CN113241759A - Power distribution network and multi-microgrid robust scheduling method, electronic equipment and storage medium - Google Patents

Power distribution network and multi-microgrid robust scheduling method, electronic equipment and storage medium Download PDF

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CN113241759A
CN113241759A CN202110525777.XA CN202110525777A CN113241759A CN 113241759 A CN113241759 A CN 113241759A CN 202110525777 A CN202110525777 A CN 202110525777A CN 113241759 A CN113241759 A CN 113241759A
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肖金星
徐冰雁
杨军
鲁晓秋
张宇威
李勇汇
叶影
张莹
周彦
唐丹红
陈龙
蔡阳
郭磊
沈杰士
翟万利
汤衡
陈文莹
曹春
骆国连
刘杨名
徐建国
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State Grid Shanghai Electric Power Co Ltd
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Abstract

The invention discloses a robust scheduling method for a power distribution network and multiple micro-networks, electronic equipment and a storage medium, wherein the method comprises the following steps: establishing a renewable energy output ellipsoid uncertain set by adopting a data driving method based on a minimum volume closed ellipsoid; establishing a day-ahead and real-time two-stage economic dispatching model considering the overall operation cost of the power distribution network and the multiple micro-grids; a renewable energy output limit scene is obtained by solving an ellipsoid uncertain set, uncertainty factors of a scheduling model are processed by adopting a limit scene method, and a two-stage robust scheduling method of a power distribution network and a plurality of micro-grids based on the limit scene method is provided. And finally, splitting the two-stage robust optimization problem into main and sub problems through an improved column constraint generation algorithm based on a limit scene method to solve the main and sub problems in an iterative manner. The method can reduce the scheduling conservatism of the power distribution network and the multi-microgrid system and improve the scheduling economy while ensuring the scheduling robustness of the power distribution network and the multi-microgrid system, and meanwhile, the model solution is simpler and the efficiency is higher.

Description

Power distribution network and multi-microgrid robust scheduling method, electronic equipment and storage medium
Technical Field
The invention relates to a dispatching method of a power distribution network, in particular to a power distribution network and multi-microgrid robust dispatching method considering the output correlation of renewable energy sources, electronic equipment and a storage medium.
Background
In recent years, distributed power generation has been favored by experts and scholars in various countries as global energy and environmental issues have become more prominent. The microgrid has a flexible and efficient distributed power generation integration function, and the consumption rate of renewable energy can be improved. The multi-microgrid is a further extension on the basis of microgrid research, and is an indispensable part in a novel intelligent power grid for coordinating energy management of adjacent distributed units, microgrids and loads in a region. Due to the volatility of the distributed power sources, access of large-scale distributed power sources can pose challenges to operation and scheduling of the power distribution network and the multi-microgrid system. At present, in the research of a power distribution network and multiple micro-networks, a processing method for uncertain factors of output of renewable energy sources mainly comprises random optimization, opportunity constraint optimization and robust optimization. Although the random optimization can simulate daily uncertain scenes, the random optimization is difficult to deal with extreme scenes; the opportunistic constraint method needs to calculate an accurate probability distribution function of an uncertain variable, and is difficult to acquire data and solve; the robustness optimization adopts an interval to describe the uncertainty of parameters, so that the robustness of the power distribution network and the multi-microgrid system is improved, but the output of each renewable energy source unit in the worst scene is assumed to be the maximum or minimum of the output of each unit, the correlation of the output of the renewable energy sources in a scheduling area is not considered, the worst case with very low actual occurrence probability is considered too much, the optimization result is over-conservative, and the economy is poor. Meanwhile, the traditional robust optimization adopts dual optimization to solve, the conversion process is complex, and more nonlinear terms are introduced, so that the model solving is difficult.
Disclosure of Invention
In order to solve the above problems, the present invention aims to provide a robust scheduling method, an electronic device, and a storage medium for a power distribution network and multiple micro-networks, which consider the output correlation of renewable energy sources, so as to achieve the purposes of reducing the scheduling cost of the power distribution network and multiple micro-networks and considering the economy of system scheduling while ensuring the robustness.
In order to achieve the above purpose, the invention is realized by the following technical scheme:
a power distribution network and multi-microgrid robust scheduling method comprises the following steps: and step S1, establishing an ellipsoid uncertain set of various renewable energy output by adopting a data driving method of a minimum volume closed ellipsoid.
And S2, establishing a day-ahead and real-time two-stage economic dispatching model considering the overall dispatching cost of the power distribution network and the multiple micro-grids.
Step S3, obtaining a renewable energy output limit scene by solving the ellipsoid uncertain set, processing uncertainty factors of the day-ahead real-time two-stage economic dispatching model by adopting a limit scene method, and establishing a power distribution network and multi-microgrid two-stage robust dispatching method based on the limit scene method, wherein the method comprises a first stage and a second stage, the first stage is a day-ahead dispatching decision stage, and a gas turbine day-ahead startup and shutdown plan, a power distribution network day-ahead power purchase plan and a power distribution network and multi-microgrid day-ahead connecting line transmission power are determined.
The second stage is a real-time scheduling stage, and under the decision-making stage before the first stage, the real-time scheduling conditions of the power distribution network and the multi-micro-grid under each limit scene are simulated, and the worst scene is optimized.
And step S4, splitting a two-stage robust optimization problem into main and sub-problems for iterative solution based on the improved C & CG algorithm of the extreme scene method, wherein the main problem in the main and sub-problems is used for solving the optimal day-ahead scheduling scheme under the extreme scene and transmitting the optimal day-ahead scheduling scheme to the sub-problems in the main and sub-problems, and the sub-problems are that the worst scene under the current day-ahead scheduling scheme is searched by the extreme scene method, the main problem decision is influenced by adding the relevant real-time scheduling constraint condition of the worst scene to the main problem, and the main and sub-problems are iteratively solved to obtain the optimal solution of the power distribution network and multi-microgrid scheduling problem.
