descending trend of wind power and the ascending trend of load in the period, the scheduling scheme given by WRO algorithm is to start all the turbine units and provide spares, while the SO scheme only gives 3 turbine units, SO that it is seen that the WRO algorithm gives the solution for the worst situation.
TABLE 4
Figure BDA0003360637030000191
Table 4 shows a comparison between different algorithm costs, which also indicates that WRO algorithm is the best conservative and therefore the cost is relatively high, the DO algorithm does not consider uncertainty of wind power at all, and only uses prediction data for planning, although the unit combination cost is low, due to wind abandoning penalty and uncertain load shedding penalty, economic performance is not the best, and in a scheduling scenario, since the first requirement of power system operation is to ensure reliable power supply, WRO itself ensures certain economic efficiency, and most importantly, the system operation is reliable and safe, and the applicable scenario is wide.
And then, carrying out algorithm calculation time comparison test, wherein the test experiments are divided into two groups, the first group is SO planning based on multiple groups of historical prediction data, the test samples gradually rise along with the test times, the total number of the experiments is 8, and 100 historical wind power prediction scenes are added in each experiment. The second set of experiments performed 8 replicates of the WRO algorithm and observed fluctuations in the algorithm run time, the results of which are shown in fig. 4.
According to the algorithm running time result, the following steps are carried out: the SO algorithm is increased along with the increase of wind power prediction scenes, the operation time is increased along with the increase of the wind power prediction scenes, and the SO algorithm cannot well meet model requirements under the conditions of considering algorithm simplicity and scheduling equipment working convenience in order to obtain more economic function reliable solutions meeting more wind power scenes and increase the number of wind power prediction scenes and along with the increase of the operation time cost of the algorithm. On the contrary, the WRO algorithm repeated experiment has extremely small fluctuation along with the rising of the test times, so that the operation of the dispatching equipment is convenient, and on the other hand, the stability of the solving time of the dispatching scheme also proves the stability of the RO algorithm.
Corresponding to the foregoing embodiments of a robust optimal scheduling method for electric-gas integrated energy distribution, the present application also provides embodiments of an apparatus for robust optimal scheduling for electric-gas integrated energy distribution.
FIG. 5 is a block diagram illustrating an electro-pneumatic integrated energy distribution robust optimization scheduling apparatus in accordance with an exemplary embodiment. Referring to fig. 5, the apparatus is applied to an electricity-gas integrated energy system, and includes:
the building module 21 is used for building a wind power output distribution fuzzy set based on empirical distribution according to historical wind power output data through Wasserstein distance;
specifically, this module includes the following sub-modules:
the calculation submodule is used for calculating to obtain a wind power prediction error set according to historical data of the wind power plant;
the first construction submodule is used for constructing experience distribution according to the wind power prediction error set;
and the second construction submodule is used for constructing a wind power output distribution fuzzy set based on empirical distribution through the Wasserstein distance according to the empirical distribution.
The preprocessing module 22 is used for preprocessing the wind power output distribution fuzzy set;
the modeling module 23 is configured to establish an optimized scheduling model of the electricity-gas integrated energy system according to the preprocessed wind power output distribution fuzzy set;
specifically, this module includes the following sub-modules:
the modeling submodule is used for establishing an optimized dispatching model of the electricity-gas integrated energy system according to the output, the start and stop, the standby cost PC, the output of the natural gas well, the output of the natural gas unit, the standby cost GC and the system operation cost IL as follows:
Figure BDA0003360637030000201
wherein P is the real wind power prediction error distribution,
Figure BDA0003360637030000202
(ii) is the empirical distribution;
the expressions of the power system unit output, the start-stop, the standby cost PC and the natural gas well output, the natural gas unit output, the standby cost GC and the system operation cost IL are as follows:
Figure BDA0003360637030000203
wherein NCU and NGU are respectively coal-fired and gas-fired unit sets, GS is natural gas well set, Ii,t、ui,t、 vi,tThe variable refers to 0-1 variable of the running and starting and stopping states of the unit i at the moment t,
Figure RE-GDA0003415600960000204
respectively rotates up and down for standby at the time t,
Figure RE-GDA0003415600960000205
is the first stage planning output of the unit and uses the linear piecewise cost
Figure RE-GDA0003415600960000206
Is expressed in the form of SWsp,tRefers to the gas production rate of a natural gas well sp in the t period, Pi,tThe actual output of the unit in the readjustment stage, H and M respectively refer to the set of nodes of the power grid and the air grid,
Figure RE-GDA0003415600960000211
and
Figure RE-GDA0003415600960000212
respectively the more limited the power grid tide and the more limited the gas grid tide, ciFor each operating state of the unit, rhogFor natural gas production, paAnd adjusting the penalty coefficient for the unit.
