CN108832665B - Electric heating integrated system distributed robust coordination optimization scheduling modeling method considering wind power uncertainty - Google Patents

Electric heating integrated system distributed robust coordination optimization scheduling modeling method considering wind power uncertainty Download PDF

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CN108832665B
CN108832665B CN201810724286.6A CN201810724286A CN108832665B CN 108832665 B CN108832665 B CN 108832665B CN 201810724286 A CN201810724286 A CN 201810724286A CN 108832665 B CN108832665 B CN 108832665B
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高红均
税月
刘友波
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Sichuan 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
    • H02J3/48—Controlling the sharing of the in-phase component
    • 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
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • 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
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A30/00—Adapting or protecting infrastructure or their operation
    • Y02A30/60—Planning or developing urban green infrastructure
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/76—Power conversion electric or electronic aspects
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin

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Abstract

The invention discloses an electric heating integrated system distributed robust coordination optimization scheduling model considering wind power uncertainty, which is used for constructing an electric heating integrated system taking a cogeneration unit and an electric boiler as coupling units, considering wind power output uncertainty and based on wind power historical data available for a scheduling system. The invention divides the model into the main problem and the sub problem by using the decomposition algorithm to carry out repeated iteration solution, and can accelerate the solution speed. The solving problem is converted into a mixed integer programming problem by performing equivalent conversion on the absolute value constraint condition, and the CPLEX of the conventional solving tool kit is adopted for effective solving.

Description

Electric heating integrated system distributed robust coordination optimization scheduling modeling method considering wind power uncertainty
Technical Field
The invention relates to the technical field of distributed robust coordinated optimization scheduling models of an electric heating integrated system, in particular to a distributed robust coordinated optimization scheduling modeling method of the electric heating integrated system considering wind power uncertainty.
Background
At present, along with the continuous access of the thermoelectric coupling element, the connection among electric heating systems is increasingly tight, and the method has very important practical significance for the coordination and optimization of an electric heating comprehensive system containing devices such as a cogeneration unit, an electric boiler and the like; meanwhile, after the large-scale clean energy is accessed into the power system, the uncertainty of the output of the large-scale clean energy brings new challenges to the electric heating comprehensive system; at present, most of uncertainty methods for an electric heating integrated system containing wind power are traditional random planning methods and robust optimization methods, but both of the two uncertainty processing methods have limitations to a certain extent; the stochastic programming method is characterized mainly through probability description, then a large number of discrete samples are generated, the calculated amount is often large, accurate probability distribution is difficult to obtain in practice, and the robust optimization method is expressed through boundary parameters of uncertain parameters, so that the problem that the optimization decision result is too conservative exists; therefore, the invention provides a data-driven distributed robust optimization method combining random planning and robust optimization, which establishes a 1-norm and infinity-norm set for constraint by extracting a large amount of available wind power historical data existing in an actual scheduling system to constrain an uncertainty probability distribution set and acquire all probability distribution functions meeting parameter information, thereby searching a robust decision scheme of a worst expected target in all distributions meeting the conditions.
Disclosure of Invention
The invention aims to provide a distributed robust coordinated optimization scheduling modeling method of an electric heating integrated system considering wind power uncertainty, which constructs an electric heating integrated system taking a cogeneration unit and an electric boiler as a coupling unit, considers wind power output uncertainty, and takes wind power historical data available for the scheduling system as a basis to form a two-stage distributed robust coordinated optimization scheduling model, wherein the model designs related variables of a heat storage device and a power storage device as first-stage robust variables and designs other variables as second-stage variables, corresponding flexible adjustment is carried out according to actual wind power output data, the first stage of the model comprises the startup and shutdown costs and the operating costs of the conventional unit, the operating costs and the wind abandoning costs of the cogeneration unit, compared with the combined model of the conventional robust optimization unit, the second stage of the model comprehensively considers 1-norm and infinity-norm constraints to determine a wind power output probability distribution confidence set, the method is used for optimizing the wind power output under the worst probability scene distribution, and meanwhile, in the second-stage solution of the model, the absolute value constraint equivalent linearization of the norm constraint condition is carried out by introducing auxiliary variables, so that the solution is carried out.
