CN114243706A - Power distribution network load recovery method, device and medium based on CCP optimization model - Google Patents

Power distribution network load recovery method, device and medium based on CCP optimization model Download PDF

Info

Publication number
CN114243706A
CN114243706A CN202111268155.XA CN202111268155A CN114243706A CN 114243706 A CN114243706 A CN 114243706A CN 202111268155 A CN202111268155 A CN 202111268155A CN 114243706 A CN114243706 A CN 114243706A
Authority
CN
China
Prior art keywords
load
distribution network
ccp
optimization model
power
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.)
Pending
Application number
CN202111268155.XA
Other languages
Chinese (zh)
Inventor
时珊珊
周健
陈颖
沈冰
黄少伟
张开宇
魏新迟
刘家妤
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.)
Tsinghua University
State Grid Shanghai Electric Power Co Ltd
Original Assignee
Tsinghua University
State Grid Shanghai 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 Tsinghua University, State Grid Shanghai Electric Power Co Ltd filed Critical Tsinghua University
Priority to CN202111268155.XA priority Critical patent/CN114243706A/en
Publication of CN114243706A publication Critical patent/CN114243706A/en
Pending legal-status Critical Current

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
    • H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
    • H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H02J3/144—Demand-response operation of the power transmission or distribution network
    • 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
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • 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
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • 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
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving

Landscapes

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

Abstract

The invention relates to a distribution network load recovery method, a distribution network load recovery device and a distribution network load recovery medium based on a CCP optimization model, and relates to a micro-grid with sustainable operation time larger than the power failure time of the whole distribution network, wherein the method comprises the following steps: calculating the upper limit value of the time for supplying power to the key load by the micro-grid, and establishing a CCP (common control protocol) optimization model for the load recovery problem of the power distribution network according to the upper limit value of the time for supplying power to the key load by the micro-grid, wherein the primary objective of the CCP optimization model is to maximize the electric energy with the importance weight supplied by the key load; the secondary objective is to minimize the expected value of the critical load average voltage deviation at recovery; and acquiring and loading the topology and the load information of the power distribution network into the CCP optimization model, and calculating to obtain a power distribution network load recovery strategy for the power distribution network load recovery. Compared with the prior art, the method has the advantages of high accuracy, guarantee of safe operation of the power distribution network and the like.

