CN114221349A - Power grid self-adaptive load recovery method and system in extreme weather - Google Patents

Power grid self-adaptive load recovery method and system in extreme weather Download PDF

Info

Publication number
CN114221349A
CN114221349A CN202111580104.0A CN202111580104A CN114221349A CN 114221349 A CN114221349 A CN 114221349A CN 202111580104 A CN202111580104 A CN 202111580104A CN 114221349 A CN114221349 A CN 114221349A
Authority
CN
China
Prior art keywords
load
air conditioner
power
power grid
value
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.)
Granted
Application number
CN202111580104.0A
Other languages
Chinese (zh)
Other versions
CN114221349B (en
Inventor
孙润稼
刘玉田
范睿
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.)
Shandong University
Original Assignee
Shandong University
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 Shandong University filed Critical Shandong University
Priority to CN202111580104.0A priority Critical patent/CN114221349B/en
Publication of CN114221349A publication Critical patent/CN114221349A/en
Application granted granted Critical
Publication of CN114221349B publication Critical patent/CN114221349B/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
    • H02J3/12—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
    • H02J3/04—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
    • 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
    • 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
    • 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
    • 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
    • Y04S20/242—Home appliances
    • Y04S20/244—Home appliances the home appliances being or involving heating ventilating and air conditioning [HVAC] units

Landscapes

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

Abstract

The invention provides a method and a system for self-adaptive load recovery of a power grid in extreme weather, which combine an air conditioner load recovery quantity rapid evaluation technology, a transformer substation maximum recoverable load quantity calculation technology and a demand side response technology to realize a self-adaptive load recovery toughening decision by considering the uncertainty change of factors such as the power grid running state, the user state, the external environment and the like after an extreme event occurs. According to the invention, real-time information of power grid operation, users and environment is acquired through the energy management system, the Internet of things system and the geographic information system, the load input amount and the maximum allowable load input amount are gradually evaluated on line, the load reduction control of the air conditioner is carried out, and the load recovery of the transformer substation is rapidly and reliably completed.

