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CN109687510A - A kind of meter and probabilistic power distribution network Multiple Time Scales optimizing operation method - Google Patents

A kind of meter and probabilistic power distribution network Multiple Time Scales optimizing operation method Download PDF

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CN109687510A
CN109687510A CN201811507881.0A CN201811507881A CN109687510A CN 109687510 A CN109687510 A CN 109687510A CN 201811507881 A CN201811507881 A CN 201811507881A CN 109687510 A CN109687510 A CN 109687510A
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time
constraints
power
distribution network
load
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CN109687510B (en
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顾伟
赵毅
吴志
窦晓波
龙寰
吴在军
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Southeast University
Liyang Research Institute of Southeast University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for AC mains or AC distribution networks
    • H02J2103/30—

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  • Power Engineering (AREA)
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Abstract

The invention discloses a kind of meter and probabilistic power distribution network Multiple Time Scales optimizing operation methods, it include: that step 1) establishes three layers of robust Optimal Operation Model of min-max-min two stages, robust Optimal Operation Model is solved using column constraint generating algorithm (CCG), determines the mode of operation of the slow motion equipment under most severe scene；Short term predicted data of the step 2) based on gray prediction sets objective function, and the mode of operation of the slow motion equipment of constraint condition and determination is run in conjunction with system, establishes the Optimal Operation Model of active distribution network under short-term time scale；Step 3): the ultra-short term prediction data based on gray prediction, comprehensively consider the number of operations of adjustable controllable device and the limitation of operating time, set objective function, the mode of operation of the slow motion equipment of constraint condition and determination is run in conjunction with system, the Optimal Operation Model for establishing active distribution network under ultra-short Time scale can guarantee the area of distributed generation resource high permeability the safety of system very well.

Description

Uncertainty-considered power distribution network multi-time scale optimization operation method
Field of the invention
The invention belongs to the technical field of operation optimization of power distribution networks, and particularly relates to a multi-time scale optimization operation method of a power distribution network, which takes uncertainty into consideration.
Background
Along with a large number of distributed power sources, adjustable loads, adjustable and controllable resources such as reactive compensation devices and the like are connected into a power distribution network, the traditional power distribution network is gradually evolving into an active power distribution network which can realize coordinated control of power generation equipment, energy storage devices and power utilization equipment, and is more flexible and friendly. However, considering that the output of the distributed power supply has randomness and volatility, the prediction precision is low, the prediction error increases along with the increase of time, and the like, higher challenges are provided for the optimal scheduling of the power distribution network, and higher requirements are provided for the safe operation of the power distribution network. How to reasonably arrange the active power output of each distributed power supply, maximize the utilization of the renewable energy output and ensure the economical efficiency and the safety of the operation of the active power distribution network is a key problem to be solved urgently.
Different from the traditional power distribution network active scheduling, due to the coupling relation between the resistance and the reactance of the active power distribution network, the active power optimization can improve the economical efficiency of the system through reasonable optimization scheduling, and the reactive power optimization can reduce the network loss and indirectly improve the economical efficiency of the system. Meanwhile, by considering the prediction error of renewable energy and load and shortening the prediction period, a fine scheduling method of the active power distribution network becomes a key point of research in recent years.
At present, the research on the optimal scheduling of the active power distribution network tends to be mature, but the fine scheduling of the active power distribution network considering uncertainty is still in an exploration stage. Some scholars can reduce the influence of the randomness of distributed power supply output on the power distribution network scheduling to a certain extent only by shortening the prediction period and using methods such as model prediction control, but still are difficult to provide a power distribution network optimal scheduling scheme under the particularly severe uncertain scene. Meanwhile, many scholars adopt a random optimization method and a Monte Carlo method to simulate the worst scene, but the selected scene is difficult to cover all possible scenes. Therefore, the key point of the problem is to establish an active power distribution network multi-time scale optimization operation model considering photovoltaic and load uncertainty, so that an optimal scheduling scheme of the active power distribution network can be provided in any scene (including the worst scene), and the economy and the safety of the system are ensured.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a distribution network multi-time scale optimization operation method considering uncertainty, which solves a robust optimization scheduling model by establishing a min-max-min two-stage three-layer robust optimization scheduling model and utilizing a column constraint generation algorithm to determine the operation state of slow-motion equipment in the worst scene; and then based on short-term prediction data of gray prediction, establishing an optimized scheduling model of the active power distribution network under a short-time scale by using the lowest total operation cost of the system in 4 hours in the future as an objective function and combining the operation constraint condition which the system should meet and the previously determined operation state of the slow-acting equipment, and establishing an optimized scheduling model of the active power distribution network under an ultra-short-time scale by comprehensively considering the operation times and the operation time limit of the adjustable controllable equipment in the ultra-short time of the system and combining the operation constraint condition which the system should meet and the previously determined operation state of the slow-acting equipment in the real-time feedback stage based on the ultra-short-term prediction data of gray prediction.
