CN106786806B - Active and reactive coordination control method for power distribution network based on model predictive control - Google Patents

Active and reactive coordination control method for power distribution network based on model predictive control Download PDF

Info

Publication number
CN106786806B
CN106786806B CN201611158742.2A CN201611158742A CN106786806B CN 106786806 B CN106786806 B CN 106786806B CN 201611158742 A CN201611158742 A CN 201611158742A CN 106786806 B CN106786806 B CN 106786806B
Authority
CN
China
Prior art keywords
power
active
reactive
day
node
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201611158742.2A
Other languages
Chinese (zh)
Other versions
CN106786806A (en
Inventor
王勇
张明
刘海波
任佳依
顾伟
嵇文路
孙昕杰
兰岚
朱红勤
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
State Grid Corp of China SGCC
Southeast University
State Grid Jiangsu Electric Power Co Ltd
Nanjing Power Supply Co of State Grid Jiangsu Electric Power Co Ltd
Original Assignee
State Grid Corp of China SGCC
Southeast University
State Grid Jiangsu Electric Power Co Ltd
Nanjing Power Supply Co of State Grid Jiangsu Electric Power Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by State Grid Corp of China SGCC, Southeast University, State Grid Jiangsu Electric Power Co Ltd, Nanjing Power Supply Co of State Grid Jiangsu Electric Power Co Ltd filed Critical State Grid Corp of China SGCC
Priority to CN201611158742.2A priority Critical patent/CN106786806B/en
Publication of CN106786806A publication Critical patent/CN106786806A/en
Application granted granted Critical
Publication of CN106786806B publication Critical patent/CN106786806B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • H02J3/48—Controlling the sharing of the in-phase component
    • 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
    • H02J3/50—Controlling the sharing of the out-of-phase component

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention discloses a power distribution network active and reactive coordination regulation method based on model predictive control, which comprises the following steps: 1) Establishing a day-ahead optimization regulation model according to the optimization target and the constraint condition; 2) Solving a day-ahead optimization regulation model, and making a day-ahead power distribution network active and reactive regulation plan; 3) Taking a power distribution network active and reactive regulation plan before the day as a reference value, based on a power distribution network running state measurement value at the current moment, taking current and future running constraint conditions into consideration, establishing a prediction model and an active and reactive coordination optimization regulation model of a rolling correction stage in the day, and solving an active and reactive control instruction sequence in the day of a limited period in the future; 4) And executing the daily active and reactive control instruction at the first moment, shifting the time window by one time interval, and repeating the daily rolling correction optimization process. The method reduces the system operation cost on the premise of ensuring the system operation safety, reduces the system network loss and realizes the maximization of the operation benefit of the active power distribution network.