Preferably, the two-stage distributed robust coordination optimization scheduling model based on data driving of the requirement 1 is firstly established according to an electric heating integrated system target function, a power grid side constraint condition, a heat supply network side constraint condition and an electric heating coordination device constraint condition, and then is established according to wind power historical output data available for the scheduling system, wind power output uncertainty is considered, and the two-stage distributed robust coordination scheduling model under data driving is established;
the electric heating integrated system coordination model objective function is as follows:
min(F1+F2+F3)
F1＝F11+F12
Figure GDA0003172873420000021
Figure GDA0003172873420000022
Figure GDA0003172873420000023
Figure GDA0003172873420000024
in the formula, F1 is a power generation cost function of a conventional unit, F2 is a power generation cost function of a cogeneration unit, and F3 is a wind curtailment cost; t is the total number of time periods; NG represents the number of conventional units; sTi、SDiRespectively the starting-up and stopping costs of the conventional unit i; i isi,tThe mark is a start-up and shut-down mark, the mark is 1 to represent a start-up state, and the mark is 0 to represent a shut-down state; a isi、bi、ciIs the coefficient of the secondary power generation cost function of the generator set i; pi,tThe active power output of the conventional unit i in the t-th time period; NC represents the number of cogeneration units;
Figure GDA0003172873420000025
is the equivalent power generation cost coefficient of the cogeneration unit i;
Figure GDA0003172873420000031
respectively representing the electric power output and the thermal power output of the ith cogeneration unit; NW represents the number of wind turbines; delta represents a wind curtailment penalty coefficient;
Figure GDA0003172873420000032
respectively representing the predicted output and the actual dispatching output of the ith fan at the time t;
the grid side constraints include:
electric power balance constraint:
Figure GDA0003172873420000033
in the formula, NESS represents the number of electric storage devices;
Figure GDA0003172873420000034
for the ith electric storage device charge and discharge power at time t,
Figure GDA0003172873420000035
indicating that the electric storage device is discharging,
Figure GDA0003172873420000036
indicating charging of the electrical storage device; pt LThe total electric load power of the system in a t period; neb represents the number of electric boilers;
Figure GDA0003172873420000037
the active power consumed by the ith electric boiler at the t moment is represented;
conventional units and cogeneration units electrical power constraints:
Ii,tPi,min≤Pi,t≤Ii,tPi,max
Figure GDA0003172873420000038
in the formula, Pi,minAnd Pi,maxRespectively the lower limit and the upper limit of the electric power of the ith conventional unit;
Figure GDA0003172873420000039
and
Figure GDA00031728734200000310
respectively the lower limit and the upper limit of the electric power of the ith cogeneration unit;
conventional unit and combined heat and power generation unit climbing restraint:
Figure GDA00031728734200000311
Figure GDA00031728734200000312
in the formula, RUi、RDiThe climbing speed and the landslide speed of the conventional unit i are respectively;
Figure GDA00031728734200000313
and
Figure GDA00031728734200000314
the climbing speed and the landslide speed of the cogeneration unit i are respectively;
conventional unit minimum on-off time constraint:
Figure GDA0003172873420000041
Figure GDA0003172873420000042
Figure GDA0003172873420000043
t＝TUi+1,TUi+2,...,T-Ti on+1
Figure GDA0003172873420000044
t＝T-Ti on+2,T-Ti on+3,...T
Figure GDA0003172873420000045
Figure GDA0003172873420000046
Figure GDA0003172873420000047
t＝TDi+1,TDi+2,...,T-Ti off+1
Figure GDA0003172873420000048
t＝T-Ti off+2,T-Ti off+3,...T
in the formula, TUi、TDiRespectively representing the time periods of the unit which must be started and stopped at the initial stage of the dispatching period; t isi on、Ti offRespectively representing the minimum starting time and the minimum stopping time of the unit;
Figure GDA0003172873420000049
respectively continuous start-up and shut-down time of the unit i at the t moment;
restraint of the electric energy storage device:
Figure GDA00031728734200000410
Figure GDA00031728734200000411
Figure GDA00031728734200000412
Figure GDA00031728734200000413
Figure GDA00031728734200000414
Figure GDA00031728734200000415
in the formula (I), the compound is shown in the specification,
Figure GDA00031728734200000416
indicating the state of charge at the moment of the ith power storage device t,
Figure GDA00031728734200000417
in the discharge state at the moment of the ith power storage device t,
Figure GDA00031728734200000418
respectively representing the discharge power, the charge power and the electric quantity of the ith electric storage device at the time t; alpha is alphadAnd alphacRespectively representing the discharge and charge coefficients,
Figure GDA0003172873420000051
and
Figure GDA0003172873420000052
the lower limit and the upper limit of the capacity of the ith power storage device are respectively set;
electric power constraint for electric boilers:
Figure GDA0003172873420000053
in the formula (I), the compound is shown in the specification,
Figure GDA0003172873420000054
indicating the rated power of the ith electric boiler;
wind power constraint:
Figure GDA0003172873420000055
and (3) direct current power flow constraint:
Figure GDA0003172873420000056
Figure GDA0003172873420000057
Figure GDA0003172873420000058
in the formula (I), the compound is shown in the specification,
Figure GDA0003172873420000059
upper limit of branch power, Pt、Pt w、Pt chp、Pt ESS、Pt LAnd Pt ebRespectively representing vector representation forms of active power of each conventional unit, each wind turbine, each cogeneration unit, each energy storage device, each load demand and each electric boiler in the t-th period in the total node dimension of the system;
the heat grid side constraints include:
heat supply network power balance constraint:
Figure GDA00031728734200000510
in the formula (I), the compound is shown in the specification,
Figure GDA00031728734200000511
indicating the heating power of the ith electric boiler, Ncr indicating the number of heat storage devices,
Figure GDA00031728734200000512
indicating the heat storage power of the ith heat storage device at the t moment,
Figure GDA00031728734200000513
representing the total heat load power of the system at the moment t;
and (3) heat supply power constraint of the cogeneration unit:
Figure GDA00031728734200000514
in the formula (I), the compound is shown in the specification,
Figure GDA00031728734200000515
and
Figure GDA00031728734200000516
the lower limit and the upper limit of the thermal power of the ith cogeneration unit;
thermal storage device restraint:
Figure GDA00031728734200000517
Figure GDA00031728734200000518
Figure GDA0003172873420000061
Figure GDA0003172873420000062
Figure GDA0003172873420000063
Figure GDA0003172873420000064
in the formula (I), the compound is shown in the specification,
Figure GDA0003172873420000065
showing the heat storage state at the moment of the ith heat storage device t,
Figure GDA0003172873420000066
showing the heat release state at the moment of the ith heat storage unit t,
Figure GDA0003172873420000067
respectively showing the heat storage power, the heat release power and the heat storage capacity of the heat storage device at the time t,
Figure GDA0003172873420000068
respectively showing the lower limit and the upper limit of the capacity of the ith heat storage device;
the electric heating coordination device constraint conditions comprise:
constraint of electric-thermal coupling relation of cogeneration unit:
Figure GDA0003172873420000069
in the formula (I), the compound is shown in the specification,
Figure GDA00031728734200000610
representing the heat-electricity ratio of the cogeneration unit;
electric boiler device electric heat coupling relation restraint:
Figure GDA00031728734200000611
in the formula, eta represents the heating efficiency of the electric boiler;
the distributed robust optimization scheduling model driven by the data is a two-stage optimization model, the first stage of the model sets the starting and stopping state of a conventional unit, an electricity storage device and a heat storage device as robust variables and does not change along with the uncertainty of the wind power output, the second stage of the model sets the output of the unit, the wind power output, an electric boiler and other devices as second-stage variables which can be correspondingly and flexibly adjusted according to the actual wind power output,
the two-stage distributed robust optimization scheduling model under the data driving is expressed in a matrix form as follows:
Figure GDA00031728734200000612
Figure GDA00031728734200000613
s.t.Ax≤d
Bx＝e
Cy≤Dξ
Gx+Hy≤g
Jx+Ky＝h
wherein x represents a first stage variable, y represents a second stage variable, aTx represents the startup and shutdown cost F11，bTy+cTXi represents the cost F12，F2And F3The method comprises the following steps that Ax is not more than D, Bx is not more than e and represents all start-stop constraints, electric storage device constraints and heat storage device constraints, Cy is not more than D xi represents the constraint relation between decision variables in the second stage and wind power predicted output vectors, xi represents the wind power predicted output vectors, Gx + Hy is not more than g, Jx + Ky is h represents the coupling relation between variables in the first stage and variables in the second stage, the model screens limited K discrete scene sets from actual M samples according to wind power historical data to represent wind power output possible values, and p represents wind power output possible valueskRepresenting the probability value of the Kth scene, and constructing two sets of 1-norm and infinity-norm to limit the value of the scene probability value, wherein the uncertainty probability confidence set is as follows:
Figure GDA0003172873420000071
in the formula, theta1And theta∞The probabilistic deviation values are represented.
Preferably, the solution method of the two-stage model is as follows:
the two-stage model is a three-layer optimization problem, the CCG algorithm in the decomposition algorithm is adopted to decompose the model into a main problem and a sub problem for iterative solution, and iteration is stopped until a specified precision value is met;
the model main problem is the optimal solution that satisfies the conditions under the known finite bad probability distribution, provides a lower limit value for the two-stage model,
Figure GDA0003172873420000072
Figure GDA0003172873420000073
the model subproblem finds the worst probability distribution condition under the condition of a first-stage variable x obtained by calculation of the main problem, so that the worst probability distribution condition is provided for the main problem, the next iteration is carried out, and an upper bound value is provided for the two-stage model;
Figure GDA0003172873420000081
the inner-layer min optimization problems under each scene of the subproblems are independent and linear programming problems, and can be processed simultaneously by adopting a parallel method to accelerate the solving speed, and a variable x is assumed to be in a given first stage*Then, the inner layer optimization target value obtained in the k scene is f (x)*,ξk) Then the sub-problem can be rewritten as:
Figure GDA0003172873420000082
the probability confidence interval constraint conditions in the two-stage model comprise absolute value constraint conditions, and 0-1 auxiliary variables are introduced
Figure GDA0003172873420000083
And
Figure GDA0003172873420000084
respectively representing the probability pkRelative to each other
Figure GDA0003172873420000085
Positive and negative offset flags:
Figure GDA0003172873420000086
Figure GDA0003172873420000087
Figure GDA0003172873420000088
Figure GDA0003172873420000089
therefore, the original absolute value constraint condition is equivalently converted into:
Figure GDA00031728734200000810
Figure GDA00031728734200000811
therefore, the two-stage distributed robust model is equivalently converted into a hybrid linear programming problem.
Preferably, the solving step is:
(1) setting LB as 0, UB as + ∞, n as 1;
(2) solving CCG main problem to obtain optimal decision result
Figure GDA00031728734200000812
Updating the lower bound value
Figure GDA00031728734200000813
(3) Fixing, solving the CCG subproblem to obtain the optimal solution and the optimal objective function value, and updating the upper bound value
Figure GDA00031728734200000814
If (UB-LB), stopping iteration and returning to the optimal solution; otherwise, updating the bad probability distribution of the main problem, defining a new variable in the main problem and adding a constraint related to the new variable;
(4) and updating n to n +1, and returning to the step (2). .
Compared with the prior art, the invention has the beneficial effects that: the invention divides the model into the main problem and the sub problem by using the decomposition algorithm to carry out repeated iteration solution, and can accelerate the solution speed. The solving problem is converted into a mixed integer programming problem by performing equivalent conversion on the absolute value constraint condition, and the CPLEX of the conventional solving tool kit is adopted for effective solving.
Drawings
FIG. 1 is a block diagram of an electric heat coordination complex system of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1
The invention provides a technical scheme that: referring to fig. 1, fig. 1 is a block diagram of an electric heat coordination integrated system according to the present invention; the model comprises a conventional unit, a wind turbine generator, an electricity storage device, an electric load and the like on the side of an electric power system, a heat storage device, a heat load and the like on the side of a thermodynamic system, and an electric and thermal coupling device such as a cogeneration unit and an electric boiler.
As shown in fig. 1, the model takes the lowest total cost of the electric-thermal integrated system as a target function, and includes constraints such as power balance at the power grid side, unit climbing rate, upper and lower unit output limits, minimum unit on-off time, charging and discharging power constraints of a power storage device, electric power constraints of an electric boiler, wind power output constraints and direct current power flow constraints, thermal power balance at the heat grid side, thermal power constraints of a cogeneration unit, power constraints of a heat storage device, and electric-thermal output coupling relations of the cogeneration unit and the electric boiler device.
According to one embodiment of the application, the two-stage distributed robust optimization model based on data driving is applied to an IEEE-39 node system for verification, wherein nodes 30 and 31 are connected to a cogeneration unit, and nodes 37 and 39 are connected to a conventional unit and a wind generating unit;
according to an embodiment of the application, the two-stage distributed robust optimization model based on the data driving is compared under different numbers of historical data, different confidence intervals and other uncertainty methods, and the results are shown in tables 1, 2 and 3:
TABLE 1 comparison of number results of different historical data
Figure GDA0003172873420000101
The comparison shows that the number of historical data is inversely proportional to the cost of the algorithm.
TABLE 2 comparison of results at different confidence intervals
Figure GDA0003172873420000102
The comparison result shows the confidence level alpha1、α∞The total cost value increases.
TABLE 3 comparison of results of different uncertainty methods
Figure GDA0003172873420000103
The comparison compares the two-stage distributed robust optimization algorithm with the traditional random planning method and the robust optimization algorithm, and the result shows that the cost of the model is higher than that of random planning in the first-stage optimization, that more units are called and the uncertainty of wind power output is stabilized, and that the model has stronger robustness; compared with a robust optimization method, the model is lower in cost and better in economy; in the second stage of optimization, the cost expectation value of the model is lower, and the economy is better.
The model construction of the invention can be summarized as a two-stage distributed robust coordination optimization scheduling model which takes a cogeneration unit and an electric boiler as coupling units, considers the uncertainty of wind power output and takes wind power historical data available for a scheduling system as the basis. According to the model, the startup and shutdown states of a conventional unit, the related variables of the heat storage device and the electricity storage device are designed into first-stage robust variables, the other variables are designed into second-stage variables, and corresponding flexible adjustment is performed according to actual wind power output data. Compared with a conventional robust optimization unit combination model, the model can give the day-ahead dispatching output of the unit, and predicted wind power output information is fused into a first-stage objective function, so that the dispatching output is more economical. And in the second stage of the model, 1-norm and infinity-norm constraints are comprehensively considered to determine a confidence set of the wind power output probability distribution for optimizing the wind power output under the worst probability scene distribution. Meanwhile, in the second-stage solution of the model, the absolute value constraint of the norm constraint condition is equivalently linearized by introducing auxiliary variables, so that the solution is carried out.
The method mainly comprises the steps of establishing an electric heating comprehensive system model, solving algorithm of the model and the steps.
When the comprehensive system model is built, the wind power output uncertainty is considered from the aspects of a power grid side, a heat supply network side and a power grid coupling device, the model which takes the lowest cost of the comprehensive system as a target function and takes electric power balance, upper and lower unit output limits, climbing rate, thermal power balance, an electric-thermal coupling device and the like as constraint conditions is built, the air abandoning amount in the system is reduced as much as possible, and the electric-thermal comprehensive system is coordinated, optimized and scheduled to run.
And (3) solving the model: the method adopts a CCG decomposition algorithm, divides a model into a main problem and a sub problem, and solves the main problem and the sub problem by transferring parameters and continuously iterating until a given precision value is met, and then stops iterating; in the solution of the model, the contained absolute value constraint conditions are subjected to equivalent transformation by adding 0-1 auxiliary variables, the solution problem is transformed into a mixed integer programming problem, and the solution is rapidly carried out.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.

Claims (3)

1. A distributed robust coordination optimization scheduling modeling method of an electric heating integrated system considering wind power uncertainty is characterized in that an electric heating integrated system taking a cogeneration unit and an electric boiler as coupling units is constructed, wind power output uncertainty is considered, a two-stage distributed robust coordination optimization scheduling model based on wind power historical data available for the scheduling system is constructed, the model designs related variables of a conventional unit in an on-off state, a heat storage device and an electric storage device as first-stage robust variables, the rest variables are second-stage variables, corresponding flexible adjustment is carried out according to actual wind power output data, the first stage of the model comprises the on-off cost and the operation cost of the conventional unit, the operation cost and the wind abandoning cost of the cogeneration unit, compared with the conventional robust optimization unit combination model, the second stage of the model comprehensively considers 1-norm and infinity norm constraints to determine a wind power output probability distribution confidence set, the method is used for optimizing the wind power output under the worst probability scene distribution, and meanwhile, in the second-stage solution of the model, the absolute value constraint equivalent linearization of the norm constraint condition is carried out by introducing auxiliary variables, so that the solution is carried out.
2. The electric heating integrated system distributed robust coordination optimization scheduling modeling method considering wind power uncertainty according to claim 1, characterized in that: the two-stage distributed robust coordinated optimization scheduling model is characterized by firstly establishing an electric heating integrated system coordination model according to an electric heating integrated system target function, a power grid side constraint condition, a heat supply network side constraint condition and an electric heating coordination device constraint condition, and then establishing a two-stage distributed robust coordinated scheduling model under the drive of data by taking wind power output uncertainty into consideration on the basis of wind power historical output data available for a scheduling system;
the electric heating integrated system coordination model objective function is as follows:
min(F1+F2+F3)
F1＝F11+F12
Figure FDA0003172873410000011
Figure FDA0003172873410000012
Figure FDA0003172873410000013
Figure FDA0003172873410000014
in the formula, F1 is a power generation cost function of a conventional unit, F2 is a power generation cost function of a cogeneration unit, and F3 is a wind curtailment cost; t is the total number of time periods; NG represents the number of conventional units; sTi、SDiStarting up and stopping the conventional unit i respectivelyA cost; i isi,tThe mark is a start-up and shut-down mark, the mark is 1 to represent a start-up state, and the mark is 0 to represent a shut-down state; a isi、bi、ciIs the coefficient of the secondary power generation cost function of the generator set i; pi,tThe active power output of the conventional unit i in the t-th time period; NC represents the number of cogeneration units;
Figure FDA0003172873410000021
is the equivalent power generation cost coefficient of the cogeneration unit i;
Figure FDA0003172873410000022
respectively representing the electric power output and the thermal power output of the ith cogeneration unit; NW represents the number of wind turbines; delta represents a wind curtailment penalty coefficient;
Figure FDA0003172873410000023
respectively representing the predicted output and the actual dispatching output of the ith fan at the time t;
the grid side constraints include:
electric power balance constraint:
Figure FDA0003172873410000024
in the formula, NESS represents the number of electric storage devices;
Figure FDA0003172873410000025
for the ith electric storage device charge and discharge power at time t,
Figure FDA0003172873410000026
indicating that the electric storage device is discharging,
Figure FDA0003172873410000027
indicating charging of the electrical storage device; pt LThe total electric load power of the system in a t period; neb represents the number of electric boilers;
Figure FDA0003172873410000028
the active power consumed by the ith electric boiler at the t moment is represented;
conventional units and cogeneration units electrical power constraints:
Ii,tPi,min≤Pi,t≤Ii,tPi,max
Figure FDA0003172873410000029
in the formula, Pi，minAnd Pi，maxRespectively the lower limit and the upper limit of the electric power of the ith conventional unit;
Figure FDA00031728734100000210
and
Figure FDA00031728734100000211
respectively the lower limit and the upper limit of the electric power of the ith cogeneration unit;
conventional unit and combined heat and power generation unit climbing restraint:
Figure FDA00031728734100000212
Figure FDA0003172873410000031
in the formula, RUi、RDiThe climbing speed and the landslide speed of the conventional unit i are respectively;
Figure FDA0003172873410000032
and
Figure FDA0003172873410000033
climbing speed of cogeneration unit iRate, landslide rate;
conventional unit minimum on-off time constraint:
Figure FDA0003172873410000034
Figure FDA0003172873410000035
Figure FDA0003172873410000036
Figure FDA0003172873410000037
Figure FDA0003172873410000038
Figure FDA0003172873410000039
Figure FDA00031728734100000310
Figure FDA00031728734100000311
in the formula, TUi、TDiRespectively representing the time periods of the unit which must be started and stopped at the initial stage of the dispatching period; t isi on、Ti offRespectively representing the minimum starting time and the minimum stopping time of the unit;
Figure FDA00031728734100000312
respectively continuous start-up and shut-down time of the unit i at the t moment;
restraint of the electric energy storage device:
Figure FDA00031728734100000313
Figure FDA00031728734100000314
Figure FDA00031728734100000315
Figure FDA00031728734100000316
Figure FDA00031728734100000317
Figure FDA00031728734100000318
in the formula (I), the compound is shown in the specification,
Figure FDA0003172873410000041
indicating the state of charge at the moment of the ith power storage device t,
Figure FDA0003172873410000042
in the discharge state at the moment of the ith power storage device t,
Figure FDA0003172873410000043
respectively represent the ith bankThe discharging power, the charging power and the electric quantity of the electric device at the moment t; alpha is alphadAnd alphacRespectively representing the discharge and charge coefficients,
Figure FDA0003172873410000044
and
Figure FDA0003172873410000045
the lower limit and the upper limit of the capacity of the ith power storage device are respectively set;
electric power constraint for electric boilers:
Figure FDA0003172873410000046
in the formula (I), the compound is shown in the specification,
Figure FDA0003172873410000047
indicating the rated power of the ith electric boiler;
wind power constraint:
Figure FDA0003172873410000048
and (3) direct current power flow constraint:
Pline＝BdiagLB-1[Pt+Pt w+Pt chp+Pt ESS-Pt L-Pt eb]
Figure FDA0003172873410000049
Figure FDA00031728734100000410
in the formula (I), the compound is shown in the specification,
Figure FDA00031728734100000411
upper limit of branch power, Pt、Pt w、Pt chp、Pt ESS、Pt LAnd Pt ebRespectively representing vector representation forms of active power of each conventional unit, each wind turbine, each cogeneration unit, each energy storage device, each load demand and each electric boiler in the t-th period in the total node dimension of the system;
the heat grid side constraints include:
heat supply network power balance constraint:
Figure FDA00031728734100000412
in the formula (I), the compound is shown in the specification,
Figure FDA00031728734100000413
indicating the heating power of the ith electric boiler, Ncr indicating the number of heat storage devices,
Figure FDA00031728734100000414
indicating the heat storage power of the ith heat storage device at the t moment,
Figure FDA00031728734100000415
representing the total heat load power of the system at the moment t;
and (3) heat supply power constraint of the cogeneration unit:
Figure FDA00031728734100000416
in the formula (I), the compound is shown in the specification,
Figure FDA00031728734100000417
and
Figure FDA00031728734100000418
is the ithThe lower limit and the upper limit of the thermal power of the platform cogeneration unit;
thermal storage device restraint:
Figure FDA0003172873410000051
Figure FDA0003172873410000052
Figure FDA0003172873410000053
Figure FDA0003172873410000054
Figure FDA0003172873410000055
Figure FDA0003172873410000056
in the formula (I), the compound is shown in the specification,
Figure FDA0003172873410000057
showing the heat storage state at the moment of the ith heat storage device t,
Figure FDA0003172873410000058
showing the heat release state at the moment of the ith heat storage unit t,
Figure FDA0003172873410000059
respectively showing the heat storage power, the heat release power and the heat storage capacity of the heat storage device at the time t,
Figure FDA00031728734100000510
respectively showing the lower limit and the upper limit of the capacity of the ith heat storage device;
the electric heating coordination device constraint conditions comprise:
constraint of electric-thermal coupling relation of cogeneration unit:
Figure FDA00031728734100000511
in the formula (I), the compound is shown in the specification,
Figure FDA00031728734100000512
representing the heat-electricity ratio of the cogeneration unit;
electric boiler device electric heat coupling relation restraint:
Figure FDA00031728734100000513
in the formula, eta represents the heating efficiency of the electric boiler;