In order to achieve the purpose, the invention adopts the technical scheme that: a multi-time scale optimization operation method for a power distribution network considering uncertainty comprises the following steps:
s1, determining the operation state of the slow-motion device before the day: establishing a min-max-min two-stage three-layer robust optimization scheduling model, solving the robust optimization scheduling model by using a column constraint generation algorithm, and determining the operation state of the slow motion equipment in the worst scene;
s2, establishing an optimized scheduling model of the active power distribution network under a rolling short time scale in a day: setting a target function based on short-term prediction data of grey prediction, and establishing an optimized scheduling model of the active power distribution network under a short-time scale by combining operation constraint conditions which should be met by the system and the operation state of the slow-motion equipment determined in the step S1;
s3, establishing an optimized scheduling model of the active power distribution network under real-time ultra-short time scale: and (4) setting a target function by comprehensively considering the operation times of the adjustable controllable equipment and the limit of the operation time based on the ultra-short-term prediction data of the grey prediction and combining the operation constraint conditions which should be met by the system and the operation state of the slow-motion equipment determined in the step S1, and establishing an optimized scheduling model of the active power distribution network under the ultra-short time scale.
As an improvement of the present invention, the slow motion device in step S1 at least includes a load voltage regulator OLTC and a group switched capacitor bank CB.
As another improvement of the present invention, the building of the robust optimized scheduling model in step S1 further includes:
s11, establishing an objective function of robust optimization scheduling considering photovoltaic and load uncertainty, wherein the objective function is as follows:
wherein,exchanging cost for power of the power distribution network and the main network connecting line;andthe gas turbine DG cost, interruptible load IL and energy storage ESS cost, respectively;andthe compensation costs of the compensation capacitor CB and the on-load voltage regulator OLTC, respectively;
s12, establishing a constraint condition: the constraint conditions at least comprise power balance constraint, uncertainty set constraint of photovoltaic and load, system safety constraint, operation constraint of a reactive compensation device SVC, operation constraint of a group switching capacitor bank CB, related constraint of a distributed power supply, energy storage constraint, operation constraint of a load voltage regulator and operation constraint of interruptible load.
As another improvement of the present invention, in step S11,
said gas turbine DG costComprises the following steps:
the interruptible load IL costComprises the following steps:
cost of the energy storage ESSComprises the following steps:
compensation cost of the compensation capacitor CBComprises the following steps:
compensation cost of the on-load voltage regulator OLTCComprises the following steps:
wherein, c1,c2,c3Is the cost coefficient of DG;and rCBCompensation cost coefficients for IL, OLTC and CB, respectively; delta UTAnd Δ UCBThe times of the whole-day adjustment of the OLTC gear and the CB gear respectively can be adjusted only every timeOne gear position;anda node set for a connected gas turbine, a medium load, an on-load voltage regulator, a compensation capacitor and an energy storage device; n is a radical oftFor the whole scheduling period, N ist＝24h。
As still another improvement of the present invention, the step S12 further includes:
s121, establishing power balance constraint
Wherein: set u (j) represents the set of head-end nodes of the branch with j as the end node; set v (j) represents the set of end nodes of a branch with j as the head-end node;andrespectively the active power and the reactive power of the ij branch at the moment t;is the voltage value of j node at the time t;the current value of the branch circuit ij at the time t;andnet injected values of active and reactive power, respectively, at j node at time t； Andrespectively representing the load active power of a j node at the time t, the charging and discharging power of the ESS, the active power of the photovoltaic PV, the active power of the gas turbine and the active power of the interruptible load; andload reactive power, reactive compensation device SVC compensation power, PV reactive power, reactive power of a grouping switching capacitor CB, reactive power of a gas turbine and reactive power of an energy storage device which are connected with j nodes at the time t respectively; r isijAnd xijThe resistance and reactance of branch ij are respectively; k is a radical ofij,tThe switching gear of the OLTC connected with the ij branch at the time t;
s122, establishing an uncertainty set of the photovoltaic and the load:
wherein:predicted values of photovoltaic output, maximum upper limitDeviation and maximum lower limit deviation value;respectively a predicted value, a maximum upper limit deviation and a maximum lower limit deviation of the load;is a variable from 0 to 1;
s123, establishing system safety constraint
Wherein:andthe upper limit and the lower limit of the j node voltage amplitude respectively;the upper limit value of the ij branch current is;
s124, establishing operation constraint of the SVC
Wherein:andrespectively of reactive power compensation meansUpper and lower limits of power output;
s125, establishing operation constraint of the group switching capacitor bank CB
Wherein:the compensation power for each group of capacitors;andrespectively are 0-1 marks of switching operation whenIndicating that at time t j node increases the commissioning of a group of CBs,the same process is carried out;the upper limit of the maximum group number is switched every time;the upper limit of the switching times of the capacitor bank;
s126, establishing related constraints of the distributed power supply, wherein the related constraints of the distributed power supply comprise photovoltaic constraints and micro gas turbine constraints, and the specific steps are as follows:
s1261, photovoltaic restraint
Wherein:representing a predicted value of photovoltaic contribution;the maximum output power of the photovoltaic inverter is obtained;
s1262, micro gas turbine constraints
Wherein:the maximum output power of the inverter;limiting the climbing of the micro gas turbine;
s127, establishing energy storage constraint
Wherein:representing the ESS charge at node j at time t ηchAnd ηdisRespectively charge and discharge efficiency;andrespectively are the maximum values of charge and discharge power;
s128, establishing operation constraint of the on-load voltage regulator
kij,t＝kij0+Mij,tΔkij,t
Wherein: mij,tThe gear of the OLTC connected with the ij branch at the time t;the upper limit and the lower limit of the OLTC gear connected with the ij branch; k is a radical ofij0Is the initial value of the gear; Δ kij,tThe difference value of two adjacent gears of the OLTC;
s129, establishing operation constraint of interruptible load
Wherein:an upper bound for the interruptible load of the j node.
As a further improvement of the present invention, the objective function in step S2 aims at the lowest total operating cost of the system in the future 4 hours, and implements 4 h-cycle rolling optimization scheduling, that is:
wherein:the communication power with the main network at the moment t of the rolling stage in the day, namely the electricity purchasing amount, is represented;andand respectively representing the controllable distributed power supply and the energy storage cost of the i node at the time t of the rolling stage in the day.
As another improvement of the present invention, in step S2, the constraint conditions of the optimized scheduling model of the active distribution network at the short time scale sequentially include: step S121-step S124, step S126, step S127 and step S129.
As a further improvement of the present invention, the step S3 objective function aims at minimizing the adjustment amount of the adjustable and controllable device within the system ultra-short time, and the system ultra-short time is set to be within 5min, so as to implement rolling optimization scheduling with 5min as a period, that is:
wherein: u represents a set of adjustable and controllable resources in a real-time feedback stage; u. ofFK.real，ΔuFKAnd uDIRespectively representing the output value of the controllable resource in the real-time feedback stage, the output adjustment value of the adjustable controllable resource and the output value of the adjustable controllable resource in the day rolling stage.
As a further improvement of the present invention, the constraint conditions of the optimized scheduling model of the active distribution network at the ultra-short time scale in step S3 sequentially include: step S121-step S124, step S126, step S127 and step S129.
Compared with the prior art, the multi-time scale optimization operation method for the power distribution network considering uncertainty is characterized in that a min-max-min three-layer robust optimization scheduling model is established to determine the operation state of the day-ahead slow-motion equipment, and multi-time scale optimization solution is realized based on short-term prediction data and ultra-short-term prediction data. The model provided by the invention mainly considers the problem of uncertainty of photovoltaic and load, a box-type uncertain set is adopted in the model to describe uncertainty variables, and a CC & G algorithm is used for solving a min-max-min three-layer robust model, so that the method has better economic benefit compared with the traditional multi-time scale optimization model in the worst scene, and meanwhile, the CC & G algorithm is used for solving, the convergence speed is high, and the iteration times are few.
Secondly, on the basis of a prior robust model, the system objective functions in different scheduling periods are comprehensively considered to be different, a refined scheduling model of the active power distribution network is established, the optimized model is a mixed integer linear programming model, and a mature solver (such as CPLEX) can be called to solve, so that the output state of the adjustable and controllable equipment in the worst scene can be determined.
In addition, the established fine scheduling model of the active power distribution network considering uncertainty can well ensure the safety of the system for the areas with high permeability of the distributed power supply.
Drawings
FIG. 1 is a flow chart of the method of optimizing operation of the present invention;
FIG. 2 is a system configuration diagram in embodiment 1 of the present invention;
fig. 3 is electricity purchase price data in embodiment 1 of the present invention;
FIG. 4 is photovoltaic and load forecast data at a previous date stage in example 1 of the present invention;
FIG. 5 is photovoltaic and load forecast data for the rolling phase within the day in example 1 of the present invention;
FIG. 6 shows photovoltaic and load forecast data during the real-time feedback phase in example 1 of the present invention;
fig. 7 is a diagram showing simulation results of each adjustable controllable device in embodiment 1 of the present invention.
Detailed Description
The invention will be explained in more detail below with reference to the drawings and examples.