Description

Active and reactive coordination control method for power distribution network based on model predictive control
Technical Field
The invention belongs to the field of power distribution network operation optimization, and relates to a power distribution network active and reactive coordination regulation method based on model predictive control.
Background
With a large number of distributed power sources, flexible loads, energy storage devices, reactive compensation devices and other devices accessing a power distribution network, traditional power distribution networks are gradually evolving into active power distribution networks with numerous adjustable and controllable resources. The power generation capacity of the distributed power supply, especially the renewable energy source, has randomness, intermittence and fluctuation, the prediction accuracy is low, the prediction error is increased with the increase of time, and great challenges are brought to the regulation and control of the power grid. Active distribution networks are urgently required to seek a regulation method capable of better coping with the uncertainty of renewable energy output, and model predictive control (model predictive control, MPC) is an effective way to solve this problem.
The existing research effectively improves the capability of the power distribution network in coping with the uncertainty of renewable energy sources through model predictive control, but only considers the active scheduling of the active power distribution network. The active power and the reactive power in the power distribution network have strong coupling performance, and the power distribution network is not complete and comprehensive because the active power and the reactive power are respectively optimized and analyzed based on the traditional active-reactive decoupling theory. From the consideration of the running economy of the power distribution network, the network loss is reduced by adjusting the non-functions, and the economic benefit is indirectly improved; from the consideration of the operation safety of the power distribution network, the system voltage can be regulated by regulating the active resources in the power distribution network, and the safe operation of the system is ensured. Therefore, from the perspective of safety and economy of operation of the active power distribution network, it is necessary to study active power and reactive power coordination scheduling of the active power distribution network, and the maximization of the operation benefit of the active power distribution network is realized under the condition of ensuring safety and stability.
Disclosure of Invention
The invention aims to solve the technical problems that: the existing active power distribution network optimization scheduling method based on model predictive control is only aimed at active power scheduling, the influence of reactive power change in the power distribution network is not considered, and the method is not complete and comprehensive for the power distribution network with strong active power and reactive power coupling.
In order to solve the technical problems, the invention provides a power distribution network active and reactive coordination regulation method based on model predictive control, which comprises the following steps:
step 10), in the day-ahead stage, a day-ahead optimization regulation model of the power distribution network is established, the model takes the minimum running cost of the power distribution network as an optimization target, and takes power flow constraint, power distribution network running safety constraint, active and reactive power coordination output constraint of a distributed power supply, flexible load constraint and reactive power compensation equipment constraint as constraint conditions;
step 20), solving a day-ahead optimal scheduling model of the power distribution network to obtain a day-ahead active and reactive power regulation plan of the power distribution network, and issuing the plan in advance;
step 30), in the intra-day stage, the active and reactive power regulation plan of the power distribution network before the day obtained by solving in the step 20) is utilized, based on the running state measurement value of the power distribution network at the current moment, a prediction model and an active and reactive power coordination optimization regulation model of the limited period in the intra-day rolling correction stage are established by considering the current and future running constraint conditions, the deviation phenomenon of the actual plan in the day caused by the pre-day prediction error is corrected, and the intra-day active and reactive power control instruction sequence of the limited period in the future is solved;
step 40), executing an active and reactive control instruction in the day at the first moment, moving the time window by one time interval, and repeating the rolling correction optimization process in the day.
As a further limiting scheme of the present invention, in step 10), the power distribution network day-ahead optimization regulation stage model is as follows:
minF DA (U DA )
s.t.G DA (U DA )≤0
Figure BDA0001181143840000021
Figure BDA0001181143840000022
in the formula ,FDA Minimum objective function of running cost of power distribution network for day-ahead stage model, G DA Is a constraint condition in a model, comprising a power flow constraint, a power distribution network operation safety constraint, a distributed power source active and reactive power coordinated output constraint, a flexible load constraint and a reactive power compensation equipment constraint, U DA As a control variable in the day-ahead phase model,
Figure BDA0001181143840000023
exchange power for main network connection line at time t before day,/->
Figure BDA0001181143840000024
and
Figure BDA0001181143840000025
For the active power of renewable energy sources and the active power of controllable distributed power sources at node i at time t before day,/->
Figure BDA0001181143840000026
The controlled active power of the load at node i at time t;
Figure BDA0001181143840000027
and
Figure BDA0001181143840000028
The charging power and the discharging power of the energy storage device at the node i at the time t before the day are respectively +.>
Figure BDA0001181143840000029
and
Figure BDA00011811438400000210
Reactive power of SVC, energy storage reactive power output, reactive power of compensation capacitor, reactive power of renewable energy source, reactive power of controllable distributed power source at node i of time t before day respectively, +/->
Figure BDA00011811438400000211
The adjustable ratio of the on-load voltage regulator OLTC at the branch ij at the time t before the day.
As a further limiting aspect of the present invention, in step 10), the objective function with the smallest running cost of the power distribution network is:
Figure BDA00011811438400000212
wherein DeltaT is the unit time interval of the day-ahead optimal regulation stage, ρ G,t For the unit cost of the power exchange between the regional distribution network and the main network tie line at the moment t, ρ RE,i 、ρ CDG,i and ρESS,i The power price of the renewable energy source, the controllable distributed power source and the stored energy at the node i are respectively used for surfing the internet,
Figure BDA0001181143840000031
for the controlled state of the load at node i at time t before day, 1 means controlled, 0 means uncontrolled, +.>
Figure BDA0001181143840000032
For compensation of load at node i ρ T Adjusting cost, deltaU for unit gear of OLTC T Total daily adjustment quantity of OLTC gear, ρ C To compensate for the unit gear adjustment cost of the capacitor, deltaU C To compensate for the total daily adjustment of the capacitor gear.
As a further limiting aspect of the present invention, in step 10), the power flow constraint is:
Figure BDA0001181143840000033
in the formula ,
Figure BDA0001181143840000034
and
Figure BDA0001181143840000035
The active power and the reactive power of the head end of the branch ij at the time t before the day are respectively +.>
Figure BDA0001181143840000036
and
Figure BDA0001181143840000037
Active power and reactive power net injection values of node j at time t before day, +.>
Figure BDA0001181143840000038
and
Figure BDA0001181143840000039
Load active power and load reactive power of node j at time t before day respectively, +.>
Figure BDA00011811438400000315
For the voltage amplitude value at the node j at the time t before the day, r ij and xij The resistance and reactance of the line ij, < >>
Figure BDA00011811438400000310
The current on the branch ij at the time t before the day;
the operation safety constraint of the power distribution network is as follows:
Figure BDA00011811438400000311
in the formula ,
Figure BDA00011811438400000312
and
Figure BDA00011811438400000313
Respectively upper and lower limits of the voltage amplitude of the node i, < + >>
Figure BDA00011811438400000314
Is the upper limit of the current amplitude of the branch ij.
As a further limiting aspect of the present invention, in step 10), the active and reactive coordinated output constraints of the distributed power supply include an energy storage active and reactive output constraint, a renewable energy active and reactive output constraint, and a controllable distributed power supply active and reactive output constraint; wherein,
the energy storage active reactive output constraint is as follows:
Figure BDA0001181143840000041
Figure BDA0001181143840000042
in the formula ,
Figure BDA0001181143840000043
the maximum apparent power that can be provided for the energy storage inverter at node i;
the energy storage active charge and discharge and the stored electric quantity need to follow the following constraint:
Figure BDA0001181143840000044
Figure BDA0001181143840000045
Figure BDA0001181143840000046
Figure BDA0001181143840000047
Figure BDA0001181143840000048
Figure BDA0001181143840000049
in the formula ,
Figure BDA00011811438400000411
and
Figure BDA00011811438400000412
Respectively, the upper limit of charge and discharge power of energy storage on a node i at the moment t, < >>
Figure BDA00011811438400000413
and
Figure BDA00011811438400000414
Respectively the charge and discharge states of energy storage on the node i at the moment t are 0-1 variable, +.>
Figure BDA00011811438400000415
For the total energy stored at node i of period t, eta ch 、η dis Charging and discharging efficiency of energy storage respectively, +.>
Figure BDA00011811438400000416
Is the energy storage capacity limit;
the renewable energy source active and reactive power output constraint comprises a photovoltaic active and reactive power output constraint and a wind power active and reactive power output constraint; wherein, photovoltaic active reactive power output constraint is:
Figure BDA00011811438400000417
Figure BDA0001181143840000051
in the formula ,
Figure BDA0001181143840000052
and
Figure BDA0001181143840000053
Active and reactive output of photovoltaic at node i at time t before day, +.>
Figure BDA0001181143840000054
The maximum apparent power of the photovoltaic inverter at the node i is set;
the wind power active and reactive output constraint is as follows:
Figure BDA0001181143840000055
Figure BDA0001181143840000056
in the formula ,
Figure BDA0001181143840000057
and
Figure BDA0001181143840000058
Active output and reactive output of wind power at node i at time t before day respectively, +.>
Figure BDA0001181143840000059
The maximum current of the stator winding of the doubly-fed induction wind turbine is s is slip ratio and X of the doubly-fed induction wind turbine s,i and Xm,i Respectively being a stator reactance and an excitation reactance of the doubly-fed induction wind turbine at the node i, < + >>
Figure BDA00011811438400000510
Maximum current for a rotor side converter of the doubly-fed induction wind turbine;
the controllable distributed power supply active and reactive output constraint is that of a miniature gas turbine; the active and reactive output constraint of the micro gas turbine is as follows:
Figure BDA00011811438400000511
in the formula ,
Figure BDA00011811438400000512
and
Figure BDA00011811438400000513
The active output and the reactive output of the micro gas turbine at the node i at the time t before the day are respectively, S MT,i The installed capacity of the miniature gas turbine at the node i;
for active output, there is a hill climbing rate constraint for a micro gas turbine:
Figure BDA00011811438400000514
in the formula ：
Figure BDA00011811438400000515
and
Figure BDA00011811438400000516
The uphill speed limit and the downhill speed limit of the micro gas turbine at the node i are respectively.
As a further limiting aspect of the present invention, in step 10), the flexible load constraint is an operational constraint of an interruptible load, and the flexible load operational constraint is:
Figure BDA0001181143840000061
Figure BDA0001181143840000062
Figure BDA0001181143840000063
in the formula ,
Figure BDA0001181143840000064
s is the upper limit value of the interruptible load at the node j CL,i An interruption period is allowed for the interruptible load at node i.
As a further limiting aspect of the present invention, in step 10), the reactive compensation device constraints include OLTC operation constraints, SVC operation constraints, and compensation capacitor operation constraints; wherein, OLTC operation constraints are:
Figure BDA0001181143840000065
Figure BDA0001181143840000066
in the formula ,
Figure BDA0001181143840000067
and Δkij Standard ratio and regulation step length of OLTC in branch ij, respectively, +.>
Figure BDA0001181143840000068
and
Figure BDA0001181143840000069
The gear at the time t of the OLTC in the branch ij and the adjustable lower limit and the adjustable upper limit of the gear are respectively shown;
the SVC operation constraint is:
Figure BDA00011811438400000610
in the formula ,
Figure BDA00011811438400000611
and
Figure BDA00011811438400000612
The upper limit value and the lower limit value of SVC adjustable power are respectively;
the compensation capacitor operating constraints are:
Figure BDA00011811438400000613
Figure BDA00011811438400000614
in the formula ,
Figure BDA00011811438400000615
and ΔQC,i Minimum output and adjustable capacity step of the compensation capacitor connected to node i, respectively, +.>
Figure BDA00011811438400000616
Figure BDA00011811438400000617
and
Figure BDA00011811438400000618
The t-time gear and the adjustable upper and lower limits of the compensation capacitor connected at the node i are respectively provided.
As a further limiting aspect of the present invention, in step 30), the prediction model of the limited period of the intra-day rolling correction phase is:
Figure BDA00011811438400000619
u＝[P G ,P RE,i ,Q RE,i ,P CDG,i ,Q CDG,i ,P ch,i ,P dis,i ,Q ESS,i ,Q SVC,i ]
in the formula ,t0 The moment is the current moment, delta t is the unit time interval of the daily rolling correction stage, M is the model solving of the daily rolling correction stageThe total time period u represents controllable means in the active power distribution network in the feedback correction stage, and comprises the exchange power P of the main network tie line G Active power output P of renewable energy source RE,i Reactive power output Q of renewable energy RE,i Active power output P of controllable distributed power supply CDG,i Reactive power Q of controllable distributed power supply CDG,i Energy storage active force P ch,i and Pdsi,i Energy storage reactive power output Q ESS,i SVC reactive power output Q SVC,I ，
Figure BDA0001181143840000071
At t 0 T obtained by time prediction 0 The output value of each controllable means at +kDeltat,/->
Figure BDA0001181143840000072
At t 0 The actual measurement feedback value of each controllable means at the moment, < >>
Figure BDA0001181143840000073
At t 0 And predicting the moment to obtain the increment of each controllable means, and solving the variables for the optimization model.
As a further limiting scheme of the present invention, in step 30), the active-reactive coordination optimization regulation model of the limited period of the intra-day rolling correction phase is:
minF DR (U DR )
Figure BDA0001181143840000074
Figure BDA0001181143840000075
in the formula ,UDR At t 0 ～t 0 A set of force values of each controllable means in +MDeltat time period, F DR G is an objective function of the rolling correction stage model in the day DR The constraint condition of the phase model is corrected for daily rolling;
the objective function of the active and reactive coordination optimization model in the limited time period of the daily rolling correction stage is as follows:
minF DR ＝||(U DR -U DA )·U max || 1
Figure BDA0001181143840000076
Figure BDA0001181143840000077
in the formula ,UDA For the day t 0 ～t 0 Set of force values of each controllable means in +MDeltat time period, U max For the reciprocal of the maximum value of the output of each controllable means, the constraint conditions of the active-reactive coordination optimization model in the limited period of the rolling correction stage in the day are the same as the constraint conditions in the previous stage except that the constraint conditions do not contain flexible load operation constraint, OLTC operation constraint and compensation capacitor operation constraint.