The solving module 24 is used for solving the optimized scheduling model through an affine rule and a dual theorem to obtain an optimized scheduling result;
specifically, this module includes the following sub-modules:
a transformation submodule, for transforming the optimized scheduling model into a compact form, as follows:
Figure BDA0003360637030000213
s.t x∈Ω
wherein a is a cost coefficient matrix in a first-stage objective function of the optimized scheduling model, ξ is a matrix formed by uncertain variables in the optimized scheduling model, x is a first-stage variable of the optimized scheduling model, Ω is a feasible domain of the first-stage variable of the optimized scheduling model, and f (x, ξ) is a second-stage function of the optimized scheduling model;
an expansion submodule, configured to expand the second stage function according to an affine rule:
f(x,ξ)＝inf gTω
Figure BDA0003360637030000214
Aξ+Gω≤b(x)
g is a cost coefficient matrix in a second-stage objective function of the optimized scheduling model, ω is a set of second-stage readjustment variables, l is the number of second-stage variables, η is the dimension of an uncertain vector ζ, and a, b and G are parameter matrices corresponding to constraint conditions of the second-stage function;
a transformation submodule for optimizing the compact form by strong dual theorem according to the expanded second stage functionIn a scheduling model
Figure BDA0003360637030000215
Conversion is carried out to give the following formula:
Figure BDA0003360637030000221
Figure BDA0003360637030000222
Figure BDA0003360637030000223
Figure BDA0003360637030000224
Figure BDA0003360637030000225
Figure BDA0003360637030000226
Figure BDA0003360637030000227
in the formula: upsilon, pi, y and z are dual variables, K is the number of sampling samples,
Figure BDA0003360637030000228
expressing a positive one-dimensional number, ζKFor wind-electric prediction error, σηAuxiliary vector, iota, representing that only the η -th term is 1 and the other terms are all zero+And iota-Measuring parameters of the wind power output distribution fuzzy set;
and the solving submodule is used for solving the optimized scheduling model processed by the affine rule and the strong dual theorem to obtain an optimized scheduling result.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
For the device embodiments, since they substantially correspond to the method embodiments, reference may be made to the partial description of the method embodiments for relevant points. The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of the application. One of ordinary skill in the art can understand and implement it without inventive effort.
Correspondingly, the present application also provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; when executed by the one or more processors, cause the one or more processors to implement an electro-pneumatic integrated energy distribution robust optimized scheduling method as described above.
Accordingly, the present application also provides a computer readable storage medium having stored thereon computer instructions, wherein the instructions, when executed by a processor, implement the electro-pneumatic energy distribution robust optimization scheduling method as described above.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It will be understood that the present application is not limited to the precise arrangements and instrumentalities shown in the drawings and described above, and that various modifications and changes can be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims (10)

1. The robust optimization scheduling method for the distribution of the electricity-gas comprehensive energy is characterized by being applied to an electricity-gas comprehensive energy system and comprising the following steps of:
according to historical wind power output data, a wind power output distribution fuzzy set based on empirical distribution is constructed through Wasserstein distance;
preprocessing the wind power output distribution fuzzy set;
establishing an optimized dispatching model of the electricity-gas integrated energy system according to the preprocessed wind power output distribution fuzzy set;
and solving the optimized scheduling model through an affine rule and a dual theorem to obtain an optimized scheduling result.
2. The method of claim 1, wherein constructing an empirical distribution-based fuzzy set of wind power output distributions from historical wind power output data via Wasserstein distance comprises:
calculating to obtain a wind power prediction error set according to historical data of a wind power plant;
establishing experience distribution according to the wind power prediction error set;
and according to the empirical distribution, constructing a wind power output distribution fuzzy set based on the empirical distribution through the Wasserstein distance.
