CN112865192A - Multi-period optimal scheduling method and system for active power distribution network - Google Patents

Multi-period optimal scheduling method and system for active power distribution network Download PDF

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CN112865192A
CN112865192A CN202011636898.3A CN202011636898A CN112865192A CN 112865192 A CN112865192 A CN 112865192A CN 202011636898 A CN202011636898 A CN 202011636898A CN 112865192 A CN112865192 A CN 112865192A
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day
ahead
standby
distribution network
power distribution
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王成福
盛宏章
梁军
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Shandong University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • 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
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
    • H02J2310/66—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads one of the loads acting as master and the other or others acting as slaves

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Abstract

The invention discloses a multi-period optimal scheduling method and a system for an active power distribution network, wherein the method comprises the following steps: performing optimization operation decision of a day-ahead stage on slow-motion resources and power transmission network side standby according to day-ahead prediction information to obtain a day-ahead scheduling reference value; the output reference value and the standby amount of each element in the power distribution network are optimized in the day-ahead stage by combining with the day-ahead scheduling reference value, and an operation reference value is provided for the real-time stage, so that day-ahead to day-ahead distribution-power transmission network standby coordination is realized; and (4) performing short-time optimization on the output of various units in a real-time stage based on the operation reference value, and solving the output value of each unit in a rolling manner. According to the distributed resource scheduling method, on the basis of considering response characteristics of various distributed resources in the power distribution network, the distributed resources are divided into a day-ahead stage, a day-in stage and a real-time stage to be respectively scheduled and optimized, so that the influence of prediction errors is reduced, and the running economy is improved.

Description

Multi-period optimal scheduling method and system for active power distribution network
Technical Field
The invention relates to the technical field of optimal scheduling of a power distribution network, in particular to a multi-period optimal scheduling method and system for an active power distribution network.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
With the access of a large number of distributed power sources to the power distribution network, the scheduling control of the conventional power distribution network becomes increasingly complex. The permeability of the renewable energy source unit mainly based on wind and light in the power distribution network is continuously improved, so that the internal operation scheduling of the power distribution network is challenged, and the power transmission network connected with the power distribution network through a connecting line is influenced. In order to solve this problem, the conventional power distribution network is gradually changing into an active power distribution network that controls various distributed resources. For a single active power distribution network, how to deal with power fluctuation in the power distribution network and reduce the influence on a superior power distribution network as much as possible is a key problem to be solved urgently.
Because the output of the renewable energy unit has randomness and volatility, the output of the renewable energy unit is difficult to predict accurately, and the prediction error is larger as the prediction advance time is longer. Existing research typically divides the scheduling process into multiple time scales and introduces model predictive control methods within a short time window. Through the coordination of scheduling stages with different time scales, the influence caused by uncertainty can be gradually reduced. Such as: in the prior art, a Model Predictive Control (MPC) method is introduced into active scheduling Control of a power system with large-scale access to wind power, and the influence caused by wind power prediction errors is gradually eliminated by a layered MPC method.
However, the prior art does not adequately consider feedback between different scheduling phases when dealing with coordination between different time scales. That is, only the assignment of scheduling commands between the long timescale scheduling phase and the short timescale scheduling phase is considered, and the feedback of the short timescale phase to the long timescale phase is not considered. When the difference of the predicted data among different scheduling links is large, the instruction issued in a certain scheduling stage is possibly not in accordance with the real operation condition of the next stage, so that the operation safety of the power distribution network is influenced.
In addition, different from a transmission network, the line resistance and the reactance value in a power distribution network are close, and a unilateral active/reactive power optimization method based on active and reactive power decoupling in the traditional scheduling theory is not suitable any more, so that active power-reactive power coordination optimization control should be carried out at the same time.
Most of the existing power distribution network scheduling methods are insufficient for considering the interaction relationship between the transmission network and the power distribution network. Most documents when modeling equate the transmission network to an infinite power supply node and equate the power delivered by the transmission network to the distribution network to the injected power at that node. During the real-time operation of the distribution network, the transmission network is subjected to power fluctuations via the tie lines without advance notice. In addition, the modeling method lacks consideration of the reserve capacity in the transmission network and the distribution network, and is actually equivalent to completely relying on the transmission network to reserve the reserve capacity for the distribution network in the scheduling process. With the increase of the permeability of the distributed power source in the power distribution network, it is difficult for the superior transmission network to acquire all information in the power distribution network, and there is a risk that the spare capacity provided by the transmission network is insufficient.
Disclosure of Invention
In order to solve the problems, the invention provides a multi-period optimization scheduling method and a multi-period optimization scheduling system for an active power distribution network. On the time level, a time-interval optimization strategy is formulated for various kinds of available active and reactive standby according to the operation characteristics, three time scales are coordinated in real time in the day ahead, and standby coordination and optimization between the power transmission network and the power distribution network and in the power distribution network are realized.
In some embodiments, the following technical scheme is adopted:
a multi-period optimization scheduling method for an active power distribution network comprises the following steps:
performing optimization operation decision of a day-ahead stage on slow-motion resources and power transmission network side standby according to day-ahead prediction information to obtain a day-ahead scheduling reference value;
the output reference value and the standby amount of each element in the power distribution network are optimized in the day-ahead stage by combining with the day-ahead scheduling reference value, and an operation reference value is provided for the real-time stage, so that day-ahead to day-ahead distribution-power transmission network standby coordination is realized;
and (4) performing short-time optimization on the output of various units in a real-time stage based on the operation reference value, and solving the output value of each unit in a rolling manner.