The invention has the beneficial effects that: aiming at the adjustment characteristics of various adjustable resources such as distributed power sources, flexible loads, reactive power adjustment equipment and the like in an active power distribution network in the aspects of time scale, control functions and the like, the coordination mechanism of active and reactive power output of the active power distribution network is analyzed, the requirements of the power distribution network in the aspects of operation economy, safety and the like are combined, prediction errors caused by large-scale renewable energy output fluctuation are considered, a multi-time scale active and reactive power coordination scheduling method of the power distribution network based on model prediction control is provided, various active and reactive power adjustable resources in the power distribution network are coordinated comprehensively, the operation cost of the system is further reduced on the premise of ensuring the operation safety of the system, the network loss of the system is reduced, and the maximization of the operation benefit of the active power distribution network is realized more comprehensively.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the method for active and reactive coordination regulation of the power distribution network based on model predictive control disclosed by the invention comprises the following steps:
1) Firstly, a power distribution network day-ahead optimization regulation model is established, the model takes the minimum running cost of the power distribution network as an optimization target, and takes power flow constraint, power distribution network running safety constraint, active and reactive power coordination output constraint of a distributed power supply, flexible load constraint and reactive power compensation equipment constraint as constraint conditions;
the day-ahead regulation and control plan performs active and reactive power coordination optimization regulation and control according to the day-ahead load and the prediction information of renewable energy sources by considering the running economy and the safety of the active power distribution network, the unit time interval of the day-ahead optimization regulation and control stage is delta T, and the optimization control variables comprise: active and reactive power output of a distributed power supply, active power exchanged by a power distribution network and a main network tie line, flexible load adjustment quantity, reactive power output of a compensation capacitor, SVC output and OLTC transformation ratio, and the minimum running cost of the power distribution network is taken as an optimization target in a day-ahead optimization stage, a basic regulation and control plan of the next day is formulated, and the plan is issued in advance;
the day-ahead optimization regulation phase model can be summarized as follows:
minF DA (U DA )
s.t.G DA (U DA )≤0
Figure BDA0001181143840000081
Figure BDA0001181143840000082
in the formula ,FDA Minimum objective function of running cost of power distribution network for day-ahead stage model, G DA Is a constraint condition in a model, comprising a power flow constraint, a power distribution network operation safety constraint, a distributed power source active and reactive power coordinated output constraint, a flexible load constraint and a reactive power compensation equipment constraint, U DA As a control variable in the day-ahead phase model,
Figure BDA0001181143840000083
exchange power for main network connection line at time t before day,/->
Figure BDA0001181143840000084
and
Figure BDA0001181143840000085
For the active power of renewable energy sources and the active power of controllable distributed power sources at node i at time t before day,/->
Figure BDA0001181143840000086
The controlled active power of the load at node i at time t;
Figure BDA0001181143840000087
and
Figure BDA0001181143840000088
The charging power and the discharging power of the energy storage device at the node i at the time t before the day are respectively +.>
Figure BDA0001181143840000089
and
Figure BDA00011811438400000810
The reactive power of the static reactive power compensation device SVC (static reactive compensator) at the node i at the time t before the day, the reactive power of the energy storage reactive power output (output reactive power is positive), the reactive power of the compensation capacitor, the reactive power of the renewable energy source (output reactive power is positive) and the reactive power of the controllable distributed power supply are respectively shown as follows>
Figure BDA0001181143840000091
The adjustable ratio of the on-load voltage regulator OLTC at the branch ij at the time t before the day;
objective function F with minimum running cost of power distribution network DA The following is shown:
Figure BDA0001181143840000092
wherein DeltaT is the unit time interval of the day-ahead optimal regulation stage, ρ G,t For the unit cost of the power exchange between the regional distribution network and the main network tie line at the moment t, ρ RE,i 、ρ CDG,i and ρESS,i The power price of the renewable energy source, the controllable distributed power source and the stored energy at the node i are respectively used for surfing the internet,
Figure BDA0001181143840000093
for the controlled state of the load at node i at time t before day, 1 means controlled, 0 means uncontrolled, +.>
Figure BDA0001181143840000094
For compensation of load at node i ρ T Adjusting cost, deltaU for unit gear of OLTC T Total daily adjustment quantity of OLTC gear, ρ C To compensate for the unit gear adjustment cost of the capacitor, deltaU C The total adjustment amount of the capacitor gear is compensated for all day;
constraint condition G of power distribution network day-ahead optimization regulation model DA The method comprises the steps of power flow constraint, power distribution network operation safety constraint, distributed power source active and reactive coordination output constraint, flexible load constraint and reactive compensation equipment constraint.
The tide constraint is as follows:
Figure BDA0001181143840000095
in the formula ,
Figure BDA0001181143840000101
and
Figure BDA0001181143840000102
The active power and the reactive power of the head end of the branch ij at the time t before the day are respectively +.>
Figure BDA0001181143840000103
And
Figure BDA0001181143840000104
active power and reactive power net injection values of node j at time t before day, +.>
Figure BDA0001181143840000105
and
Figure BDA0001181143840000106
Load active power and load reactive power of node j at time t before day respectively, +.>
Figure BDA0001181143840000107
For the voltage amplitude value at the node j at the time t before the day, r ij and xij The resistance and reactance of the line ij, < >>
Figure BDA0001181143840000108
The current on the branch ij at the time t before the day;
the operation safety constraint of the power distribution network is as follows:
Figure BDA0001181143840000109
in the formula ,
Figure BDA00011811438400001010
and
Figure BDA00011811438400001011
Respectively upper and lower limits of the voltage amplitude of the node i, < + >>
Figure BDA00011811438400001012
The current amplitude upper limit of the branch ij is set;
the distributed power source active and reactive power coordinated output constraint comprises an energy storage active and reactive output constraint, a renewable energy source active and reactive output constraint and a controllable distributed power source active and reactive output constraint; wherein,
the energy storage active reactive output constraint is as follows:
Figure BDA00011811438400001013
Figure BDA00011811438400001014
in the formula ,
Figure BDA00011811438400001015
the maximum apparent power that can be provided for the energy storage inverter at node i;
the energy storage active charge and discharge and the stored electric quantity need to follow the following constraint:
Figure BDA00011811438400001016
Figure BDA00011811438400001017
Figure BDA00011811438400001018
Figure BDA00011811438400001019
Figure BDA00011811438400001020
Figure BDA0001181143840000111
in the formula ,
Figure BDA0001181143840000112
and
Figure BDA0001181143840000113
Respectively, the upper limit of charge and discharge power of energy storage on a node i at the moment t, < >>
Figure BDA0001181143840000114
and
Figure BDA0001181143840000115
Respectively the charge and discharge states of energy storage on the node i at the moment t are 0-1 variable, +.>
Figure BDA0001181143840000116
For the total energy stored at node i of period t, eta ch 、η dis Charging and discharging efficiency of energy storage respectively, +.>
Figure BDA0001181143840000117
Is the energy storage capacity limit;
the renewable energy source active and reactive power output constraint comprises a photovoltaic active and reactive power output constraint and a wind power active and reactive power output constraint; wherein, photovoltaic active reactive power output constraint is:
Figure BDA0001181143840000118
Figure BDA0001181143840000119
in the formula ,
Figure BDA00011811438400001110
and
Figure BDA00011811438400001111
Active and reactive output of photovoltaic at node i at time t before day, +.>
Figure BDA00011811438400001112
The maximum apparent power of the photovoltaic inverter at the node i is set; />
The wind power active and reactive output constraint is as follows:
Figure BDA00011811438400001113
Figure BDA00011811438400001114
in the formula ,
Figure BDA00011811438400001115
and
Figure BDA00011811438400001116
Active output and reactive output of wind power at node i at time t before day respectively, +.>
Figure BDA00011811438400001117
The maximum current of the stator winding of the doubly-fed induction wind turbine is s is slip ratio and X of the doubly-fed induction wind turbine s,i and Xm,i Respectively being a stator reactance and an excitation reactance of the doubly-fed induction wind turbine at the node i, < + >>
Figure BDA00011811438400001118
Maximum current for a rotor side converter of the doubly-fed induction wind turbine;
the controllable distributed power supply active and reactive output constraint is that of a miniature gas turbine; the active and reactive output constraint of the micro gas turbine is as follows:
Figure BDA00011811438400001119
in the formula ,
Figure BDA0001181143840000121
and
Figure BDA0001181143840000122
Respectively the day beforeActive output and reactive output of miniature gas turbine at node i at t moment S MT,i The installed capacity of the miniature gas turbine at the node i;
for active output, there is a hill climbing rate constraint for a micro gas turbine:
Figure BDA0001181143840000123
in the formula ：
Figure BDA0001181143840000124
and
Figure BDA0001181143840000125
Respectively limiting the upward climbing speed and the downward climbing speed of the micro gas turbine at the node i;
the flexible load constraint mainly considers the operation constraint of the interruptible load, and the flexible load operation constraint is as follows:
Figure BDA0001181143840000126
Figure BDA0001181143840000127
Figure BDA0001181143840000128
in the formula ,
Figure BDA0001181143840000129
s is the upper limit value of the interruptible load at the node j CL,i Allowing an interruption period for an interruptible load at node i;
reactive compensation equipment constraints mainly include OLTC operating constraints, SVC operating constraints and compensation capacitor operating constraints; wherein, OLTC operation constraints are:
Figure BDA00011811438400001210
Figure BDA00011811438400001211
in the formula ,
Figure BDA00011811438400001212
and Δkij Standard ratio and regulation step length of OLTC in branch ij, respectively, +.>
Figure BDA00011811438400001213
and
Figure BDA00011811438400001214
The gear at the time t of the OLTC in the branch ij and the adjustable lower limit and the adjustable upper limit of the gear are respectively shown;
the SVC operation constraint is:
Figure BDA00011811438400001215
in the formula ,
Figure BDA00011811438400001216
and
Figure BDA00011811438400001217
The upper limit value and the lower limit value of SVC adjustable power are respectively;
the compensation capacitor operating constraints are:
Figure BDA0001181143840000131
Figure BDA0001181143840000132
in the formula ,
Figure BDA0001181143840000133
and ΔQC,i Minimum output and adjustable capacity step of the compensation capacitor connected to node i, respectively, +.>
Figure BDA0001181143840000134
Figure BDA0001181143840000135
and
Figure BDA0001181143840000136
The t-time gear and the adjustable upper and lower limits of the compensation capacitor connected at the node i are respectively;
2) Solving a day-ahead optimization regulation model by using mature commercial software, making a day-ahead power distribution network active and reactive regulation plan, and issuing the plan in advance, wherein slow dynamic equipment such as an OLTC, a compensation capacitor, an interruptible load and the like are not changed in a day-ahead regulation stage after determining a power output plan due to limited regulation speed;
3) Utilizing the regulation and control plan obtained in the step 2), taking current and future operation constraint conditions into consideration, establishing a prediction model and an optimized regulation and control model in a daily rolling correction stage, and correcting a daily actual plan deviation phenomenon caused by a daily prediction error;
in order to eliminate the deviation phenomenon of actual plan in the day caused by the prediction error before the day, rolling correction is carried out in the day by taking Δt (Δt < Δt) as a period, in each period, taking the current moment and the load in a period of MΔt in the future and the prediction information of renewable energy sources as input variables, taking the actual measurement value of the output of each controllable means in the power distribution network at the current moment as an initial value, taking the increment of the output of each controllable means in a finite time domain MΔt in the future as a control variable, predicting the state of the future MΔt time of the power distribution network based on a certain prediction model, simultaneously taking the current and future operation constraint conditions into consideration, establishing a finite period active-reactive coordination optimization model in the rolling correction stage in the day, optimizing and solving the control instruction sequence of the period MΔt in the future,
the prediction model of the limited period of the daily rolling correction stage is as follows:
Figure BDA0001181143840000137
u＝[P G ,P RE,i ,Q RE,i ,P CDG,i ,Q CDG,i ,P ch,i ,P dis,i ,Q ESS,i ,Q SVC,i ]
in the formula ,t0 The moment is the current moment, deltat is the unit time interval of the daily rolling correction stage, M is the total time period of the daily rolling correction stage model solution, u represents the controllable means in the active power distribution network of the feedback correction stage, and the controllable means comprise the main network tie line exchange power P G Active power output P of renewable energy source RE,i Reactive power output Q of renewable energy RE,i Active power output P of controllable distributed power supply CDG,i Reactive power Q of controllable distributed power supply CDG,i Energy storage active force P ch,i and Pdsi,i Energy storage reactive power output Q ESS,i SVC reactive power output Q SVC,I ，
Figure BDA0001181143840000138
At t 0 T obtained by time prediction 0 The output value of each controllable means at +kDeltat,/->
Figure BDA0001181143840000139
At t 0 The actual measurement feedback value of each controllable means at the moment, < >>
Figure BDA00011811438400001310
At t 0 Predicting the moment to obtain the increment of each controllable means, and solving the variables for the optimization model;
the active and reactive coordination optimization regulation model of the limited period in the daily rolling correction stage is as follows:
minF DR (U DR )
Figure BDA0001181143840000141
Figure BDA0001181143840000142
in the formula ：UDR At t 0 ～t 0 A set of force values of each controllable means in +MDeltat time period, F DR G is an objective function of the rolling correction stage model in the day DR Constraints of the phase model are corrected for intra-day scrolling.
The objective function of the optimization model for the limited period of the daily roll correction phase is as follows:
minF DR ＝||(U DR -U DA )·U max || 1
Figure BDA0001181143840000143
Figure BDA0001181143840000144
in the formula ：UDA For the day t 0 ～t 0 Set of force values of each controllable means in +MDeltat time period, U max Constraint condition G of finite period active-reactive coordination optimization model in daily rolling correction stage for reciprocal of maximum value of output of each controllable means DR Constraint condition G of day-ahead optimization regulation stage DA Similarly, the difference is that: since the regulation and control plans of full dynamic devices such as interruptible loads, OLTC, and compensation capacitors have been determined before the day, and have not been changed during the day phase, the constraints of the day roll correction phase do not include flexible load operation constraints, OLTC operation constraints, and compensation capacitor operation constraints.
4) And solving according to the active and reactive power optimization regulation model in the daily rolling correction stage to obtain a daily active and reactive power control instruction sequence of a limited period in the future, executing the daily active and reactive power control instruction at the first moment, shifting the time window by one time interval, and repeating the daily rolling correction optimization process.