Description

Power grid self-adaptive load recovery method and system in extreme weather
Technical Field
The invention belongs to the technical field of power system recovery, and particularly relates to a power grid self-adaptive load recovery method and system in extreme weather.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
With the continuous deterioration of global climate, the frequent and intensive extreme cold and hot weather events seriously affect the grid safety and the user power supply. Extreme weather leads to the electric heating and refrigerated demand to obviously rise, and the adverse external environment can cause the electric power system trouble and influence power supply, appears obvious unbalance between the electric power supply and demand, and then causes the power failure accident. In addition, extreme weather can also lead to the circuit to trip because of the increase of sag, icing scheduling accident in a large number, and then increase the risk of power system large tracts of land outage.
Modern power supply plays a very important role in social production and life, and power failure accidents can cause serious economic and social influences. With the development of social economy, the air conditioner load ratio is continuously improved, and the air conditioner load ratio in summer of partial cities in China exceeds 50%. In the sudden extreme hot and cold weather, the demands of people on refrigeration and heating equipment such as an air conditioner and the like are increased rapidly, and the life of people can be seriously influenced by power failure accidents. Therefore, the method pays attention to the rapid load recovery in extreme weather, enhances the toughness of the power grid, and is very important for guaranteeing social production and people's life.
The construction of a tough power grid aiming at high-risk and small-probability events such as extreme weather and natural disasters also draws great attention in academic circles and industrial circles in recent years, and one key characteristic of the tough power grid is that the operation situation of the power grid can be comprehensively and accurately sensed, and important loads can be quickly recovered from the disturbance of the extreme events.
The load recovery of the transformer substation is the basis of the recovery of the power system after power failure, the cold load starting problem caused by a large amount of air conditioner loads and the continuous influence of extreme weather are considered, the change of the running state of a power grid, the user state and the external environment is adapted, the electric energy support provided for the load recovery of the transformer substation by a superior power grid and a near-area microgrid is fully utilized, the rapid and reliable power grid recovery scheduling is realized, and the purpose of enhancing the toughness of the power grid is achieved. The existing load recovery method mainly makes a recovery scheme including a full recovery process in advance from a power grid level, however, the scheme often cannot match with an actual recovery process, so that single-step recovery operation in a transformer substation cannot be reliably implemented, and the overall recovery process is influenced.
Disclosure of Invention
In order to solve the problems, the invention provides a power grid self-adaptive load recovery method and a power grid self-adaptive load recovery system under extreme weather, which take the uncertain changes of factors such as the power grid running state, the user state, the external environment and the like after an extreme event occurs into consideration, combine the air conditioner load recovery quantity rapid evaluation technology, the transformer substation maximum recoverable load quantity calculation technology and the demand side response technology, and realize the self-adaptive load recovery toughening decision. According to the invention, real-time information of power grid operation, users and environment is acquired through the energy management system, the Internet of things system and the geographic information system, the load input amount and the maximum allowable load input amount are gradually evaluated on line, the load reduction control of the air conditioner is carried out, and the load recovery of the transformer substation is rapidly and reliably completed.
According to some embodiments, the invention adopts the following technical scheme:
a self-adaptive load recovery method for a power grid in extreme weather comprises the following steps:
acquiring a power grid running state, a user terminal state and environmental data, respectively processing the data, extracting a power value which can be provided by a superior power grid and a power value which can be provided by a microgrid in the power grid running state, and extracting user power failure time, a user air conditioner temperature set value and environmental temperature in the environmental data in the user terminal state;
estimating the load aggregate power of the air conditioner based on the environmental temperature and the power failure time, and estimating the load recovery amount after the outgoing line of the transformer substation is switched on by considering the fixed load amount;
considering the power support from top to bottom and from bottom to top, calculating to obtain the limit value of the single-time input load capacity of the transformer substation based on the power grid running state, the power value which can be provided by the upper-level power grid and the power value which can be provided by the micro-grid;
comparing the load recovery amount after the outgoing line and the closing of the transformer substation with the limit value of the single-time investable load amount, and calculating the required load reduction amount;
calculating the difference value between the indoor temperature of each user air conditioner and the temperature set value of the user air conditioner, calculating the ratio of the difference value to the temperature dead zone of the air conditioner, determining the survivability index of the corresponding user, and sequencing the current controllable air conditioner load according to the survivability index;
and selecting a reduction air conditioner set in the current control period according to the required load reduction amount and the controllable air conditioner load sequence, setting a switch of the reduction air conditioner set to be in a closed state, and executing the switching-on operation of the transformer substation load outgoing line.
As an alternative, the individual steps of the method are executed cyclically according to a control cycle.
As an alternative implementation, when the power grid operation state, the user terminal state and the environment data are obtained, the environment data are obtained through the geographic information system, the user terminal state data are obtained through the internet of things system, and the power grid operation state data are obtained through the energy management system.
