CN109687510B - Uncertainty-considered power distribution network multi-time scale optimization operation method - Google Patents

Uncertainty-considered power distribution network multi-time scale optimization operation method Download PDF

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CN109687510B
CN109687510B CN201811507881.0A CN201811507881A CN109687510B CN 109687510 B CN109687510 B CN 109687510B CN 201811507881 A CN201811507881 A CN 201811507881A CN 109687510 B CN109687510 B CN 109687510B
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CN109687510A (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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Liyang Research Institute of Southeast University
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    • H02J3/382—
    • 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
    • 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
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Abstract

The invention discloses a power distribution network multi-time scale optimization operation method considering uncertainty, which comprises the following steps: step 1) 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 (CCG), and determining the operation state of slow motion equipment in the worst scene; step 2) 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 system operation constraint conditions and the determined operation state of the slow-motion equipment; step 3): the method comprises the steps of setting a target function by comprehensively considering the operation times and the operation time limit of adjustable controllable equipment based on ultra-short-term prediction data of gray prediction, and establishing an optimized scheduling model of the active power distribution network under an ultra-short time scale by combining system operation constraint conditions and the determined operation state of slow-acting equipment, so that the safety of the system can be well guaranteed for areas with high permeability of distributed power supplies.

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:
Figure GDA0002898625760000031
wherein the content of the first and second substances,
Figure GDA0002898625760000032
exchanging cost for power of the power distribution network and the main network connecting line;
Figure GDA0002898625760000033
and
Figure GDA0002898625760000034
the gas turbine DG cost, interruptible load IL and energy storage ESS cost, respectively;
Figure GDA0002898625760000035
and
Figure GDA0002898625760000036
the 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 cost
Figure GDA0002898625760000037
Comprises the following steps:
Figure GDA0002898625760000038
the interruptible load IL cost
Figure GDA0002898625760000039
Comprises the following steps:
Figure GDA00028986257600000310
cost of the energy storage ESS
Figure GDA00028986257600000311
Comprises the following steps:
Figure GDA00028986257600000312
compensation cost of the compensation capacitor CB
Figure GDA00028986257600000313
Comprises the following steps:
Figure GDA00028986257600000314
compensation cost of the on-load voltage regulator OLTC
Figure GDA00028986257600000315
Comprises the following steps:
Figure GDA00028986257600000316
wherein, c1,c2,c3Is the cost coefficient of DG;
Figure GDA00028986257600000317
and rCBCompensation cost coefficients for IL, OLTC and CB, respectively; 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;
Figure GDA00028986257600000318
and
Figure GDA00028986257600000319
a 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
Figure GDA0002898625760000041
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;
Figure GDA0002898625760000042
and
Figure GDA0002898625760000043
respectively the active power and the reactive power of the ij branch at the moment t;
Figure GDA0002898625760000044
is the voltage value of j node at the time t;
Figure GDA0002898625760000045
the current value of the branch circuit ij at the time t;
Figure GDA0002898625760000046
and
Figure GDA0002898625760000047
respectively the net injection values of the active power and the reactive power of the j node at the time t;
Figure GDA0002898625760000048
Figure GDA0002898625760000049
and
Figure GDA00028986257600000410
respectively 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;
Figure GDA00028986257600000411
Figure GDA00028986257600000412
and
Figure GDA00028986257600000413
load 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:
Figure GDA00028986257600000414
Figure GDA00028986257600000415
wherein:
Figure GDA00028986257600000416