Claims (6)

1. The active and reactive coordination regulation and control method for the power distribution network based on model predictive control is characterized by comprising the following steps of:
step 10), in the day-ahead stage, a day-ahead optimization regulation model of the power distribution network is established, the model takes the minimum running cost of the power distribution network as an optimization target, and takes power flow constraint, power distribution network running safety constraint, active and reactive power coordination output constraint of a distributed power supply, flexible load constraint and reactive power compensation equipment constraint as constraint conditions;
step 20), solving a day-ahead optimal scheduling model of the power distribution network to obtain a day-ahead active and reactive power regulation plan of the power distribution network, and issuing the plan in advance;
step 30), in the intra-day stage, the active and reactive power regulation plan of the power distribution network before the day obtained by solving in the step 20) is utilized, based on the running state measurement value of the power distribution network at the current moment, a prediction model and an active and reactive power coordination optimization regulation model of the limited period in the intra-day rolling correction stage are established by considering the current and future running constraint conditions, the deviation phenomenon of the actual plan in the day caused by the pre-day prediction error is corrected, and the intra-day active and reactive power control instruction sequence of the limited period in the future is solved;
step 40), executing an active and reactive control instruction in the day at the first moment, moving a time window backwards by a time interval, and repeating the rolling correction optimization process in the day;
in the step 10), the power distribution network day-ahead optimization regulation stage model is as follows:
minF DA (U DA )
s.t.G DA (U DA )≤0
Figure FDA0004116446840000011
Figure FDA0004116446840000012
in the formula ,FDA Minimum objective function of running cost of power distribution network for day-ahead stage model, G DA Is a constraint condition in a model, comprising a power flow constraint, a power distribution network operation safety constraint, a distributed power source active and reactive power coordinated output constraint, a flexible load constraint and a reactive power compensation equipment constraint, U DA As a control variable in the day-ahead phase model,
Figure FDA0004116446840000013
exchange power for main network connection line at time t before day,/->
Figure FDA0004116446840000014
and
Figure FDA0004116446840000015
The active power of renewable energy sources and controllable distributed power sources at the node i at the moment t before the day,
Figure FDA0004116446840000016
the controlled active power of the load at node i at time t;
Figure FDA0004116446840000017
and
Figure FDA0004116446840000018
The charging power and the discharging power of the energy storage device at the node i at the time t before the day are respectively +.>
Figure FDA0004116446840000019
and
Figure FDA00041164468400000110
Reactive power of SVC, energy storage reactive power output, reactive power of compensation capacitor, reactive power of renewable energy source, reactive power of controllable distributed power source at node i of time t before day respectively, +/->
Figure FDA00041164468400000111
The adjustable ratio of the on-load voltage regulator OLTC at the branch ij at the time t before the day; in step 10), the objective function with the minimum running cost of the power distribution network is as follows:
Figure FDA0004116446840000021
wherein DeltaT is the unit time interval of the day-ahead optimal regulation stage, ρ G,t For the unit cost of the power exchange between the regional distribution network and the main network tie line at the moment t, ρ RE,i 、ρ CDG,i and ρESS,i The power price of the renewable energy source, the controllable distributed power source and the stored energy at the node i are respectively used for surfing the internet,
Figure FDA0004116446840000022
for the controlled state of the load at node i at time t before day, 1 means controlled, 0 means uncontrolled, +.>
Figure FDA0004116446840000023
For compensation of load at node i ρ T Adjusting cost, deltaU for unit gear of OLTC T Total daily adjustment quantity of OLTC gear, ρ C To compensate for the unit gear adjustment cost of the capacitor, deltaU C The total adjustment amount of the capacitor gear is compensated for all day;
wherein ,ψRE Representing a set of renewable energy nodes; psi phi type DG Representing a controllable distributed power node set; psi phi type IL Representing a set of interruptible load nodes; psi phi type ESS Representing a set of energy storage nodes;
in step 10), the power flow constraint is:
Figure FDA0004116446840000024
in the formula ,
Figure FDA0004116446840000025
and
Figure FDA0004116446840000026
The active power and the reactive power of the head end of the branch ij at the time t before the day are respectively +.>
Figure FDA0004116446840000027
and
Figure FDA0004116446840000028
Active power and reactive power net injection values of node j at time t before day, +.>
Figure FDA0004116446840000029
and
Figure FDA00041164468400000210
Load active power and load reactive power of node j at time t before day respectively, +.>
Figure FDA00041164468400000211
For the voltage amplitude value at the node j at the time t before the day, r ij and xij The resistance and reactance of the line ij, < >>
Figure FDA00041164468400000212
The current on the branch ij at the time t before the day;
wherein ,
Figure FDA00041164468400000213
and
Figure FDA00041164468400000214
The active power and the reactive power of the head end of the branch jk at the time t before the day are respectively;
Figure FDA00041164468400000215
and
Figure FDA00041164468400000216
Respectively charging power and discharging power of the energy storage device at a node j at the time t before the day;
Figure FDA0004116446840000031
and
Figure FDA0004116446840000032
The active power of renewable energy sources and controllable distributed power sources at the node j at the time t before the day;
Figure FDA0004116446840000033
The controlled active power of the load at node j at time t;
Figure FDA0004116446840000034
The reactive power of the static reactive power compensation device SVC, the energy storage reactive power output, the reactive power of the compensation capacitor, the reactive power of the renewable energy source and the reactive power of the controllable distributed power source at the node j at the time t before the day are respectively;
Figure FDA0004116446840000035
Representing the voltage amplitude at node i at time t before day;
the operation safety constraint of the power distribution network is as follows:
Figure FDA0004116446840000036
in the formula ,
Figure FDA0004116446840000037
and
Figure FDA0004116446840000038
Respectively upper and lower limits of the voltage amplitude of the node i, < + >>
Figure FDA0004116446840000039
Is the upper limit of the current amplitude of the branch ij.
2. The method for active and reactive coordination control of a power distribution network based on model predictive control according to claim 1, wherein in step 10), the active and reactive coordination output constraints of the distributed power source include an energy storage active and reactive output constraint, a renewable energy active and reactive output constraint and a controllable active and reactive output constraint of the distributed power source; wherein,
the energy storage active reactive output constraint is as follows:
Figure FDA00041164468400000310
Figure FDA00041164468400000311
in the formula ,
Figure FDA00041164468400000312
the maximum apparent power that can be provided for the energy storage inverter at node i;
the energy storage active charge and discharge and the stored electric quantity need to follow the following constraint:
Figure FDA00041164468400000313
Figure FDA00041164468400000314
Figure FDA00041164468400000315
Figure FDA00041164468400000316
Figure FDA00041164468400000317
Figure FDA00041164468400000318
in the formula ,
Figure FDA00041164468400000319
and
Figure FDA00041164468400000320
Respectively, the upper limit of charge and discharge power of energy storage on a node i at the moment t, < >>
Figure FDA00041164468400000321
and
Figure FDA00041164468400000322
Respectively the charge and discharge states of energy storage on the node i at the moment t are 0-1 variable, +.>
Figure FDA0004116446840000041
For the total energy stored at node i of period t, eta ch 、η dis Charging and discharging efficiency of energy storage respectively, +.>
Figure FDA0004116446840000042
Is the energy storage capacity limit;
Figure FDA0004116446840000043
Representing the power of the energy storage active charge and discharge on the node i at the moment t;
Figure FDA0004116446840000044
Representing the total energy stored at node i during the t+Δt period;
the renewable energy source active and reactive power output constraint comprises a photovoltaic active and reactive power output constraint and a wind power active and reactive power output constraint; wherein, photovoltaic active reactive power output constraint is:
Figure FDA0004116446840000045