As an alternative embodiment, the relevant parameters include an ambient temperature, a power outage time of a user, a fixed load and a cold load before the power outage, a temperature set value of an air conditioner of the user, an access condition of the internet of things of the user, an operation state of a power grid, a power value which can be provided by the power grid, and a power value which can be provided by the microgrid.
As an alternative embodiment, the specific process of estimating the aggregate power of the air conditioning load based on the ambient temperature and the power failure time in the relevant parameters includes:
a first-order thermodynamic model is adopted to represent the indoor and outdoor heat transfer processes, and the starting time and the shutdown time of the air conditioner in a normal operation state are deduced based on the external environment temperature and the air conditioner temperature set point;
considering the power failure time of the air conditioner and the on-off time of the air conditioner, calculating the power-on probability of a single air conditioner after the load of the air conditioner has power failure for a period of time;
and based on a law of large numbers, the starting probability and the power of the air conditioners are considered, and the aggregate power of the plurality of air conditioners is estimated to be the sum of the products of the starting probability and the power.
As an alternative embodiment, the specific process of calculating and obtaining the limit value of the single-time investable load capacity of the transformer substation based on the power grid operation state, the power value available to the upper-level power grid and the power value available to the microgrid comprises the following steps:
initializing a maximum recoverable load capacity search interval, determining an interval upper limit and an interval lower limit, and taking a golden section point of the interval as a load recovery capacity;
transient voltage, load flow, frequency and available power constraint of the load recovery quantity are verified;
if all the constraints are met, setting the interval lower limit to be the same as the load recovery amount, otherwise, setting the interval upper limit to be the same as the load recovery amount, and continuously selecting the regulated interval golden section point as the load recovery amount;
judging whether the difference between the updated upper limit and the updated lower limit of the interval is smaller than a threshold value, if so, carrying out the next step, otherwise, carrying out the verification step again;
and calculating the sum of the load recovery amount and the power value which can be provided by the microgrid, and setting the sum as the maximum recoverable load amount of the transformer substation.
As an alternative embodiment, the specific process of sequentially sorting the current controllable air-conditioning load includes: the controllable air conditioner loads are sorted in a descending order according to survivability indexes; and if the survivability indexes are the same, sorting the air conditioner loads in an ascending order according to the set temperature value.
As an alternative implementation, the specific process of selecting the air conditioner reduction set of the current control period according to the required load reduction amount and the controllable air conditioner load sequence includes:
judging whether the air conditioner at the head of the sorting is connected with the Internet of things, if so, deleting the air conditioner from the sorting and entering the next step, otherwise, deleting the air conditioner from the sorting and continuing the judgment;
adding the air conditioner into a reduced air conditioner set, and calculating the load of the reduced air conditioner set by adopting an air conditioner load recovery quantity rapid evaluation technology;
and judging whether the load of the air conditioner reduction set is larger than the required load reduction amount or not, if so, outputting the reduction air conditioner set, and otherwise, judging whether the air conditioner at the head of the sequencing is connected with the Internet of things or not.
A power grid adaptive load recovery system under extreme weather, comprising:
the parameter acquisition module is configured to acquire a power grid operation state, a user terminal state and environmental data, perform data processing respectively, extract a power value which can be provided by a superior power grid and a power value which can be provided by a microgrid in the power grid operation state, and extract user power failure time, a user air conditioner temperature set value and environmental temperature in the environmental data in the user terminal state;
the load recovery quantity estimation module is configured to estimate the air conditioner load aggregation power based on the environment temperature and the power failure time, and estimate the load recovery quantity after the outgoing line of the transformer substation is switched on by considering the fixed load quantity;
the limit value calculation module is configured to consider power support from top to bottom and from bottom to top, and calculate and obtain the limit value of the single-time input load of the transformer substation based on the power grid operation state, the power value which can be provided by the upper-level power grid and the power value which can be provided by the micro-grid;
the load reduction calculation module is configured to compare the load recovery amount after the outgoing line of the transformer substation is switched on with the limit value of the single input load amount and calculate the required load reduction amount;
the air conditioner load sorting module is configured to calculate a difference value between the indoor temperature of each user air conditioner and a set value of the temperature of the user air conditioner, calculate a ratio of the difference value to the air conditioner temperature dead zone, determine survivability indexes of corresponding users, and sort the current controllable air conditioner loads in sequence according to the survivability indexes;
and the load recovery module is configured to select a reduced air conditioner set in the current control period according to the required load reduction amount and the controllable air conditioner load sequence, set a switch of the reduced air conditioner set in a closed state and execute the switching-on operation of the transformer substation load outgoing line.
An electronic device comprising a memory and a processor and computer instructions stored on the memory and executed on the processor, the computer instructions, when executed by the processor, performing the steps of the above method.
A computer readable storage medium storing computer instructions which, when executed by a processor, perform the steps of the above method.
Compared with the prior art, the invention has the beneficial effects that:
the method for restoring the self-adaptive load of the power grid in the extreme weather has the core innovation points that the change of the running state, the user state and the environment state of the power grid can be adapted according to the real-time information of the energy management system, the Internet of things system and the geographic information system, the input of a transformer substation load outgoing line is decided on line, the power supply restoration of a user in the extreme weather is guided, and the toughness of the power grid is enhanced;
the self-adaptive load recovery method for the power grid in extreme weather can adapt to load recovery of different substations in extreme cold weather and extreme hot weather, and has self-adaptive capacity;
according to the power grid self-adaptive load recovery method in extreme weather, power support provided by a superior power grid and a distributed power supply in a transformer substation is considered, and the load recovery speed is effectively improved;
according to the power grid self-adaptive load recovery method under extreme weather, a first-order thermodynamic model is adopted to establish a rapid estimation model of air conditioner load aggregated power, and load recovery quantity is rapidly estimated based on the environmental temperature and the power failure time before load input, so that the efficiency of online decision is improved;
according to the self-adaptive load recovery method for the power grid in extreme weather, provided by the invention, the allowable maximum load recovery of the transformer substation which considers transient voltage, current and frequency and can provide power constraint is quickly solved by adopting a golden section search algorithm, so that the load recovery amount is prevented from exceeding the maximum value, and the reliability of load recovery is improved;
according to the power grid self-adaptive load recovery method in extreme weather, the user survivability indexes are adopted to carry out sequencing control on air conditioner load reduction, and the effect on the livelihood is considered, so that the load recovery benefit is improved.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a schematic diagram illustrating the principle of adaptive load recovery toughening of a power grid;
FIG. 2 is a schematic diagram of load recovery based on top-down and bottom-up power support;
FIG. 3 is a flow chart of a calculation of the maximum recoverable load capacity of the substation;
FIG. 4 is a logic diagram of air conditioner load ranking based on user survivability indicators;
FIG. 5 is a diagram of an embodiment of an urban power grid architecture;
fig. 6 is a diagram of the air conditioner reduction result of the power grid adaptive load recovery method under the condition that different power grids can provide power, wherein the abscissa represents the power that the power grid can provide, and the ordinate represents the air conditioner load reduction quantity.
Fig. 7 is a diagram of air conditioner reduction results of the power grid adaptive load recovery method under different load compositions, wherein the abscissa represents the load compositions respectively, and the ordinate represents the air conditioner load reduction number.
Fig. 8 is a diagram of the air conditioner reduction result of the power grid adaptive load recovery method under different ambient temperatures, wherein the abscissa represents the ambient temperature respectively, and the ordinate represents the air conditioner load reduction number.
The specific implementation mode is as follows:
the invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The first embodiment is as follows:
a self-adaptive load recovery method for a power grid in extreme weather comprises the following specific steps:
(1) and acquiring the current time step load recovery multi-source real-time data. Collecting power grid running state, user state and environment real-time data through an energy management system, an Internet of things system and a geographic information system, obtaining relevant parameters through data processing, and inputting the parameters into the system;
(2) based on the environmental temperature and the power failure time, estimating the air conditioner load aggregate power by adopting an air conditioner load recovery quantity rapid estimation technology, and estimating the load recovery quantity after the outgoing line of the transformer substation is switched on by considering the fixed load quantity;
(3) considering the power support from top to bottom and from bottom to top, and adopting a maximum recoverable load calculation technology of the transformer substation to obtain the limit value of the single-time investable load of the transformer substation;
(4) comparing the load recovery amount after the outgoing line of the transformer substation is switched on with the limit value of the single-time investable load amount, calculating the required load reduction amount, and sequencing the controllable air conditioner load based on the survivability index of a user;
(5) based on the required load reduction and the controllable air conditioner load sequencing, a reduction air conditioner set of the current time step is selected, a switch of the reduction air conditioner set is set to be in a closed state by adopting a demand response technology, and a dispatcher executes the switching-on operation of the transformer substation load outgoing line and enters the next time step.
In the step (1), a specific power grid applied by the toughening method is determined, and multi-source real-time information in the power grid area is obtained. After the load recovery in the previous step is completed, acquiring external environment temperature data at the current moment through a geographic information system, acquiring a user temperature set value and user Internet of things access condition data through an Internet of things system, and acquiring user power failure time, fixed load and cold load before power failure, a power grid running state, a power value which can be provided by a power grid and power value data which can be provided by a microgrid through an energy management system. A schematic diagram of a scrolling update of multi-source real-time information is shown in fig. 1.
In the step (2), the rapid evaluation of the air conditioner load recovery amount is necessary work before the load is put into use. In normal operation of the power system, the random switching of a group of temperature controlled loads results in a total power less than the sum of their capacities. However, when the load supply is restored after a continuous power outage, the diversity of the temperature controlled loads is lost, resulting in a total power after the power outage that may be much greater than the value before the power outage. This phenomenon is particularly evident on air conditioning loads in extremely hot or cold weather, and is called cold load start. Therefore, the evaluation of the air conditioner load recovery amount is the basis for preventing the load recovery amount from exceeding the limit, and the specific evaluation steps are as follows:
1. using first-order thermodynamicsThe model represents the indoor and outdoor heat transfer process, and deduces the starting time of the air conditioner in the normal operation state to be T based on the external environment temperature and the air conditioner temperature set pointonShutdown time of Toff；
2. Considering the power-off time of the air conditioner and the on-off time of the air conditioner, calculating the power-on probability p of a single air conditioner after the load of the air conditioner has power-off for a period of timeon,i；
3. Based on the law of large numbers, the starting probability of the air conditioner and the power P of the air conditioner are considerediEstimating the aggregate power of the plurality of air conditioners as ∑ Pi*pon,i。
Boot time TonAnd shutdown time ToffAs shown in the following formula:
Figure BDA0003425805940000111
wherein, thetaoutIs the external environment temperature, C is the equivalent heat capacity, R is the equivalent heat resistance, P is the air-conditioning power, eta is the air-conditioning performance coefficient, thetasetThe temperature is set for the air conditioner, and delta is the air conditioner temperature dead zone.
Starting probability p of single air conditioner loadon,iAs shown in the following formula:
pon,i＝(Ton,i+To)/(Ton,i+Toff,i)
wherein, TOThe power failure time.
And (3) performing load recovery by adopting top-down and bottom-up power support. After power failure, the micro-grid in the transformer substation keeps normal operation, a distributed power supply in the micro-grid provides recovered power support for a surrounding area, partial load is recovered, and after the upper-level power grid receives power, the area and the upper-level power grid perform synchronous operation, so that the purpose of power support from top to bottom and from bottom to top is achieved. A schematic diagram of load recovery based on top-down and bottom-up power support is shown in fig. 2.
In the step (3), the maximum recoverable load of the transformer substation is calculated and used for obtaining the upper limit of the current investable load, and the maximum recoverable load of the transformer substation in the current time step can be quickly obtained by adopting a golden section search algorithm according to the real-time power grid operation state, the power value which can be provided by the power grid and the power value which can be provided by the microgrid. The specific calculation steps are as follows:
1. initializing a maximum recoverable load search interval and determining an interval upper limit PmaxAnd a lower limit PminAnd taking the golden section point of the interval as the load recovery quantity Pnew；
2. For load recovery amount PnewThe transient voltage, the power flow and the frequency can provide power constraint for verification;
3. if all of the above constraints are satisfied, then P is setmin＝PnewOtherwise, P is setmax＝PnewContinuously selecting the golden section point of the adjusted interval as the load recovery quantity Pnew；
4. Judgment of PmaxAnd PminWhether the difference is smaller than a threshold value epsilon or not, if so, carrying out the next step, otherwise, returning to the step 2);
5. and calculating the sum of the load recovery amount and the power value which can be provided by the micro-grid, and setting the sum as the maximum recoverable load amount of the transformer substation.
The maximum recoverable load calculation flow of the transformer substation is shown in fig. 3.
In the step (4), the air conditioner loads are sorted based on the survivability index of the user, the indoor temperature and the set temperature are represented, and the air conditioner precision is represented by considering the air conditioner temperature dead zone.
the survivability index of the user at the time t is shown as the following formula:
Figure BDA0003425805940000121
wherein theta (t) is the indoor temperature at the time t, thetasetThe temperature is set for the air conditioner, and delta is the air conditioner temperature dead zone.
In the step (4), the air conditioner loads are sorted based on the user survivability indexes, the related principle firstly takes the user survivability indexes as the main, and if the survivability indexes are the same, the air conditioner temperature set value is considered. The specific ordering logic is shown in fig. 4.
In the step (5), based on the load reduction amount and the controllable air-conditioning load arrangement sequence, the air-conditioning load reduced at the current time step is selected, and the specific method comprises the following steps:
1. judging whether the first air conditioner in the sequence is connected with the Internet of things or not, if so, deleting the air conditioner from the sequence and entering the next step, otherwise, deleting the air conditioner from the sequence and continuing the judgment;
2. adding the air conditioner into a reduced air conditioner set, and calculating the load of the reduced air conditioner set by adopting an air conditioner load recovery quantity rapid evaluation technology;
3. and (4) judging whether the load of the reduced air-conditioning set is greater than the required load reduction amount, if so, outputting the reduced air-conditioning set, and otherwise, returning to the step 1.
The judgment expression for reducing whether the load of the air conditioner set is larger than the required load reduction amount is as follows:
Figure BDA0003425805940000131
wherein M represents a reduction in the number of air conditioners of the air conditioner set, PiIndicating air conditioning power, pon,iIndicating the probability of air-conditioning starting, PSIndicating the amount of recovery of the load after a power failure, PMRepresenting the maximum recoverable load amount.
In the step (5), the switch of the air conditioner is set to be in a closed state by adopting a demand response technology, and direct load control of the air conditioner is completed by deploying bidirectional interaction equipment, load control equipment and the like by relying on an automatic demand response system based on the internet of things technology.
And (5) executing the transformer substation load outgoing line closing operation, generating a transformer substation outgoing line closing operation ticket, and issuing the transformer substation outgoing line closing operation ticket to a power system dispatcher for execution.
Example two:
a power grid adaptive load recovery system under extreme weather, comprising:
the device is used for acquiring and processing data of the energy management system, the Internet of things system and the geographic information system;
the device is used for rapidly evaluating the load capacity of the substation after the outgoing line is switched on;
the device is used for optimizing and acquiring the maximum recoverable load of the transformer substation by adopting a golden section search algorithm;