Description

Power distribution network load recovery method, device and medium based on CCP optimization model
Technical Field
The invention relates to the field of distribution network load recovery methods, in particular to a distribution network load recovery method, a distribution network load recovery device and a distribution network load recovery medium based on a CCP optimization model.
Background
From the point of view of energy production and conversion, the devices in the microgrid can be divided into 4 categories: renewable energy distributed generation (renewable DGs), such as photovoltaic cells and wind generators; dispatchable Distributed Generation (DGs) such as fuel and natural gas generators; microgrid internal loads, including critical and non-critical loads; an energy storage device.
Different kinds of devices in the microgrid have different operating characteristics. The output power and load demand of the renewable energy DG vary with the weather conditions, the user behavior and other factors, with uncertainty. The schedulable DG and the energy storage device continuously operate depending on the power supply resources stored in the microgrid, such as Fuel Reserve (FR) of the DG and state of charge (SOC) of the energy storage device.
When the distribution network has a power failure, the microgrid can be in two running states: an off-network state and a recovery state. When the micro-grid is disconnected from the distribution network and only supplies power to the internal load, the micro-grid is called to be in an off-grid state (island mode). If the micro-grid is connected with a feeder of the distribution network and supplies power to critical loads on the feeder, the micro-grid is called to operate in a restoration mode. In both states, the dispatchable DG and the energy storage device are used to maintain power balance inside the microgrid.
During and after an extreme natural disaster, the total amount of power supply resources stored in the microgrid is usually limited and difficult to supplement in time, and a microgrid restoration strategy can be formulated according to loads running in a restoration state, so that a microgrid load restoration method considering the restoration time of a power distribution network needs to be provided.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a method, a device and a medium for recovering the load of a power distribution network based on a CCP optimization model, wherein the CCP optimization model is used for considering the recovery time of the power distribution network.
The purpose of the invention can be realized by the following technical scheme:
a distribution network load recovery method based on a CCP optimization model, which takes into account a micro-grid with sustainable operation time larger than the power failure time of the whole distribution network, and comprises the following steps:
calculating the time upper limit value of the micro-grid for supplying power to the key load,
establishing a CCP optimization model of the power distribution network load recovery problem according to the upper limit value of the time for supplying power to the key load by the micro-grid, wherein the primary objective of the CCP optimization model is to maximize the electric energy with the importance weight supplied by the key load; the secondary objective is to minimize the expected value of the critical load average voltage deviation at recovery;
and acquiring and loading the topology and the load information of the power distribution network into the CCP optimization model, and calculating to obtain a power distribution network load recovery strategy for the power distribution network load recovery.
Further, the time upper limit value of the micro-grid for supplying power to the critical load is a maximum value meeting a first constraint, and an expression of the first constraint is as follows:
Figure BDA0003327656080000021
Figure BDA0003327656080000022
in the formula (I), the compound is shown in the specification,
Figure BDA0003327656080000023
the upper limit of time for supplying the micro grid with power for the critical load c,
Figure BDA0003327656080000024
the remaining power supply resources stored in the microgrid can ensure the time length of the microgrid running in an off-grid state, k is an internal load of the microgrid, TOThe power failure time of the whole power distribution network is obtained.
Further, the calculation expression of the primary objective of the CCP optimization model is as follows:
Figure BDA0003327656080000025
wherein C is a set consisting of the key loads recovered by the microgrid; c is any load in C, namely C belongs to C; wcIs the weight of the load c, which represents the importance of the load;
Figure BDA0003327656080000026
is the rated active power of the critical load c,
Figure BDA0003327656080000027
is composed of
Figure BDA0003327656080000028
Percentile of (d);
the calculation expression of the secondary target of the CCP optimization model is as follows:
Figure BDA0003327656080000029