Preferably, the step S1 includes: constructing a historical set omega of renewable energy output: assuming that the power distribution network and the multi-microgrid region have N in commonwA renewable energy output unit for collecting historical data of renewable energy output
Figure BDA0003064440130000021
Dividing by day, and setting the number of days of the collected historical data as Nd: the history set ω is represented by the following formula:
Figure BDA0003064440130000022
Figure BDA0003064440130000023
in the formula, ωiRepresenting the output historical data of the renewable energy source unit of the ith day in each time period, T representing the scheduling time period, i representing the number of days, and taking 1-Nd；
Constructing an ellipsoid uncertain set of the renewable energy output according to the historical data of the renewable energy output;
constructing an N by a data-driven algorithm of a minimum-volume closed ellipsoidWThe T-dimensional ellipsoid surrounds all historical data of renewable energy output, and an optimization model is obtained as follows:
minρdetQ-1/2
(ωi-c)TQ(ωi-c)≤1,i＝1,2,…Nd
Q＝QT,
Figure BDA0003064440130000031
where ρ is a constant and represents NWDimension T encloses the volume of the ellipsoid; q, c is a waiting quantity, Q is NWThe deviation direction of the symmetry axis of the T-dimensional ellipsoid relative to the coordinate axis, and c is NWCentral point of T-dimensional ellipsoid, NdRepresenting the number of days of historical data collected.
Solving the optimization model to obtain the uncertain set E (Q, c) of ellipsoids of renewable energy output as follows:
Figure BDA0003064440130000032
in the formula (I), the compound is shown in the specification,
Figure BDA0003064440130000033
represents NwAnd (4) T-dimensional real number set.
Preferably, the step S2 includes: the objective function of the day-ahead real-time two-stage economic dispatching model mainly comprises day-ahead dispatching cost and real-time dispatching cost of the power distribution network and the multiple micro-grids; the day-ahead real-time two-stage economic dispatching model comprises the following two stages, wherein the first stage of the two stages is a day-ahead dispatching stage, the constraint condition adopts a day-ahead dispatching constraint condition, and the objective function is to realize that the day-ahead dispatching cost and the real-time dispatching cost of the power distribution network and the microgrid are minimum; the day-ahead schedule phase constraints include gas turbine minimum start/shut-down time constraints and tie-line power delivery constraints.
The second stage is a real-time scheduling stage, the constraint condition adopts a real-time scheduling constraint condition, and the objective function is to realize the minimum cost of the real-time scheduling of the power distribution network and the micro-grid under the determined day-ahead scheduling scheme.
The real-time scheduling stage constraint conditions comprise power distribution network node power balance constraint, power distribution network line voltage balance constraint, power distribution network line capacity constraint, micro-grid gas turbine output climbing constraint, micro-grid power balance constraint, energy storage charging and discharging constraint, renewable energy abandonment and load loss constraint and gas turbine output maximum/minimum constraint.
Preferably, the step S3 includes: solving the ellipsoid uncertainty set E (Q, c) of the renewable energy output to obtain the vertex omega of the ellipsoid uncertainty sete,i,i＝1,2,…,2NwT, then NwThe uncertain set of T-dimensional ellipsoids has 2N in totalwT vertexes; 2N is mixedwThe scenes at the T vertices are called extreme scenes.
Preferably, in step S3, the power distribution network and multi-microgrid two-stage robust scheduling method based on the limit scenario method is represented by using the following mathematical model:
Figure BDA0003064440130000041
Dx≤d
Figure BDA0003064440130000042
Figure BDA0003064440130000043
wherein x is a day-ahead scheduling decision variable; subscript s denotes the limit scene number; n is a radical ofsRepresenting the number of limit scenes; xisRepresenting the value of the renewable energy output under the limit scene s; y issRepresenting real-time scheduling decision variables such as line power, voltage and the like in a limit scene; ax is the day-ahead scheduling cost; bys+CξsReal-time scheduling cost under a limit scene s; A. b C, D, F, H, L, M, D, F,h each represents a coefficient matrix.
Preferably, the step S4 includes: the main problem is expressed by the following formula:
Figure BDA0003064440130000044
Dx≤d
Figure BDA0003064440130000045
Figure BDA0003064440130000046
Figure BDA0003064440130000047
wherein n represents the current C&The iteration times of the CG algorithm; k is a radical ofmRepresenting the number of the limit scene returned by the subproblem during the mth iteration;
Figure BDA0003064440130000048
as extreme scene kmReal-time scheduling decision variables; eta is the maximum real-time scheduling cost in the current main problem limit scene;
Figure BDA0003064440130000049
indicating extreme scenarios kmThe value of the output of the lower renewable energy source;
Figure BDA0003064440130000051
as extreme scene kmReal-time scheduling cost.
Solving the main problem to obtain the value of a day-ahead scheduling decision variable x, transmitting the value to the subproblem, searching the worst scene under the day-ahead scheduling condition of the main problem by the subproblem through a limit scene method, and adding the real-time scheduling constraint of the worst scene to the main problem;
the subproblems are represented by a formula:
Figure BDA0003064440130000052
Figure BDA0003064440130000053
Figure BDA0003064440130000054
wherein f isSPA mathematical expression representing a subproblem.
The sub-problems adopt an enumeration method to obtain real-time scheduling cost under each limit scene, and if the sub-problems under all the limit scenes have solutions, the scene with the maximum real-time scheduling cost is the worst scene under the current scheduling condition; if a certain limit scene exists, so that no feasible solution exists for the real-time scheduling problem, the limit scene is the worst scene.
Preferably, the iterative solution of the main and sub-problems by the C & CG algorithm in step S4 to obtain the optimal solution of the power distribution network and multi-microgrid scheduling problem includes the following processes:
step S4.1, setting a lower bound LBInfinity, upper bound UBThe number of iterations n of the algorithm is 1 ∞.
S4.2, solving the main problem to obtain the optimal solution of the day-ahead scheduling decision
Figure BDA0003064440130000055
Day-ahead scheduling cost
Figure BDA0003064440130000056
And real-time scheduling cost under the scenario
Figure BDA0003064440130000057
And updating the lower bound value
Figure BDA0003064440130000058
Step S4.3, fixing scheduling decision variables before the day
Figure BDA0003064440130000059
Solving the sub-problems to obtain the sub-problems under all limit scenes
Figure BDA00030644401300000510
S4.4, judging the subproblems in all the limit scenes
Figure BDA00030644401300000511
Whether feasible solutions exist all over, if all the problems exist, the sub-problems under all the limit scenes
Figure BDA00030644401300000512
If all the solutions are feasible, the step S4.5 is carried out; if a sub-problem under a certain limit scene s
Figure BDA00030644401300000513
If there is no feasible solution, the process goes to step S4.6.