means for calculating a user survivability index and sequencing controllable air conditioning loads;
means for selecting a reduced air conditioning set based on the load reduction and the controllable air conditioning load ranking;
and the device is used for generating an outgoing line and closing operation ticket of the transformer substation and issuing the outgoing line and closing operation ticket to a power system dispatcher.
The following describes a power grid adaptive load recovery method flow by simulating a power grid actual system in the south-Ji city.
The power grid structure in the city of denna is shown in fig. 5, and the problem in summer is high and can even exceed 40 ℃. Suppose that the urban power grid is subjected to extreme hot weather, causing a power outage in some areas. In the recovery process, the right half part of the power grid in the figure is recovered to a normal state, the load of the east-door substation is subjected to power recovery through the power support of the power grid and the microgrid, the input of the load outgoing line of the substation is realized by adopting a power grid self-adaptive load recovery method, and the method comprises the following specific steps:
s1: and acquiring the current time step load recovery multi-source real-time data. The power grid operation state, the user state and the environment real-time data are collected through an energy management system, an internet of things system and a geographic information system, relevant parameters are obtained through data processing, and the relevant parameters are input into the system.
After an extreme thermal event, in the process of load recovery, the external environment temperature data at the current moment is obtained to be 36 ℃ through a geographic information system, the set values of the user temperatures are all 26 ℃ through an internet of things system, a total of 2269 air conditioners are connected to the internet of things under the transformer substation, the power failure time of the user is 60min, the proportion of the fixed load to the cold load before power failure is 5:5, the total load is 10.25MW, the power provided by a power grid is 20MW, and the power provided by a microgrid is 1.5MW are obtained through an energy management system.
S2: under the environment temperature of 36 ℃ and the power failure time of 60min, estimating the air conditioner load aggregate power to be 18.14MW by adopting an air conditioner load recovery quantity rapid estimation technology, and estimating the load recovery quantity after the outgoing line of the transformer substation is closed by considering the fixed load quantity to be 6.48MW, wherein the process takes about 1ms, and the specific implementation steps are as follows:
1. a first-order thermodynamic model is adopted to represent the indoor and outdoor heat transfer processes, and the starting time of the air conditioner in a normal operation state is deduced to be T based on the external environment temperature and the air conditioner temperature set pointonShutdown time of Toff；
2. Considering the power-off time of the air conditioner and the on-off time of the air conditioner, calculating the power-on probability p of a single air conditioner after the load of the air conditioner has power-off for a period of timeon,i；
3. Based on the law of large numbers, the starting probability of the air conditioner and the power P of the air conditioner are considerediEstimating the aggregate power of the plurality of air conditioners as ∑ Pi*pon,i＝18.14MW。
S3: considering the power support from top to bottom and from bottom to top, after power failure, the micro-grid inside the transformer substation keeps normal operation, the distributed power supply in the micro-grid provides 1.5MW recovery power for the surrounding area, and the power support provided by the superior power grid at the current time step is 20 MW.
Initializing a maximum recoverable load search interval and determining an upper limit P of a golden section intervalmax20MW and lower limit PminThe maximum recoverable load is calculated to be 14.62MW when the maximum transient voltage drop value is 0.8p.u., and the maximum frequency deviation is-0.14 Hz. And in addition, 1.5MW recovery power provided by the distributed power supply in the microgrid, the recovery load limit value of the substation is 16.12MW at the moment.
S4: and comparing the load recovery amount after the outgoing line of the transformer substation is switched on with the limit value of the single-time investable load amount, calculating the required load reduction amount, and sequencing the controllable air conditioner load based on the survivability index of the user.
The load recovery amount after the outgoing line and the closing of the transformer substation is 18.14MW, the limit value of the load recovery amount of the transformer substation is 16.12MW, and therefore the required load reduction amount is 2.02 MW.
The survivability index of the air conditioner load after 60min of power failure is calculated by adopting the following formula, and the value range of the finally obtained survivability index of 2269 air conditioners is [0.64-1.64 ].
Figure BDA0003425805940000161
Wherein theta (t) is the indoor temperature at the time t, thetasetThe temperature is set for the air conditioner, and delta is the air conditioner temperature dead zone.
Further, the air conditioner loads are sorted based on the user survivability indexes, the related principles firstly take the user survivability indexes as the main, and if the survivability indexes are the same, the air conditioner temperature set value is considered.
S5: and selecting a reduction air conditioner set of the current time step based on the required load reduction amount and the controllable air conditioner load sequencing, implementing demand response based on the Internet of things, and setting air conditioner switches in the reduction air conditioner set to be in a closed state through direct load control. And (4) executing the switching-on operation of the transformer substation load outgoing line by a dispatcher, and entering the next time step.
According to the sequencing of the air-conditioning loads and the reduction of the required air-conditioning loads, the number of the air-conditioners to be reduced is 354, switches of the air-conditioners are set to be in a closed state through direct load control based on the Internet of things, and a dispatcher can perform closing operation of the east-door substation relative to load outgoing lines.
In order to illustrate the adaptability of the power grid adaptive load recovery method to different power grid states, user states and environment states, the power, load composition and environment temperature which can be provided by different power grids are respectively selected, and the number of air conditioner reductions is calculated, and the related results are shown in fig. 6, fig. 7 and fig. 8.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.