in the formula (I), the compound is shown in the specification,
Figure BDA00033276560800000210
is the average voltage of the critical load c,
Figure BDA00033276560800000211
is the rated voltage of the critical load c.
Further, the
Figure BDA00033276560800000212
The calculation expression of the percentile is as follows:
Figure BDA00033276560800000213
in the formula (I), the compound is shown in the specification,
Figure BDA00033276560800000214
is composed of
Figure BDA00033276560800000215
Alpha is a preselected probability level, Pr(. is) the occurrence probability of random events in (. smallcircle.), and sup {. is the supremum of the set in { }.
Further, the constraint conditions of the CCP optimization model include the recovered critical load of the micro grid participating in load recovery and the runtime constraint of the internal load:
Figure BDA0003327656080000031
Figure BDA0003327656080000032
Figure BDA0003327656080000033
wherein M is a set composed of micro-grids participating in load recovery, M is any micro-grid in M, CmIs a subset of C, representing a set of critical loads recovered by the microgrid m, KmIs micro-electricitySet of internal loads of net m, K being KmAny load in (1).
Further, the constraint conditions of the CCP optimization model further include:
the microgrid participating in load recovery is at tr+TOThe power supply resource is reserved at any time, namely:
Figure BDA0003327656080000034
in the formula (I), the compound is shown in the specification,
Figure BDA0003327656080000035
is the lowest resource reserve of the microgrid m.
Further, the adjustment is carried out according to the reservation degree of the micro-grid power supply resource
Figure BDA0003327656080000036
The size of (a) to
Figure BDA0003327656080000037
Is in direct proportion to the reservation degree of the micro-grid power supply resource.
Further, the constraint conditions of the CCP optimization model further include:
and the constraint of the three-phase unbalanced load flow equation is satisfied:
Figure BDA0003327656080000038
Figure BDA0003327656080000039
Figure BDA00033276560800000310
Figure BDA00033276560800000311
u1,u2,u∈B,p1,p2∈{a,b,c},l∈L,d∈D,t∈[tr,tr+TO]
in the formula, B is a set formed by buses in the power distribution network, L is a set formed by lines in the power distribution network, D is a set formed by DGs in the power distribution network, and u is1,u2U is any bus in B, L is any line in L, D is any DG in D, a, B and c respectively represent a, B and c three phases, and p1,p2Is any of three phases, Vu,t、
Figure BDA00033276560800000312
And
Figure BDA00033276560800000313
respectively limiting the voltage amplitude of the bus u at the moment t and the lower limit and the upper limit thereof; (.)*In order to take the conjugate operation,
Figure BDA00033276560800000314
is u1P of (a)1And phase with u2P of (a)2Admittance between phases, Il,t、
Figure BDA00033276560800000315
Respectively, the current of the line l at the time t and its upper limit value, Pd,t、Qd,tAnd
Figure BDA00033276560800000316
DGd, respectively, the upper limit values, beta, of the active power, reactive power and apparent power at time t1、β2And beta3Is a probability level given in advance.
The invention also provides a distribution network load recovery device based on the CCP optimization model, which is characterized by comprising a memory and a processor, wherein the memory stores a computer program, and the processor calls the computer program to execute the steps of the method.
The present invention also provides a computer-readable storage medium having stored thereon a computer program for execution by a processor of a method as described above.
Compared with the prior art, the invention has the following advantages:
according to the power distribution network load recovery method based on the CCP optimization model, the electric energy with the importance weight, which is supplied to the key load in a maximized mode, is determined as the primary target of the CCP optimization model according to the time for the micro-grid to supply power to the key load; the secondary objective is to minimize the expected value of the average voltage deviation of the critical load at the time of recovery, and the constraint conditions of the CCP optimization model comprise the recovered critical load of the micro-grid participating in load recovery and the operation time constraint of the internal load; the power supply resource reservation constraint and the three-phase unbalanced load flow equation constraint of the micro-grid participating in load recovery are carried out, so that the power distribution network load recovery strategy calculated according to the power distribution network parameters has the advantages of high accuracy, guarantee of safe operation of the power distribution network and the like.
Drawings
Fig. 1 is a schematic flow chart of a power distribution network load recovery method based on a CCP optimization model in an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
Example 1
As shown in fig. 1, this embodiment provides a distribution network load recovery method based on a CCP optimization model, which accounts for a micro-grid with a sustainable operation time greater than the blackout time of the entire distribution network, and includes:
s1: calculating the time upper limit value of the micro-grid for supplying power to the key load,
s2: establishing a CCP optimization model of the power distribution network load recovery problem according to the upper limit value of the time for supplying power to the key load by the micro-grid, wherein the primary objective of the CCP optimization model is to maximize the electric energy with the importance weight supplied by the key load; the secondary objective is to minimize the expected value of the critical load average voltage deviation at recovery;
s3: and acquiring and loading the topology and the load information of the power distribution network into the CCP optimization model, and calculating to obtain a power distribution network load recovery strategy for the power distribution network load recovery.
For this example Em(t) represents the total amount of stored power supply resources of the microgrid m at the moment t, and is described by the equivalent electric energy that they can convert into. When the FR and the SOC are converted into equivalent electric energy, the conversion efficiency of the corresponding DG and the energy storage device needs to be considered.
1. Sustainable runtime of microgrid
When the distribution network has a power failure, the microgrid can be in two running states: an off-network state and a recovery state. When the micro-grid is disconnected from the distribution network and only supplies power to the internal load, the micro-grid is called to be in an off-grid state (island mode). If the micro-grid is connected with a feeder of the distribution network and supplies power to critical loads on the feeder, the micro-grid is called to operate in a restoration mode. In both states, the dispatchable DG and the energy storage device are used to maintain power balance inside the microgrid.
During and after extreme natural disasters, the total amount of power supply resources stored in the microgrid is usually limited and difficult to supplement in time. In this embodiment, a sustainable operating time (COT) of the microgrid is defined as a maximum expected time that the microgrid can maintain uninterrupted power supply of the internal load in a specified operating mode under a given power supply resource (FR and SOC). When considering the uncertainty of renewable energy generation and load demand, the consumption rate of the power supply resources is also random due to the fact that the output power of the DG and the energy storage device needs to be changed accordingly to maintain the power balance inside the microgrid. The COT of the microgrid is a random variable, and the probability distribution of the COT must be considered in calculation.
The microgrid COT is conceptually very similar to the discharge reserve time of the battery, which represents the maximum time the battery can supply power to a given load. The two differences are that the uncertainty of renewable energy power generation and load is considered by the microgrid COT and cannot be directly obtained by dividing the total amount of power supply resources by the load power. The present embodiment is based on research on a power distribution network recovery method based on acquisition of a microgrid COT, and another difference between the COT and the discharge remaining time is that the COT considers various types of power supply resources (such as diesel oil and natural gas) in the microgrid, and the discharge remaining time only considers electric energy stored in a storage battery.
2. Restored availability of microgrid
If and only if one microgrid can be powered down for the entire distribution grid (denoted T)O) The micro-grid can only participate in the load recovery of the power distribution network when the micro-grid continuously supplies power for internal important loads and has redundant power supply resources and capacity. It is assumed that the microgrid is operating in an off-grid state and its internal non-essential loads have been removed all before. Then the following conclusions can be drawn: if the COT of the micro-grid is more than or equal to TOIt can be used for distribution network load recovery; otherwise, it has no restoration availability.
For a microgrid with restoration availability, which is used for restoring some critical loads on a feeder of a distribution network, let c denote one of the critical loads. The power supply resources stored in the micro-grid areThere is an upper limit to the time that the microgrid can supply power to the critical load c, to
Figure BDA0003327656080000061
And (4) showing. In that
Figure BDA0003327656080000062
The microgrid then transitions to an off-grid operating state. Let k denote an internal load of the microgrid, and the remaining power supply resources stored in the microgrid can ensure that the microgrid is operated in an off-grid state for a period of time
Figure BDA0003327656080000063
At this time, COT of the microgrid is equal to
Figure BDA0003327656080000064
In order to prevent the micro-grid from power failure, the requirements must be met
Figure BDA0003327656080000065
On the other hand, after the power supply of the distribution network is recovered, the key load no longer needs the micro-grid to supply power to the key load, so that the micro-grid is not needed to supply power to the key load any more
Figure BDA0003327656080000066
In summary, it is shown that,
Figure BDA0003327656080000067
is the maximum that satisfies the following constraint:
Figure BDA0003327656080000068
Figure BDA0003327656080000069
in the formula (I), the compound is shown in the specification,
Figure BDA00033276560800000610
the upper limit of time for supplying the micro grid with power for the critical load c,
Figure BDA00033276560800000611
the remaining power supply resources stored in the microgrid can ensure the time length of the microgrid running in an off-grid state, k is an internal load of the microgrid, TOThe power failure time of the whole power distribution network is obtained.
Inequalities (1) and (2) give a limited power supply resource pair
Figure BDA00033276560800000612
Is constrained by the upper limit of (c). Is provided with
Figure BDA00033276560800000613
Increase of
Figure BDA00033276560800000614
When the temperature of the water is higher than the set temperature,
Figure BDA00033276560800000615
will be reduced
Figure BDA00033276560800000616
The total load of the microgrid in the recovery state is greater than that of the microgrid in the off-grid state
Figure BDA00033276560800000617
And thus, will be reduced. Therefore, if the power supply resource reserve can satisfy the condition that the micro-grid is operated in the recovery state in the whole power failure time,
Figure BDA00033276560800000618
is given by formula (2); if not, then,
Figure BDA00033276560800000619
is given by formula (1). When considering uncertainty, the COT of the microgrid is a random variable. At this time, given by the constraint (1)
Figure BDA00033276560800000620
Also random variables, are difficult to directly consider in the optimization. For this purpose, define
Figure BDA00033276560800000621
Percentile of (c):
Figure BDA00033276560800000622
in the formula (I), the compound is shown in the specification,
Figure BDA00033276560800000623
is composed of
Figure BDA00033276560800000624
Alpha is a preselected probability level, Pr(. is) the occurrence probability of random events in (. smallcircle.), and sup {. is the supremum of the set in { }. Typically, α is selected as a larger probability value, e.g., 0.95. Formula (3) ensures that greater than 95% of the total
Figure BDA00033276560800000625
Similar to the definition of Value-at-Risk, will
Figure BDA00033276560800000626
For representing random variables in an optimization problem
Figure BDA00033276560800000627
Is generally horizontal. It is worth noting that the constraint COT ≧ TOCan only ensure the load demand in the micro-grid to be at TOIs satisfied. In practice, the microgrid participating in load recovery should be at TOSome power supply resources are reserved later to improve the reliability of the internal load. In this case, an AND-gate needs to be added
Figure BDA0003327656080000071
Related constraint where Em(T) at TOThe end time should not be less than a predetermined reserve level of resources
3. Establishment of power distribution network load recovery optimization model
CCP, proposed by Charnes and Cooper, has been used to address uncertainties in the reconstruction (reconfiguration) problem of power distribution networks. This section establishes a CCP model of the distribution network load recovery problem: firstly, the hypothesis conditions in the study are proposed; specific optimization objectives and problems are described subsequently; finally, a method of converting opportunistic constraints to deterministic constraints is introduced.
In the present embodiment the system function f (t) is selected as the power to be supplied for the critical load at time t, te [ t ], taking into account the importance weightr,tr+TO]。
Figure BDA0003327656080000072
Wherein C is a set consisting of the key loads recovered by the microgrid; c is any load in C, namely C belongs to C; wcIs the weight of the load c, which represents the importance of the load; pc(t) is the active power of the load c at time t. For t e [ t ∈ [ [ t ]r,tr+TO]，Pc(t) is equal to
Figure BDA0003327656080000073
Wherein
Figure BDA0003327656080000074
Rated active power for the critical load c; for the
Figure BDA0003327656080000075
Pc(t) is equal to 0.
The primary goal of the load recovery problem is selected to maximize the electrical energy supplied by the critical load with the importance weight, namely:
Figure BDA0003327656080000076
wherein C is a set consisting of the key loads recovered by the microgrid; c is an arbitrary load in C, namely C epsilonC；WcIs the weight of the load c, which represents the importance of the load;
Figure BDA0003327656080000077
is the rated active power of the critical load c,
Figure BDA0003327656080000078
is composed of
Figure BDA0003327656080000079
Percentile of (d);
a secondary objective of the selected optimization model is to minimize the expected value of the critical load average voltage deviation at recovery, i.e.:
the calculation expression of the secondary target of the CCP optimization model is as follows:
Figure BDA00033276560800000710
in the formula (I), the compound is shown in the specification,
Figure BDA00033276560800000711
is the average voltage of the critical load c,
Figure BDA00033276560800000712
is the rated voltage of the critical load c.
For the micro-grid participating in load recovery, the operating time constraints of the recovered critical load and the internal load are as follows:
Figure BDA00033276560800000713
wherein M is a set composed of micro-grids participating in load recovery, M is any micro-grid in M, CmIs a subset of C, representing a set of critical loads recovered by the microgrid m, KmSet of internal loads of the microgrid m, K being KmAny load in (1).
To promote a micro-gridReliable internal load, microgrid participating in load recovery at tr+TOThe power supply resource is reserved at any time, namely:
Figure BDA0003327656080000081
in the formula (I), the compound is shown in the specification,
Figure BDA0003327656080000082
is the lowest resource reserve of the microgrid m.
Figure BDA0003327656080000083
The value of (c) can be set according to the needs of the microgrid owner, which determines the conservatism of the power supply resource constraints. If the owner desires greater reliability of the microgrid internal load, it may be desirable to have
Figure BDA0003327656080000084
Set to a larger value; otherwise, it may be set to a smaller value. If it is
Figure BDA0003327656080000085
Indicating that the microgrid does not need to reserve resource reserves.
At any time during the recovery process, the three-phase unbalanced load flow equation must be satisfied to ensure the balance of the active and reactive power. The variables in the power flow, such as bus voltage, line current and DG output power, should all be within given ranges:
Figure BDA0003327656080000086
in the formula, B is a set formed by buses in the power distribution network, L is a set formed by lines in the power distribution network, D is a set formed by DGs in the power distribution network, and u is1,u2U is any bus in B, L is any line in L, D is any DG in D, a, B and c respectively represent a, B and c three phases, and p1,p2Is any of three phases, Vu,t、
Figure BDA0003327656080000087
And
Figure BDA0003327656080000088
respectively limiting the voltage amplitude of the bus u at the moment t and the lower limit and the upper limit thereof; (.)*In order to take the conjugate operation,
Figure BDA0003327656080000089
is u1P of (a)1And phase with u2P of (a)2Admittance between phases, Il,t、
Figure BDA00033276560800000810
Respectively, the current of the line l at the time t and its upper limit value, Pd,t、Qd,tAnd
Figure BDA00033276560800000811
DGd, respectively, the upper limit values, beta, of the active power, reactive power and apparent power at time t1、β2And beta3Is a probability level given in advance.
The power distribution network needs to keep a radial structure during operation, namely an electromagnetic looped network cannot appear during recovery. Furthermore, each critical load cannot be recovered by more than 1 microgrid at the same time. Due to TOIs the power distribution network power failure time estimated by DSO, so TOThe power failure time may not be exactly the same as the actual power distribution network power failure time. The key load recovery strategy proposed by the embodiment is only at TOAnd the method is an optimal recovery strategy of the power distribution network when the value is an accurate value. If the actual system blackout time is greater than the expected time, the microgrid may have a power outage due to exhaustion of power supply resources. In this case, the supply resource constraints help to reduce the risk of a complete blackout of the microgrid. If the actual system blackout time is less than the estimated TOThen, the key load recovery strategy obtained by the method provided in this embodiment may have a certain conservative property.
The present embodiment further provides a distribution network load recovery apparatus based on a CCP optimization model, which is characterized by including a memory and a processor, where the memory stores a computer program, and the processor calls the computer program to execute the steps of the distribution network load recovery method based on the CCP optimization model as described above.
The present embodiment further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to perform the method for recovering load of a power distribution network based on a CCP optimization model as described above.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.

Claims (10)

1. A distribution network load recovery method based on a CCP optimization model, which takes into account a micro-grid with sustainable operation time larger than the power failure time of the whole distribution network, and is characterized by comprising the following steps:
calculating the time upper limit value of the micro-grid for supplying power to the key load,
establishing a CCP optimization model of the power distribution network load recovery problem according to the upper limit value of the time for supplying power to the key load by the micro-grid, wherein the primary objective of the CCP optimization model is to maximize the electric energy with the importance weight supplied by the key load; the secondary objective is to minimize the expected value of the critical load average voltage deviation at recovery;
and acquiring and loading the topology and the load information of the power distribution network into the CCP optimization model, and calculating to obtain a power distribution network load recovery strategy for the power distribution network load recovery.
2. The CCP optimization model-based power distribution network load recovery method according to claim 1, wherein an upper limit value of time for the micro grid to supply power to the critical load is a maximum value satisfying a first constraint, and an expression of the first constraint is:
Figure FDA0003327656070000011
Figure FDA0003327656070000012
in the formula (I), the compound is shown in the specification,
Figure FDA0003327656070000013
the upper limit of time for supplying the micro grid with power for the critical load c,
Figure FDA0003327656070000014
the remaining power supply resources stored in the microgrid can ensure the time length of the microgrid running in an off-grid state, k is an internal load of the microgrid, TOThe power failure time of the whole power distribution network is obtained.
3. The method for recovering the load of the power distribution network based on the CCP optimization model as claimed in claim 2, wherein the computational expression of the primary objective of the CCP optimization model is:
Figure FDA0003327656070000015
wherein C is a set consisting of the key loads recovered by the microgrid; c is any load in C, namely C belongs to C; wcIs the weight of the load c, which represents the importance of the load;
Figure FDA0003327656070000016
is the rated active power of the critical load c,
Figure FDA0003327656070000017
is composed of
Figure FDA0003327656070000018
Percentile of (d);
the calculation expression of the secondary target of the CCP optimization model is as follows:
Figure FDA0003327656070000019
in the formula (I), the compound is shown in the specification,
Figure FDA00033276560700000110
is the average voltage of the critical load c,
Figure FDA00033276560700000111
is the rated voltage of the critical load c.
4. The CCP optimization model-based distribution network load recovery method according to claim 3, wherein the load recovery method is based on the CCP optimization model
Figure FDA00033276560700000112
The calculation expression of the percentile is as follows:
Figure FDA0003327656070000021
in the formula (I), the compound is shown in the specification,
Figure FDA0003327656070000022
is composed of
Figure FDA0003327656070000023
Alpha is a preselected probability level, Pr(. is) the occurrence probability of random events in (. smallcircle.), and sup {. is the supremum of the set in { }.
5. The CCP optimization model-based power distribution network load recovery method according to claim 4, wherein the constraint conditions of the CCP optimization model comprise the key load recovered by the micro-grid participating in load recovery and the runtime constraint of the internal load:
Figure FDA0003327656070000024
Figure FDA0003327656070000025
Figure FDA0003327656070000026
wherein M is a set composed of micro-grids participating in load recovery, M is any micro-grid in M, CmIs a subset of C, representing a set of critical loads recovered by the microgrid m, KmSet of internal loads of the microgrid m, K being KmAny load in (1).
6. The method for recovering the load of the power distribution network based on the CCP optimization model as claimed in claim 5, wherein the constraint conditions of the CCP optimization model further comprise:
the microgrid participating in load recovery is at tr+TOThe power supply resource is reserved at any time, namely:
Figure FDA0003327656070000027
in the formula (I), the compound is shown in the specification,
Figure FDA0003327656070000028
is the lowest resource reserve of the microgrid m.
7. The CCP optimization model-based power distribution network load recovery method according to claim 6, wherein the adjustment is performed according to the retention degree of the power supply resources of the microgrid
Figure FDA0003327656070000029
The size of (a) is (b),
Figure FDA00033276560700000210
is in direct proportion to the reservation degree of the micro-grid power supply resource.
8. The method for recovering the load of the power distribution network based on the CCP optimization model as claimed in claim 5, wherein the constraint conditions of the CCP optimization model further comprise:
and the constraint of the three-phase unbalanced load flow equation is satisfied:
Figure FDA00033276560700000211
Figure FDA00033276560700000212
Figure FDA00033276560700000213
Figure FDA00033276560700000214
u1,u2,u∈B,p1,p2∈{a,b,c},l∈L,d∈D,t∈[tr,tr+TO]
in the formula, B is a set formed by buses in the power distribution network, L is a set formed by lines in the power distribution network, D is a set formed by DGs in the power distribution network, and u is1,u2U is any bus in B, L is any line in L, D is any DG in D, a, B and c respectively represent a, B and c three phases, and p1,p2Is any of three phases, Vu,t、
Figure FDA0003327656070000031
And Vu MRespectively limiting the voltage amplitude of the bus u at the moment t and the lower limit and the upper limit thereof; (.)*In order to take the conjugate operation,
Figure FDA0003327656070000032
is u1P of (a)1And phase with u2P of (a)2Admittance between phases, Il,t、
Figure FDA0003327656070000033
Respectively, the current of the line l at the time t and its upper limit value, Pd,t、Qd,tAnd
Figure FDA0003327656070000034
DGd, respectively, the upper limit values, beta, of the active power, reactive power and apparent power at time t1、β2And beta3Is a probability level given in advance.
9. A distribution network load recovery device based on a CCP optimization model, characterized by comprising a memory and a processor, said memory storing a computer program, said processor invoking said computer program to perform the steps of the method according to any of claims 1 to 8.
10. A computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, which computer program is executed by a processor for performing the method according to any one of claims 1 to 8.
CN202111268155.XA 2021-10-29 2021-10-29 Power distribution network load recovery method, device and medium based on CCP optimization model Pending CN114243706A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111268155.XA CN114243706A (en) 2021-10-29 2021-10-29 Power distribution network load recovery method, device and medium based on CCP optimization model

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111268155.XA CN114243706A (en) 2021-10-29 2021-10-29 Power distribution network load recovery method, device and medium based on CCP optimization model

Publications (1)

Publication Number Publication Date
CN114243706A true CN114243706A (en) 2022-03-25

Family

ID=80743341

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111268155.XA Pending CN114243706A (en) 2021-10-29 2021-10-29 Power distribution network load recovery method, device and medium based on CCP optimization model

Country Status (1)

Country Link
CN (1) CN114243706A (en)

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105914753A (en) * 2016-05-25 2016-08-31 成都信息工程大学 Active distribution network (ADN) voltage real-time fuzzy control method
CN109802387A (en) * 2019-03-11 2019-05-24 燕山大学 A kind of elastic power distribution network multistage service restoration method containing microgrid

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105914753A (en) * 2016-05-25 2016-08-31 成都信息工程大学 Active distribution network (ADN) voltage real-time fuzzy control method
CN109802387A (en) * 2019-03-11 2019-05-24 燕山大学 A kind of elastic power distribution network multistage service restoration method containing microgrid

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
HAIXIANG GAO: "Resilience-Oriented Critical Load Restoration Using Microgrids in Distribution Systems", IEEE TRANSACTIONS ON SMART GRID, pages 2837 - 2848 *

Similar Documents

Publication Publication Date Title
Wong et al. Review on the optimal placement, sizing and control of an energy storage system in the distribution network
CN107862405B (en) Power system grid reconstruction optimization method taking microgrid as black-start power supply
CN105703393B (en) A kind of micro-capacitance sensor voltage restoration methods based on Distributed Predictive Control strategy
Wang et al. Optimal planning of stand-alone microgrids incorporating reliability
CN112383086B (en) Island micro-grid day-ahead energy-standby combined optimization scheduling method
CN110797921A (en) Microgrid control method
Choobineh et al. Emergency electric service restoration in the aftermath of a natural disaster
CN111641205A (en) Active power distribution network fault management method based on random optimization
Liu et al. Bi-level coordinated power system restoration model considering the support of multiple flexible resources
CN114362218A (en) Deep Q learning-based multi-type energy storage scheduling method and device in microgrid
Qachchachi et al. Optimal power flow for a hybrid AC/DC microgrid
CN114243706A (en) Power distribution network load recovery method, device and medium based on CCP optimization model
CN113572171B (en) Emergency resource optimal scheduling method and device for power distribution network and storage medium
Lou et al. A new method for planning of stand-alone hybrid PV/WT/BESS/diesel microgrids considering DR based on DARTP
CN115549067A (en) Power distribution network sequence recovery optimization method considering area division, recovery path and power supply sequence cooperation
Tongge et al. The Research on Black Start Strategy of Distributed Photovoltaic-Battery Energy Storage Systems Based on Cluster Division
CN111106622B (en) Active power distribution network power supply recovery method based on RMPC
Park et al. Power sharing algorithm for voltage regulation in islanded dc multi-microgrid system
CN113937777A (en) Power distribution network load recovery method and device based on two-stage solving strategy
Jacob et al. Planning of battery energy storage system in distribution network considering uncertainty
Abdulgalil et al. One-step solution for sizing and allocation of battery energy storage system using dc optimal power flow
Anam et al. An Energy Management System for Enhancing Resiliency of Microgrids Considering Uncertain Events
Fattaheian-Dehkordi et al. Hydrogen Storage System Allocation in a Distribution Grid Considering Congestion Problem
Guo et al. Distribution System Resilience Enhancement with Battery Swapping Stations of Electric Vehicles
Liu et al. Post-Disturbance Dynamic Distribution System Restoration with DGs and Mobile Resources

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