S4.5, searching the maximum value of feasible solutions under all limit scenes
Figure BDA00030644401300000514
The maximum value of the corresponding limit scene is the current worst scene S, and the process proceeds to step S4.7.
And S4.6, the current limit scene S is the current worst scene S, and the step S4.9 is carried out.
Step S4.7, according to the maximum value
Figure BDA0003064440130000061
Update the upper bound value
Figure BDA0003064440130000062
Step S4.8 is entered.
S4.8, setting epsilon as convergence criterion, if UB-LBLess than or equal to epsilon to obtain the optimal solution of the day-ahead scheduling decision
Figure BDA0003064440130000063
The iteration process is finished; if U isB-LB> epsilon and proceed to said step S4.9.
S4.9, updating the worst limit scene S of the main problem under the current scheduling condition, and adding the constraint condition corresponding to the worst limit scene S to the main problem; the algorithm iteration number is updated to n ═ n +1, the process returns to step S4.2, and step S4.2 to step S4.9 are repeated.
In another aspect, the present invention further provides an electronic device, including a processor and a memory, where the memory stores a computer program, and the computer program, when executed by the processor, is used for the power distribution network and the multi-piconet robust scheduling method as described above.
In still another aspect, the present invention further provides a readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the method for robust scheduling of a power distribution network and multiple piconets as described above is implemented.
The invention has at least one of the following advantages:
the method provided by the invention can effectively improve the capability of the power distribution network and the multi-microgrid system for dealing with the uncertainty of the renewable energy sources, simultaneously considers the correlation of the output of the renewable energy sources, adopts a data driving method to construct the uncertain set of the output of the renewable energy sources, and reduces the scheduling cost of the power distribution network and the multi-microgrid while ensuring the robustness compared with the traditional robust optimization adopting a box-type uncertain set, and also considers the economical efficiency of system scheduling. Meanwhile, compared with a two-stage robust scheduling model adopting a conventional method, the robust optimization method has the advantages that the sub-problem is a problem without integer variables, the solution is simpler, and the calculation efficiency is higher.
According to the method, the power distribution network and multi-microgrid scheduling model considering the uncertainty and the correlation of the output of the renewable energy sources is established, a scientific scheduling basis is provided for scheduling personnel of the power distribution network and the multi-microgrid system, and the reliability and the economical efficiency of the scheduling of the power distribution network and the multi-microgrid system are improved.
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Fig. 1 is a schematic flowchart of a power distribution network and multi-microgrid robust scheduling method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an indeterminate set of ellipsoids provided in accordance with an embodiment of the present invention;
fig. 3 is a flowchart of a C & CG algorithm based on an extreme scene method in a power distribution network and multi-microgrid robust scheduling method provided by an embodiment of the present invention.
Detailed Description
The following describes in detail a robust scheduling method for a power distribution network and multiple micro-grids according to the present invention with reference to the accompanying drawings and the detailed description. The advantages and features of the present invention will become more apparent from the following description. It is to be noted that the drawings are in a very simplified form and are all used in a non-precise scale for the purpose of facilitating and distinctly aiding in the description of the embodiments of the present invention. To make the objects, features and advantages of the present invention comprehensible, reference is made to the accompanying drawings. It should be understood that the structures, ratios, sizes, and the like shown in the drawings and described in the specification are only used for matching with the disclosure of the specification, so as to be understood and read by those skilled in the art, and are not used to limit the implementation conditions of the present invention, so that the present invention has no technical significance, and any structural modification, ratio relationship change or size adjustment should still fall within the scope of the present invention without affecting the efficacy and the achievable purpose of the present invention.
As shown in fig. 1, the power distribution network and multi-microgrid robust scheduling method provided in this embodiment is specifically a power distribution network and multi-microgrid robust scheduling method considering output correlation of renewable energy, and includes:
and S1, aiming at the uncertainty and the correlation of the various renewable energy sources, establishing an ellipsoid uncertainty set of the various renewable energy sources by adopting a data driving method of a minimum volume closed ellipsoid.
Step S2: aiming at the uncertainty of output of various renewable energy sources, a day-ahead and real-time two-stage economic dispatching model considering the overall dispatching cost of the power distribution network and the multi-microgrid is established by taking a day-ahead starting and stopping plan of the gas turbine and the day-ahead power transmission power of the power distribution network and the multi-microgrid as day-ahead dispatching decision content.
Step S3: the method comprises the following two stages (a first stage and a second stage), wherein the first stage is a day-ahead scheduling stage, and a day-ahead startup and shutdown plan of the gas turbine, a charge-discharge plan of energy storage and a day-ahead connecting line transmission power of the power distribution network and the multi-microgrid are determined. The second stage is a real-time scheduling stage, and under the first-stage scheduling scheme, the real-time scheduling conditions of the power distribution network and the multiple microgrids under each limit scene are simulated, and the worst limit scene is optimized.
Step S4: the method comprises the steps that a two-stage robust optimization problem is split (converted) into a main sub-problem for iterative solution by an improved C & CG (column-and-constraint generation) algorithm based on a limit scene method, the main problem is solved and an optimal day-ahead scheduling scheme under the limit scene contained in the main problem is obtained and transmitted to a sub-problem, the sub-problem is that the worst limit scene under the current day-ahead scheduling scheme is searched by the limit scene method, the main problem decision is influenced by adding relevant real-time scheduling constraint conditions of the worst limit scene to the main problem, and finally the optimal solution of the robust scheduling problem is obtained by iterative solution of the main sub-problem.