3. The method of claim 1, wherein establishing an optimized dispatching model of the electric-gas integrated energy system according to the preprocessed fuzzy set of wind power output distribution comprises:
establishing an optimized dispatching model of the electricity-gas integrated energy system according to the output, the start and stop, the standby cost PC, the output of the natural gas well, the output of the natural gas unit, the standby cost GC and the system operation cost IL of the power system unit as follows:
Figure RE-FDA0003415600950000011
wherein P is the real wind power prediction error distribution,
Figure RE-FDA0003415600950000012
(ii) is the empirical distribution;
the expressions of the power system unit output, the start-stop, the standby cost PC and the natural gas well output, the natural gas unit output, the standby cost GC and the system operation cost IL are as follows:
Figure RE-FDA0003415600950000021
wherein NCU and NGU are respectively coal-fired and gas-fired unit sets, GS is natural gas well set, Ii,t、ui,t、vi,tThe variable refers to 0-1 variable of the running and starting and stopping states of the unit i at the moment t,
Figure RE-FDA0003415600950000022
respectively rotates up and down for standby at the time t,
Figure RE-FDA0003415600950000023
is the first stage planning output of the unit and uses the linear piecewise cost
Figure RE-FDA0003415600950000024
Is expressed in the form of SWsp,tRefers to the gas production rate of a natural gas well sp in the t period, Pi,tThe actual output of the unit in the readjustment stage, H and M respectively refer to the set of nodes of the power grid and the air grid,
Figure RE-FDA0003415600950000025
and
Figure RE-FDA0003415600950000026
respectively the more limited the power grid flow and the more limited the gas grid flow, ciFor each operating state of the unit, rhogFor natural gas production, paAnd adjusting the penalty coefficient for the unit.
4. The method of claim 1, wherein solving the optimized scheduling model through affine rules and strong dual theorem to obtain an optimized scheduling result comprises:
converting the optimized scheduling model into a compact form as follows:
Figure FDA0003360637020000027
s.t x∈Ω
wherein a is a cost coefficient matrix in a first-stage objective function of the optimized scheduling model, ξ is a matrix formed by uncertain variables in the optimized scheduling model, x is a first-stage variable of the optimized scheduling model, Ω is a feasible domain of the first-stage variable of the optimized scheduling model, and f (x, ξ) is a second-stage function of the optimized scheduling model;
expanding the second stage function through an affine rule:
Figure FDA0003360637020000028
Figure FDA0003360637020000029
Aξ+Gω≤b(x)
g is a cost coefficient matrix in a second-stage objective function of the optimized scheduling model, ω is a set of readjustment variables in the second stage, l is the number of variables in the second stage, η is the dimension of an uncertain vector ζ, and a, b and G are parameter matrices corresponding to constraint conditions of the second-stage function;
according to the expanded second-stage function, the optimized scheduling model in a compact form is subjected to strong dual theorem
Figure FDA0003360637020000031
Conversion is carried out to give the following formula:
Figure FDA0003360637020000032
Figure FDA0003360637020000033
Figure FDA0003360637020000034
Figure FDA0003360637020000035
Figure FDA0003360637020000036
Figure FDA0003360637020000037
Figure FDA0003360637020000038
in the formula: upsilon, pi, y and z are dual variables, K is the number of sampling samples,
Figure FDA0003360637020000039
expressing a positive one-dimensional number, ζKFor wind power prediction error, σηAuxiliary vector, iota, representing that only the η -th term is 1 and the other terms are all zero+And iota-Measuring parameters of the wind power output distribution fuzzy set;
and solving the optimized scheduling model processed by the affine rule and the strong dual theorem to obtain an optimized scheduling result.
5. An electric-gas integrated energy distribution robust optimization scheduling device is applied to an electric-gas integrated energy system and comprises the following components:
the building module is used for building a wind power output distribution fuzzy set based on empirical distribution according to historical wind power output data through Wasserstein distance;
the preprocessing module is used for preprocessing the wind power output distribution fuzzy set;
the modeling module is used for establishing an optimized dispatching model of the electricity-gas integrated energy system according to the preprocessed wind power output distribution fuzzy set;
and the solving module is used for solving the optimized scheduling model through an affine rule and a dual theorem to obtain an optimized scheduling result.
6. The apparatus of claim 5, wherein the building module comprises:
the calculation submodule is used for calculating to obtain a wind power prediction error set according to historical data of the wind power plant;
the first construction submodule is used for constructing experience distribution according to the wind power prediction error set;
and the second construction submodule is used for constructing a wind power output distribution fuzzy set based on empirical distribution through the Wasserstein distance according to the empirical distribution.