In other embodiments, the following technical solutions are adopted:
a multi-period optimization scheduling system for an active power distribution network comprises:
the day-ahead optimization module is used for carrying out day-ahead stage optimization operation decision on slow-motion resources and power transmission network side standby according to day-ahead prediction information to obtain a day-ahead scheduling reference value;
the in-day optimization module is used for optimizing the output reference value and the standby amount of each element in the power distribution network in the in-day stage by combining with the day-ahead scheduling reference value, and providing an operation reference value for the real-time stage so as to realize the standby coordination between the in-day distribution and power transmission networks from the day-ahead;
and the real-time optimization module is used for carrying out short-time optimization on the output of various units in a real-time stage based on the operation reference value and solving the output value of each unit in a rolling manner.
In other embodiments, the following technical solutions are adopted:
a terminal device comprising a processor and a memory, the processor being arranged to implement instructions; the computer memory is used for storing a plurality of instructions, and the instructions are suitable for being loaded by the processor and executing the active power distribution network multi-period optimization scheduling method.
In other embodiments, the following technical solutions are adopted:
a computer-readable storage medium, wherein a plurality of instructions are stored, and the instructions are adapted to be loaded by a processor of a terminal device and execute the active power distribution network multi-period optimization scheduling method.
Compared with the prior art, the invention has the beneficial effects that:
(1) the invention provides a multi-type standby space-time coordination optimization method considering correlation influence between power transmission and distribution networks. On a spatial level, a standby coordination strategy among different areas in the power distribution network is established, and standby coordination optimization among the power transmission and distribution networks is considered. On the time level, a time-interval optimization strategy is formulated for various kinds of available active and reactive standby power according to operation characteristics (such as response speed), three time scales are coordinated in real time in the day ahead, and standby coordination and optimization between the power transmission network and the power distribution network and in the power distribution network are realized. Therefore, the reasonable utilization of standby resources can be realized, the capability of the power distribution network for coping with uncertainty is improved, and the influence of power fluctuation on a superior power grid is reduced.
(2) According to the distributed resource scheduling method, on the basis of considering response characteristics of various distributed resources in the power distribution network, the distributed resources are divided into a day-ahead stage, a day-in stage and a real-time stage to be respectively scheduled and optimized, so that the influence of prediction errors is reduced, and the running economy is improved.
(3) The invention establishes a multi-period power distribution network operation scheduling framework with a standby margin feedback mechanism. The framework takes the spare margin as a feedback signal among three scheduling stages (day ahead, day in and real time), and feeds back the spare margin to the previous scheduling stage for readjustment in the day in and real time stages according to the spare capacity margin. Through the standby margin feedback mechanism, the bidirectional optimization cooperation among different scheduling stages can be improved, and the problem of difficult adjustment caused by larger deviation between the scheduling result of the previous stage and the operation condition of the current stage is avoided.
Additional features and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
Fig. 1 is a schematic diagram of a multi-period optimal scheduling method for an active power distribution network in an embodiment of the present invention;
FIG. 2 is a diagram illustrating an alternative spatio-temporal coordination strategy in accordance with an embodiment of the present invention;
FIG. 3 is a schematic diagram of a line with an OLTC connected at the head end in the embodiment of the present invention;
FIG. 4 is a block diagram of an alternate embodiment of the method of the present invention;
fig. 5(a) is a schematic diagram of comparison results of power grid side purchase reserve and usage in accordance with methods and methods 1 of the present invention;
FIG. 5(b) is a schematic power diagram of a tie line of method 1 and method of embodiments of the present invention;
FIG. 6(a) is a graph comparing the system loss of methods 2 and 3 according to the embodiment of the present invention;
fig. 6(b) is a comparison of the behavior of the reactive device in the method and method 3 according to the embodiment of the present invention.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Example one
In one or more embodiments, a multi-period optimization scheduling method for an active power distribution network is disclosed, and with reference to fig. 1, the method includes:
step S101: performing optimization operation decision of a day-ahead stage on slow-motion resources and power transmission network side standby according to day-ahead prediction information to obtain a day-ahead scheduling reference value;
specifically, the optimized duration of the day-ahead scheduling stage is 24 hours in the future, the resolution is 1h, the main purpose is to establish an operation plan of slow-motion resources and power grid side standby, and provide a tie line power reference value for the subsequent day-ahead and real-time stages. The resources with larger inertia time constants in the power distribution network mainly comprise an On Load Tap Changer (OLTC) transformer, a parallel Capacitor Bank (CB) and an interruptible Load, the action speeds and the action times of the On Load Tap Changer (OLTC) transformer and the parallel Capacitor Bank (CB) are limited to a certain extent, and the On Load Tap Changer and the interruptible Load are not suitable for frequent action within one day; the latter needs to be informed in advance of its blackout schedule. After the previous stage is completed, the rest of resources in the power distribution network can be further optimized in the next day link.
Step S102: the output reference value and the standby amount of each element in the power distribution network are optimized in the day-ahead stage by combining with the day-ahead scheduling reference value, and an operation reference value is provided for the real-time stage, so that day-ahead to day-ahead distribution-power transmission network standby coordination is realized;
in particular, the purpose of the in-day phase is to further optimize the output values and the standby values of the remaining components in the active distribution network and to provide operating reference values for the following real-time control phase. Based on the latest prediction data, the daily stage is executed every 4h, the optimization range is from the current time period to the future 4h, and the resolution is 15 min. In the process of optimizing the in-day phase, the active power distribution network detects the standby margin in the current power distribution network, and when the standby margin is detected to be insufficient, the standby margin is fed back to the day-ahead scheduling phase, namely, the day-ahead calculation is carried out again on the basis of the current operation state and the prediction data, and a new day-ahead reference value is obtained so as to ensure that the standby in the power distribution network is sufficient.
Step S103: and (4) performing short-time optimization on the output of various units in a real-time stage based on the operation reference value, and solving the output value of each unit in a rolling manner.
Specifically, the purpose of the real-time phase is to track an operation reference value given by a link in a day and stabilize power fluctuation in the power distribution network. A Model Predictive Control (MPC) method is introduced at the stage, the optimization time length of each time is 15min from the current moment to the future, and the resolution is 5 min. At each sampling moment, the short-term period is optimized according to a set target by combining the sampling value at the current moment (the moment t 0) and the latest predicted value in a future period. Although each sampling moment is optimized in real time, each time, only the optimization result of the next sampling moment is adopted and issued as a control instruction. Therefore, the output of various units can be subjected to feedback correction in the real-time control stage, and the deviation of the scheduling result caused by the prediction error can be eliminated in time. In addition, similar to the in-day phase, the real-time phase can also detect the available standby margin in the current power distribution network and feed back the in-day phase so as to ensure that the standby margin is sufficient when the power distribution network operates.
In order to reduce the influence of power fluctuation of distributed resources on the power distribution network and the upper-level power grid, the standby coordination strategy proposed in this embodiment is shown in fig. 2. The strategy aims to comprehensively consider various standby resources which can be called by the active power distribution network, and meanwhile, based on the action characteristics (such as available capacity, adjusting speed and the like) of the standby resources, different active and reactive standby resources are coordinately distributed according to two scales of time and space.
From the perspective of spatial scale, standby resources that can be called by an active power distribution network can be mainly divided into power distribution network side standby and power transmission network side standby. The standby on the transmission network side is provided by a standby unit in the transmission network, and because the active distribution network in the model is used as an independent operation subject to participate in market transaction, a Distribution System Operator (DSO) can purchase power and standby for a Transmission System Operator (TSO) according to a certain price. The power distribution network side standby is mainly composed of controllable elements in the power distribution network and interruptible loads and can be divided into active standby and reactive standby. Active standby has controllable DG, energy storage, interruptible load, and reactive standby has parallel Capacitor Bank (CB) and Static Var Compensator (SVC) according to the supply standby source division.
The characteristics of the above various types of standby operation speed, operation times, and the like are also different from the time scale. In the active standby source, the standby capacity of the transmission network side is large, but the climbing speed is slow, and the standby source needs to be notified and purchased in advance; interruptible loads can be used as positive rotation standby when the power inside the power distribution network is insufficient, but the outage cost is high, the participation capacity is limited, and the notification is required in advance; the controllable distributed power supply and the energy storage have higher climbing speed but relatively smaller capacity. In the reactive standby mode, the parallel capacitor bank is not suitable for frequent actions in one day due to the limitation of service life and switching times; the SVC can realize continuous power compensation within a certain range and has a high action speed.
Therefore, the embodiment optimizes the standby resources according to the operation characteristics of the standby resources before and during the day. The day-ahead standby comprises power transmission network side standby, interruptible load standby and capacitor bank standby; the intra-day standby includes gas turbine standby, energy storage standby, and SVC standby.
This has two benefits:
1) the standby power transmission network side can be greatly saved, and the operation cost is further reduced;
2) after the internal standby of the power distribution network is considered, the power distribution network can be improved in response to power fluctuation, and the influence on a superior power grid is reduced.
1. Regarding the power distribution network optimization model considering the standby space-time coordination:
in the model used in this embodiment, it is assumed that the active distribution network has control right for Distributed Energy Resources (DERs) in the distribution network, and mainly includes transformer taps, parallel capacitor banks, Distributed power sources, Energy storage and SVC. Furthermore, the active distribution grid may purchase backup to the transmission grid during the day-ahead scheduling phase and may purchase power to the transmission grid throughout the scheduling. The model considers the standby characteristics of various resources in the power distribution network and carries out coordination optimization on the standby. Although the scheduling architecture proposed in this embodiment is divided into three stages, namely, a day-ahead stage, a day-in stage and a real-time stage, the model used by the same element in different time periods is the same unless otherwise specified.
(1) Day ahead-day intra-day scheduling objective
In the day-ahead scheduling stage, the active power distribution network optimizes the running state of the power distribution network in one day, and informs the power reference value of the tie line and the standby demand to a power transmission network operator. Once the interruptible load, OLTC range and CB output values are established at the earlier stages, they are not changed at the later dispatch stages except for emergency situations. The optimization goal of the day-ahead phase is to minimize the economic cost and the network loss weighting in one day:
Figure BDA0002878734500000091
Figure BDA0002878734500000092
Figure BDA0002878734500000093
wherein DA represents the day-ahead stage, T is a scheduling time window (24h), T is a time index (1h resolution), L is a set of all lines in the power distribution network, ij is a line index,
Figure BDA0002878734500000094
and
Figure BDA0002878734500000095
mu is the system cost and the loss of the network1And mu2Respectively, the cost and the loss of the network.
Figure BDA0002878734500000096
And
Figure BDA0002878734500000097
the active power and active backup provided for the transmission network,
Figure BDA0002878734500000098
and
Figure BDA0002878734500000099
the active power of the controllable DG, interruptible load,
Figure BDA00028787345000000910
and
Figure BDA00028787345000000911
for the price of active power and active back-up,
Figure BDA00028787345000000912
in order to control the cost of electricity generation by DG,
Figure BDA00028787345000000913
in order to be able to interrupt the load compensation costs,
Figure BDA00028787345000000914
and
Figure BDA00028787345000000915
in order to store the charging and discharging power,
Figure BDA00028787345000000916
in order to save the charge-discharge cost of energy storage,
Figure BDA00028787345000000917
and rijThe current and resistance of line ij.
And in the scheduling stage in the day, the active power distribution network calculates the scheduling reference value of the next short period, and corrects the error of scheduling in the day ahead. The objective function of scheduling in the day is the same as that of the previous stage, and the economic cost and the network loss are weighted to be minimum:
Figure BDA00028787345000000918
Figure BDA00028787345000000919
Figure BDA00028787345000000920
wherein, ID represents the in-day phase, T is the scheduling time window (4h), and T is the time index (15min resolution).
Figure BDA00028787345000000921
The first three terms are the same as those in the objective function at the day-ahead stage, and are not described herein.
Figure BDA00028787345000000922
And the output penalty item is a junctor power fluctuation penalty item, and the output penalty item is related to the charging principle of the power grid operator on the junctor power in the intraday stage. In the model used herein, it is assumed that the real active power of the actual tie line will be punished according to certain charging rules if it exceeds the reserve range given in the day-ahead stage. Order to
Figure BDA00028787345000000923
For the amount of power fluctuation of the tie line at time t, charging penalty term
Figure BDA0002878734500000101
The calculation method is as follows:
Figure BDA0002878734500000102
wherein the content of the first and second substances,
Figure BDA0002878734500000103
respectively for link power fluctuation exceeding
Figure BDA0002878734500000104
Or
Figure BDA0002878734500000105
Or
Figure BDA0002878734500000106
Penalty factor for range, λ1、λ2Represents the proportional relation (lambda) of the penalty factor and the electricity price2≥λ1≥1)；
Figure BDA0002878734500000107
And
Figure BDA0002878734500000108
and the positive rotation standby reference value and the negative rotation standby reference value of the power transmission network are the active power of the tie line in the day-ahead scheduling. By adding the charging punishment item in the objective function, the planned output fluctuation range of the connecting line of the daily scheduling link can be limited. Furthermore, the method also provides a charging principle when the active power distribution network is operated as a relatively independent main body and the electricity is purchased to the power transmission system, and the charging principle can be used as a reference in the actual scheduling operation in the future.
The penalty term can be summarized as follows: when the tie line power fluctuation is at the moment of t
Figure BDA0002878734500000109
When the range is reached, the penalty item is marked as 0; when out of this range, but still in
Figure BDA00028787345000001010
When the excess is additionally according to lambda1Charging punishment fee according to the power price; when the above range is exceeded again, the excess portion is determined by λ2The power rate is charged with a penalty fee.
(2) MPC based real-time control objective
In the real-time control stage, the active power distribution network tracks the reference value of each unit in the day, and the power fluctuation in the power distribution network is stabilized. The target of the real-time control stage mainly comprises two parts, namely the minimum weighted deviation of the output and the reference value of each unit, the minimum wind abandoning amount and the minimum light abandoning amount. In the real-time stage, the renewable energy source units mainly based on wind and light can generate strong power fluctuation, so that the active power distribution network needs to make a balance between stabilizing the power fluctuation and abandoning the wind and abandoning the light by each unit, and a penalty item of abandoning the wind and abandoning the light is embodied in a target function in the real-time stage. The specific objective function is as follows:
Figure BDA0002878734500000111
Figure BDA0002878734500000112
Figure BDA0002878734500000113
Figure BDA0002878734500000114
Figure BDA0002878734500000115
where RT represents the real-time phase, T is the scheduling window (30min), T is the time index (5min resolution),
Figure BDA0002878734500000116
the weighted output deviation of each unit in the real-time link,
Figure BDA0002878734500000117
for the penalty items of wind abandoning and light abandoning,
Figure BDA0002878734500000118
the coefficient of the deviation between the actual output value and the reference value of the type of the machine set in a real-time link,
Figure BDA0002878734500000119
is a penalty coefficient of wind abandon and light abandon,
Figure BDA00028787345000001110
the real-time link output of the ith DER at the time t,
Figure BDA00028787345000001111
and (4) giving an output reference value for the ith DER at the moment t in the day link.
Formula (11) gives the set of unit output power participating in stabilizing power in the real-time link, GRES gives the set of renewable energy unit output active power, and for
Figure BDA00028787345000001112
For the unit in (1), the coefficient corresponding to the unit
Figure BDA00028787345000001113
The lower the real-time link, the more the real-time link is biased to call the set to cope with power fluctuation.
2. Constraint conditions
(1) Standby coordination constraints
In the model used in this embodiment, active backup is provided by the grid, gas turbines, energy storage and interruptible loads, and reactive backup is provided primarily by CB and SVC. In the process of dispatching the distribution network, the spare quantity needs to meet certain requirements, for example, the spare quantity needs to be larger than a certain proportion of the prediction error. In addition, since different types of backup sources differ in reliability, ramp rate, etc., the proportion of certain types of backup capacity in the total backup may be specified during the scheduling process. Therefore, the standby operation constraint used in the present embodiment mainly has three parts, namely, an active operation constraint, a reactive operation constraint and a specific standby constraint. Wherein, the first two respectively specify the active and reactive reserve total requirements, and the last constraint specifies a single type of reserve range.
Active standby operation constraint
Figure BDA0002878734500000121
Wherein the content of the first and second substances,
Figure BDA0002878734500000122
respectively providing positive and negative rotation standby values for the transmission network unit, the controllable distributed power supply unit and the energy storage unit in the current dispatching stage,
Figure BDA0002878734500000123
an equivalent positive rotation reserve value is available for interruptible loads.
The superscript Stage represents the day-ahead or intra-day scheduling phase in which the positive and negative spinning reserve of the grid block and the positive spinning reserve of the interruptible load are variables, and the intra-day scheduling phase in which the positive and negative spinning reserve of the grid block and the positive spinning reserve of the interruptible load are reference values given according to the day-ahead phase. The positive and negative rotation standby provided by the controllable distributed power supply and the energy storage are decision variables in the day-ahead and day-in stages.
Figure BDA0002878734500000124
The minimum positive rotation and negative rotation standby requirements are met during active power distribution network scheduling respectively.
② idle spare operation restriction
Figure BDA0002878734500000125
Wherein the content of the first and second substances,
Figure BDA0002878734500000126
the standby values of positive and negative rotation of the parallel capacitor bank in the current dispatching stage;
Figure BDA0002878734500000127
and the SVC positive and negative rotation standby values of the current scheduling stage are obtained. In the day-ahead scheduling stage, the standby value of the parallel capacitor bank is a variable; in the day scheduling phase, the standby value of the parallel capacitor bank is a constant and is only used as emergency operation standby. The SVC standby value is a decision variable in the day-ahead and day-in stages.
③ specific reserve constraints:
Figure BDA0002878734500000131
wherein the content of the first and second substances,
Figure BDA0002878734500000132
the reserve amount for the specified type of positive rotation and negative rotation may be any one.
Figure BDA0002878734500000133
The spare is the proportion of the total spare requirement at time t for the specified type. It should be noted that it is preferable that,
Figure BDA0002878734500000134
still subject to the output limitations of this type of unit.
(2) Flow restraint
The example uses the Distflow power flow model as follows:
Figure BDA0002878734500000135
the superscripts U and D represent the upstream node and downstream node sets of the node j respectively。Pj,t，Qj,tActive power and reactive power, P, respectively, injected at time t for node jjk,t，Qjk,tFor active power, reactive power, V, at the beginning of the line jki,tIs the voltage magnitude at node i.
(3) Power distribution network safe operation constraint
In order to ensure the operation safety of the distribution network, the deviation range of the node voltage should be limited, and simultaneously, the line current is enabled to be in a bearable range:
Figure BDA0002878734500000136
in the formula (I), the compound is shown in the specification,
Figure BDA0002878734500000137
upper and lower current limits, V, respectively, of the line iji max，Vi minRespectively the upper and lower limits of the rated voltage amplitude of the node i.
(4) Tie line operational constraints
In order to limit the influence of power fluctuation of a power distribution network on a power transmission network, the upper limit and the lower limit of power climbing on a contact line are regulated as follows:
Figure BDA0002878734500000141
meanwhile, the backup climbing rate of the power transmission network also has certain limitation:
Figure BDA0002878734500000142
(5) distributed power supply operation constraints
The distributed power sources considered by the model of the embodiment mainly have two types: the other type is a controllable unit represented by a gas turbine, and the other type is a renewable energy unit represented by a distributed fan and a photovoltaic.
For a distributed renewable energy unit connected to a power grid through an inverter, certain reactive power can be generated through the inverter. The constraints are as follows:
Figure BDA0002878734500000143
Figure BDA0002878734500000144
PRES,i,trespectively predicting an active force output value and a planned force output value of the renewable energy source unit; qRES,i,tFor reactive power, S, produced by the inverter of the unitRES,iIs the rated capacity of the inverter.
The grid-connected mode of a controllable distributed power source such as a gas turbine is similar to that of a renewable energy source unit, namely, the controllable distributed power source is connected with a power grid through an inverter and outputs active power and reactive power. The output characteristics are as follows:
Figure BDA0002878734500000145
PCDG,i,t，QCDG,i,tactive and reactive power output of a controllable distributed power supply accessed to the node i at the moment t; sCDG,iThe rated power of the controllable distributed power supply;
Figure BDA0002878734500000146
positive rotation and negative rotation of the controllable distributed power supply are respectively used for active standby;
Figure BDA0002878734500000151
the upper limit and the lower limit of the climbing slope of the active power output of the controllable distributed power supply;
Figure BDA0002878734500000152
the upper limit and the lower limit of reactive power output climbing of the controllable distributed power supply.
(6) Energy storage operation restraint
In the energy storage, the service life of an energy storage system is considered, the electric quantity of the stored energy is generally limited within a certain range in actual operation, the stored energy is not allowed to be fully charged and fully discharged, and the model of the embodiment limits the electric quantity to be between 10% and 90% of rated capacity in the energy storage operation. The operating constraints of the stored energy are as follows:
Figure BDA0002878734500000153
wherein the content of the first and second substances,
Figure BDA0002878734500000154
capacity limit for energy storage, EES,i,tThe charging/discharging value of the stored energy at the current moment is shown, and if the value is positive, the stored energy is in a charging state; otherwise, the discharge state is obtained.
In order to ensure that the action of the energy storage in this period does not affect the use of the next scheduling period, it is specified that the capacity of the energy storage at the end of this scheduling period must be greater than the capacity reference at this moment:
Figure BDA0002878734500000155
wherein
Figure BDA0002878734500000156
The value of (c) will vary depending on the scheduling period. In the scheduling link in the day-ahead,
Figure BDA0002878734500000157
the value of (a) is the energy storage capacity of the first time node at the beginning of the day-ahead scheduling; for the intra-day scheduling and real-time control links,
Figure BDA0002878734500000158
the values of the energy storage capacity and the scheduling capacity are the energy storage capacity reference values at the corresponding time given in the previous scheduling period, namely the energy storage capacity reference value scheduled at the time in the day and the scheduling capacity reference value scheduled in the day.
Considering the remaining output margin of the energy storage device as a backup, the positive and negative rotational backup constraints of the energy storage device can be summarized as follows:
Figure BDA0002878734500000159
wherein
Figure BDA00028787345000001510
And positive rotation spare quantity and negative rotation spare quantity are respectively provided for the energy storage equipment accessed by the node i at the moment t. This constraint indicates that for some time t in the future, an energy storage in a planned charging state can treat its charging power and dischargeable power as a positive spinning reserve; likewise, an energy storage in a planned discharge state may treat its discharged and chargeable power as a negative spinning reserve.
(7) Interruptible load constraints
In the model used in the embodiment, the active power of the interruptible load which does not participate in the response whether the interruptible load participates in the response or not can be used as a part of the power distribution network standby, but is only used as an emergency operation standby and is not called in normal operation. The operational constraints of interruptible loads are as follows:
Figure BDA0002878734500000161
PIL,i,tparticipating in the amount of active power, S, of the demand side response for the interruptible load of node iIL,i,tThe variable is 0-1, representing the interruptible load state of the node, and when the value is 1, representing participation in the demand-side response, and when the value is 0, representing non-participation.
Figure BDA0002878734500000162
The active power is predicted for the interruptible load of node i,
Figure BDA0002878734500000163
spare capacity available for the interruptible load of node i.
(8) OLTC constraints
For the line ij, if the front end of the node j is connected to the transformer, the transformer can be set at the time tThe voltage at the head end of the tap is Vm,tIs obtained by
Figure BDA0002878734500000164
Wherein the content of the first and second substances,
Figure BDA0002878734500000165
is a discrete quantity representing the OLTC tap ratio.
Figure BDA0002878734500000166
Maximum and minimum gears of the OLTC are respectively; sij,tIs an integer, representing the gear sequence of the OLTC; n is a radical ofOLTCThe total number of OLTC gears.
(9) Capacitor bank constraint
In consideration of the limitations of the capacitor bank in terms of operating life and operating speed, the capacitor bank should not be operated frequently during one operating cycle, and each operation is performed on a bank-by-bank basis. The behavior characteristic constraints of a capacitor bank can be expressed as follows:
Figure BDA0002878734500000171
wherein the content of the first and second substances,
Figure BDA0002878734500000172
the compensation power and the reserved reserve power for the capacitor bank connected to the node i at the time t,
Figure BDA0002878734500000173
is an integer variable representing the number of groups of capacitors switched into operation and the number of reserved spare groups of the capacitors connected to the node i at the time t,
Figure BDA0002878734500000174
the compensation power for each set of capacitors connected to node i. SCB,i,tIs a variable from 0 to 1 and represents the action state of the capacitor bank of the i node at the time t when S CB,i,t1 represents the action of the capacitor bank, 0 representsThe capacitor bank is not active. In order to prevent the capacitor from large power fluctuation during the action, the compensation capacity of one capacitor group can be increased/decreased at most when the capacitor group is in action.
Figure BDA0002878734500000175
The upper limit of the action times of the capacitor bank specified in the current scheduling period.
(10) SVC operating constraints
Figure BDA0002878734500000176
Wherein Q isSVC,i,tReactive power output of SVC;
Figure BDA0002878734500000177
the output upper and lower limits of the SVC are obtained;
Figure BDA0002878734500000178
Figure BDA0002878734500000179
the upper and lower climbing limits of the SVC are shown.
Figure BDA00028787345000001710
And the reserved reactive positive and negative rotation in the SVC operation process are respectively reserved for standby.
3. Solving method
The original problem is a non-convex problem caused by the tidal current and A-B constraints in the OLTC constraints, and the original problem can be solved through convex relaxation. The relaxation step is detailed in this chapter, and the problem after relaxation is a mixed-integer Second-Order Cone Programming (SOCP) problem.
The standard form of second order cone programming is as follows:
Minimize fTx
Figure BDA0002878734500000181
wherein x ∈ Rn,
Figure BDA0002878734500000182
F∈Rp×n。
(1) Convex relaxation of tidal current equation
Figure BDA0002878734500000183
The equation constraint of quadratic terms appears in the formulas (3) and (4) in the power flow equation, so that the original problem is not convex. Therefore, under the premise that the node load has no upper limit and the system is a radial network, the node load is subjected to relaxation processing. The relaxed power flow equation is as follows:
Figure BDA0002878734500000184
wherein equation (9) is a second order cone constraint. Up to this point, the original non-convex power flow equation can be converted into a second-order cone constraint consisting of (1), (2), (7) and (9).
(2) Convex relaxation of transformer model equation
For lines ij with a transformer connected at the head end as shown in fig. 3, the transformer constraint equation can be converted into the form of Distflow power flow equation as shown in (10):
Figure BDA0002878734500000191
due to the gear of the transformer
Figure BDA0002878734500000192
Is a discrete variable with a certain fixed step size, so that the original expression is not solvable. To this end, equation (31) is converted to constraint (32):
Figure BDA0002878734500000193
wherein N isijIs taken to be such that
Figure BDA0002878734500000194
It holds that the smallest value, λ, can be obtainedij,n,tIs a relaxation variable of the type 0-1, KijNumber of steps of tap of transformer, mij,t，xij,n,t，yij,n,tIs a relaxation variable of the real number type,
Figure BDA0002878734500000195
is the tap minimum transformation ratio, Δ kijFor the step size of tap adjustment, 0.1 is taken in this embodiment, and M is a sufficiently large positive number. So far, through the above steps, the original problem can be converted into a mixed Integer Second-Order Cone Programming problem (MISOCP).
4. Conversion processing of energy storage model in real-time control link
Through the processing, the original problem is converted into a mixed integer second-order cone programming problem, and effective solving can be achieved. However, the requirement of the real-time control link on the solving speed may not be met because the variable still contains 0-1. In the real-time link, the energy storage model can be simplified by the following method:
let PES,i,tThe charge and discharge power for energy storage is positive when the energy storage is charged and negative when the energy storage is discharged:
Figure BDA0002878734500000196
substituting (33) into (22), the simplified energy storage model can be described as follows:
Figure BDA0002878734500000201
through the conversion processing, the problem in the real-time link can be converted into a second-order cone programming problem containing continuous variables, so that the solving speed of the real-time link can meet the requirement.
5. Example simulation
The simulation is performed on an improved IEEE 33 node system, and the details of example systems and parameters are shown in the appendix. Example simulation modeling was performed in a MATLAB 2018b environment using a Yalmip (version 20190425) optimization toolbox, and a Gurobi solver (version 8.1.1) was invoked for solution. Running the simulation program computer configured as Inter (R) core (TM) i 5-65003.20 Ghz,8G memory.
To illustrate the effectiveness of the method of this embodiment, four comparison methods are set in this link:
the method comprises the following steps: only considering the active and reactive power optimization method of the multi-period power distribution network on the side of the transmission network for standby;
the method 2 comprises the following steps: an active multi-period power distribution network optimization method;
the method 3 comprises the following steps: a reactive-only multi-period power distribution network optimization method;
the method 4 comprises the following steps: a day-ahead-day active and reactive power distribution network optimization method.
The four comparison methods are mainly different from the method provided in this embodiment as follows: the method 1 does not consider the standby resources in the power distribution network and the standby coordination relationship of the power transmission network and the power distribution network, and all the used standby resources are purchased to the power transmission network side; the control variable of the method 2 is only active output inside the power distribution network, namely active output of a tie line, active output of a controllable distributed power supply, active output of a fan and active output of energy storage, and all standby power is provided by the power transmission network side; the control variables of the method 3 are OLTC gears, CB switching gears and SVC reactive power output, and all the standby power is provided by the transmission network side; method 4 is the same as the method described herein except that the real-time phase is not included in the control strategy and control variables.
(1) Cost analysis
The method proposed in this example and all the comparative methods were subjected to simulation calculations, using the economic costs shown in table 1:
TABLE 1 comparison of economic costs
Figure BDA0002878734500000211
As can be seen from the above table, the method proposed in this embodiment can greatly reduce the economic cost generated by the operation of the power distribution network compared with other methods. Compared with the method 1, the method of the embodiment can enable the units or equipment which can provide the standby in the power distribution network to reserve certain positive rotation and negative rotation for standby, can still keep the units in the power distribution network to operate with certain power when the power price at the power transmission network side is low, and can also keep certain electricity purchasing amount when the power price at the power transmission network side is high, so that the electricity purchasing cost is slightly higher than that of the method 1; however, since the method provided in this embodiment can greatly reduce the spare amount purchased on the grid side, the total economic cost is still lower than that of the comparative method 1. Compared with the methods 2 and 3, the method takes the output and coordination characteristics of active and reactive devices in the power distribution network into consideration, and can effectively reduce the network loss and the economic cost required by power purchasing at the transmission network side. Compared with the method 4, the method provided by the embodiment adds a real-time control link, so that power fluctuation can be effectively stabilized through various devices in the power distribution network, economic loss caused by wind and light abandonment is reduced, and the final electricity purchasing cost is still lower than that of the method 4.
(2) Alternate composition and Effect analysis
The standby components in the active power distribution network 24h obtained by the method provided by the embodiment are shown in fig. 4. A comparison of the proposed method and comparative method 4 in terms of alternate purchase, usage and tie line power is given in fig. 5. As can be seen from fig. 5(a), compared to method 1, the method of the present embodiment has fewer grid-side backups, either purchased or used, which reduces the impact on the grid-side backup aggregates; as can be seen from fig. 5(b), the method provided herein can also reduce the peak-to-valley difference of the tie line power, and reduce the impact on the power transmission network after the distributed power supply is connected into the power distribution network.
To further explore the inhibitory effect of the method on tie power, the tie power peak-to-valley difference, standard deviation, can be defined as follows:
Figure BDA0002878734500000221
Figure BDA0002878734500000222
wherein P isGrid,tIs the active power of the tie line,
Figure BDA0002878734500000223
is the average power over the 24 hours of the tie line.
TABLE 2 results of the method presented in this example and comparative method 1
Figure BDA0002878734500000224
The calculated tie line peak to valley differences and standard deviations for the two methods are given in table 2. As can be seen from table 2, compared with method 1 (without considering the backup method in the power distribution network), the method provided in this embodiment can effectively reduce the active peak-to-valley difference of the tie line, and simultaneously can reduce the active and reactive fluctuation degrees of the tie line, thereby reducing the power uncertainty on the power distribution network tie line.
(3) Loss of network contrast analysis
Fig. 6(a) - (b) show the comparison of the two methods in the system loss and reactive device operation. As can be seen from fig. 6(a), the comparative method 3 (i.e., the reactive only method) has a higher network loss, especially during peak load periods. This is mainly due to two reasons: on one hand, the active power cannot be controlled; on the other hand, the reactive power regulation function of a fan, a photovoltaic and the like which are connected into a power grid distributed power supply through an inverter is not considered. Meanwhile, as can be seen from fig. 6(b), because there are fewer available reactive power sources inside the system, reactive power regulation is not flexible enough during operation, and the CB and the SVC are often in a fully loaded state. The method provided by the embodiment can fully and comprehensively utilize various active and reactive resources in the power distribution network, ensure sufficient reactive power in the power distribution network, and realize a better network loss reduction effect.
(4) System payload analysis
To illustrate the effect of the MPC-based real-time control phase on the consumption of renewable energy in the method of the present example, the method of the present example can be compared to comparative method 4.
Defining the system net load and the system net load as follows:
Figure BDA0002878734500000231
Figure BDA0002878734500000232
where N is the set of all nodes in the system, PLoad,i,tIs the active load of node i, PRES,i,tThe active power output of the renewable energy source unit accessed by the node i,
Figure BDA0002878734500000233
as a result of the net load of the method,
Figure BDA0002878734500000234
is the actual payload. The real system payload calculated according to the above formula, the payload of the method of this embodiment, and the payload of the method 4 are sampled according to the resolution of 5min within one day, after the deviation rate of the payload within one day is calculated respectively, the average deviation rate of the payload, the maximum deviation rate of the payload, and the ratio of the sampling time at which the deviation rate of the payload exceeds 1%, 5%, and 10% in the statistical data to the total data are counted, and the data are sorted as shown in table 3.
TABLE 3 Net load deviation Rate statistics
Figure BDA0002878734500000241
As can be seen from the table, the average and maximum net load deviation rate of the method of this embodiment is much lower than that of the comparison method 4, and the net load deviation rate can be controlled within 5%. Therefore, compared with the comparison method 4, the method provided by the embodiment can effectively reduce the net load deviation by introducing the model predictive control method in a real-time stage, and improve the consumption capability of renewable energy.
The embodiment considers the interaction capacity of the power distribution network and the power transmission network, and coordinates the backup of the power distribution network and the power transmission network, and has the following two advantages: on one hand, from the aspect of operation economy, the standby purchased by the power distribution network from the power transmission network side can be reduced, and further the operation cost of the system is reduced; on the other hand, from the aspect of operation safety, the power fluctuation of the tie line can be reduced by establishing the internal backup of the power distribution network, so that the influence of uncertainty on the power transmission network is reduced.
The method fully utilizes various active and reactive standby resources in the power distribution network, and compared with a power distribution network scheduling method which does not consider the standby resources in the power distribution network and only purchases standby resources from a power transmission network, the method can effectively reduce the system operation cost, reduce the power fluctuation degree of a connecting line, and reduce the influence of a renewable energy source unit in the power distribution network on a superior power grid.
The method can effectively coordinate active and reactive resources in the power distribution network, and can effectively reduce network loss on the premise of ensuring the operation economy and provide sufficient reactive support for the power distribution network compared with single active optimization or reactive optimization.
According to the method, after the model prediction control method is introduced as the real-time stage in the scheduling process, the power fluctuation caused by the renewable energy prediction error can be effectively coped with, and the renewable energy consumption degree is improved.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.

Claims (10)

1. A multi-period optimization scheduling method for an active power distribution network is characterized by comprising the following steps:
performing optimization operation decision of a day-ahead stage on slow-motion resources and power transmission network side standby according to day-ahead prediction information to obtain a day-ahead scheduling reference value;
the output reference value and the standby amount of each element in the power distribution network are optimized in the day-ahead stage by combining with the day-ahead scheduling reference value, and an operation reference value is provided for the real-time stage, so that day-ahead to day-ahead distribution-power transmission network standby coordination is realized;
and (4) performing short-time optimization on the output of various units in a real-time stage based on the operation reference value, and solving the output value of each unit in a rolling manner.
2. The active power distribution network multi-period optimization scheduling method of claim 1, wherein the optimization objective of the day-ahead stage is that the economic cost and the network loss weight in one day are minimum; the optimization target of the in-day phase is that the economic cost and the network loss weight in the set time period are minimum.
3. The active power distribution network multi-period optimization scheduling method of claim 1, wherein in the day-in-day phase optimization process, the active power distribution network detects a standby margin in the current power distribution network, and feeds back the standby margin to the day-ahead scheduling phase when the standby margin is detected to be insufficient, that is, based on the current operating state and the prediction data, the day-ahead calculation is performed again to obtain a new day-ahead reference value so as to ensure that the standby in the power distribution network is sufficient.
4. The active power distribution network multi-period optimization scheduling method of claim 1, wherein in the real-time phase, each sampling time is optimized in real time, and only the optimization result of the next sampling time is adopted and issued as a control command each time.
5. The active power distribution network multi-period optimization scheduling method of claim 1, wherein a real-time stage detects available reserve margin in a current power distribution network and feeds the reserve margin back to a day optimization stage to ensure that the reserve margin is sufficient when the power distribution network operates.
6. The active power distribution network multi-period optimization scheduling method according to claim 1, wherein the coordination of day-ahead to day intra-distribution-inter-transmission-network backup specifically comprises:
carrying out scheduling optimization on the standby resources at a day-ahead stage and a day-in stage according to the operating characteristics of the standby resources; the day-ahead standby comprises power transmission network side standby, interruptible load standby and capacitor bank standby; the intra-day standby includes gas turbine standby, energy storage standby, and SVC standby.
7. The active power distribution network multi-period optimization scheduling method of claim 1, wherein the optimization objective of the real-time control phase comprises: the weighted deviation of the output force of each unit and the reference value is minimum, and the abandoned wind and the abandoned light quantity are minimum.
8. A multi-period optimization scheduling system for an active power distribution network is characterized by comprising:
the day-ahead optimization module is used for carrying out day-ahead stage optimization operation decision on slow-motion resources and power transmission network side standby according to day-ahead prediction information to obtain a day-ahead scheduling reference value;
the in-day optimization module is used for optimizing the output reference value and the standby amount of each element in the power distribution network in the in-day stage by combining with the day-ahead scheduling reference value, and providing an operation reference value for the real-time stage so as to realize the standby coordination between the in-day distribution and power transmission networks from the day-ahead;
and the real-time optimization module is used for carrying out short-time optimization on the output of various units in a real-time stage based on the operation reference value and solving the output value of each unit in a rolling manner.
9. A terminal device comprising a processor and a memory, the processor being arranged to implement instructions; the memory is used for storing a plurality of instructions, wherein the instructions are suitable for being loaded by the processor and executing the active power distribution network multi-period optimization scheduling method of any one of claims 1 to 7.
10. A computer-readable storage medium having stored thereon a plurality of instructions, wherein the instructions are adapted to be loaded by a processor of a terminal device and to perform the active power distribution network multi-period optimization scheduling method according to any one of claims 1 to 7.
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