The step S1 includes: first, a historical set omega of the output of various renewable energy sources is constructed. Assuming that the power distribution network and the multi-microgrid region have N in commonwA renewable energy output unit for collecting historical output data of the renewable energy output unit
Figure BDA0003064440130000081
The method is divided by days, and the scheduling time interval of each day is T. Setting the number of days of the collected historical data to be Nd. Due to the need to consider the space and time between new energy stations (renewable energy output units)Correlation, therefore, when constructing the ellipsoid uncertainty set, all new energy station outputs in a scheduling period T should be considered, and an expression of the history set ω is as follows:
Figure BDA0003064440130000082
in the formula, ωiHistorical data representing the output of the renewable energy source unit at each time period on the ith day; i represents the number of days, 1 to Nd。
And then constructing an ellipsoid uncertain set of the output of various renewable energy sources based on the historical data omega of the output of the renewable energy sources. Constructing a high-dimensional (N) through a data-driven algorithm of Minimum Volume Enclosing Ellipsoid (MVEE)WT dimension) to enclose all historical scenarios (historical data of renewable energy output), the optimization model is obtained as follows:
Figure BDA0003064440130000091
where ρ is a constant and represents NWVolume of unit sphere of dimension T; q and c are quantities to be calculated, Q is the deviation direction of the symmetry axis of the high-dimensional ellipsoid relative to the coordinate axis, c is the central point of the high-dimensional ellipsoid, and N isdIndicating historical data days.
The optimization model is a convex optimization model, and can be quickly solved in polynomial time, so that an ellipsoid uncertain set E (Q, c) of renewable energy output is obtained and is represented by the following formula:
Figure BDA0003064440130000092
in the formula (I), the compound is shown in the specification,
Figure BDA0003064440130000093
represents NwAnd (4) T-dimensional real number set.
Compared with the traditional box-type uncertain set, the ellipsoid uncertain set considers the correlation among renewable energy sources, the enclosed uncertain output space is smaller, the scenes which cannot occur are less considered, and the conservation of robust optimization is reduced. As an example, a single-period ellipsoid uncertainty set and a box uncertainty set of adjacent wind power generation units in a system region are shown in fig. 2, and it can be seen that on the premise of wrapping all historical data, an uncertainty space surrounded by the ellipsoid uncertainty set is smaller and conservatism is smaller.
The objective function of the day-ahead real-time two-stage economic dispatching model (the two-stage dispatching model of the power distribution network and the multi-microgrid system) in the step S2 mainly includes the day-ahead dispatching cost and the real-time dispatching cost of the power distribution network and the multi-microgrid. The process of performing optimization scheduling by adopting the day-ahead-real-time two-stage economic scheduling model is divided into two stages, wherein the first stage in the two stages is a day-ahead scheduling stage, and the constraint is day-ahead scheduling constraint (the constraint condition adopts a day-ahead scheduling constraint condition); the second of the two phases is a real-time scheduling phase, and the constraint is a real-time scheduling constraint (the constraint is a real-time scheduling constraint). In the two-stage model, a day-ahead scheduling stage model determines a day-ahead scheduling decision variable and transmits the day-ahead scheduling decision variable to a real-time scheduling layer, and the real-time scheduling stage influences the day-ahead scheduling decision by adding renewable energy real-time scheduling constraints under different limit scenes to the day-ahead scheduling layer.
Specifically, the first stage of the two stages is a day-ahead scheduling stage, the constraint condition is a day-ahead scheduling constraint condition, and the objective function is to minimize the day-ahead scheduling cost and the real-time scheduling cost of the power distribution network and the microgrid; the day-ahead schedule phase constraints include gas turbine minimum start/shut-down time constraints and tie-line power delivery constraints.
The second stage is a real-time scheduling stage, the constraint condition adopts a real-time scheduling constraint condition, and the objective function is to realize the minimum cost of the real-time scheduling of the power distribution network and the micro-grid under the determined day-ahead scheduling scheme.
The real-time scheduling stage constraint conditions comprise power distribution network node power balance constraint, power distribution network line voltage balance constraint, power distribution network line capacity constraint, micro-grid gas turbine output climbing constraint, micro-grid power balance constraint, energy storage charging and discharging constraint, renewable energy abandonment and load loss constraint and gas turbine output maximum/minimum constraint.
The specific objective function of the two-stage model is as follows:
Figure BDA0003064440130000101
wherein OC is the day-ahead scheduling cost of the power distribution network and the multi-microgrid system; RC is the real-time scheduling cost of the power distribution network and the multi-microgrid system; x is a day-ahead scheduling decision variable; y is a real-time scheduling decision variable; xi is an uncertain variable; f. oflossThe network loss cost of the power distribution network; f. ofmainPurchasing power cost from the main network for the power distribution network;
Figure BDA0003064440130000102
abandoning load cost for the micro-grid k;
Figure BDA0003064440130000103
the cost of renewable energy is abandoned for the micro-grid k;
Figure BDA0003064440130000104
real-time output cost of the micro-grid k gas turbine is obtained; k is the number of the micro-grid; n is a radical ofMGThe number of the micro-grids; ctr、Closs、Cd、CreRespectively obtaining unit electricity purchasing cost, distribution network loss cost, micro-grid load abandoning punishment coefficient and abandoning renewable energy punishment coefficient of the distribution network slave main network;
Figure BDA0003064440130000111
purchasing power from a main network for the power distribution network;
Figure BDA0003064440130000112
real-time output of a gas turbine in the microgrid k; i isk,tThe variable is 0-1, and represents the output state variable of the gas turbine set in the microgrid k;
Figure BDA0003064440130000113
load power is cut for the micro-grid k, and renewable energy power is abandoned;
Figure BDA0003064440130000114
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; a isk、bk、ckRespectively, gas turbine output cost coefficients; t is the total scheduling time interval; t represents a scheduling time; i. j represents the distribution network node number.
The constraint conditions of the scheduling stage in step S2 mainly include minimum startup/shutdown time constraint of the gas turbine and tie line power transmission constraint, and specifically include:
2.1), gas turbine minimum on/off time constraints
Figure BDA0003064440130000115
Wherein the content of the first and second substances,
Figure BDA0003064440130000116
the minimum start-up and shut-down time of the gas turbine unit.