Figure FDA0004116446840000046
in the formula ,
Figure FDA0004116446840000047
and
Figure FDA0004116446840000048
Active and reactive output of photovoltaic at node i at time t before day, +.>
Figure FDA0004116446840000049
The maximum apparent power of the photovoltaic inverter at the node i is set;
Figure FDA00041164468400000410
and
Figure FDA00041164468400000411
The load active power and the load reactive power of the node i at the time t before the day are respectively;
the wind power active and reactive output constraint is as follows:
Figure FDA00041164468400000412
Figure FDA00041164468400000413
in the formula ,
Figure FDA00041164468400000414
and
Figure FDA00041164468400000415
Active output and reactive output of wind power at node i at time t before day respectively, +.>
Figure FDA00041164468400000416
The maximum current of the stator winding of the doubly-fed induction wind turbine is s is slip ratio and X of the doubly-fed induction wind turbine s,i and Xm,i Respectively being a stator reactance and an excitation reactance of the doubly-fed induction wind turbine at the node i, < + >>
Figure FDA00041164468400000417
Maximum current for a rotor side converter of the doubly-fed induction wind turbine;
the controllable distributed power supply active and reactive output constraint is that of a miniature gas turbine; the active and reactive output constraint of the micro gas turbine is as follows:
Figure FDA00041164468400000418
in the formula ,
Figure FDA00041164468400000419
and
Figure FDA00041164468400000420
The active output and the reactive output of the micro gas turbine at the node i at the time t before the day are respectively, S MT,i For miniature gas turbines at node iIs a function of the installed capacity of the device;
for active output, there is a hill climbing rate constraint for a micro gas turbine:
Figure FDA0004116446840000051
in the formula ：
Figure FDA0004116446840000052
and
Figure FDA0004116446840000053
The uphill speed limit and the downhill speed limit of the micro gas turbine at the node i are respectively.
3. The method for active and reactive coordination control of a power distribution network based on model predictive control as claimed in claim 1, wherein in step 10), the flexible load constraint is an operational constraint of interruptible load, and the flexible load operational constraint is:
Figure FDA0004116446840000054
Figure FDA0004116446840000055
Figure FDA0004116446840000056
in the formula ,
Figure FDA0004116446840000057
s is the upper limit value of the interruptible load at the node j CL,i An interruption period is allowed for the interruptible load at node i.
4. The method for active and reactive coordination regulation of a power distribution network based on model predictive control according to claim 1, wherein in step 10), reactive compensation equipment constraints include OLTC operation constraints, SVC operation constraints, and compensation capacitor operation constraints; wherein, OLTC operation constraints are:
Figure FDA0004116446840000058
Figure FDA0004116446840000059
in the formula ,
Figure FDA00041164468400000510
and Δkij Standard ratio and regulation step length of OLTC in branch ij, respectively, +.>
Figure FDA00041164468400000511
and
Figure FDA00041164468400000512
The gear at the time t of the OLTC in the branch ij and the adjustable lower limit and the adjustable upper limit of the gear are respectively shown;
the SVC operation constraint is:
Figure FDA00041164468400000513
in the formula ,
Figure FDA00041164468400000514
and
Figure FDA00041164468400000515
The upper limit value and the lower limit value of SVC adjustable power are respectively;
the compensation capacitor operating constraints are:
Figure FDA00041164468400000516
Figure FDA00041164468400000517
in the formula ,
Figure FDA00041164468400000518
and ΔQC,i Minimum output and adjustable capacity step of the compensation capacitor connected to node i, respectively, +.>
Figure FDA00041164468400000519
Figure FDA00041164468400000520
and
Figure FDA00041164468400000521
The t-time gear and the adjustable upper and lower limits of the compensation capacitor connected at the node i are respectively provided.
5. The method for active and reactive coordination control of a power distribution network based on model predictive control according to claim 1, wherein in step 30), the prediction model of the limited period of the intra-day rolling correction phase is:
Figure FDA0004116446840000061
u＝[P G ,P RE,i ,Q RE,i ,P CDG,i ,Q CDG,i ,P ch,i ,P dis,i ,Q ESS,i ,Q SVC,i ]
in the formula ,t0 The moment is the current moment, delta t is the single rolling correction phase in the dayThe bit time interval M is the total time period of the model solution of the daily rolling correction stage, u represents the controllable means in the active power distribution network of the feedback correction stage, and comprises the main network tie line exchange power P G Active power output P of renewable energy source RE,i Reactive power output Q of renewable energy RE,i Active power output P of controllable distributed power supply CDG,i Reactive power Q of controllable distributed power supply CDG,i Energy storage active force P ch,i and Pdsi,i Energy storage reactive power output Q ESS,i SVC reactive power output Q SVC,I ，
Figure FDA0004116446840000062
At t 0 T obtained by time prediction 0 The output value of each controllable means at +kDeltat,/->
Figure FDA0004116446840000063
At t 0 The actual measurement feedback value of each controllable means at the moment, < >>
Figure FDA0004116446840000064
At t 0 And predicting the moment to obtain the increment of each controllable means, and solving the variables for the optimization model.
6. The method for active and reactive coordination control of a power distribution network based on model predictive control according to claim 1, wherein in step 30), the active and reactive coordination optimization control model for a limited period of time in a daily rolling correction stage is:
minF DR (U DR )
Figure FDA0004116446840000065
Figure FDA0004116446840000066
in the formula ,UDR At t 0 ～t 0 A set of force values of each controllable means in +MDeltat time period, F DR G is an objective function of the rolling correction stage model in the day DR The constraint condition of the phase model is corrected for daily rolling;
Figure FDA0004116446840000067
representing t 0 The controlled active power of the load at node i at time +kΔt;
Figure FDA0004116446840000068
Representing the time t 0 The adjustable ratio of the on-load voltage regulator OLTC at the +kΔt moment branch ij;
Figure FDA0004116446840000069
Representing the time t 0 Compensating the reactive power of the capacitor at the node i at the moment +kDeltat;
the objective function of the active and reactive coordination optimization model in the limited time period of the daily rolling correction stage is as follows:
min F DR ＝||(U DR -U DA )·U max || 1
Figure FDA00041164468400000610
Figure FDA00041164468400000611
in the formula ,UDA For the day t 0 ～t 0 Set of force values of each controllable means in +MDeltat time period, U max For the reciprocal of the maximum value of the output of each controllable means, the constraint conditions of the active-reactive coordination optimization model in the limited period of the rolling correction stage in the day are the same as the constraint conditions in the previous stage except that the constraint conditions do not contain flexible load operation constraint, OLTC operation constraint and compensation capacitor operation constraint;
wherein ,
Figure FDA0004116446840000071
representing t 0 The force value of each controllable means at the moment +delta t;
Figure FDA0004116446840000072
Representing t 0 The force value of each controllable means at +2Δt;
Figure FDA0004116446840000073
Representing t 0 The force value of each controllable means at +MDeltat;
wherein ,
Figure FDA0004116446840000074
representing the maximum value of the exchange power of the main network interconnecting line;
Figure FDA0004116446840000075
representing the maximum value of the active power of the renewable energy source at the node i;
Figure FDA0004116446840000076
representing the maximum value of the active power of the controllable distributed power supply at the node i;
Figure FDA0004116446840000077
representing a maximum value of the charging power of the energy storage device at node i;
Figure FDA0004116446840000078
representing a maximum value of discharge power of the energy storage device at node i;
Figure FDA0004116446840000079
representing the maximum value of the reactive power of the static var compensator SVC at node i;
Figure FDA00041164468400000710
representing the maximum value of the energy storage reactive power output at the node i;
Figure FDA00041164468400000711
representing the maximum value of the reactive power output of the renewable energy source at the node i;
Figure FDA00041164468400000712
representing the maximum value of the reactive power of the controllable distributed power supply at node i. />
CN201611158742.2A 2016-12-15 2016-12-15 Active and reactive coordination control method for power distribution network based on model predictive control Active CN106786806B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201611158742.2A CN106786806B (en) 2016-12-15 2016-12-15 Active and reactive coordination control method for power distribution network based on model predictive control