Example 1
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The system structure in this embodiment is shown in fig. 2, the system is composed of photovoltaic (PV1, PV2), gas turbine (MT1, MT2), reactive power compensation device (SVC1, SVC2), energy storage device (ESS1, ESS2), Interruptible Load (IL), and group-switched Capacitor Bank (CB), and the parameters and connection positions of each device are shown in table 1; meanwhile, the comparison of the total operation cost of the system in the worst scene is shown in a table 2; the system is connected with the power grid, and the electricity is purchased from the power grid but not sold to the power grid, and the electricity price data and the load data are respectively shown in figures 3-6.
Table 1 equipment parameters in the calculation
TABLE 2 comparison of operating costs
Text model Traditional multi-time scale model
Total cost (Yuan) 32931.2 34221.5
In this case, the Cplex algorithm package is used to develop the active power distribution network scheduling model considering the uncertainty of the photovoltaic and the load in the Matlab environment, and the output of each adjustable and controllable device is shown in fig. 7.
A multi-time scale optimization operation method of a power distribution network based on photovoltaic and load uncertainty is shown in figure 1 and comprises the following steps:
s1, determining the operation state of the slow-motion device before the day: establishing a min-max-min two-stage three-layer robust optimization scheduling model, solving the robust optimization scheduling model by using a column constraint generation algorithm, and determining the operation state of slow-acting equipment in the worst scene, wherein the slow-acting equipment at least comprises a load voltage regulator OLTC and a group switching capacitor bank CB;
the establishment of the robust optimized scheduling model further comprises the following steps:
s11, establishing an objective function of robust optimization scheduling considering photovoltaic and load uncertainty, wherein the objective function is as follows:
wherein,exchanging cost for power of the power distribution network and the main network connecting line;andthe gas turbine DG cost, interruptible load IL and energy storage ESS cost, respectively;anda compensation capacitor CB and an on-load voltage regulator are respectively arranged;
said gas turbine DG costComprises the following steps:
the interruptible load IL costComprises the following steps:
cost of the energy storage ESSComprises the following steps:
compensation cost of the compensation capacitor CBComprises the following steps:
compensation cost of the on-load voltage regulator OLTCComprises the following steps:
wherein, c1,c2,c3Is the cost coefficient of DG;and rCBAre IL, OLTC and CB, respectivelyThe compensation cost factor of (2); delta UTAnd Δ UCBThe times of all-day adjustment of the OLTC gear and the CB gear are respectively, and only one gear can be adjusted each time;anda node set for a connected gas turbine, a medium load, an on-load voltage regulator, a compensation capacitor and an energy storage device; n is a radical oftFor the whole scheduling period, N istCompensation cost of 24 hloltc;
s12, establishing a constraint condition: the constraint conditions at least comprise power balance constraint, uncertainty set constraint of photovoltaic and load, system safety constraint, operation constraint of a reactive compensation device SVC, operation constraint of a group switching capacitor bank CB, related constraint of a distributed power supply, energy storage constraint, operation constraint of a load voltage regulator and operation constraint of interruptible load, and specifically comprise the following steps:
s121, establishing power balance constraint
Wherein: set u (j) represents the set of head-end nodes of the branch with j as the end node; set v (j) represents the set of end nodes of a branch with j as the head-end node;andrespectively the active power and the reactive power of the ij branch at the moment t;is the voltage value of j node at the time t;the current value of the branch circuit ij at the time t;andrespectively the net injection values of the active power and the reactive power of the j node at the time t; andrespectively representing the load active power of a j node at the time t, the charging and discharging power of the ESS, the active power of the photovoltaic PV, the active power of the gas turbine and the active power of the interruptible load; andload reactive power, reactive compensation device SVC compensation power, PV reactive power, reactive power of a grouping switching capacitor CB, reactive power of a gas turbine and reactive power of an energy storage device which are connected with j nodes at the time t respectively; r isijAnd xijThe resistance and reactance of branch ij are respectively; k is a radical ofij,tThe switching gear of the OLTC connected with the ij branch at the time t;
s122, establishing an uncertainty set of the photovoltaic and the load:
wherein:respectively a predicted value, a maximum upper limit deviation value and a maximum lower limit deviation value of the photovoltaic output;respectively a predicted value, a maximum upper limit deviation and a maximum lower limit deviation of the load;is a variable from 0 to 1;
s123, establishing system safety constraint
Wherein:andthe upper limit and the lower limit of the j node voltage amplitude respectively;the upper limit value of the ij branch current is;
s124, establishing operation constraint of the SVC
Wherein:andthe upper limit value and the lower limit value of the reactive power output of the reactive power compensation device are respectively;
s125, establishing operation constraint of the group switching capacitor bank CB
Wherein:the compensation power for each group of capacitors;and0 + of switching operation respectively1 denotes whenIndicating that at time t j node increases the commissioning of a group of CBs,the same process is carried out;the upper limit of the maximum group number is switched every time;the upper limit of the switching times of the capacitor bank;
s126, establishing related constraints of the distributed power supply, wherein the related constraints of the distributed power supply comprise photovoltaic constraints and micro gas turbine constraints, and the specific steps are as follows:
s1261, photovoltaic restraint
Wherein:representing a predicted value of photovoltaic contribution;the maximum output power of the photovoltaic inverter is obtained;
s1262, micro gas turbine constraints
Wherein:the maximum output power of the inverter;limiting the climbing of the micro gas turbine;
s127, establishing energy storage constraint
Wherein:representing the ESS charge at node j at time t ηchAnd ηdisRespectively charge and discharge efficiency;andrespectively are the maximum values of charge and discharge power;
s128, establishing operation constraint of the on-load voltage regulator
kij,t＝kij0+Mij,tΔkij,t
Wherein: mij,tThe gear of the OLTC connected with the ij branch at the time t;the upper limit and the lower limit of the OLTC gear connected with the ij branch; k is a radical ofij0Is the initial value of the gear; Δ kij,tThe difference value of two adjacent gears of the OLTC;
s129, establishing operation constraint of interruptible load
Wherein:an upper bound for the interruptible load of the j node.