2.2), the tie line power transfer constraints are as follows:
Figure BDA0003064440130000117
wherein the content of the first and second substances,
Figure BDA0003064440130000118
transmitting power to the microgrid k for the power distribution network;
Figure BDA0003064440130000119
respectively the lower limit and the upper limit of the transmission power between the micro-grid and the distribution network.
The constraint conditions of the real-time scheduling stage in the step S2 include power distribution network node power balance constraint, power distribution network line voltage balance constraint, power distribution network line current, voltage capacity constraint, gas turbine output climbing constraint, micro-grid power balance constraint, energy storage charging and discharging constraint, renewable energy abandonment and load loss constraint, and gas turbine output maximum/minimum constraint. The method comprises the following specific steps:
2.3), power balance constraint of the power distribution network nodes:
Figure BDA00030644401300001110
wherein, delta(j)Is a line set with j as the head end; pi(j)Is a line set with j as the terminal; omegaPBThe method comprises the steps of (1) collecting power grid nodes; pij,t,Qij,tRespectively the active and reactive power of line ij; x is the number ofijIs the reactance value of line ij;
Figure BDA0003064440130000121
the active load demand and the reactive load demand of the node j are respectively;
Figure BDA0003064440130000122
transmitting reactive power for the main network; pjh,tRepresenting the active power of the line jh at the moment t; omegaMGRepresenting a microgrid set; omegaTRRepresenting a set of transformer nodes; qjk,tRepresenting the reactive power of the line jk at time t; i. j, h and k all represent nodes of the power distribution network; t denotes a scheduling time.
2.4), power distribution network line voltage balance restraint:
Figure BDA0003064440130000123
wherein the content of the first and second substances,
Figure BDA0003064440130000124
is the square of the voltage at node i of the distribution network,
Figure BDA0003064440130000125
representing the square of the voltage at distribution network node j.
2.5), power distribution network line current and voltage capacity constraint:
Figure BDA0003064440130000126
Figure BDA0003064440130000127
wherein the content of the first and second substances,
Figure BDA0003064440130000128
is the upper square limit of the line current;
Figure BDA0003064440130000129
the upper and lower limits of the square of the node voltage.
2.6), output climbing restraint of the gas turbine:
Figure BDA00030644401300001210
wherein, RUg,RDgThe maximum upward and downward climbing rates of the gas turbine unit are respectively;
Figure BDA00030644401300001211
the output of a gas turbine in the microgrid k at the moment t is obtained;
Figure BDA00030644401300001212
and (4) outputting power at the moment t-1 for the gas turbine in the microgrid k.
2.7), microgrid power balance constraint:
Figure BDA00030644401300001213
wherein the content of the first and second substances,
Figure BDA00030644401300001214
outputting uncertain variables for renewable energy sources;
Figure BDA00030644401300001215
is the load of the micro-grid k,
Figure BDA00030644401300001216
charging power and discharging power for stored energy respectively
2.8), energy storage charging and discharging restraint:
Figure BDA00030644401300001217
wherein the content of the first and second substances,
Figure BDA00030644401300001218
is a variable of 0-1, which is respectively in an energy storage charging state and a discharging state;
Figure BDA00030644401300001219
respectively storing charging power and discharging power of the power distribution network k at the moment t,
Figure BDA00030644401300001220
respectively storing the charging power and the discharging power of the power distribution network k at the time t-1;
Figure BDA00030644401300001221
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 BDA0003064440130000131
respectively, charging efficiency and discharging efficiency; ek,tIs the energy storage charge capacity; kE、
Figure BDA0003064440130000132
the energy storage charge capacity upper and lower limits; Δ t represents a scheduling period length; ek,0Indicating initial charge capacity of stored energyAn amount; ek,TAnd represents the energy storage charge capacity at the moment T.
2.9), renewable energy curtailment and loss of load constraints:
Figure BDA0003064440130000133
wherein the content of the first and second substances,
Figure BDA0003064440130000134
respectively the maximum value of the power of the lost load and the abandoned renewable energy source;
2.10), gas turbine output max/min constraints:
Figure BDA0003064440130000135
wherein the content of the first and second substances,
Figure BDA0003064440130000136
respectively the upper and lower limits of the active power output of the gas turbine unit.
In step S3, the uncertainty and the correlation of the renewable energy are processed by using a limit scenario method, where the limit scenario method specifically includes:
defining an error scene as a scene of a renewable energy output deviation predicted value, wherein robust optimization must meet real-time scheduling constraints of all error scenes, but the number of the error scenes is infinite, and solution cannot be performed, so that a limit scene method is introduced.
Firstly, solving the ellipsoid uncertainty set E (Q, c) of the output of the various renewable energy sources established in the step S1 to obtain the vertex omega of the ellipsoid uncertainty set E (Q, c)e,i,i＝1,2,…,2NwT, N abovewThe uncertain set of T-dimensional ellipsoids has 2N in totalwT vertices.
In the convex optimization model, an extreme value necessarily exists at a certain vertex of a polyhedral solution space, and a scene at the vertex of an uncertain set is called as an extreme scene. The extreme scenes have complete robustness for all error scenes, and for the day-ahead scheduling decision variable x, all error scenes can be adapted as long as the real-time scheduling variable y is adjusted to be adapted to all the extreme scenes. And a two-stage robust scheduling model of the power distribution network and the multi-microgrid system can be established according to an extreme scene method.
The two-stage robust scheduling method of the power distribution network and the multi-microgrid system based on the limit scene method established in the step S3 specifically comprises the following steps:
the first stage of the two-stage robust scheduling model of the power distribution network and the multi-microgrid is a day-ahead scheduling stage, namely a day-ahead starting and stopping plan of a gas turbine, a charging and discharging plan of stored energy and power transmission of a day-ahead connecting line of the power distribution network and the multi-microgrid. The second stage is a real-time scheduling stage, and real-time simulation operation is carried out under the day-ahead scheduling scheme of the first stage, so that the optimal economy is taken as a target. The specific mathematical model is as follows:
Figure BDA0003064440130000141
wherein x is a day-ahead scheduling decision variable; subscript s denotes the limit scene number; n is a radical ofsRepresenting the number of limit scenes; xisRepresenting the value of the renewable energy output under the limit scene s; y issRepresenting real-time scheduling decision variables such as line power, voltage and the like in a limit scene; ax is the day-ahead scheduling cost; bys+CξsReal-time scheduling cost under a limit scene s; a is a cost coefficient matrix scheduled day before; B. c is a cost coefficient matrix of real-time scheduling; D. d is a correlation coefficient matrix of the day-ahead scheduling constraint; F. h, L, M, f and H are related coefficient matrixes which are real-time scheduling constraints.
In the step S4, the two-stage robust optimization problem is split into a main sub-problem and a sub-problem by an improved C & CG algorithm based on a limit scenario method, the main problem is solved for an optimal day-ahead scheduling scheme in a single limit scenario and is transmitted to the sub-problem, the sub-problem is to find a worst renewable energy output scenario in the current day-ahead scheduling scheme by the limit scenario method, the main problem decision is affected by adding a relevant real-time scheduling constraint condition of the worst scenario to the main problem, and finally the optimal solution of the robust scheduling problem is iteratively solved by the main sub-problem.
In step S4, the mathematical model of the main problem is:
Figure BDA0003064440130000142
wherein x is day-ahead scheduling decision variables such as a day-ahead startup and shutdown plan of the gas turbine, a charge-discharge plan of stored energy, transmission power of a power distribution network and a multi-microgrid day-ahead connecting line and the like; n represents the current C&The iteration times of the CG algorithm; k is a radical ofmRepresenting the number of the selected limit scene in the mth iteration;
Figure BDA0003064440130000151
as extreme scene kmReal-time scheduling decision variables; eta is the maximum real-time scheduling cost in the main problem limit scene;
Figure BDA0003064440130000152
indicating extreme scenarios kmThe value of the output of the lower renewable energy source;
Figure BDA0003064440130000153
as extreme scene kmReal-time scheduling cost.
Solving the main problem to obtain the value of the day-ahead scheduling decision variable x (solving result)
Figure BDA0003064440130000154
And transmitting to the subproblems, searching the worst scene under the day-ahead scheduling decision of the main problem by the subproblems through a limit scene method, and adding the real-time scheduling constraint of the worst scene to the main problem.
The mathematical model of the sub-problem in step S4 is as follows:
Figure BDA0003064440130000155
wherein, fSPA mathematical expression representing a sub-problem,
Figure BDA0003064440130000156
and obtaining the solution result of the day-ahead scheduling decision variable of the main problem in the nth iteration.
Step S4, solving the real-time scheduling cost of the subproblems in each limit scene by an enumeration method, wherein if the subproblems in all limit scenes have solutions, the severe scene with the largest real-time scheduling cost is the worst scene under the current scheduling condition; if a certain severe scene exists, so that no feasible solution exists in scheduling, the severe scene is the worst scene. Compared with the conventional dual method, the sub-problems of each limit scene in the method are scheduling problems without integer variables, and the solution is relatively simple.
As shown in fig. 3, the iterative solution of the main and sub-problems by the C & CG algorithm in step S4 to obtain the optimal solution of the power distribution network and multi-microgrid scheduling problem includes the following processes:
step S4.1, setting a lower bound LBInfinity, upper bound UBThe number of iterations n of the algorithm is 1 ∞.
S4.2, solving the main problem to obtain the optimal solution of the day-ahead scheduling decision
Figure BDA0003064440130000157
Day-ahead scheduling cost
Figure BDA0003064440130000158
And real-time scheduling cost under the scenario
Figure BDA0003064440130000161
And updating the lower bound value
Figure BDA0003064440130000162
Step S4.3, fixing scheduling decision variables before the day
Figure BDA0003064440130000163
Solving the sub-problems to obtain the sub-problems under all limit scenes
Figure BDA0003064440130000164
S4.4, judging the subproblems in all the limit scenes
Figure BDA0003064440130000165
Whether feasible solutions exist all over, if all the problems exist, the sub-problems under all the limit scenes
Figure BDA0003064440130000166
If all the solutions are feasible, the step S4.5 is carried out; if a sub-problem under a certain limit scene s
Figure BDA0003064440130000167
If there is no feasible solution, the process goes to step S4.6.
S4.5, searching the maximum value of feasible solutions under all limit scenes
Figure BDA0003064440130000168
The maximum value of the corresponding limit scene is the current worst scene S, and the process proceeds to step S4.7.
And S4.6, the current limit scene S is the current worst scene S, and the step S4.9 is carried out.
Step S4.7, according to the maximum value
Figure BDA0003064440130000169
Update the upper bound value
Figure BDA00030644401300001610
Step S4.8 is entered.
S4.8, setting epsilon as convergence criterion, if UB-LBLess than or equal to epsilon to obtain the optimal solution of the day-ahead scheduling decision
Figure BDA00030644401300001611
The iteration process is finished; if U isB-LB> epsilon and proceed to said step S4.9.
S4.9, updating the worst limit scene S of the main problem under the current scheduling condition, and adding the constraint condition corresponding to the worst limit scene S to the main problem; the algorithm iteration number is updated to n ═ n +1, the process returns to step S4.2, and step S4.2 to step S4.9 are repeated.
In another aspect, the present embodiment further provides an electronic device, which includes a processor and a memory, where the memory stores a computer program, and when the computer program is executed by the processor, the method for robust scheduling of a power distribution network and multiple piconets as described above is implemented.
In still another aspect, the present embodiment further provides a readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the method for robust scheduling of a power distribution network and multiple piconets as described above is implemented.
In summary, the embodiment discloses a power distribution network and multi-microgrid robust scheduling method considering output correlation of renewable energy, which includes: firstly, aiming at the uncertainty and the correlation of renewable energy sources, establishing an uncertain set of renewable energy source output ellipsoids by adopting a data driving method based on a minimum volume closed ellipsoid; then, aiming at the uncertainty of adapting to renewable energy sources, taking a gas turbine day-ahead starting and stopping plan, a power distribution network day-ahead electricity purchasing plan and the power transmission power of a distribution network and a multi-microgrid day-ahead connecting line as day-ahead scheduling decision content, and establishing a day-ahead-real-time two-stage economic scheduling model considering the overall operation cost of the distribution network and the multi-microgrid; a renewable energy output limit scene is obtained by solving an ellipsoid uncertain set, uncertainty factors of a scheduling model are processed by adopting a limit scene method, and a two-stage robust scheduling method of a power distribution network and a plurality of micro-grids based on the limit scene method is provided. And finally, splitting the two-stage robust optimization problem into main and sub problems through an improved column constraint generation algorithm based on a limit scene method to solve the main and sub problems in an iterative manner. According to the method and the device, the scheduling robustness of the power distribution network and the multi-microgrid can be guaranteed, meanwhile, the scheduling conservatism of the power distribution network and the multi-microgrid system is reduced, the scheduling economy is improved, meanwhile, the model solution is simple, and the efficiency is high.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
It should be noted that the apparatuses and methods disclosed in the embodiments herein can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments herein. In this regard, each block in the flowchart or block diagrams may represent a module, a program, or a portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
While the present invention has been described in detail with reference to the preferred embodiments, it should be understood that the above description should not be taken as limiting the invention. Various modifications and alterations to this invention will become apparent to those skilled in the art upon reading the foregoing description. Accordingly, the scope of the invention should be determined from the following claims.

Claims (9)

1. A power distribution network and multi-microgrid robust scheduling method is characterized by comprising the following steps:
s1, establishing an ellipsoid uncertain set of various renewable energy output by adopting a data driving method of a minimum volume closed ellipsoid;
step S2, establishing a day-ahead and real-time two-stage economic dispatching model considering the overall dispatching cost of the power distribution network and the multiple micro-grids;
step S3, obtaining a renewable energy output limit scene by solving the ellipsoid uncertain set, processing uncertainty factors of the day-ahead-real-time two-stage economic dispatching model by adopting a limit scene method, and establishing a power distribution network and multi-microgrid two-stage robust dispatching method based on the limit scene method, wherein the method comprises a first stage and a second stage, the first stage is a day-ahead dispatching decision stage and is used for determining a day-ahead startup and shutdown plan of a gas turbine, a day-ahead power purchase plan of the power distribution network and the transmission power of a day-ahead connecting line of the power distribution network and the multi-microgrid;
the second stage is a real-time scheduling stage, and under the decision-making stage before the first stage, the real-time scheduling conditions of the power distribution network and the multi-micro-grid under each limit scene are simulated, and the worst scene is optimized;
and step S4, splitting a two-stage robust optimization problem into main and sub-problems for iterative solution based on the improved C & CG algorithm of the extreme scene method, wherein the main problem in the main and sub-problems is used for solving the optimal day-ahead scheduling scheme under the extreme scene and transmitting the optimal day-ahead scheduling scheme to the sub-problems in the main and sub-problems, and the sub-problems are that the worst scene under the current day-ahead scheduling scheme is searched by the extreme scene method, the main problem decision is influenced by adding the relevant real-time scheduling constraint condition of the worst scene to the main problem, and the main and sub-problems are iteratively solved to obtain the optimal solution of the power distribution network and multi-microgrid scheduling problem.
2. The power distribution network and multi-microgrid robust scheduling method of claim 1,
the step S1 includes: constructing a historical set omega of renewable energy output;
assuming that the power distribution network and the multi-microgrid region have N in commonwA renewable energy output unit for collecting historical data of renewable energy output
Figure FDA0003064440120000023
Dividing by day, and setting the number of days of the collected historical data as Nd: the history set ω is represented by the following formula:
Figure FDA0003064440120000021
Figure FDA0003064440120000022
in the formula, ωiRepresenting output historical data of the renewable energy source unit in each time period on the ith day; t represents a scheduling period, i represents the number of days, and 1-N are takend；
Constructing an ellipsoid uncertain set of the renewable energy output according to the historical data of the renewable energy output;
constructing an N by a data-driven algorithm of a minimum-volume closed ellipsoidWThe T-dimensional ellipsoid surrounds all the historical data of renewable energy output to obtain an optimization model as follows:
minρdetQ-1/2
(ωi-c)TQ(ωi-c)≤1,i＝1,2,…Nd
Figure FDA0003064440120000024
where ρ is a constant and represents NWDimension T encloses the volume of the ellipsoid; q, c is a waiting quantity, Q is NWThe deviation direction of the symmetry axis of the T-dimensional ellipsoid relative to the coordinate axis, and c is NWCentral point of T-dimensional ellipsoid, NdRepresents the number of days of the collected historical data;
solving the optimization model to obtain the uncertain set E (Q, c) of ellipsoids of renewable energy output as follows:
Figure FDA0003064440120000025
in the formula (I), the compound is shown in the specification,
Figure FDA0003064440120000026
represents NwAnd (4) T-dimensional real number set.
3. The power distribution network and multi-microgrid robust scheduling method of claim 2,
the step S2 includes: the objective function of the day-ahead real-time two-stage economic dispatching model mainly comprises day-ahead dispatching cost and real-time dispatching cost of the power distribution network and the multiple micro-grids; the day-ahead real-time two-stage economic dispatching model comprises the following two stages, wherein the first stage of the two stages is a day-ahead dispatching stage, the constraint condition adopts a day-ahead dispatching constraint condition, and the objective function is to realize that the day-ahead dispatching cost and the real-time dispatching cost of the power distribution network and the microgrid are minimum; the day-ahead scheduling phase constraints include gas turbine minimum start/shut-down time constraints and tie-line power transmission constraints;
the second stage is a real-time scheduling stage, the constraint condition adopts a real-time scheduling constraint condition, and the objective function is to realize the minimum cost of the real-time scheduling of the power distribution network and the micro-grid under the determined day-ahead scheduling scheme;
the real-time scheduling stage constraint conditions comprise power distribution network node power balance constraint, power distribution network line voltage balance constraint, power distribution network line capacity constraint, micro-grid gas turbine output climbing constraint, micro-grid power balance constraint, energy storage charging and discharging constraint, renewable energy abandonment and load loss constraint and gas turbine output maximum/minimum constraint.