Claims (10)

1. A self-adaptive load recovery method for a power grid in extreme weather is characterized by comprising the following steps: the method comprises the following steps:
acquiring a power grid running state, a user terminal state and environmental data, respectively processing the data, extracting a power value which can be provided by a superior power grid and a power value which can be provided by a microgrid in the power grid running state, and extracting user power failure time, a user air conditioner temperature set value and environmental temperature in the environmental data in the user terminal state;
estimating the load aggregate power of the air conditioner based on the environmental temperature and the power failure time, and estimating the load recovery amount after the outgoing line of the transformer substation is switched on by considering the fixed load amount;
considering the power support from top to bottom and from bottom to top, calculating to obtain the limit value of the single-time input load capacity of the transformer substation based on the power grid running state, the power value which can be provided by the upper-level power grid and the power value which can be provided by the micro-grid;
comparing the load recovery amount after the outgoing line and the closing of the transformer substation with the limit value of the single-time investable load amount, and calculating the required load reduction amount;
calculating the difference value between the indoor temperature of each user air conditioner and the temperature set value of the user air conditioner, calculating the ratio of the difference value to the temperature dead zone of the air conditioner, determining the survivability index of the corresponding user, and sequencing the current controllable air conditioner load according to the survivability index;
and selecting a reduction air conditioner set in the current control period according to the required load reduction amount and the controllable air conditioner load sequence, setting a switch of the reduction air conditioner set to be in a closed state, and executing the switching-on operation of the transformer substation load outgoing line.
2. The method for restoring the self-adaptive load of the power grid in the extreme weather as claimed in claim 1, wherein: and the steps are circularly executed according to the control period.
3. The method for restoring the self-adaptive load of the power grid in the extreme weather as claimed in claim 1, wherein: when the power grid operation state, the user terminal state and the environment data are obtained, the environment data are obtained through a geographic information system, the user terminal state data are obtained through an internet of things system, and the power grid operation state data are obtained through an energy management system.
4. The method for restoring the self-adaptive load of the power grid in the extreme weather as claimed in claim 1, wherein: the specific process of estimating the air conditioner load aggregate power based on the ambient temperature and the power failure time in the relevant parameters comprises the following steps:
a first-order thermodynamic model is adopted to represent the indoor and outdoor heat transfer processes, and the starting time and the shutdown time of the air conditioner in a normal operation state are deduced based on the external environment temperature and the air conditioner temperature set point;
considering the power failure time of the air conditioner and the on-off time of the air conditioner, calculating the power-on probability of a single air conditioner after the load of the air conditioner has power failure for a period of time;
and based on a law of large numbers, the starting probability and the power of the air conditioners are considered, and the aggregate power of the plurality of air conditioners is estimated to be the sum of the products of the starting probability and the power.
5. The method for restoring the self-adaptive load of the power grid in the extreme weather as claimed in claim 1, wherein: the specific process of calculating and obtaining the limit value of the single-time investable load capacity of the transformer substation based on the power grid running state, the power value provided by the superior power grid and the power value provided by the micro-grid comprises the following steps:
initializing a maximum recoverable load capacity search interval, determining an interval upper limit and an interval lower limit, and taking a golden section point of the interval as a load recovery capacity;
transient voltage, load flow, frequency and available power constraint of the load recovery quantity are verified;
if all the constraints are met, setting the interval lower limit to be the same as the load recovery amount, otherwise, setting the interval upper limit to be the same as the load recovery amount, and continuously selecting the regulated interval golden section point as the load recovery amount;
judging whether the difference between the updated upper limit and the updated lower limit of the interval is smaller than a threshold value, if so, carrying out the next step, otherwise, carrying out the verification step again;
and calculating the sum of the load recovery amount and the power value which can be provided by the microgrid, and setting the sum as the maximum recoverable load amount of the transformer substation.
6. The method for restoring the self-adaptive load of the power grid in the extreme weather as claimed in claim 1, wherein: the specific process of sequencing the current controllable air conditioner load comprises the following steps: the controllable air conditioner loads are sorted in a descending order according to survivability indexes; and if the survivability indexes are the same, sorting the air conditioner loads in an ascending order according to the set temperature value.
7. The method for restoring the self-adaptive load of the power grid in the extreme weather as claimed in claim 1, wherein: the specific process of selecting the air conditioner reduction set of the current control period according to the required load reduction amount and the controllable air conditioner load sequence comprises the following steps:
judging whether the air conditioner at the head of the sorting is connected with the Internet of things, if so, deleting the air conditioner from the sorting and entering the next step, otherwise, deleting the air conditioner from the sorting and continuing the judgment;
adding the air conditioner into a reduced air conditioner set, and calculating the load of the reduced air conditioner set by adopting an air conditioner load recovery quantity rapid evaluation technology;
and judging whether the load of the air conditioner reduction set is larger than the required load reduction amount or not, if so, outputting the reduction air conditioner set, and otherwise, judging whether the air conditioner at the head of the sequencing is connected with the Internet of things or not.
8. The utility model provides a power grid self-adaptation load recovery system under extreme weather which characterized in that: the method comprises the following steps:
the parameter acquisition module is configured to acquire a power grid operation state, a user terminal state and environmental data, perform data processing respectively, extract a power value which can be provided by a superior power grid and a power value which can be provided by a microgrid in the power grid operation state, and extract user power failure time, a user air conditioner temperature set value and environmental temperature in the environmental data in the user terminal state;
the load recovery quantity estimation module is configured to estimate the air conditioner load aggregation power based on the environment temperature and the power failure time, and estimate the load recovery quantity after the outgoing line of the transformer substation is switched on by considering the fixed load quantity;
the limit value calculation module is configured to consider power support from top to bottom and from bottom to top, and calculate and obtain the limit value of the single-time input load of the transformer substation based on the power grid operation state, the power value which can be provided by the upper-level power grid and the power value which can be provided by the micro-grid;
the load reduction calculation module is configured to compare the load recovery amount after the outgoing line of the transformer substation is switched on with the limit value of the single input load amount and calculate the required load reduction amount;
the air conditioner load sorting module is configured to calculate a difference value between the indoor temperature of each user air conditioner and a set value of the temperature of the user air conditioner, calculate a ratio of the difference value to the air conditioner temperature dead zone, determine survivability indexes of corresponding users, and sort the current controllable air conditioner loads in sequence according to the survivability indexes;
and the load recovery module is configured to select a reduced air conditioner set in the current control period according to the required load reduction amount and the controllable air conditioner load sequence, set a switch of the reduced air conditioner set in a closed state and execute the switching-on operation of the transformer substation load outgoing line.
9. An electronic device, characterized in that: comprising a memory and a processor and computer instructions stored on the memory and executed on the processor, which when executed by the processor, perform the steps of the method of any one of claims 1 to 7.
10. A computer-readable storage medium characterized by: for storing computer instructions which, when executed by a processor, perform the steps of the method of any one of claims 1 to 7.
CN202111580104.0A 2021-12-22 2021-12-22 Power grid self-adaptive load recovery method and system in extreme weather Active CN114221349B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111580104.0A CN114221349B (en) 2021-12-22 2021-12-22 Power grid self-adaptive load recovery method and system in extreme weather

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111580104.0A CN114221349B (en) 2021-12-22 2021-12-22 Power grid self-adaptive load recovery method and system in extreme weather

Publications (2)

Publication Number Publication Date
CN114221349A true CN114221349A (en) 2022-03-22
CN114221349B CN114221349B (en) 2022-10-04

Family

ID=80705004

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111580104.0A Active CN114221349B (en) 2021-12-22 2021-12-22 Power grid self-adaptive load recovery method and system in extreme weather

Country Status (1)

Country Link
CN (1) CN114221349B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114726000A (en) * 2022-05-23 2022-07-08 国网重庆市电力公司电力科学研究院 Light-vehicle-diesel-load coordination optimization method applied in power failure scene

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH07227041A (en) * 1994-02-09 1995-08-22 Toshiba Corp Device for supporting restoration of electric power system from fault
CN108631306A (en) * 2018-05-21 2018-10-09 西安交通大学 The appraisal procedure of recovery capability after a kind of electric system calamity
CN113036760A (en) * 2021-03-31 2021-06-25 国网上海市电力公司 Method and system for recovering power grid load after disaster and computer readable medium
CN113422387A (en) * 2020-08-10 2021-09-21 谭世克 Fault processing method for power distribution network equipment

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH07227041A (en) * 1994-02-09 1995-08-22 Toshiba Corp Device for supporting restoration of electric power system from fault
CN108631306A (en) * 2018-05-21 2018-10-09 西安交通大学 The appraisal procedure of recovery capability after a kind of electric system calamity
CN113422387A (en) * 2020-08-10 2021-09-21 谭世克 Fault processing method for power distribution network equipment
CN113036760A (en) * 2021-03-31 2021-06-25 国网上海市电力公司 Method and system for recovering power grid load after disaster and computer readable medium

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
范睿等: "考虑空调负荷需求响应的负荷恢复量削减方法", 《电工技术学报》 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114726000A (en) * 2022-05-23 2022-07-08 国网重庆市电力公司电力科学研究院 Light-vehicle-diesel-load coordination optimization method applied in power failure scene
CN114726000B (en) * 2022-05-23 2024-07-02 国网重庆市电力公司电力科学研究院 Light-vehicle-firewood-load coordination optimization method under power outage scene

Also Published As

Publication number Publication date
CN114221349B (en) 2022-10-04

Similar Documents

Publication Publication Date Title
CN111932402B (en) Short-term power load bidirectional combination prediction method based on similar day and LSTM
CN109726872A (en) A kind of energy consumption prediction technique, device, storage medium and electronic equipment
CN105005878B (en) A kind of comprehensive estimation method of strong intelligent grid
CN114221349B (en) Power grid self-adaptive load recovery method and system in extreme weather
CN112865093B (en) Combined prediction method for short-time power load
CN112232575A (en) Comprehensive energy system regulation and control method and device based on multivariate load prediction
Joseph et al. Predictive mitigation of short term voltage instability using a faster than real-time digital replica
Yang et al. Real-time adaptive UVLS by optimized fuzzy controllers for short-term voltage stability control
Liu et al. Online short-term reliability evaluation using a fast sorting technique
Tavarov et al. Model and Algorithm of Electricity Consumption Management for Household Consumers in the Republic of Tajikistan.
CN113887809A (en) Power distribution network supply and demand balance method, system, medium and computing equipment under double-carbon target
CN113253646A (en) Zero-energy-consumption and zero-carbon building judgment method and energy control system
CN116976200A (en) Air conditioner real-time monitoring and dynamic modeling method and system based on electricity behavior division
CN113449909B (en) Cold load prediction method of cold accumulation air conditioner based on improved long-term and short-term memory network
CN107992986B (en) Long-term power grid planning method based on reliability index of power grid topological structure
CN115271168A (en) Method and device for predicting response potential of electrical load and storage medium
CN114759579A (en) Power grid active power optimization control system, method and medium based on data driving
CN108460479A (en) A kind of public building air-conditioning baseline load forecasting method in short-term
Xing et al. Post-evaluation method and index system for energy Internet projects based on attribute theory
Hou et al. Cooling load prediction based on the combination of rough set theory and support vector machine
Li et al. Electricity consumption forecast of hunan province using combined model based on multivariate linear regression and BP neural network
ZhiYuan et al. A Mixed Integer Linear Programming Method for Generating Backbone Grid
Yao et al. Short-term load forecasting of office building microgrid based on EA-NN
CN113822483A (en) Power load prediction method and device based on weather parameters and mobile terminal
Ozawa et al. Data-driven HVAC Control Using Symbolic Regression: Design and Implementation

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