S2, establishing an optimized scheduling model of the active power distribution network under a rolling short time scale in a day: setting a target function based on short-term prediction data of grey prediction, and establishing an optimized scheduling model of the active power distribution network under a short-time scale by combining operation constraint conditions which should be met by the system and the operation state of the slow-motion equipment determined in the step S1;
s21, establishing an objective function:
and (4) realizing rolling optimization scheduling with a period of 4h based on short-term prediction data of photovoltaic and load. The objective function of the rolling optimization is to minimize the running cost in one rolling scheduling period (4h), namely:
in the formula:the communication power with the main network at the moment t of the rolling stage in the day, namely the electricity purchasing amount, is represented;andand respectively representing the controllable distributed power supply and the energy storage cost of the i node at the time t of the rolling stage in the day.
S22, establishing a constraint condition: the conditions include in sequence: step S121-step S124, step S126, step S127 and step S129.
S3, establishing an optimized scheduling model of the active power distribution network under real-time ultra-short time scale: the ultra-short-term prediction data based on gray prediction comprehensively considers the operation times of the adjustable controllable equipment and the limitation of operation time, sets a target function, and establishes an optimized scheduling model of the active power distribution network under the ultra-short time scale by combining the operation constraint condition which the system should meet and the operation state of the slow-motion equipment determined in the step S1, specifically as follows:
s31, establishing an objective function:
and rolling optimization scheduling with 5min as a period is realized based on ultra-short-term prediction data of photovoltaic and load. Considering the operation time of the adjustable controllable resource, the objective function of the real-time feedback is to minimize the output adjustment of the adjustable controllable resource in one scheduling period:
in the formula: u represents a set of adjustable and controllable resources in a real-time feedback stage; u. ofFK.real，ΔuFKAnd uDIRespectively representing the output value of the controllable resource in the real-time feedback stage, the output adjustment value of the adjustable controllable resource and the output value of the adjustable controllable resource in the day rolling stage.
S32, establishing a constraint condition: the constraint conditions sequentially comprise: step S121-step S124, step S126, step S127 and step S129.
In this embodiment, step S1 may be to separately identify a main problem and a sub problem, where the sub problem may be converted into a linear max problem through a dual algorithm and a large _ M algorithm, and the model is solved through a CCG algorithm; the refined scheduling models in step S2 and step S3 are both mixed integer nonlinear problems, and both can be solved using a mature solver.
Therefore, according to the established objective function and the set constraint conditions, the real-time output of various adjustable and controllable devices in the worst scene of the active power distribution network is determined, and the safe and economic operation of the system is ensured.
In summary, in the embodiments of the present invention, the operation state of the slow-motion device determined by the previous robust model of the active power distribution network is first established, and then a model for actively refining the power distribution network scheduling is established based on the short-term and ultra-short-term prediction data of the gray prediction. The influence of the uncertainty of the renewable energy sources on the optimal scheduling of the power distribution network can be well dealt with.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited by the foregoing examples, which are provided to illustrate the principles of the invention, and that various changes and modifications may be made without departing from the spirit and scope of the invention, which is also intended to be covered by the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (9)

1.一种计及不确定性的配电网多时间尺度优化运行方法，其特征在于，包括如下步骤：1. a multi-time-scale optimal operation method for a distribution network taking into account uncertainty, is characterized in that, comprises the steps: S1，日前慢动作设备的操作状态确定：建立min-max-min两阶段三层鲁棒优化调度模型，利用列约束生成算法对鲁棒优化调度模型求解，确定最恶劣场景下的慢动作设备的操作状态；S1, determine the operating state of the slow-motion equipment recently: establish a min-max-min two-stage three-layer robust optimal scheduling model, use the column constraint generation algorithm to solve the robust optimal scheduling model, and determine the slow-motion equipment in the worst scenario. operating state; S2，日内滚动短时间尺度下主动配电网的优化调度模型建立：基于灰色预测的短期预测数据，设定目标函数，结合系统应该满足的运行约束条件以及步骤S1确定的慢动作设备的操作状态，建立短时间尺度下主动配电网的优化调度模型；S2, the establishment of an optimal scheduling model for the active distribution network under the rolling short time scale within a day: based on the short-term forecast data of the grey forecast, set the objective function, combine the operating constraints that the system should meet and the operating state of the slow-motion equipment determined in step S1 , to establish the optimal dispatch model of active distribution network in short time scale; S3，实时超短时间尺度下主动配电网的优化调度模型建立：基于灰色预测的超短期预测数据，综合考虑可调可控设备的操作次数和操作时间的限制，设定目标函数，结合系统应该满足的运行约束条件以及步骤S1确定的慢动作设备的操作状态，建立超短时间尺度下主动配电网的优化调度模型。S3. Establishment of optimal dispatching model for active distribution network under real-time ultra-short time scale: Based on the ultra-short-term forecast data of grey forecast, comprehensively consider the limitation of the number of operations and operation time of the adjustable and controllable equipment, set the objective function, combine the system The operating constraints that should be satisfied and the operating state of the slow-motion equipment determined in step S1 are used to establish an optimal scheduling model for the active distribution network on an ultra-short time scale. 2.如权利要求1所述的一种计及不确定性的配电网多时间尺度优化运行方法，其特征在于所述步骤S1中的慢动作设备至少包括有载调压器OLTC和分组投切电容器组CB。2. The multi-time-scale optimal operation method for a distribution network considering uncertainty as claimed in claim 1, characterized in that the slow-motion equipment in the step S1 at least includes an on-load voltage regulator OLTC and a grouped switch. Cut capacitor bank CB. 3.如权利要求1或2所述的一种计及不确定性的配电网多时间尺度优化运行方法，其特征在于所述步骤S1中鲁棒优化调度模型的建立进一步包括：3. The multi-time-scale optimal operation method for a distribution network considering uncertainty as claimed in claim 1 or 2, wherein the establishment of the robust optimal scheduling model in the step S1 further comprises: S11，建立考虑光伏和负荷不确定性的鲁棒优化调度的目标函数，所述目标函数为：S11, establish an objective function for robust optimal scheduling considering photovoltaic and load uncertainties, where the objective function is: 其中，为配电网与主网联络线功率交换成本；和分别为燃气轮机DG成本，可中断负荷IL与储能装置ESS成本；和分别为补偿电容器CB和有载调压器OLTC的补偿成本；in, The cost of power exchange between the distribution network and the main network tie line; and are the gas turbine DG cost, the interruptible load IL and the energy storage device ESS cost; and are the compensation costs of the compensation capacitor CB and the on-load voltage regulator OLTC, respectively; S12，建立约束条件：所述约束条件至少包括功率平衡约束、光伏和负荷的不确定性集约束、系统安全约束、无功补偿装置SVC的运行约束、分组式投切电容器组CB的运行约束、分布式电源相关约束、储能约束、有载调压器的运行约束及可中断负荷的运行约束。S12, establishing constraints: the constraints at least include power balance constraints, uncertainty set constraints of photovoltaics and loads, system safety constraints, operation constraints of the reactive power compensation device SVC, and operation constraints of the grouped switching capacitor bank CB, Distributed power-related constraints, energy storage constraints, operating constraints of on-load voltage regulators and operational constraints of interruptible loads. 4.如权利要求3所述的一种计及不确定性的配电网多时间尺度优化运行方法，其特征在于所述步骤S11中，4. A multi-time-scale optimization operation method for power distribution network considering uncertainty as claimed in claim 3, characterized in that in the step S11, 所述燃气轮机DG成本为： The gas turbine DG cost for: 所述可中断负荷IL成本为： The interruptible load IL cost for: 所述储能装置ESS的成本为： The cost of the energy storage device ESS for: 所述补偿电容器CB的补偿成本为： The compensation cost of the compensation capacitor CB for: 所述有载调压器OLTC的补偿成本为： Compensation cost of the on-load regulator OLTC for: 其中，c1,c2,c3是DG的成本系数；和rCB分别是IL，OLTC和CB的补偿成本系数；ΔUT和ΔUCB分别为OLTC档位和CB档位的全天调整次数，每次仅能调节一个档位；以及为连接的燃气轮机，可中负荷，有载调压器，补偿电容器以及储能装置的节点集合；Nt为整个调度周期，所述Nt＝24h。Among them, c 1 , c 2 , c 3 are the cost coefficients of DG; and r CB are the compensation cost coefficients of IL, OLTC and CB respectively; ΔU T and ΔU CB are the adjustment times of OLTC gear and CB gear in the whole day, and only one gear can be adjusted at a time; as well as It is the node set of the connected gas turbine, the mid-load, the on-load voltage regulator, the compensation capacitor and the energy storage device; N t is the entire dispatch period, and the N t =24h. 5.如权利要求4所述的一种计及不确定性的配电网多时间尺度优化运行方法，其特征在于所述步骤S12进一步包括：5. The multi-time-scale optimal operation method for power distribution network considering uncertainty as claimed in claim 4, characterized in that the step S12 further comprises: S121，建立功率平衡约束S121, establish power balance constraints 其中：集合u(j)表示以j为末端节点的支路的首端节点的集合；集合v(j)表示以j为首端节点的支路的末端节点的集合；和分别为t时刻ij支路的有功功率和无功功率；为t时刻j节点的电压值；为t时刻ij支路的电流值；和分别为t时刻j节点的有功功率和无功功率的净注入值； 以及分别代表t时刻j节点的负荷有功功率，ESS充放电功率，光伏PV的有功功率，燃气轮机的有功功率以及可中断负荷的有功功率； 以及分别t时刻j节点所连接的负荷无功功率，无功补偿装置SVC补偿功率，PV的无功功率，分组投切电容器CB的无功功率，燃气轮机的无功功率以及储能装置的无功功率；rij和xij分别为支路ij的电阻和电抗；kij,t为t时刻ij支路所连接的OLTC的投切档位；Wherein: set u(j) represents the set of head-end nodes of the branch with j as the end node; set v(j) represents the set of end nodes of the branch with j as the head-end node; and are the active power and reactive power of the ij branch at time t, respectively; is the voltage value of node j at time t; is the current value of the ij branch at time t; and are the net injection values of active power and reactive power at node j at time t, respectively; as well as respectively represent the active power of the load at node j at time t, the charging and discharging power of the ESS, the active power of the photovoltaic PV, the active power of the gas turbine and the active power of the interruptible load; as well as Respectively, the reactive power of the load connected to node j at time t, the compensation power of the reactive power compensation device SVC, the reactive power of the PV, the reactive power of the group switching capacitor CB, the reactive power of the gas turbine and the reactive power of the energy storage device ; r ij and x ij are the resistance and reactance of the branch ij respectively; k ij, t is the switching gear of the OLTC connected to the branch ij at time t; S122，建立光伏和负荷的不确定性集：S122, establish the uncertainty set of photovoltaic and load: 其中：分别为光伏出力的预测值，最大上限偏差和最大下限偏差值；分别为负荷的预测值，最大上限偏差和最大下限偏差值；为0-1变量；in: are the predicted value of photovoltaic output, the maximum upper limit deviation and the maximum lower limit deviation; are the predicted value of the load, the maximum upper limit deviation and the maximum lower limit deviation; is a 0-1 variable; S123，建立系统安全约束S123, establish system security constraints 其中：和分别为j节点电压幅值的上下限；为ij支路电流的上限值；in: and are the upper and lower limits of the voltage amplitude at node j, respectively; is the upper limit value of the ij branch current; S124，建立无功补偿装置SVC的运行约束S124, establish the operating constraints of the reactive power compensation device SVC 其中：和分别为无功补偿装置的无功出力的上下限值；in: and are the upper and lower limits of the reactive power output of the reactive power compensation device; S125，建立分组式投切电容器组CB的运行约束S125, establish the operation constraints of the grouped switching capacitor bank CB 其中：为每组电容器的补偿功率；和分别为投切操作的0-1标识，当表示t时刻j节点增加一组CB的投运，同理；为每次投切最大组数的上限；为电容器组投切次数的上限；in: Compensation power for each group of capacitors; and are the 0-1 flags of the switching operation, respectively, when Indicates that the node j at time t adds a group of CBs to the operation, the same; It is the