4. The power distribution network and multi-microgrid robust scheduling method of claim 3, wherein the step S3 comprises: solving the ellipsoid uncertainty set E (Q, c) of the renewable energy output to obtain the vertex omega of the ellipsoid uncertainty sete,i,i＝1,2,…,2NwT, then NwThe uncertain set of T-dimensional ellipsoids has 2N in totalwT vertexes; 2N is mixedwThe scenes at the T vertices are called extreme scenes.
5. The power distribution network and multi-microgrid robust scheduling method of claim 1, wherein the power distribution network and multi-microgrid two-stage robust scheduling method based on the limit scenario method in the step S3 is represented by using the following mathematical model:
Figure FDA0003064440120000031
Dx≤d
Figure FDA0003064440120000032
Figure FDA0003064440120000033
wherein x is a day-ahead scheduling decision variable; subscript s denotes the limit scene number; n is a radical ofsRepresenting the number of limit scenes; xisRepresenting the value of the renewable energy output under the limit scene s; y issRepresenting real-time scheduling decision variables such as line power, voltage and the like in a limit scene; ax is the day-ahead scheduling cost; bys+CξsReal-time scheduling cost under a limit scene s; A. b C, D, F, H, L, M, D, F, H all represent coefficient matrices.
6. The power distribution network and multi-microgrid robust scheduling method of claim 5,
the step S4 includes:
the main problem is expressed by the following formula:
Figure FDA0003064440120000041
Dx≤d
Figure FDA0003064440120000042
Figure FDA0003064440120000043
Figure FDA0003064440120000044
wherein n represents the current C&The iteration times of the CG algorithm; k is a radical ofmRepresenting the number of the limit scene returned by the subproblem during the mth iteration;
Figure FDA0003064440120000045
as extreme scene kmReal-time scheduling decision variables; eta is the maximum real-time scheduling cost in the current main problem limit scene;
Figure FDA0003064440120000046
indicating extreme scenarios kmThe value of the output of the lower renewable energy source;
Figure FDA0003064440120000047
as extreme scene km(ii) a real-time scheduling cost;
solving the main problem to obtain the value of a day-ahead scheduling decision variable x, transmitting the value to the subproblem, searching the worst scene under the day-ahead scheduling condition of the main problem by the subproblem through a limit scene method, and adding the real-time scheduling constraint of the worst scene to the main problem;
the subproblems are represented by a formula:
Figure FDA0003064440120000048
Figure FDA0003064440120000049
Figure FDA00030644401200000410
wherein f isSPA mathematical expression representing a subproblem;
the sub-problems adopt an enumeration method to obtain real-time scheduling cost under each limit scene, and if the sub-problems under all the limit scenes have solutions, the scene with the maximum real-time scheduling cost is the worst scene under the current scheduling condition; if a certain limit scene exists, so that no feasible solution exists for the real-time scheduling problem, the limit scene is the worst scene.
7. The power distribution network and multi-microgrid robust scheduling method of claim 6,
in the step S4, the iterative solution of the main and sub problems by the C & CG algorithm to obtain the optimal solution of the scheduling problem of the power distribution network and the multiple micro grids includes the following steps:
step S4.1, setting a lower bound LBInfinity, upper bound UBThe iteration number n of the algorithm is 1;
s4.2, solving the main problem to obtain the optimal solution of the day-ahead scheduling decision
Figure FDA0003064440120000051
Day-ahead scheduling cost
Figure FDA0003064440120000052
And real-time scheduling cost under the scenario
Figure FDA0003064440120000053
And updating the lower bound value
Figure FDA0003064440120000054
Step S4.3, fixing scheduling decision variables before the day
Figure FDA0003064440120000055
Solving the sub-problems to obtain the sub-problems under all limit scenes
Figure FDA0003064440120000056
S4.4, judging the subproblems in all the limit scenes
Figure FDA0003064440120000057
Whether feasible solutions exist all over, if all the problems exist, the sub-problems under all the limit scenes
Figure FDA0003064440120000058
If all the solutions are feasible, the step S4.5 is carried out; if a sub-problem under a certain limit scene s
Figure FDA0003064440120000059
If no feasible solution exists, the step S4.6 is carried out;
s4.5, searching the maximum value of feasible solutions under all limit scenes
Figure FDA00030644401200000510
The corresponding limit scene of the maximum value is the current worst scene S, and the step S4.7 is carried out;
s4.6, the current extreme scene S is the current worst scene S, and the step S4.9 is entered;
step S4.7, according to the maximum value
Figure FDA00030644401200000511
Update the upper bound value
Figure FDA00030644401200000512
Entering step S4.8;
s4.8, setting epsilon as convergence criterion, if UB-LBLess than or equal to epsilon to obtain the optimal solution of the day-ahead scheduling decision
Figure FDA00030644401200000513
The iteration process is finished; if U isB-LBIf is more than epsilon, entering the step S4.9;
s4.9, updating the worst limit scene S of the main problem under the current scheduling condition, and adding the constraint condition corresponding to the worst limit scene S to the main problem; the algorithm iteration number is updated to n ═ n +1, the process returns to step S4.2, and step S4.2 to step S4.9 are repeated.
8. An electronic device comprising a processor and a memory, the memory having stored thereon a computer program which, when executed by the processor, implements the method of any of claims 1 to 7.
9. A readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method of any one of claims 1 to 7.
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CN114243750A (en) * 2021-11-09 2022-03-25 国网江苏省电力有限公司电力科学研究院 Photovoltaic absorption capacity assessment method and device considering time-space correlation and active management
CN114336749A (en) * 2021-12-30 2022-04-12 国网北京市电力公司 Power distribution network optimization method, system, device and storage medium
CN114726008A (en) * 2022-06-10 2022-07-08 武汉大学 Active power distribution network and multi-microgrid combined robust optimization method and system
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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114243750A (en) * 2021-11-09 2022-03-25 国网江苏省电力有限公司电力科学研究院 Photovoltaic absorption capacity assessment method and device considering time-space correlation and active management
CN114336749A (en) * 2021-12-30 2022-04-12 国网北京市电力公司 Power distribution network optimization method, system, device and storage medium
CN114336749B (en) * 2021-12-30 2023-10-27 国网北京市电力公司 Power distribution network optimization method, system, device and storage medium
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