7. The apparatus of claim 5, wherein the modeling module comprises:
the modeling submodule is used for establishing an optimized dispatching model of the electricity-gas integrated energy system according to the output, the start and stop, the standby cost PC, the output of the natural gas well, the output of the natural gas unit, the standby cost GC and the system operation cost IL as follows:
Figure RE-FDA0003415600950000041
wherein P is the real wind power prediction error distribution,
Figure RE-FDA0003415600950000042
(ii) is the empirical distribution;
the expressions of the power system unit output, the start-stop, the standby cost PC and the natural gas well output, the natural gas unit output, the standby cost GC and the system operation cost IL are as follows:
Figure RE-FDA0003415600950000043
wherein NCU and NGU are respectively coal-fired and gas-fired unit sets, GS is natural gas well set, Ii,t、ui,t、vi,tThe variable refers to 0-1 variable of the running and starting and stopping states of the unit i at the moment t,
Figure RE-FDA0003415600950000044
respectively rotates up and down for standby at the time t,
Figure RE-FDA0003415600950000045
is the first stage planning output of the unit and uses the linear piecewise cost
Figure RE-FDA0003415600950000046
Is expressed in the form of SWsp,tRefers to the gas production rate of a natural gas well sp in the t period, Pi,tIs the actual output of the unit at the readjustment stage,h and M refer to the set of grid and gas grid nodes respectively,
Figure RE-FDA0003415600950000047
and
Figure RE-FDA0003415600950000048
respectively the more limited the power grid flow and the more limited the gas grid flow, ciFor each operating state of the unit, rhogFor natural gas production, paAnd adjusting the penalty coefficient for the unit.
8. The method of claim 1, wherein the solution module comprises:
a transformation submodule, for transforming the optimized scheduling model into a compact form, as follows:
Figure FDA0003360637020000049
s.t x∈Ω
wherein a is a cost coefficient matrix in a first-stage objective function of the optimized scheduling model, ξ is a matrix formed by uncertain variables in the optimized scheduling model, x is a first-stage variable of the optimized scheduling model, Ω is a feasible domain of the first-stage variable of the optimized scheduling model, and f (x, ξ) is a second-stage function of the optimized scheduling model;
an expansion submodule, configured to expand the second stage function according to an affine rule:
Figure FDA0003360637020000051
Figure FDA0003360637020000052
Aξ+Gω≤b(x)
g is a cost coefficient matrix in a second-stage objective function of the optimized scheduling model, ω is a set of readjustment variables in the second stage, l is the number of variables in the second stage, η is the dimension of an uncertain vector ζ, and a, b and G are parameter matrices corresponding to constraint conditions of the second-stage function;
a conversion submodule for optimizing the scheduling model in a compact form by strong dual theorem according to the expanded second stage function
Figure FDA0003360637020000053
Conversion is carried out to give the following formula:
Figure FDA0003360637020000054
Figure FDA0003360637020000055
Figure FDA0003360637020000056
Figure FDA0003360637020000057
Figure FDA0003360637020000058
Figure FDA0003360637020000059
Figure FDA00033606370200000510
in the formula: upsilon, pi, y and z are dual variables, K is the number of sampling samples,
Figure FDA00033606370200000511
expressing a positive one-dimensional number, ζKFor wind power prediction error, σηAuxiliary vector, iota, representing that only the η -th term is 1 and the other terms are all zero+And iota-Measuring parameters of the wind power output distribution fuzzy set;
and the solving submodule is used for solving the optimized scheduling model processed by the affine rule and the strong dual theorem to obtain an optimized scheduling result.
9. An electronic device, comprising:
one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-4.
10. A computer-readable storage medium having stored thereon computer instructions, which, when executed by a processor, carry out the steps of the method according to any one of claims 1 to 4.
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Cited By (2)

* Cited by examiner, † Cited by third party
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CN116541968A (en) * 2023-06-28 2023-08-04 南京航空航天大学 Determination method of ground-moon DRO optimal transfer orbit
CN116780649A (en) * 2023-06-16 2023-09-19 国网浙江省电力有限公司嘉兴供电公司 Multi-energy complementary utilization distributed robust optimization operation method

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116780649A (en) * 2023-06-16 2023-09-19 国网浙江省电力有限公司嘉兴供电公司 Multi-energy complementary utilization distributed robust optimization operation method
CN116780649B (en) * 2023-06-16 2024-03-01 国网浙江省电力有限公司嘉兴供电公司 Multi-energy complementary utilization distributed robust optimization operation method
CN116541968A (en) * 2023-06-28 2023-08-04 南京航空航天大学 Determination method of ground-moon DRO optimal transfer orbit
CN116541968B (en) * 2023-06-28 2023-09-22 南京航空航天大学 Determination method of ground-moon DRO optimal transfer orbit

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