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201611158742.2A CN106786806B (en) 2016-12-15 2016-12-15 Active and reactive coordination control method for power distribution network based on model predictive control

Publications (2)

Publication Number Publication Date
CN106786806A CN106786806A (en) 2017-05-31
CN106786806B true CN106786806B (en) 2023-06-06

Family

ID=58888332

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201611158742.2A Active CN106786806B (en) 2016-12-15 2016-12-15 Active and reactive coordination control method for power distribution network based on model predictive control

Country Status (1)

Country Link
CN (1) CN106786806B (en)

Families Citing this family (42)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107565576B (en) * 2017-09-21 2023-09-15 国网福建省电力有限公司 Reactive voltage optimization method for active power distribution network coordinated by multiple active management means
CN108011366B (en) * 2017-11-30 2023-10-20 中国电力科学研究院有限公司 Power distribution network controllable resource coordination optimization control method and device
CN108075468B (en) * 2017-12-11 2020-04-21 囯网河北省电力有限公司电力科学研究院 Multi-distribution-node load adjusting method, control device and management system
CN108493948B (en) * 2018-04-13 2021-06-29 江苏方天电力技术有限公司 Multi-time scale coordinated voltage reactive power control method
CN108683179B (en) * 2018-05-03 2020-04-21 国网山东省电力公司潍坊供电公司 Active power distribution network optimal scheduling method and system based on mixed integer linear programming
CN108923435B (en) * 2018-07-04 2020-05-08 山东大学 Wind power reactive voltage coordination control system based on layered MPC
CN109120003A (en) * 2018-09-07 2019-01-01 中国南方电网有限责任公司 Distributed photovoltaic energy storage system optimization control method based on MPC algorithm
CN109347137A (en) * 2018-09-21 2019-02-15 中国农业大学 A kind of distributed photovoltaic cluster-coordinator optimal control method and device
CN109193715A (en) * 2018-10-24 2019-01-11 云南电网有限责任公司电力科学研究院 A kind of Poewr control method and system of active distribution network
CN109672216A (en) * 2018-11-26 2019-04-23 南瑞集团有限公司 One kind being based on polyfactorial active distribution network hierarchical control method and system
CN109687530B (en) * 2019-01-08 2022-06-10 南京工程学院 Power grid hybrid rolling scheduling method considering blocking and energy storage time-of-use electricity price
CN109765787B (en) * 2019-01-30 2022-05-13 南方电网科学研究院有限责任公司 Power distribution network source load rapid tracking method based on intraday-real-time rolling control
CN109873447B (en) * 2019-02-19 2022-10-04 国网江苏省电力有限公司南京供电分公司 Multi-time-level active-reactive power regulation and control method for multi-source cooperative active power distribution network
CN109659947B (en) * 2019-02-20 2022-02-18 重庆城市管理职业学院 Urban distribution network voltage partition active control method containing high-proportion controllable source load
CN110148969B (en) * 2019-03-26 2023-07-21 上海电力学院 Active power distribution network optimized operation method based on model predictive control technology
CN110034587B (en) * 2019-04-22 2023-03-28 广东电网有限责任公司 Optimized scheduling method
CN110137942B (en) * 2019-04-23 2022-09-16 河海大学 Multi-time scale flexible load rolling scheduling method and system based on model predictive control
CN110581571A (en) * 2019-08-29 2019-12-17 昆明理工大学 dynamic optimization scheduling method for active power distribution network
CN110535180B (en) * 2019-09-17 2020-11-27 杭州电子科技大学 Method for coordinately controlling voltage of power distribution network based on flexible resources and traditional voltage regulating equipment
CN110826880B (en) * 2019-10-24 2023-07-28 成都信息工程大学 Active power distribution network optimal scheduling method for large-scale electric automobile access
CN110970903A (en) * 2019-12-27 2020-04-07 山东大学 Voltage coordination control optimization method and system applied to active power distribution network
CN111259315B (en) * 2020-01-17 2022-04-19 福州大学 Decentralized scheduling method of multi-subject coordinated pricing mode
CN111446719B (en) * 2020-05-13 2022-09-09 国网江苏省电力有限公司电力科学研究院 Distribution network voltage stability control method and system
CN111799847B (en) * 2020-07-16 2022-11-22 国网北京市电力公司 Predictive control method of risk-considering two-stage random model of active power distribution network
CN112039126B (en) * 2020-08-24 2022-10-04 国网山东省电力公司潍坊供电公司 Multi-time-scale coordinated scheduling method and system for power distribution network containing distributed power supply
CN112600218B (en) * 2020-11-30 2022-07-29 华北电力大学（保定） Multi-time-scale optimization control method for reactive voltage of power grid comprising photovoltaic energy storage system
CN112865192A (en) * 2020-12-31 2021-05-28 山东大学 Multi-period optimal scheduling method and system for active power distribution network
CN113067344A (en) * 2021-03-08 2021-07-02 南京理工大学 Active power distribution network reactive power optimization method based on model predictive control
CN113054671A (en) * 2021-04-28 2021-06-29 重庆大学 Day-ahead and real-time optimization control method for power distribution network based on energy storage reactive power regulation model
CN113364059B (en) * 2021-05-20 2022-07-22 国网湖北省电力有限公司电力科学研究院 Multi-period decoupling friendly interactive power distribution network voltage reactive rolling optimization method
CN113394805B (en) * 2021-07-26 2024-03-22 国网江苏省电力有限公司 Method for optimizing intra-day rolling of flexible interconnection power distribution network based on self-adaptive dynamic update
CN113659627A (en) * 2021-08-25 2021-11-16 中南大学 Optimal scheduling method for micro-grid containing photovoltaic power generation and liquid air energy storage
CN113937752A (en) * 2021-09-02 2022-01-14 国网山东省电力公司济南供电公司 Active power distribution network optimal scheduling method and system based on stochastic model predictive control
CN114243675B (en) * 2021-09-02 2024-09-13 国网山东省电力公司济南供电公司 Active power distribution network optimization method and system considering energy storage system and reactive compensation
CN113629781A (en) * 2021-09-16 2021-11-09 广西电网有限责任公司电力科学研究院 Multi-VPP day-ahead coordinated optimization method and device based on EMPC
CN114006379B (en) * 2021-11-25 2023-08-15 华北电力大学（保定） Double-time-scale voltage control method based on dynamic-static reactive power replacement mechanism
CN114123341A (en) * 2021-12-02 2022-03-01 国网山东省电力公司经济技术研究院 Time-varying tracking control method, controller and system for grid-connected operation of photovoltaic power station
CN114123220B (en) * 2021-12-13 2024-04-23 南京理工大学 Reactive power optimization scheduling method and system for active power distribution network with participation of mobile energy storage
CN114884049B (en) * 2022-07-12 2022-10-25 东南大学溧阳研究院 Optimized operation control method for flexible direct-current power distribution network
CN116073452A (en) * 2023-03-31 2023-05-05 天津电力工程监理有限公司 Active power distribution network double-order optimization method, system and equipment considering electric automobile
CN116488264B (en) * 2023-06-21 2023-11-21 国网浙江省电力有限公司经济技术研究院 Optimal scheduling method, device and equipment for power distribution network and storage medium
CN117996861B (en) * 2024-04-02 2024-06-11 浙江大学 Scheduling method for light-water coupling residual electricity hydrogen production of power distribution network and energy management device

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103886388B (en) * 2014-03-06 2017-08-29 国家电网公司 A kind of multicycle generation schedule coordination optimization and closed loop control method
CN105006843A (en) * 2014-04-17 2015-10-28 国家电网公司 Multi-time-scale flexible load scheduling method for handling wind power uncertainties
CN104951899A (en) * 2015-07-02 2015-09-30 东南大学 Multi-time-scale optimal scheduling method for power distribution company containing large-scale renewable energy sources

Also Published As

Publication number Publication date
CN106786806A (en) 2017-05-31

Similar Documents

Publication Publication Date Title
CN106786806B (en) Active and reactive coordination control method for power distribution network based on model predictive control
CN109687510B (en) Uncertainty-considered power distribution network multi-time scale optimization operation method
CN106099960B (en) A kind of method of small power station&#39;s group of planes composition distributed energy storage system
CN102299527B (en) Wind power station reactive power control method and system
CN103199542B (en) Method of optimal control of wind power plant reactive voltage
CN109217374B (en) Wind power system reactive voltage advanced multi-time scale optimization control method
CN103401248B (en) A kind of containing wind energy turbine set power distribution network random reactive optimization method for power
CN110581571A (en) dynamic optimization scheduling method for active power distribution network
CN105140936B (en) A kind of micro-capacitance sensor frequency modulation control method for minimizing cost of electricity-generating
CN112865174B (en) Micro-energy network multi-time scale optimization control method based on double-layer model prediction control
CN102684201B (en) Voltage threshold probability-based reactive power optimizing method for grid containing wind power plant
CN104065093B (en) A kind of wind based on low-pass filtering storage predictive coordinated control method
CN103219732A (en) reactive voltage controlling method of power distribution network with variable speed constant frequency wind farm
CN106877344A (en) A kind of grid-connected photovoltaic power station based on power prediction is idle-voltage control method
CN108494020B (en) Wind-powered electricity generation transmitting system reactive voltage Active Control Method under fluctuations in wind speed
CN110867907B (en) Power system scheduling method based on multi-type power generation resource homogenization
CN115149580A (en) Wind, light, water, fire and storage combined secondary frequency modulation method considering uncertainty delay
CN117833273B (en) Power system look-ahead scheduling model solving method considering frequency constraint based on neural network assistance
CN110768265A (en) Power distribution network scheduling method considering time sequence
Wang et al. Frequency response methods for grid-connected wind power generations: A review
CN202178583U (en) Reactive power control system of wind power station
CN116073400A (en) Active and reactive power loss reduction control method and system for two-stage active power distribution network
Wang et al. Multi-objective optimal dispatch of wind-integrated power system based on distributed energy storage
CN116316666A (en) Reactive power compensation device and energy storage coordination optimization configuration method and system
CN112600202B (en) Method for calculating optimal power flow of power grid with controllable phase shifter considering randomness of new energy

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant