CN113541191A - Multi-time scale scheduling method considering large-scale renewable energy access - Google Patents

Multi-time scale scheduling method considering large-scale renewable energy access Download PDF

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CN113541191A
CN113541191A CN202110831790.8A CN202110831790A CN113541191A CN 113541191 A CN113541191 A CN 113541191A CN 202110831790 A CN202110831790 A CN 202110831790A CN 113541191 A CN113541191 A CN 113541191A
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scheduling
ahead
distribution network
renewable energy
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肖金星
徐冰雁
杨军
鲁小秋
张宇威
孙俭
李勇汇
叶影
周彦
唐丹红
骆国连
曹春
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Wuhan University WHU
State Grid Shanghai Electric Power Co Ltd
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State Grid Shanghai Electric Power Co Ltd
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Abstract

The invention provides a multi-time scale scheduling method considering large-scale renewable energy access, which comprises the following steps: s1, forecasting the day-ahead output upper and lower limits of the renewable energy through the probability distribution of the day-ahead forecasting error of the renewable energy, and constructing a robust uncertain set; s2, constructing a two-stage robust scheduling model of the power distribution network and the multi-microgrid system, wherein the two-stage robust scheduling model comprises a day-ahead stage model and a regulation and control stage model; s3, splitting a day-ahead robust scheduling problem into major and minor problems through a column constraint generation algorithm based on a day-ahead stage model, and iteratively solving to obtain an optimal day-ahead scheduling decision of the power distribution network and the multi-microgrid system in the worst scene; and S4, based on the regulation and control stage model, on the basis of the optimal day-ahead scheduling decision, performing rolling optimization on the day-ahead scheduling of the power distribution network and the multi-microgrid system by adopting a day-ahead rolling optimization algorithm, and realizing multi-time scale scheduling of the power distribution network and the multi-microgrid system.

Description

Multi-time scale scheduling method considering large-scale renewable energy access
Technical Field
The invention relates to the technical field of power scheduling, in particular to a multi-time-scale scheduling method considering large-scale renewable energy access.
Background
With the rapid development and the large-scale application of the renewable energy distributed power generation technology, the micro-grid is receiving wide attention as an effective access mode of a distributed power supply. The micro-grid group coordinates energy management of adjacent distributed units, micro-grids and loads in a region, so that the permeability of a distributed power supply can be improved, and the efficient utilization of renewable energy sources is realized. Due to the fluctuation of the output of the renewable energy sources, the large-scale access of the distributed renewable energy sources can bring challenges to the operation and scheduling of the power distribution network and the multi-microgrid system. In the scheduling research of a power distribution network and multiple micro-grids considering the uncertainty of renewable energy sources, the random optimization is difficult to cope with extreme scenes; the opportunity constraint method needs a large amount of data and is difficult to solve; robust optimization enables scheduling robustness, but its day-ahead decisions are too conservative. In addition, the scheduling models are all scheduling for the day-ahead scale, and because the prediction error of the day-ahead renewable energy is larger than the prediction error in the day, the accuracy of the scheduling models used in actual scheduling is poor, and the scheduling models need to be adjusted on the basis of the day-ahead scheduling.
Disclosure of Invention
The invention aims to provide a multi-time scale scheduling method considering large-scale renewable energy access, which is used for scheduling large-scale renewable energy accessed to a power distribution network and a multi-microgrid system in two scales of day-ahead and day-within. In the day-ahead scheduling, the power distribution network and the multi-microgrid system predict the output of the renewable energy within 24 hours in the future, and system scheduling personnel arrange a system day-ahead scheduling plan according to day-ahead prediction information; due to the fact that the prediction error is large day ahead, day-in-day real-time scheduling is conducted on the basis of day-ahead scheduling in order to accurately reflect the fluctuation of the output of the renewable energy. And the in-day scheduling updates the renewable energy in-day output predicted value in a plurality of time intervals in the future in a rolling mode through ultra-short-term (generally less than 1 hour) prediction, and the scheduling center performs economic scheduling on the real-time output of each controllable unit at the next moment based on the in-day output predicted value.
In order to achieve the above object, the present invention provides a multi-time scale scheduling method considering large-scale renewable energy access, which is suitable for a power distribution network and a multi-microgrid system, and comprises the steps of:
s1, forecasting the day-ahead output upper and lower limits of the renewable energy through the probability distribution of the day-ahead forecasting error of the renewable energy, and constructing a robust uncertain set;
s2, constructing a two-stage robust scheduling model of the power distribution network and the multi-microgrid system; the two-stage robust scheduling model comprises a day-ahead stage model and a regulation and control stage model; the day-ahead stage model makes a day-ahead scheduling decision based on the output of renewable energy predicted day-ahead; the regulation and control stage model identifies the worst scene of the output of the renewable energy sources based on the day-ahead scheduling decision, and corrects the day-ahead scheduling decision aiming at the worst scene;
s3, splitting a day-ahead robust scheduling problem into main and sub problems through a column constraint generation algorithm, and iteratively solving to obtain an optimal day-ahead scheduling decision of the power distribution network and the multi-microgrid system in the worst scene;
and S4, performing rolling optimization on the day-to-day scheduling of the power distribution network and the multi-microgrid system by adopting a day-to-day rolling optimization algorithm on the basis of the optimal day-to-day scheduling decision, and realizing multi-time scale scheduling of the power distribution network and the multi-microgrid system.
Optionally, step S1 includes:
s11, order
Figure BDA0003175849150000021
The maximum positive and negative prediction error of the output of the nth renewable energy source set,
Figure BDA0003175849150000022
predicting an error of the output of the nth renewable energy unit before the day; according to
Figure BDA0003175849150000023
Respectively obtaining the lambda quantile and the 1-lambda quantile of the cumulative probability density function
Figure BDA0003175849150000024
Wherein lambda is a preset value;
s12, representing the uncertainty of the renewable energy contribution as:
Figure BDA0003175849150000025
Figure BDA0003175849150000026
wherein
Figure BDA0003175849150000027
The actual output of the nth renewable energy source unit is obtained;
Figure BDA0003175849150000028
predicting output for the nth renewable energy unit in the day ahead;
s13, order
Figure BDA0003175849150000029
And the method is a robust uncertain set of the nth renewable energy unit.
Optionally, the objective function of the day-ahead stage model is:
Figure BDA00031758491500000210
Figure BDA00031758491500000211
Figure BDA0003175849150000031
wherein i and j represent the node number of the power distribution network; t is the total scheduling time interval; t represents the current scheduling time; ij is a power distribution network line between the ith power distribution network node and the jth power distribution network node; f. ofda1Scheduling cost for the power distribution network and the multi-microgrid system day ahead; f. ofda2For distribution network and many microgrid systemsRegulating and controlling cost; x is a day-ahead scheduling decision variable; y is a regulation decision variable; xi is an uncertain variable; k is the number of the micro-grid; n is a radical ofMGThe number of the micro-grids; ctr、Closs、CIDRRespectively obtaining unit electricity purchasing cost, distribution network loss cost and distribution network node load demand response cost of the slave main network of the distribution network; cre、CesRespectively discarding a punishment coefficient of renewable energy resources and a punishment coefficient of energy storage loss cost for the micro-grid;
Figure BDA0003175849150000032
purchasing power from a main network for the power distribution network;
Figure BDA0003175849150000033
the output of a gas turbine in the kth micro-grid is obtained; i isk,tIs [0,1 ]]The internal variable represents the output state variable of the gas turbine unit in the kth micro-grid; dj,tThe translatable load power of the j distribution network node at the scheduling time t before the demand response;
Figure BDA0003175849150000034
the translatable load power of the j distribution network node at the scheduling time t after the demand response;
Figure BDA0003175849150000035
the renewable energy power is abandoned for the kth micro-grid;
Figure BDA0003175849150000036
the square of the line current of the power distribution network; r isijIs a line resistance; omegaPE、ΩTRRespectively a power distribution network line set and a power distribution network transformer node set;
Figure BDA0003175849150000037
charging power and discharging power for stored energy respectively; a isk、bk、ckRespectively, gas turbine output cost coefficients;
the objective function of the regulation phase model is: realizing distribution network and multi-network under determined day-ahead scheduling decisionRegulation and control cost f of micro-grid system in worst renewable energy output sceneda2And minimum.
Optionally, the constraint conditions of the day-ahead phase model include: a main grid output constraint, a gas turbine minimum start/shut down time constraint, a tie line power transmission constraint;
the primary grid output force constraints include:
Figure BDA0003175849150000038
Figure BDA0003175849150000039
wherein
Figure BDA00031758491500000310
Transmitting reactive power for the main network;
Figure BDA00031758491500000311
respectively transmitting upper and lower limits of reactive power for the main network;
Figure BDA00031758491500000312
and transmitting upper and lower limits of active power for the main network.
The gas turbine minimum on/off time constraints include:
Figure BDA00031758491500000313
Figure BDA00031758491500000314
Figure BDA0003175849150000041
Figure BDA0003175849150000042
wherein the content of the first and second substances,
Figure BDA0003175849150000043
the minimum starting time and the minimum closing time of the unit.
The tie-line power transfer constraints include
Figure BDA0003175849150000044
Wherein the content of the first and second substances,
Figure BDA0003175849150000045
transmitting power to the kth micro-grid for the power distribution network;
Figure BDA0003175849150000046
respectively the lower limit and the upper limit of the transmission power between the micro-grid and the distribution network.
Optional, objective function f of the intraday roll optimization algorithmdiComprises the following steps:
Figure BDA0003175849150000047
Figure BDA0003175849150000048
wherein, tinA step size optimized for rolling in days; PF is an energy storage penalty item; pf is a penalty coefficient;
Figure BDA0003175849150000049
the energy storage charge capacity obtained by day-ahead scheduling.
Optionally, the constraint condition of the rolling optimization algorithm in the day is the same as the constraint condition of the regulation phase model, and includes: the method comprises the following steps of power distribution network power flow constraint, power distribution network safety constraint, static reactive compensator constraint, gas turbine output constraint, micro-grid power balance constraint, energy storage charging and discharging constraint, renewable energy abandonment constraint and demand response load constraint;
the power flow constraint of the power distribution network comprises:
Figure BDA00031758491500000410
Figure BDA00031758491500000411
Figure BDA00031758491500000412
Figure BDA00031758491500000413
wherein i, j, h and k are power distribution network node numbers; deltajThe method comprises the steps that a line set with a jth power distribution network node as a head end is formed; pijThe network node is a line set taking the jth power distribution network node as a tail end; omegaPBThe method comprises the steps of (1) collecting power grid nodes; omegaSVCIs a reactive compensator set; pij,t，Qij,tRespectively the active and reactive power of line ij; x is the number ofijIs the reactance value of line ij;
Figure BDA00031758491500000414
the active load and the reactive load of the j power distribution network node are respectively required;
Figure BDA00031758491500000415
the reactive power of the static reactive compensator;
Figure BDA00031758491500000416
the square of the voltage of the ith power distribution network node and the jth power distribution network node at the moment t is obtained; pjh,tRepresenting line jh active power; omegaMGRepresenting a microgrid set; omegaTRRepresenting a set of transformer nodes;Qjk,trepresents the reactive power of line jk;
the power distribution network safety constraint comprises:
Figure BDA0003175849150000051
Figure BDA0003175849150000052
wherein the content of the first and second substances,
Figure BDA0003175849150000053
is the upper square limit of the line current;
Figure BDA0003175849150000054
the square upper and lower limits of the node voltage are defined;
the static var compensator constraints include:
Figure BDA0003175849150000055
wherein the content of the first and second substances,
Figure BDA0003175849150000056
and the upper and lower limit of reactive power output of the static reactive compensator is arranged for the node j of the power distribution network.
The gas turbine output constraints include:
Figure BDA0003175849150000057
Figure BDA0003175849150000058
wherein, RUg,RDgThe maximum upward and downward climbing rates of the gas turbine unit;
Figure BDA0003175849150000059
respectively representing the upper limit and the lower limit of the output active power of the gas turbine unit;
the microgrid power balance constraint comprises:
Figure BDA00031758491500000510
wherein the content of the first and second substances,
Figure BDA00031758491500000511
outputting uncertain variables for renewable energy sources;
Figure BDA00031758491500000512
is the load of the kth microgrid;
the energy storage charge-discharge constraint comprises:
Figure BDA00031758491500000513
Figure BDA00031758491500000514
Figure BDA00031758491500000515
Figure BDA00031758491500000516
Ek,0＝Ek,T
Figure BDA00031758491500000517
wherein the content of the first and second substances,
Figure BDA00031758491500000518
is [0,1 ]]Internal variableRespectively representing an energy storage charging state and an energy storage discharging state;
Figure BDA00031758491500000519
charging power and discharging power for stored energy respectively;
Figure BDA00031758491500000520
charging power and discharging power stored in the kth power distribution network at the moment of t-1 are respectively stored;
Figure BDA00031758491500000521
respectively setting an upper limit and a lower limit of energy storage charging power and an upper limit and a lower limit of discharging power;
Figure BDA00031758491500000522
respectively, charging efficiency and discharging efficiency; ek,tIs the energy storage charge capacity; kE、
Figure BDA00031758491500000523
the energy storage charge capacity upper and lower limits; Δ t represents a scheduling period length; ek,0Representing the initial charge capacity of energy storage; ek,TRepresenting the energy storage charge capacity at the moment T;
the curtailment renewable energy constraint comprises:
Figure BDA0003175849150000061
wherein
Figure BDA0003175849150000062
The maximum value of the power of renewable energy is abandoned;
the demand response load constraints include:
Figure BDA0003175849150000063
wherein D isj,tThe translatable load power of the j-th distribution network node in the time period t before the demand response;
Figure BDA0003175849150000064
the translatable load power of the j distribution network node in the time period t after the demand response;
Figure BDA0003175849150000065
respectively, the maximum and minimum power requirements of the demand response load during time t.
Optionally, step S3 includes:
s31, writing the robust scheduling problem in the day ahead into the following form:
Figure BDA0003175849150000066
wherein x is a day-ahead scheduling decision variable; u is a predicted value of the day-ahead output of the renewable energy; u. ofmin、umaxRespectively representing the upper limit and the lower limit of the predicted value of the day-ahead output of the renewable energy; y is a regulation decision variable such as line power and voltage; hx is the day-ahead scheduling cost; ay is the regulation and control cost; h is a day-ahead scheduling cost coefficient matrix; a is a regulation cost coefficient matrix; A. b is a day-ahead scheduling constraint condition correlation coefficient matrix; C. d, E, G, G and f are regulation constraint cost correlation coefficient matrixes;
s32, dividing the robust scheduling problem in the day ahead into a main problem and a sub problem;
the mathematical model of the main problem is as follows:
Figure BDA0003175849150000067
wherein f isMPIs a main problem expression; n represents the maximum iteration number of the column constraint generation algorithm; m represents the iteration number of the column constraint generation algorithm; u. ofmThe predicted value of the day-ahead output of the renewable energy source under the worst scene of the mth iteration is obtained; y ismA regulation decision variable in the worst scene of the mth iteration; eta is the maximum regulation and control cost of the main problem in the worst scene;
the mathematical model of the subproblem is:
Figure BDA0003175849150000071
wherein f isSPIs a subproblem expression;
Figure BDA0003175849150000072
and the solution result of the day-ahead scheduling decision variable of the main problem in the mth iteration is obtained.
Optionally, the step of obtaining the optimal day-ahead scheduling scheme of the power distribution network and the multi-microgrid system in the severe scene through iterative solution includes:
s34, setting the lower bound LBInfinity, upper bound UBThe column constraint generation algorithm iterates for a number m of 1.
S35, solving the main problem to obtain the best solution of the day-ahead scheduling decision
Figure BDA0003175849150000073
Day-ahead scheduling cost
Figure BDA0003175849150000074
And maximum regulatory cost in the worst scenario of major problems
Figure BDA0003175849150000075
And update the lower bound
Figure BDA0003175849150000076
S36, fixing scheduling decision variables before day
Figure BDA0003175849150000077
Solving the subproblems; carrying out duality on the subproblems by adopting a Lagrangian duality method to obtain the duality problem of the subproblems; solving the dual problem to obtain a renewable energy day-ahead output value u under the worst scene in the mth iterationmAnd update the upper bound
Figure BDA0003175849150000078
For the dual problem, proceed to S37;
s37, if UB-LBLess than epsilon, obtaining the optimal solution of the day-ahead scheduling decision
Figure BDA0003175849150000079
Finishing the iteration; if U isB-LBEntering S38 when the value is more than or equal to epsilon;
s38, updating the renewable energy output predicted value u under the worst scene of main problemsmAnd adding the constraint conditions of the regulation and control phase model of the current worst scene to the main problem, wherein m is m +1, and returning to the step S35.
Optionally, step S4 includes:
s41, setting the day initial scheduling time t to be 1;
s42, obtaining t + 1-t + t based on intraday rolling optimization algorithminPredicted value of renewable energy output within day of time, where tinA step size optimized for rolling in days;
s43, on the basis of the day-ahead scheduling decision, based on the objective function and constraint conditions of the rolling optimization algorithm in day, optimizing and solving t + 1-t + tinScheduling control variables in the day of the moment, and performing real-time scheduling on the power distribution network and the multi-microgrid system in the day based on the scheduling control variables in the day of the t +1 moment; let t be t +1, repeat steps S42, S43.
Optionally, step S42 includes:
s421, according to the upper limit and the lower limit of the output predicted value of the renewable energy sources day ahead, dividing the output predicted value of the renewable energy sources into a first interval to an nth interval at equal intervals, and respectively corresponding to the discretized n states S1～Sn；
S422, establishing a one-step state transition matrix pi (t, t +1) based on the Markov chain:
Figure BDA0003175849150000081
where t denotes the current time, Pa,bRepresents the slave state SaTransition to State SbThe probability of (a) of (b) being,a,b∈[1,n]；
s423 according to the currenttThe output predicted value of the renewable energy source is obtained at the moment to obtain the corresponding state Si，i∈[1,n]；
Finding and obtaining the maximum probability P of the ith row in the one-step state transition matrixij(ii) a Then at the present timetTime state SiIn the case of (1), the renewable energy output at the next time is at the maximum probability PijTransition to State Sj(ii) a Taking the average value of the corresponding j interval as a predicted value of the output of the renewable energy source in the day at the time t + 1;
s424, let t be t +1, and repeat step S423 until t + t is obtained, while continuing to scroll the time domaininThe output prediction value of the renewable energy within the time and day; wherein t isinStep size optimized for intra-day scrolling.
Compared with the prior art, the invention has the beneficial effects that:
according to the multi-time-scale scheduling method considering large-scale renewable energy access, day-ahead scheduling and day-in scheduling can be coordinated, the influence of renewable energy uncertainty on the scheduling of the power distribution network and the multi-microgrid system is reduced, and compared with a single day-ahead scheduling method, the scheduling accuracy is improved, so that the capability of the power distribution network and the multi-microgrid system for dealing with the renewable energy uncertainty can be effectively improved.
According to the method, a two-stage robust scheduling model with multiple time scales (including a day-ahead scale and a day-inside scale) is established, so that scientific scheduling basis is provided for scheduling personnel of the power distribution network and the multi-microgrid system, and multi-time scale coordinated scheduling of the power distribution network and the multi-microgrid system is realized.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings used in the description will be briefly introduced, and it is obvious that the drawings in the following description are an embodiment of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts according to the drawings:
fig. 1 is a multi-time scale scheduling framework of a power distribution network and a multi-microgrid system;
FIG. 2 is a flow chart of a multi-time scale scheduling method of the present invention;
fig. 3 is a flowchart of a power distribution network and a multi-microgrid system day-ahead robust scheduling solution;
fig. 4 is a flowchart of the intraday rolling scheduling solving process of the power distribution network and the multi-microgrid system.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The multi-time scale scheduling method firstly establishes a multi-time scale scheduling framework, and divides the scheduling of the power distribution network and the multi-microgrid system into 2 stages in the day and the day, as shown in fig. 1, the method specifically comprises the following steps:
stage 1: and (5) scheduling day by day. In the day-ahead scheduling, the power distribution network and the multi-microgrid system predict the output of renewable energy sources within 24 hours in the future. And arranging a system day-ahead scheduling plan by the system scheduling personnel according to the day-ahead prediction information. In this stage, the prediction information includes not only the predicted value of the renewable energy output in the next 24 hours, but also the upper and lower prediction limits thereof. And considering that the prediction error in the day ahead is larger, constructing an uncertain set of renewable energy output by adopting robust optimization, and establishing a day ahead stage model of the power distribution network and the multi-microgrid system. In order to meet the real-time power balance of the system, the day-ahead scheduling decision at the stage only comprises a scheduling strategy which needs to be made one day ahead of time, such as a gas turbine day-ahead starting and stopping plan, a power distribution network day-ahead electricity purchasing plan, power transmission of a power distribution network and a multi-microgrid day-ahead connecting line and the like.
Stage 2: and (5) short-term scheduling in the day. Due to the large prediction range and the low prediction accuracy, the renewable energy output prediction result of one day ahead of time cannot meet the requirement of power balance. In order to accurately reflect the fluctuation of the output of the renewable energy sources and reduce the deviation between the advance scheduling and the real-time scheduling, the short-term scheduling in the day is introduced. In the scheduling in the day, ultra-short-term prediction is carried out through a regulation and control stage model, and a predicted value of the output of the renewable energy in a plurality of time periods in the future is obtained. The dispatching center can carry out economic dispatching on the output of each controllable unit on the basis of day-ahead dispatching and make an output plan of each unit in the next time period. This stage is typically completed 15 minutes to 1 hour in advance and the intra-day schedule plan is revised in a rolling fashion based on the update of the ultra-short term predictions, and is therefore a rolling schedule.
The invention provides a multi-time scale scheduling method considering large-scale renewable energy access, which is suitable for a power distribution network and a multi-microgrid system, and as shown in figure 2, the method comprises the following steps:
s1, forecasting the day-ahead output upper and lower limits of the renewable energy through the probability distribution of the day-ahead forecasting error of the renewable energy, and constructing a robust uncertain set;
step S1 includes:
s11, order
Figure BDA0003175849150000101
The maximum positive and negative prediction error of the output of the nth renewable energy source set,
Figure BDA0003175849150000102
predicting an error of the output of the nth renewable energy unit before the day; according to
Figure BDA0003175849150000103
Respectively obtaining the lambda quantile and the 1-lambda quantile of the cumulative probability density function
Figure BDA0003175849150000104
Wherein lambda is a preset value; the lambda can be selected according to the scheduling requirement of the system, and the smaller the lambda is, the higher the scheduling robustness is;
taking a wind and light unit as an example, the output prediction error of the wind and light unit meets the three-order mixed Gaussian distribution, namely the probability density function form of the maximum output prediction error of the wind turbine and the photovoltaic is as follows:
Figure BDA0003175849150000105
wherein x is a prediction error value; mu, delta and alpha are respectively expectation, standard deviation and weight phasor of Gaussian distribution of each group, and alpha isiThe weight of the ith group of Gaussian distribution of the mixed Gaussian distribution; mu.si(ii) an expectation of an ith set of Gaussian distributions for the mixed Gaussian distributions; deltaiThe standard deviation of the ith set of gaussian distributions of the hybrid gaussian distribution. i is 1,2, 3.
S12, representing the uncertainty of the renewable energy contribution as:
Figure BDA0003175849150000111
Figure BDA0003175849150000112
wherein
Figure BDA0003175849150000113
The actual output of the nth renewable energy source unit is obtained;
Figure BDA0003175849150000114
predicting output for the nth renewable energy unit in the day ahead;
s13, order
Figure BDA0003175849150000115
And the method is a robust uncertain set of the nth renewable energy unit.
S2, constructing a two-stage robust scheduling model of the power distribution network and the multi-microgrid system; the two-stage robust scheduling model comprises a day-ahead stage model and a regulation and control stage model; the day-ahead stage model makes a day-ahead scheduling decision based on the output of renewable energy predicted day-ahead; and the regulation and control stage model identifies the worst scene of the output of the renewable energy sources based on the day-ahead scheduling decision, and corrects the day-ahead scheduling decision aiming at the worst scene.
The worst scheduling scene refers to a renewable energy uncertain variable value scene which enables the scheduling cost of the system to be the maximum under the current determined day-ahead scheduling decision. According to the convex optimization theory, the worst scene is generally a scene in which the output of the renewable energy takes an extreme value at a certain vertex of a polyhedron solution space.
The day-ahead scale scheduling is divided into a day-ahead stage (based on a day-ahead stage model) and a regulation and control stage (based on a regulation and control stage model), and an objective function of the two-stage robust scheduling model mainly comprises the day-ahead scheduling cost and the regulation and control cost of the power distribution network and the multiple micro-grids.
The objective function of the day-ahead stage model mainly comprises day-ahead scheduling cost and regulation and control cost of the power distribution network and the multiple micro-grids. The objective function of the day-ahead phase model is:
Figure BDA0003175849150000116
Figure BDA0003175849150000117
Figure BDA0003175849150000118
wherein i and j represent the node number of the power distribution network; t is the total scheduling time interval; t represents the current scheduling time; ij is a power distribution network line between the ith power distribution network node and the jth power distribution network node; f. ofda1Scheduling cost for the power distribution network and the multi-microgrid system day ahead; f. ofda2Cost is regulated and controlled for the power distribution network and the multi-microgrid system; x is a day-ahead scheduling decision variable; y is a regulation decision variable; xi is an uncertain variable; k is the number of the micro-grid; n is a radical ofMGThe number of the micro-grids; ctr、Closs、CIDRRespectively obtaining unit electricity purchasing cost, distribution network loss cost and distribution network node load demand response cost of the slave main network of the distribution network; cre、CesRespectively abandon for the microgridA penalty coefficient of renewable energy resources and a penalty coefficient of energy storage loss cost;
Figure BDA0003175849150000121
purchasing power from a main network for the power distribution network;
Figure BDA0003175849150000122
the output of a gas turbine in the kth micro-grid is obtained; i isk,tIs [0,1 ]]The internal variable represents the output state variable of the gas turbine unit in the kth micro-grid; dj,tThe translatable load power of the j distribution network node at the scheduling time t before the demand response;
Figure BDA0003175849150000123
the translatable load power of the j distribution network node at the scheduling time t after the demand response;
Figure BDA0003175849150000124
the renewable energy power is abandoned for the kth micro-grid;
Figure BDA0003175849150000125
the square of the line current of the power distribution network; r isijIs a line resistance; omegaPE、ΩTRRespectively a power distribution network line set and a power distribution network transformer node set;
Figure BDA0003175849150000126
charging power and discharging power for stored energy respectively; a isk、bk、ckRespectively, gas turbine output cost coefficients;
the constraints of the day-ahead phase model include: a main grid output constraint, a gas turbine minimum start/shut down time constraint, a tie line power transmission constraint;
the primary grid output force constraints include:
Figure BDA0003175849150000127
Figure BDA0003175849150000128
wherein
Figure BDA0003175849150000129
Transmitting reactive power for the main network;
Figure BDA00031758491500001210
respectively transmitting upper and lower limits of reactive power for the main network;
Figure BDA00031758491500001211
and transmitting upper and lower limits of active power for the main network.
The gas turbine minimum on/off time constraints include:
Figure BDA00031758491500001212
Figure BDA00031758491500001213
Figure BDA00031758491500001214
Figure BDA00031758491500001215
wherein the content of the first and second substances,
Figure BDA00031758491500001216
the minimum starting time and the minimum closing time of the unit.
The tie-line power transfer constraints include
Figure BDA0003175849150000131
Wherein，
Figure BDA0003175849150000132
Transmitting power to the kth micro-grid for the power distribution network;
Figure BDA0003175849150000133
respectively the lower limit and the upper limit of the transmission power between the micro-grid and the distribution network.
The objective function of the regulation phase model is: under the determined day-ahead scheduling decision, the regulation and control cost f of the power distribution network and the multi-microgrid system under the worst renewable energy output scene is realizedda2And minimum.
The constraint conditions of the regulation phase model comprise: the method comprises the following steps of power distribution network power flow constraint, power distribution network safety constraint, static reactive compensator constraint, gas turbine output constraint, micro-grid power balance constraint, energy storage charging and discharging constraint, renewable energy abandonment constraint and demand response load constraint;
the power flow constraint of the power distribution network comprises:
Figure BDA0003175849150000134
Figure BDA0003175849150000135
Figure BDA0003175849150000136
Figure BDA0003175849150000137
wherein the content of the first and second substances,ij, h and k are the numbers of the nodes of the power distribution network; deltajThe method comprises the steps that a line set with a jth power distribution network node as a head end is formed; pijThe network node is a line set taking the jth power distribution network node as a tail end; omegaPBThe method comprises the steps of (1) collecting power grid nodes; omegaSVCIs a reactive compensator set; pij,t,Qij,tRespectively the active and reactive power of line ij; x is the number ofijIs the reactance value of line ij;
Figure BDA0003175849150000138
the active load and the reactive load of the j power distribution network node are respectively required;
Figure BDA0003175849150000139
the reactive power of the static reactive compensator;
Figure BDA00031758491500001310
the square of the voltage of the ith power distribution network node and the jth power distribution network node at the moment t is obtained; pjh,tRepresenting line jh active power; omegaMGRepresenting a microgrid set; omegaTRRepresenting a set of transformer nodes; qjk,tRepresents the reactive power of line jk;
the power distribution network safety constraint comprises:
Figure BDA00031758491500001311
Figure BDA00031758491500001312
wherein the content of the first and second substances,
Figure BDA00031758491500001313
is the upper square limit of the line current;
Figure BDA00031758491500001314
the square upper and lower limits of the node voltage are defined;
the static var compensator constraints include:
Figure BDA00031758491500001315
wherein the content of the first and second substances,
Figure BDA00031758491500001316
and the upper and lower limit of reactive power output of the static reactive compensator is arranged for the node j of the power distribution network.
The gas turbine output constraints include:
Figure BDA00031758491500001317
Figure BDA00031758491500001318
wherein, RUg,RDgThe maximum upward and downward climbing rates of the gas turbine unit;
Figure BDA00031758491500001319
respectively representing the upper limit and the lower limit of the output active power of the gas turbine unit;
the microgrid power balance constraint comprises:
Figure BDA0003175849150000141
wherein the content of the first and second substances,
Figure BDA0003175849150000142
outputting uncertain variables for renewable energy sources;
Figure BDA0003175849150000143
is the load of the kth microgrid;
the energy storage charge-discharge constraint comprises:
Figure BDA0003175849150000144
Figure BDA0003175849150000145
Figure BDA0003175849150000146
Figure BDA0003175849150000147
Ek,0＝Ek,T
Figure BDA0003175849150000148
wherein the content of the first and second substances,
Figure BDA0003175849150000149
is [0,1 ]]The internal variables respectively represent the energy storage charging state and the discharging state;
Figure BDA00031758491500001410
Figure BDA00031758491500001411
charging power and discharging power for stored energy respectively;
Figure BDA00031758491500001412
charging power and discharging power stored in the kth power distribution network at the moment of t-1 are respectively stored;
Figure BDA00031758491500001413
respectively setting an upper limit and a lower limit of energy storage charging power and an upper limit and a lower limit of discharging power;
Figure BDA00031758491500001414
respectively, charging efficiency and discharging efficiency; ek,tIs the energy storage charge capacity; kE、
Figure BDA00031758491500001415
the energy storage charge capacity upper and lower limits; Δ t represents a scheduling period length; ek,0Representing the initial charge capacity of energy storage;Ek,Trepresenting the energy storage charge capacity at the moment T;
the curtailment renewable energy constraint comprises:
Figure BDA00031758491500001416
wherein
Figure BDA00031758491500001417
The maximum value of the power of renewable energy is abandoned;
the demand response load constraints include:
Figure BDA00031758491500001418
wherein D isj,tThe translatable load power of the j-th distribution network node in the time period t before the demand response;
Figure BDA00031758491500001419
the translatable load power of the j distribution network node in the time period t after the demand response;
Figure BDA00031758491500001420
respectively, the maximum and minimum power requirements of the demand response load during time t.
S3: splitting a day-ahead robust scheduling problem into main and sub problems through a column-and-constraint generation (C & CG) algorithm, and iteratively solving to obtain an optimal day-ahead scheduling decision of the power distribution network and the multi-microgrid system in the worst scene; the method comprises the steps that a main problem is solved to obtain an optimal day-ahead scheduling scheme in the worst scene, the optimal day-ahead scheduling scheme is transmitted to sub-problems, the sub-problems are solved through a dual principle, the regulation and control cost in the current worst scene and the output of renewable energy in the worst scene are obtained through solving, the worst scene in the day-ahead scheduling scheme is identified, the main problem decision is influenced by adding the regulation and control stage constraint in the worst scene, and finally the optimal solution of the day-ahead robust scheduling problem is obtained through iteration of the main and sub-problems. The main problem objective function comprises the day-ahead scheduling cost and the regulation and control cost under the worst scene, and is the same as the day-ahead scheduling model, but the constraint conditions of the main problem not only comprise the constraint conditions of the day-ahead stage model, but also comprise the constraint conditions of the m worst-scene regulation and control stage models generated in iteration.
The subproblem objective function is the regulation cost and is the same as the objective function of the regulation stage model; the constraint of the sub-problem is the constraint of a regulation and control stage model, and the regulation and control cost under the worst scene under the day-ahead decision of the current main problem is obtained through dual solution.
Step S3 includes:
s31, writing the robust scheduling problem in the day ahead into the following form based on the objective function and the constraint condition of the day ahead stage model:
Figure BDA0003175849150000151
wherein x is a day-ahead scheduling decision variable; u is a predicted value of the day-ahead output of the renewable energy; u. ofmin、umaxRespectively representing the upper limit and the lower limit of the predicted value of the day-ahead output of the renewable energy; y is a regulation decision variable such as line power and voltage; hx is the day-ahead scheduling cost; ay is the regulation and control cost; h is a day-ahead scheduling cost coefficient matrix; a is a regulation cost coefficient matrix; A. b is a day-ahead scheduling constraint condition correlation coefficient matrix; C. d, E, G, G and f are regulation constraint cost correlation coefficient matrixes;
s32, dividing the robust scheduling problem in the day ahead into a main problem and a sub problem;
the mathematical model of the main problem is as follows:
Figure BDA0003175849150000161
wherein f isMPIs a main problem expression; n represents the maximum iteration number of the column constraint generation algorithm; m represents the iteration number of the column constraint generation algorithm; u. ofmThe day-ahead output of renewable energy sources in the worst scene of the mth iteration is predictedMeasuring; y ismA regulation decision variable in the worst scene of the mth iteration; eta is the maximum regulation and control cost of the main problem in the worst scene;
the mathematical model of the subproblem is:
Figure BDA0003175849150000162
wherein f isSPIs a subproblem expression;
Figure BDA0003175849150000163
and the solution result of the day-ahead scheduling decision variable of the main problem in the mth iteration is obtained.
As shown in fig. 3, the iterative solution to obtain the optimal day-ahead scheduling scheme of the power distribution network and the multi-microgrid system in the severe scene includes the steps of:
s34, setting the lower bound LBInfinity, upper bound UBThe column constraint generation algorithm iterates for a number m of 1.
S35, solving the main problem to obtain the best solution of the day-ahead scheduling decision
Figure BDA0003175849150000164
Day-ahead scheduling cost
Figure BDA0003175849150000165
And maximum regulatory cost in the worst scenario of major problems
Figure BDA0003175849150000166
And update the lower bound
Figure BDA0003175849150000167
S36, fixing scheduling decision variables before day
Figure BDA0003175849150000168
Solving the subproblems; carrying out duality on the subproblems by adopting a Lagrangian duality method to obtain the duality problem of the subproblems; solving the dual problem if a feasible solution existsAnd obtaining the predicted value u of the day-ahead output of the renewable energy source under the worst scene in the mth iterationmAnd update the upper bound
Figure BDA0003175849150000171
For the dual problem, proceed to S37; if no feasible solution exists in the dual problem, a renewable energy day-ahead output predicted value u under the worst scene in the mth iteration is obtainedmGo to S38;
in this embodiment, the expression of the dual problem of the sub-problem is:
Figure BDA0003175849150000172
CTπ1+DTπ2+ETπ3≤a
π1≤0
umin≤u≤umax
wherein, pi1、π3、π2Is the dual variable introduced. According to the dual principle, the dual problem is the same as the original problem solving result, so that the sub-problem is solved by directly solving the dual problem. The subproblems can be solved through a dual algorithm, the regulation and control cost under the current worst scene and the renewable energy output under the worst scene are obtained through solving, and the identification of the worst scene is realized by obtaining the renewable energy output under the worst scene.
S37, if UB-LBLess than epsilon, obtaining the optimal solution of the day-ahead scheduling decision
Figure BDA0003175849150000173
Finishing the iteration; if U isB-LBEntering S38 when the value is more than or equal to epsilon;
s38, updating the renewable energy output predicted value u under the worst scene of main problemsmAnd adding the constraint of the regulation and control phase model of the current worst scene to the main problem, wherein m is m +1, and returning to the step S35.
S4: on the basis of the optimal day-ahead scheduling decision, a day-in rolling optimization algorithm is adopted to perform rolling optimization on day-in scheduling of the power distribution network and the multi-microgrid system, so that a day-in rolling decision of the power distribution network and the multi-microgrid system is obtained (in the embodiment, the day-in rolling decision is used for scheduling for 15 minutes to 1 hour in the future), and multi-time scale scheduling of the power distribution network and the multi-microgrid system is realized.
It is considered that the accuracy of prediction of renewable energy output decreases as the prediction time scale increases. The two-stage robust scheduling model based on the day-ahead prediction result is difficult to meet the requirement of real-time scheduling, so that the adjustment needs to be carried out on the basis of day-ahead scheduling decisions by adopting an intra-day rolling optimization algorithm.
And the day-by-day rolling optimization algorithm follows the day-by-day energy storage charging and discharging state, the unit starting and stopping plan and the tie line power obtained in the step S3, and the day-by-day decision variables such as the unit output and the like are adjusted according to the short-term prediction of the day-by-day renewable energy fluctuation.
Objective function f of rolling optimization algorithm in daydiComprises the following steps:
Figure BDA0003175849150000181
Figure BDA0003175849150000182
wherein, tinA step size optimized for rolling in days; PF is an energy storage penalty item; pf is a penalty coefficient;
Figure BDA0003175849150000183
the energy storage charge capacity obtained by day-ahead scheduling.
The constraint conditions of the rolling optimization algorithm in the day are the same as those of the model in the regulation and control stage.
As shown in fig. 4, step S4 includes:
s41, setting the day initial scheduling time t to be 1;
s42, obtaining t + 1-t + t based on intraday rolling optimization algorithminDay of the dayInternal renewable energy output prediction value, where tinA step size optimized for rolling in days;
s43, on the basis of the day-ahead scheduling decision, based on the objective function and constraint conditions of the rolling optimization algorithm in day, optimizing and solving t + 1-t + tinScheduling control variables in the day of the moment, and performing real-time scheduling on the power distribution network and the multi-microgrid system in the day based on the scheduling control variables in the day of the t +1 moment; let t be t +1, repeat steps S42, S43.
Step S42 includes:
s421, according to the upper limit and the lower limit of the output predicted value of the renewable energy sources day ahead, dividing the output predicted value of the renewable energy sources into a first interval to an nth interval at equal intervals, and respectively corresponding to the discretized n states S1～Sn；
S422, establishing a one-step state transition matrix pi (t, t +1) based on the Markov chain:
Figure BDA0003175849150000191
where t denotes the current time, Pa,bRepresents the slave state SaTransition to State SbProbability of, a, b ∈ [1, n ]]；
S423 according to the currenttThe output predicted value of the renewable energy source is obtained at the moment to obtain the corresponding state Si，i∈[1,n]；
Finding and obtaining the maximum probability P of the ith row in the one-step state transition matrixij(ii) a Then at the present timetTime state SiIn the case of (1), the renewable energy output at the next time is at the maximum probability PijTransition to State Sj(ii) a Taking the average value of the corresponding j interval as a predicted value of the output of the renewable energy source in the day at the time t + 1;
s424, let t be t +1, and repeat step S423 until t + t is obtained, while continuing to scroll the time domaininThe output prediction value of the renewable energy within the time and day; wherein t isinStep size optimized for intra-day scrolling.
According to the multi-time-scale scheduling method considering large-scale renewable energy access, day-ahead scheduling and day-in scheduling can be coordinated, the influence of renewable energy uncertainty on the scheduling of the power distribution network and the multi-microgrid system is reduced, and compared with a single day-ahead scheduling method, the scheduling accuracy is improved, so that the capability of the power distribution network and the multi-microgrid system for dealing with the renewable energy uncertainty can be effectively improved.
According to the method, a two-stage robust scheduling model with multiple time scales (including a day-ahead scale and a day-inside scale) is established, so that scientific scheduling basis is provided for scheduling personnel of the power distribution network and the multi-microgrid system, and multi-time scale coordinated scheduling of the power distribution network and the multi-microgrid system is realized.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (10)

1. A multi-time scale scheduling method considering large-scale renewable energy access is suitable for a power distribution network and a multi-microgrid system, and is characterized by comprising the following steps:
s1, forecasting the day-ahead output upper and lower limits of the renewable energy through the probability distribution of the day-ahead forecasting error of the renewable energy, and constructing a robust uncertain set;
s2, constructing a two-stage robust scheduling model of the power distribution network and the multi-microgrid system; the two-stage robust scheduling model comprises a day-ahead stage model and a regulation and control stage model; the day-ahead stage model makes a day-ahead scheduling decision based on the predicted day-ahead output of the renewable energy; the regulation and control stage model is based on the day-ahead scheduling decision, and the regulation and control cost under the current worst scene and the output of the renewable energy under the worst scene are obtained by solving the regulation and control stage model, so that the worst scene of the output of the renewable energy is identified, and the day-ahead scheduling decision is corrected aiming at the worst scene;
s3: splitting a day-ahead robust scheduling problem into main and sub problems through a column constraint generation algorithm, and iteratively solving to obtain an optimal day-ahead scheduling decision of the power distribution network and the multi-microgrid system in the worst scene;
and S4, performing rolling optimization on the day-to-day scheduling of the power distribution network and the multi-microgrid system by adopting a day-to-day rolling optimization algorithm on the basis of the optimal day-to-day scheduling decision, and realizing multi-time scale scheduling of the power distribution network and the multi-microgrid system.
2. The method for multi-time-scale scheduling considering large-scale renewable energy access according to claim 1, wherein step S1 comprises:
s11, order
Figure FDA0003175849140000011
The maximum positive and negative prediction error of the output of the nth renewable energy source set,
Figure FDA0003175849140000012
predicting an error of the output of the nth renewable energy unit before the day; according to
Figure FDA0003175849140000013
Respectively obtaining the lambda quantile and the 1-lambda quantile of the cumulative probability density function
Figure FDA0003175849140000014
Wherein lambda is a preset value;
s12, representing the uncertainty of the renewable energy contribution as:
Figure FDA0003175849140000015
Figure FDA0003175849140000016
wherein
Figure FDA0003175849140000017
The actual output of the nth renewable energy source unit is obtained;
Figure FDA0003175849140000018
predicting output for the nth renewable energy unit in the day ahead;
s13, order
Figure FDA0003175849140000019
And the method is a robust uncertain set of the nth renewable energy unit.
3. The multi-time scale scheduling method considering large-scale renewable energy access of claim 2, wherein the objective function of the stage-of-day model is:
Figure FDA0003175849140000021
Figure FDA0003175849140000022
Figure FDA0003175849140000023
wherein i and j represent the node number of the power distribution network; t is the total scheduling time interval; t represents the current scheduling time; ij is a power distribution network line between the ith power distribution network node and the jth power distribution network node; f. ofda1Scheduling cost for the power distribution network and the multi-microgrid system day ahead; f. ofda2Cost is regulated and controlled for the power distribution network and the multi-microgrid system; x is a day-ahead scheduling decision variable; y is a regulation decision variable; xi is an uncertain variable; k is the number of the micro-grid; n is a radical ofMGThe number of the micro-grids; ctr、Closs、CIDRThe unit electricity purchasing cost and the distribution network loss of the main network are respectively selected for the distribution networkCost and distribution network node load demand response cost; cre、CesRespectively discarding a punishment coefficient of renewable energy resources and a punishment coefficient of energy storage loss cost for the micro-grid;
Figure FDA0003175849140000024
purchasing power from a main network for the power distribution network;
Figure FDA0003175849140000025
the output of a gas turbine in the kth micro-grid is obtained; i isk,tIs [0,1 ]]The internal variable represents the output state variable of the gas turbine unit in the kth micro-grid; dj,tThe translatable load power of the j distribution network node at the scheduling time t before the demand response;
Figure FDA0003175849140000026
the translatable load power of the j distribution network node at the scheduling time t after the demand response;
Figure FDA0003175849140000027
the renewable energy power is abandoned for the kth micro-grid;
Figure FDA0003175849140000028
the square of the line current of the power distribution network; r isijIs a line resistance; omegaPE、ΩTRRespectively a power distribution network line set and a power distribution network transformer node set;
Figure FDA0003175849140000029
charging power and discharging power for stored energy respectively; a isk、bk、ckRespectively, gas turbine output cost coefficients;
the objective function of the regulation phase model is: under the determined day-ahead scheduling decision, the regulation and control cost f of the power distribution network and the multi-microgrid system under the worst renewable energy output scene is realizedda2And minimum.
4. The method of claim 3, wherein the constraints of the pre-day phase model include: a main grid output constraint, a gas turbine minimum start/shut down time constraint, a tie line power transmission constraint;
the primary grid output force constraints include:
Figure FDA00031758491400000210
Figure FDA00031758491400000211
wherein
Figure FDA00031758491400000212
Transmitting reactive power for the main network;
Figure FDA00031758491400000213
respectively transmitting upper and lower limits of reactive power for the main network;
Figure FDA0003175849140000031
transmitting upper and lower limits of active power for the main network;
the gas turbine minimum on/off time constraints include:
Figure FDA0003175849140000032
Figure FDA0003175849140000033
Figure FDA0003175849140000034
Figure FDA0003175849140000035
wherein the content of the first and second substances,
Figure FDA0003175849140000036
the minimum starting time and the minimum closing time of the unit are obtained;
the tie-line power transfer constraints include
Figure FDA0003175849140000037
Wherein the content of the first and second substances,
Figure FDA0003175849140000038
transmitting power to the kth micro-grid for the power distribution network;
Figure FDA0003175849140000039
respectively the lower limit and the upper limit of the transmission power between the micro-grid and the distribution network.
5. The method of multi-time scale scheduling with consideration of large-scale renewable energy access of claim 4, wherein an objective function f of a rolling-in-the-day optimization algorithmdiComprises the following steps:
Figure FDA00031758491400000310
Figure FDA00031758491400000311
wherein, tinA step size optimized for rolling in days; PF is an energy storage penalty item; pf is a penalty coefficient;
Figure FDA00031758491400000312
the energy storage charge capacity obtained by day-ahead scheduling.
6. The multi-time scale scheduling method considering large-scale renewable energy access according to claim 5, wherein the constraint condition of the rolling optimization algorithm in the day is the same as the constraint condition of the regulation phase model, and comprises: the method comprises the following steps of power distribution network power flow constraint, power distribution network safety constraint, static reactive compensator constraint, gas turbine output constraint, micro-grid power balance constraint, energy storage charging and discharging constraint, renewable energy abandonment constraint and demand response load constraint;
the power flow constraint of the power distribution network comprises:
Figure FDA0003175849140000041
Figure FDA0003175849140000042
Figure FDA0003175849140000043
Figure FDA0003175849140000044
wherein i, j, h and k are power distribution network node numbers; deltajThe method comprises the steps that a line set with a jth power distribution network node as a head end is formed; pijThe network node is a line set taking the jth power distribution network node as a tail end; omegaPBThe method comprises the steps of (1) collecting power grid nodes; omegaSVCIs a reactive compensator set; pij,t，Qij,tRespectively the active and reactive power of line ij; x is the number ofijIs the reactance value of line ij;
Figure FDA0003175849140000045
the active load and the reactive load of the j power distribution network node are respectively required;
Figure FDA0003175849140000046
the reactive power of the static reactive compensator;
Figure FDA0003175849140000047
the square of the voltage of the ith power distribution network node and the jth power distribution network node at the moment t is obtained; pjh,tRepresenting line jh active power; omegaMGRepresenting a microgrid set; omegaTRRepresenting a set of transformer nodes; qjk,tRepresents the reactive power of line jk;
the power distribution network safety constraint comprises:
Figure FDA0003175849140000048
Figure FDA0003175849140000049
wherein the content of the first and second substances,
Figure FDA00031758491400000410
is the upper square limit of the line current;
Figure FDA00031758491400000411
the square upper and lower limits of the node voltage are defined;
the static var compensator constraints include:
Figure FDA00031758491400000412
wherein the content of the first and second substances,
Figure FDA00031758491400000413
static var compensator for power distribution network node j installationThe reactive power output upper and lower limits of the compensator;
the gas turbine output constraints include:
Figure FDA00031758491400000414
Figure FDA00031758491400000415
wherein, RUg，RDgThe maximum upward and downward climbing rates of the gas turbine unit;
Figure FDA00031758491400000416
respectively representing the upper limit and the lower limit of the output active power of the gas turbine unit;
the microgrid power balance constraint comprises:
Figure FDA00031758491400000417
wherein the content of the first and second substances,
Figure FDA00031758491400000418
outputting uncertain variables for renewable energy sources;
Figure FDA00031758491400000419
is the load of the kth microgrid;
the energy storage charge-discharge constraint comprises:
Figure FDA0003175849140000051
Figure FDA0003175849140000052
Figure FDA0003175849140000053
Figure FDA0003175849140000054
Ek,0＝Ek,T
Figure FDA0003175849140000055
wherein the content of the first and second substances,
Figure FDA0003175849140000056
is [0,1 ]]The internal variables respectively represent the energy storage charging state and the discharging state;
Figure FDA0003175849140000057
charging power and discharging power for stored energy respectively;
Figure FDA0003175849140000058
charging power and discharging power stored in the kth power distribution network at the moment of t-1 are respectively stored;
Figure FDA0003175849140000059
respectively setting an upper limit and a lower limit of energy storage charging power and an upper limit and a lower limit of discharging power;
Figure FDA00031758491400000510
respectively, charging efficiency and discharging efficiency; ek,tIs the energy storage charge capacity;
Figure FDA00031758491400000511
the energy storage charge capacity upper and lower limits; Δ t represents a scheduling period length; ek,0Representing the initial charge capacity of energy storage; ek,TRepresents time T storeAn energy-to-charge capacity;
the curtailment renewable energy constraint comprises:
Figure FDA00031758491400000512
wherein
Figure FDA00031758491400000513
The maximum value of the power of renewable energy is abandoned;
the demand response load constraints include:
Figure FDA00031758491400000514
wherein D isj,tThe translatable load power of the j-th distribution network node in the time period t before the demand response;
Figure FDA00031758491400000515
the translatable load power of the j distribution network node in the time period t after the demand response;
Figure FDA00031758491400000516
respectively, the maximum and minimum power requirements of the demand response load during time t.
7. The method for multi-time-scale scheduling considering large-scale renewable energy access according to claim 6, wherein step S3 comprises:
s31, writing the robust scheduling problem in the day ahead into the following form based on the objective function and the constraint condition of the day ahead stage model:
Figure FDA0003175849140000061
wherein x is a day-ahead scheduling decision variable; u is renewable energy day-ahead outputMeasuring; u. ofmin、umaxRespectively representing the upper limit and the lower limit of the predicted value of the day-ahead output of the renewable energy; y is a regulation decision variable such as line power and voltage; hx is the day-ahead scheduling cost; ay is the regulation and control cost; h is a day-ahead scheduling cost coefficient matrix; a is a regulation cost coefficient matrix; A. b is a day-ahead scheduling constraint condition correlation coefficient matrix; C. d, E, G, G and f are regulation constraint cost correlation coefficient matrixes;
s32, dividing the robust scheduling problem in the day ahead into a main problem and a sub problem;
the mathematical model of the main problem is as follows:
Figure FDA0003175849140000062
wherein f isMPIs a main problem expression; n represents the maximum iteration number of the column constraint generation algorithm; m represents the iteration number of the column constraint generation algorithm; u. ofmThe predicted value of the day-ahead output of the renewable energy source under the worst scene of the mth iteration is obtained; y ismA regulation decision variable in the worst scene of the mth iteration; eta is the maximum regulation and control cost of the main problem in the worst scene;
the mathematical model of the subproblem is:
Figure FDA0003175849140000063
wherein f isSPIs a subproblem expression;
Figure FDA0003175849140000071
and the solution result of the day-ahead scheduling decision variable of the main problem in the mth iteration is obtained.
8. The multi-time-scale scheduling method considering large-scale renewable energy access of claim 7, wherein the step of iteratively solving in step S3 to obtain the optimal day-ahead scheduling scheme of the power distribution network and the multi-microgrid system in the severe scene comprises the steps of:
s34, setting the lower bound LBInfinity, upper bound UB1, defining the iteration number m of the column constraint generation algorithm as 1;
s35, solving the main problem to obtain the best solution of the day-ahead scheduling decision
Figure FDA0003175849140000072
Day-ahead scheduling cost
Figure FDA0003175849140000073
And maximum regulatory cost in the worst scenario of major problems
Figure FDA0003175849140000074
And update the lower bound
Figure FDA0003175849140000075
S36, fixing scheduling decision variables before day
Figure FDA0003175849140000076
Solving the subproblems; carrying out duality on the subproblems by adopting a Lagrangian duality method to obtain the duality problem of the subproblems; solving the dual problem to obtain a renewable energy day-ahead output value u under the worst scene in the mth iterationmAnd update the upper bound
Figure FDA0003175849140000077
Figure FDA0003175849140000078
For the dual problem, proceed to S37;
s37, if UB-LBLess than epsilon, obtaining the optimal solution of the day-ahead scheduling decision
Figure FDA0003175849140000079
Finishing the iteration; if U isB-LBEntering S38 when the value is more than or equal to epsilon;
s38, updating the main questionRenewable energy output predicted value u in worst scenemAnd adding a regulation and control stage model constraint condition in the current worst scene to the main problem, wherein m is m +1, and returning to the step S35.
9. The method according to claim 8, wherein the step S4 comprises:
s41, setting the day initial scheduling time t to be 1;
s42, obtaining t + 1-t + t based on intraday rolling optimization algorithminPredicted value of renewable energy output within day of time, where tinA step size optimized for rolling in days;
s43, on the basis of the day-ahead scheduling decision, based on the objective function and constraint conditions of the rolling optimization algorithm in day, optimizing and solving t + 1-t + tinScheduling control variables in the day of the moment, and performing real-time scheduling on the power distribution network and the multi-microgrid system in the day based on the scheduling control variables in the day of the t +1 moment; let t be t +1, repeat steps S42, S43.
10. The method for multi-time-scale scheduling considering large-scale renewable energy access of claim 9, wherein step S42 comprises:
s421, according to the upper limit and the lower limit of the output predicted value of the renewable energy sources day ahead, dividing the output predicted value of the renewable energy sources into a first interval to an nth interval at equal intervals, and respectively corresponding to the discretized n states S1～Sn；
S422, establishing a one-step state transition matrix pi (t, t +1) based on the Markov chain:
Figure FDA0003175849140000081
where t denotes the current time, Pa,bRepresents the slave state SaTransition to State SbProbability of, a, b ∈ [1, n ]]；
S423, obtaining pairs according to the output predicted value of the renewable energy at the current t momentCorresponding state Si，i∈[1,n]；
Finding and obtaining the maximum probability P of the ith row in the one-step state transition matrixij(ii) a Then state S at the current time tiIn the case of (1), the renewable energy output at the next time is at the maximum probability PijTransition to State Sj(ii) a Taking the average value of the corresponding j interval as a predicted value of the output of the renewable energy source in the day at the time t + 1;
s424, let t be t +1, and repeat step S423 until t + t is obtained, while continuing to scroll the time domaininThe output prediction value of the renewable energy within the time and day; wherein t isinStep size optimized for intra-day scrolling.
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