respectively a predicted value, a maximum upper limit deviation value and a maximum lower limit deviation value of the photovoltaic output;
Figure GDA00028986257600000417
predicted values of load, respectivelyA large upper limit deviation and a maximum lower limit deviation;
Figure GDA00028986257600000418
is a variable from 0 to 1;
s123, establishing system safety constraint
Figure GDA0002898625760000051
Figure GDA0002898625760000052
Wherein:
Figure GDA0002898625760000053
and
Figure GDA0002898625760000054
the upper limit and the lower limit of the j node voltage amplitude respectively;
Figure GDA0002898625760000055
the upper limit value of the ij branch current is;
s124, establishing operation constraint of the SVC
Figure GDA0002898625760000056
Wherein:
Figure GDA0002898625760000057
and
Figure GDA0002898625760000058
the 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
Figure GDA0002898625760000059
Figure GDA00028986257600000510
Figure GDA00028986257600000511
Figure GDA00028986257600000512
Figure GDA00028986257600000513
Wherein:
Figure GDA00028986257600000514
the compensation power for each group of capacitors;
Figure GDA00028986257600000515
and
Figure GDA00028986257600000516
respectively are 0-1 marks of switching operation when
Figure GDA00028986257600000517
Indicating that at time t j node increases the commissioning of a group of CBs,
Figure GDA00028986257600000518
the same process is carried out;
Figure GDA00028986257600000519
the upper limit of the maximum group number is switched every time;
Figure GDA00028986257600000520
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
Figure GDA00028986257600000521
Wherein:
Figure GDA00028986257600000522
representing a predicted value of photovoltaic contribution;
Figure GDA00028986257600000523
the maximum output power of the photovoltaic inverter is obtained;
s1262, micro gas turbine constraints
Figure GDA00028986257600000524
Wherein:
Figure GDA00028986257600000525
the maximum output power of the inverter;
Figure GDA00028986257600000526
limiting the climbing of the micro gas turbine;
s127, establishing energy storage constraint
Figure GDA0002898625760000061
Figure GDA0002898625760000062
Wherein:
Figure GDA0002898625760000063
representing the ESS electric quantity of the j node at the time t; etachAnd ηdisRespectively charge and discharge efficiency;
Figure GDA0002898625760000064
and
Figure GDA0002898625760000065
respectively 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
Figure GDA0002898625760000066
Wherein: mij,tThe gear of the OLTC connected with the ij branch at the time t;
Figure GDA0002898625760000067
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
Figure GDA0002898625760000068
Wherein:
Figure GDA0002898625760000069
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:
Figure GDA00028986257600000610
wherein:
Figure GDA00028986257600000611
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;
Figure GDA00028986257600000612
and
Figure GDA00028986257600000613
and 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:
Figure GDA0002898625760000071
Figure GDA0002898625760000072
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, a column constraint generation algorithm is used for solving a min-max-min three-layer robust model, the method is better in economic benefit compared with the traditional multi-time scale optimization model in the worst scene, and meanwhile, the column constraint generation algorithm is used for solving, so that 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
Figure GDA0002898625760000081
Figure GDA0002898625760000091
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:
Figure GDA0002898625760000092
wherein the content of the first and second substances,
Figure GDA0002898625760000093
exchanging cost for power of the power distribution network and the main network connecting line;
Figure GDA0002898625760000094
and
Figure GDA0002898625760000095
the gas turbine DG cost, interruptible load IL and energy storage ESS cost, respectively;
Figure GDA0002898625760000096
and
Figure GDA0002898625760000097
a compensation capacitor CB and an on-load voltage regulator are respectively arranged;
said gas turbine DG cost
Figure GDA0002898625760000101
Comprises the following steps:
Figure GDA0002898625760000102
the interruptible load IL cost
Figure GDA0002898625760000103
Comprises the following steps:
Figure GDA0002898625760000104
cost of the energy storage ESS
Figure GDA0002898625760000105
Comprises the following steps:
Figure GDA0002898625760000106
compensation cost of the compensation capacitor CB
Figure GDA0002898625760000107
Comprises the following steps:
Figure GDA0002898625760000108
compensation cost of the on-load voltage regulator OLTC
Figure GDA0002898625760000109
Comprises the following steps:
Figure GDA00028986257600001010
wherein, c1,c2,c3Is the cost coefficient of DG;
Figure GDA00028986257600001011
and rCBCompensation cost coefficients for IL, OLTC and CB, respectively; 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;
Figure GDA00028986257600001012
and
Figure GDA00028986257600001013
a 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
Figure GDA00028986257600001014
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;
Figure GDA00028986257600001015
and
Figure GDA00028986257600001016
respectively the active power and the reactive power of the ij branch at the moment t;
Figure GDA0002898625760000111
is the voltage value of j node at the time t;
Figure GDA0002898625760000112
the current value of the branch circuit ij at the time t;
Figure GDA0002898625760000113
and
Figure GDA0002898625760000114
respectively the net injection values of the active power and the reactive power of the j node at the time t;
Figure GDA0002898625760000115
Figure GDA0002898625760000116
and
Figure GDA0002898625760000117
respectively 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;
Figure GDA0002898625760000118
Figure GDA0002898625760000119
and
Figure GDA00028986257600001110
load 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,tConnected to branch ij at time tThe switching gear of the OLTC;
s122, establishing an uncertainty set of the photovoltaic and the load:
Figure GDA00028986257600001111
Figure GDA00028986257600001112
wherein:
Figure GDA00028986257600001113
respectively a predicted value, a maximum upper limit deviation value and a maximum lower limit deviation value of the photovoltaic output;
Figure GDA00028986257600001114
respectively a predicted value, a maximum upper limit deviation and a maximum lower limit deviation of the load;
Figure GDA00028986257600001115
is a variable from 0 to 1;
s123, establishing system safety constraint
Figure GDA00028986257600001116
Figure GDA00028986257600001117
Wherein:
Figure GDA00028986257600001118
and
Figure GDA00028986257600001119
the upper limit and the lower limit of the j node voltage amplitude respectively;
Figure GDA00028986257600001120
is an ij branchAn upper limit value of the current;
s124, establishing operation constraint of the SVC
Figure GDA00028986257600001121
Wherein:
Figure GDA00028986257600001122
and
Figure GDA00028986257600001123
the 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
Figure GDA0002898625760000121
Figure GDA0002898625760000122
Figure GDA0002898625760000123
Figure GDA0002898625760000124
Figure GDA0002898625760000125
Wherein:
Figure GDA0002898625760000126
the compensation power for each group of capacitors;
Figure GDA0002898625760000127
and
Figure GDA0002898625760000128
respectively are 0-1 marks of switching operation when
Figure GDA0002898625760000129
Indicating that at time t j node increases the commissioning of a group of CBs,
Figure GDA00028986257600001210
the same process is carried out;
Figure GDA00028986257600001211
the upper limit of the maximum group number is switched every time;
Figure GDA00028986257600001212
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
Figure GDA00028986257600001213
Wherein:
Figure GDA00028986257600001214
representing a predicted value of photovoltaic contribution;
Figure GDA00028986257600001215
the maximum output power of the photovoltaic inverter is obtained;
s1262, micro gas turbine constraints
Figure GDA00028986257600001216
Wherein:
Figure GDA00028986257600001217
the maximum output power of the inverter;
Figure GDA00028986257600001218
limiting the climbing of the micro gas turbine;
s127, establishing energy storage constraint
Figure GDA00028986257600001219
Figure GDA00028986257600001220
Wherein:
Figure GDA00028986257600001221
representing the ESS electric quantity of the j node at the time t; etachAnd ηdisRespectively charge and discharge efficiency;
Figure GDA00028986257600001222
and
Figure GDA00028986257600001223
respectively 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
Figure GDA0002898625760000131
Wherein: mij,tThe gear of the OLTC connected with the ij branch at the time t;
Figure GDA0002898625760000132
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
Figure GDA0002898625760000133
Wherein:
Figure GDA0002898625760000134
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:
Figure GDA0002898625760000135
in the formula:
Figure GDA0002898625760000136
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;
Figure GDA0002898625760000137
and
Figure GDA0002898625760000138
and 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:
Figure GDA0002898625760000141
Figure GDA0002898625760000142
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 column constraint generation 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 (8)

1. A multi-time scale optimization operation method for a power distribution network, which takes uncertainty into account, is characterized by comprising 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, wherein the establishing of the robust optimization scheduling model in the step S1 further comprises:
s11, establishing an objective function of robust optimization scheduling considering photovoltaic and load uncertainty, wherein the objective function is as follows:
Figure FDA0002898625750000011
wherein the content of the first and second substances,
Figure FDA0002898625750000012
exchanging cost for power of the power distribution network and the main network connecting line;
Figure FDA0002898625750000013
and
Figure FDA0002898625750000014
the gas turbine DG cost, interruptible load IL and energy storage ESS cost, respectively;
Figure FDA0002898625750000015
and
Figure FDA0002898625750000016
the compensation costs of the compensation capacitor CB and the on-load voltage regulator OLTC, respectively;
Figure FDA0002898625750000017
and
Figure FDA0002898625750000018
respectively marking 0-1 of the switching operation of the capacitor bank; mijAnd kijThe gear and the actual voltage regulation ratio of the OLTC connected with the ij branch are obtained; pijAnd QijRespectively the active power and the reactive power of the ij branch; viIs the voltage value of node i; pi,DGAnd Qi,DGThe active power and the reactive power of the micro gas turbine connected with the node i are respectively; pi PVAnd
Figure FDA0002898625750000019
the active and reactive power of the photovoltaic connected with the i node are respectively; pi ch,Pi disAnd
Figure FDA00028986257500000110
the charging power, the discharging power and the electric quantity of the energy storage device connected with the i node are respectively; p and d are values under a certain specific situation of photovoltaic and load respectively; PV and Pd being photovoltaic and loaded, respectivelyValue collection; x and y are respectively a set of a first-stage discrete variable and a second-stage continuous variable;
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;
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.
2. The method according to claim 1, wherein the slow-acting devices in step S1 at least include an on-load voltage regulator OLTC and a group-switched capacitor bank CB.
3. The method according to claim 2, wherein in step S11,
said gas turbine DG cost
Figure FDA0002898625750000021
Comprises the following steps:
Figure FDA0002898625750000022
the interruptible load IL cost
Figure FDA0002898625750000023
Comprises the following steps:
Figure FDA0002898625750000024
cost of the energy storage ESS
Figure FDA0002898625750000025
Comprises the following steps:
Figure FDA0002898625750000026
compensation cost of the compensation capacitor CB
Figure FDA0002898625750000027
Comprises the following steps:
Figure FDA0002898625750000028
compensation cost of the on-load voltage regulator OLTC
Figure FDA0002898625750000029
Comprises the following steps:
Figure FDA00028986257500000210
wherein, c1,c2,c3Is the cost coefficient of DG;
Figure FDA00028986257500000211
and rCBCompensation cost coefficients for IL, OLTC and CB, respectively; 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;
Figure FDA00028986257500000212
and
Figure FDA00028986257500000213
a 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。
4. The method of claim 3, wherein the step S12 further comprises:
s121, establishing power balance constraint
Figure FDA0002898625750000031
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;
Figure FDA0002898625750000032
and
Figure FDA0002898625750000033
respectively the active power and the reactive power of the ij branch at the moment t;
Figure FDA0002898625750000034
is the voltage value of j node at the time t;
Figure FDA0002898625750000035
the current value of the branch circuit ij at the time t;
Figure FDA0002898625750000036
and
Figure FDA0002898625750000037
respectively at j nodes at time tNet injected values of active and reactive power;
Figure FDA0002898625750000038
Figure FDA0002898625750000039
and
Figure FDA00028986257500000310