upper limit of the maximum number of groups for each switch; is the upper limit of the switching times of the capacitor bank; S126，建立分布式电源相关约束，所述分布式电源相关约束包括光伏约束和微型燃气轮机约束，具体如下：S126, establish distributed power related constraints, the distributed power related constraints include photovoltaic constraints and micro gas turbine constraints, as follows: S1261，光伏约束S1261, PV Constraints 其中：表示光伏出力的预测值；为光伏逆变器最大输出功率；in: Indicates the predicted value of photovoltaic output; is the maximum output power of the photovoltaic inverter; S1262，微型燃气轮机约束S1262, Microturbine Constraints 其中：为逆变器最大输出功率；为微型燃气轮机爬坡约束限值；in: is the maximum output power of the inverter; is the limit value of the micro-turbine climbing constraint; S127，建立储能约束S127, establish energy storage constraints 其中：表示t时刻j节点的ESS电量；ηch和ηdis分别为充放电效率；和分别为充放电功率的最大值；in: Represents the ESS power of node j at time t; ηch and ηdis are charge and discharge efficiencies, respectively; and are the maximum values of charge and discharge power, respectively; S128，建立有载调压器的运行约束S128, establish operating constraints for on-load voltage regulators kij,t＝kij0+Mij,tΔkij,t k ij,t =k ij0 +M ij,t Δk ij,t 其中：Mij,t为t时刻ij支路所连接的OLTC的档位；为ij支路所连接的OLTC档位的上下限；kij0为档位的初始值；Δkij,t为OLTC两个相邻档位差值；Wherein: M ij,t is the gear of the OLTC connected by the ij branch at time t; is the upper and lower limits of the OLTC gears connected by the ij branch; k ij0 is the initial value of the gear; Δk ij,t is the difference between two adjacent gears of the OLTC; S129，建立可中断负荷的运行约束S129, establish operating constraints for interruptible loads 其中：为j节点可中断负荷的上限。in: It is the upper limit of the interruptible load of the j node. 6.如权利要求1或2所述的一种计及不确定性的配电网多时间尺度优化运行方法，其特征在于所述步骤S2中目标函数以系统未来4小时内的总运行成本最低为目标，实现以4h为周期的滚动优化调度，即：6. A multi-time-scale optimal operation method for a distribution network considering uncertainty as claimed in claim 1 or 2, characterized in that in the step S2, the objective function is the lowest total operation cost of the system in the next 4 hours To achieve the goal of rolling optimization scheduling with a period of 4h, namely: 其中：表示日内滚动阶段t时刻的与主网的联络功率即购电量；和分别表示日内滚动阶段t时刻i节点的可控分布式电源和储能成本。in: Indicates the contact power with the main network at time t in the rolling phase of the day, namely the purchased electricity; and are the controllable distributed power and energy storage costs of node i at time t in the intraday rolling stage, respectively. 7.如权利要求5所述的一种计及不确定性的配电网多时间尺度优化运行方法，其特征在于所述步骤S2中短时间尺度下主动配电网的优化调度模型的约束条件依次包括：步骤S121-步骤S124、步骤S126、步骤S127及步骤S129。7. The multi-time-scale optimal operation method for a distribution network considering uncertainty as claimed in claim 5, characterized in that the constraints of the optimal scheduling model of the active distribution network under short time scales in the step S2 The sequence includes: step S121-step S124, step S126, step S127 and step S129. 8.如权利要求1或2所述的一种计及不确定性的配电网多时间尺度优化运行方法，其特征在于所述步骤S3目标函数以系统超短时间内可调可控设备的调整量最小为目标，所述系统超短时间设定为5min内，实现以5min为周期的滚动优化调度，即：8. A multi-time-scale optimal operation method for a distribution network considering uncertainty as claimed in claim 1 or 2, characterized in that the objective function of step S3 is based on the value of the adjustable and controllable equipment in a system ultra-short time. The minimum adjustment amount is the goal, and the ultra-short time of the system is set to within 5 minutes to realize the rolling optimization scheduling with a period of 5 minutes, namely: 其中：U代表实时反馈阶段可调可控资源的集合；uFK.real，ΔuFK和uDI分别代表实时反馈阶段的可控资源的输出值，可调可控资源的出力调整值和日内滚动阶段可调可控资源的输出值。Among them: U represents the set of adjustable and controllable resources in the real-time feedback stage; u FK.real , Δu FK and u DI respectively represent the output value of the controllable resources in the real-time feedback stage, the output adjustment value of the adjustable and controllable resources and the intraday rolling A stage adjusts the output value of a controllable resource. 9.如权利要求5所述的一种计及不确定性的配电网多时间尺度优化运行方法，其特征在于所述步骤S3中超短时间尺度下主动配电网的优化调度模型的约束条件依次包括：步骤S121-步骤S124、步骤S126、步骤S127及步骤S129。9. A multi-time-scale optimal operation method for a distribution network considering uncertainty as claimed in claim 5, characterized in that the constraints of the optimal dispatch model of the active distribution network under the ultra-short time scale in the step S3 The sequence includes: step S121-step S124, step S126, step S127 and step S129.
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