the distributed robust optimization scheduling model driven by the data is a two-stage optimization model, the first stage of the model sets the starting and stopping state of a conventional unit, the power storage device and the heat storage device as robust variables and does not change along with the uncertainty of the wind power output, the second stage of the model sets the output of the unit, the wind power output and the output of the electric boiler device as second-stage variables which can be correspondingly and flexibly adjusted according to the actual wind power output,
the two-stage distributed robust optimization scheduling model under the data driving is expressed in a matrix form as follows:
Figure FDA00031728734100000514
Figure FDA00031728734100000515
s.t.Ax≤d
Bx＝e
Cy≤Dξ
Gx+Hy≤g
Jx+Ky＝h
wherein x represents a first stage variable, y represents a second stage variable, aTx represents the startup and shutdown cost F11，bTy+cTXi represents the cost F12，F2And F3The method comprises the following steps that Ax is not more than D, Bx is not more than e and represents all start-stop constraints, electric storage device constraints and heat storage device constraints, Cy is not more than D xi represents the constraint relation between decision variables in the second stage and wind power predicted output vectors, xi represents the wind power predicted output vectors, Gx + Hy is not more than g, Jx + Ky is h represents the coupling relation between variables in the first stage and variables in the second stage, the model screens limited K discrete scene sets from actual M samples according to wind power historical data to represent wind power output possible values, and p represents wind power output possible valueskRepresenting the probability value of the Kth scene, and constructing two sets of 1-norm and infinity-norm to limit the value of the scene probability value, wherein the uncertainty probability confidence set is as follows:
Figure FDA0003172873410000061
in the formula, theta1And theta∞The probabilistic deviation values are represented.
3. The electric heating integrated system distributed robust coordination optimization scheduling modeling method considering wind power uncertainty according to claim 2, characterized in that: the solving method of the two-stage distributed robust coordinated optimization scheduling model comprises the following steps:
the two-stage distributed robust coordinated optimization scheduling model is a three-layer optimization problem, a CCG algorithm in a decomposition algorithm is adopted to decompose the model into a main problem and a sub problem for iterative solution, and iteration is stopped until a specified precision value is met;
the main problem of the two-stage distributed robust coordinated optimization scheduling model is that the two-stage distributed robust coordinated optimization scheduling model is an optimal solution which meets the condition under the condition of known limited severe probability distribution, a lower limit value is provided for the two-stage distributed robust coordinated optimization scheduling model,
Figure FDA0003172873410000062
Figure FDA00031728734100000712
the two-stage distributed robust coordinated optimization scheduling model sub-problem finds the worst probability distribution condition under the condition of a first-stage variable x obtained by calculating a main problem, so that the worst probability distribution condition is provided for the main problem, the next iteration is carried out, and an upper bound value is provided for the two-stage distributed robust coordinated optimization scheduling model;
Figure FDA0003172873410000071
the inner-layer min optimization problems under each scene of the subproblems are independent and linear programming problems, and can be processed simultaneously by adopting a parallel method to accelerate the solving speed, and a variable x is assumed to be in a given first stage*Then, the inner layer optimization target value obtained in the k scene is f (x)*,ξk) Then the sub-problem can be rewritten as:
Figure FDA0003172873410000072
the probability confidence interval constraint condition in the two-stage distributed robust coordinated optimization scheduling model contains an absolute value constraint condition, and 0-1 auxiliary variable is introduced
Figure FDA0003172873410000073
And
Figure FDA0003172873410000074
respectively representing the probability pkRelative to each other
Figure FDA0003172873410000075
Positive and negative offset flags:
Figure FDA0003172873410000076
Figure FDA0003172873410000077
Figure FDA0003172873410000078
Figure FDA0003172873410000079
therefore, the original absolute value constraint condition is equivalently converted into:
Figure FDA00031728734100000710
Figure FDA00031728734100000711
therefore, the two-stage distributed robust coordinated optimization scheduling model is equivalently converted into a hybrid linear programming problem.
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