respectively 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;
Figure FDA00028986257500000311
Figure FDA00028986257500000312
and
Figure FDA00028986257500000313
load 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:
Figure FDA00028986257500000314
Figure FDA00028986257500000315
Figure FDA00028986257500000316
Figure FDA00028986257500000317
wherein:
Figure FDA00028986257500000318
respectively a predicted value, a maximum upper limit deviation value and a maximum lower limit deviation value of the photovoltaic output;
Figure FDA00028986257500000319
respectively a predicted value, a maximum upper limit deviation and a maximum lower limit deviation of the load;
Figure FDA00028986257500000320
is a variable from 0 to 1;
s123, establishing system safety constraint
Figure FDA0002898625750000041
Figure FDA0002898625750000042
Wherein:
Figure FDA0002898625750000043
and
Figure FDA0002898625750000044
the upper limit and the lower limit of the j node voltage amplitude respectively;
Figure FDA0002898625750000045
the upper limit value of the ij branch current is;
s124, establishing operation constraint of the SVC
Figure FDA0002898625750000046
Wherein:
Figure FDA0002898625750000047
and
Figure FDA0002898625750000048
the 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
Figure FDA0002898625750000049
Figure FDA00028986257500000410
Figure FDA00028986257500000411
Figure FDA00028986257500000412
Figure FDA00028986257500000413
Wherein:
Figure FDA00028986257500000414
the compensation power for each group of capacitors;
Figure FDA00028986257500000415
and
Figure FDA00028986257500000416
respectively are 0-1 marks of switching operation when
Figure FDA00028986257500000417
Indicating that at time t j node increases the commissioning of a group of CBs,
Figure FDA00028986257500000418
the same process is carried out;
Figure FDA00028986257500000419
the upper limit of the maximum group number is switched every time;
Figure FDA00028986257500000420
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
Figure FDA00028986257500000421
Wherein:
Figure FDA00028986257500000422
representing a predicted value of photovoltaic contribution;
Figure FDA00028986257500000423
the maximum output power of the photovoltaic inverter is obtained;
s1262, micro gas turbine constraints
Figure FDA00028986257500000424
Figure FDA00028986257500000425
Wherein:
Figure FDA00028986257500000426
the maximum output power of the inverter;
Figure FDA00028986257500000427
limiting the climbing of the micro gas turbine;
s127, establishing energy storage constraint
Figure FDA0002898625750000051
Figure FDA0002898625750000052
Wherein:
Figure FDA0002898625750000053
representing the ESS electric quantity of the j node at the time t; etachAnd ηdisRespectively charge and discharge efficiency;
Figure FDA0002898625750000054
and
Figure FDA0002898625750000055
respectively 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
Figure FDA0002898625750000056
Wherein: mij,tThe gear of the OLTC connected with the ij branch at the time t;
Figure FDA0002898625750000057
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
Figure FDA0002898625750000058
Wherein:
Figure FDA0002898625750000059
an upper bound for the interruptible load of the j node.
5. The method according to claim 1 or 2, wherein the objective function in step S2 is to achieve a rolling optimization schedule with a period of 4h, with the objective function targeting the lowest total operation cost of the system in 4 hours in the future, that is:
Figure FDA00028986257500000510
wherein:
Figure FDA00028986257500000511
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;
Figure FDA00028986257500000512
and
Figure FDA00028986257500000513
and 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.
6. The method according to claim 5, wherein the constraints of the optimized scheduling model of the active distribution network at the short time scale in step S2 sequentially include: step S121-step S124, step S126, step S127 and step S129.
7. The method according to claim 1 or 2, wherein the step S3 objective function aims at minimizing the adjustment amount of the adjustable and controllable device within the system ultra-short time, the system ultra-short time is set to be within 5min, and a rolling optimization scheduling with 5min as a period is implemented, that is:
Figure FDA0002898625750000061
Figure FDA0002898625750000062
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.
8. The method according to claim 4, wherein the constraints 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.
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