CN113204862A - Modeling method, device and application of power grid cold load - Google Patents
Modeling method, device and application of power grid cold load Download PDFInfo
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Abstract
The invention relates to a modeling method, equipment and application of a power grid cold load, wherein the modeling method comprises the following steps: step 1: classifying loads in the post-disaster power grid recovery process into temperature control loads and non-temperature control loads; step 2: establishing a preliminary model for the power grid cold load aiming at the temperature control load and the non-temperature control load; and step 3: simplifying the model established in the step 2 to obtain a simplified model; and 4, step 4: increasing influencing factors in the cold load recovery process in the simplified model; and 5: and (4) completing modeling of the cold load in the power grid recovery process after the disaster to obtain a cold load recovery model. Compared with the prior art, the invention has the advantages of high reliability, high safety, good stability and the like.
Description
Technical Field
The invention relates to the technical field of post-disaster power grid restoration, in particular to a modeling method, equipment and application of a power grid cold load in a post-disaster power grid restoration process.
Background
Typically, when a distribution line is restored after a long power outage, the load demand is greater than before the outage. Because the system power supply has not been able to power the load for a significant period of time, the load has reached a "cold" state before being re-energized. In the load recovery phase, the load demand suddenly rises due to the wide range of power equipment being put into service again. The load increase phenomenon that occurs for such cases is referred to as cold load start.
The reason why the cold load phenomenon occurs is as follows: 1) this process will generally not exceed 1s due to transformer magnetizing inrush current; 2) sudden change of the starting current of the asynchronous motor, this process being generally less than 10 s; 3) this phenomenon is particularly pronounced at the terminals of power grids where the permeability of the automatic control or thermostat (such as air conditioners, refrigerators, water heaters, heat pumps, etc.) is high, due to the loss of load diversity during the starting process, which generally lasts ten minutes or even several hours. The first two factors generally only affect protection coordination and can be addressed by proper coordination of the protection devices. The third factor has raised greater concern because long lasting load currents can increase the thermal load on the device, increase recovery time and may damage the device. At present, the research on the cold load in the post-disaster power grid recovery process is less, so a modeling method for the cold load in the post-disaster power grid recovery process is urgently needed, and a research basis is provided for the research on the subsequent cold load.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a modeling method, equipment and application of a power grid cold load with high reliability.
The purpose of the invention can be realized by the following technical scheme:
a modeling method for power grid cooling load comprises the following steps:
step 1: classifying loads in the post-disaster power grid recovery process into temperature control loads and non-temperature control loads;
step 2: establishing a preliminary model for the power grid cold load aiming at the temperature control load and the non-temperature control load;
and step 3: simplifying the model established in the step 2 to obtain a simplified model;
and 4, step 4: increasing influencing factors in the cold load recovery process in the simplified model;
and 5: and (5) completing modeling of the cold load in the recovery process of the power grid after the disaster to obtain a cold load model.
Preferably, the temperature control load comprises a cooling load or a heating load and an automatic cooling equipment load; the non-temperature control load comprises a manual control load and a non-control load.
Preferably, the step 2 specifically comprises:
the following model was established:
wherein, Ptem(t) is a power function of the temperature controlled load over time; t is0To restore the start time; t is1Is the decay start time; alpha is a cold load attenuation factor; u (t) is a unit step function.
Preferably, the step 3 specifically comprises:
considering only the steady-state process of load recovery, the model is simplified as follows:
wherein, P (t) is the active load required by the load in the fault recovery process; pbaseA load value that is a normal condition; lambda [ alpha ]CLPUAnd τ are the initial growth factor and duration of the cold load phenomenon, respectively;
discretizing the simplified model:
wherein, Δ tiFor each time step, i 1.. n, n is the number of discrete time intervals.
Preferably, the step 4 specifically includes:
step 4-1: adding an ambient temperature influence P to a simplified modeltem′；
Step 4-2: adding blackout time impact P to simplified modeltem″；
Step 4-3: adding temperature control load proportional influence P to simplified modeltem″′。
More preferably, the environmental temperature influence is specifically:
the relationship between the power and the ambient temperature when the cold load is recovered is as follows:
Ptem′＝ηtemPtem
ηtem＝0.0024T2-0.06147T+1.375
wherein eta istemIs an ambient temperature influencing factor; ptem' is the temperature controlled load power taking into account the ambient temperature factor. More preferably, the influence of the power failure time is specifically:
Ptem″＝Ptemηtout
in the formula: ptem"temperature controlled load power considering blackout time factor; etatoutThe power failure time influence factor; t is toutThe unit is the power failure time which is hour; a and b are both time coefficients.
More preferably, the proportional influence of the temperature control load is specifically:
from the ratio mu of the power value of all temperature-controlled loads before the system fault occurs to the total loadtemIs shown, i.e.
Ptem″′＝μtemPtem。
A modeling apparatus for grid cooling load, the modeling apparatus comprising:
a memory for storing a computer program;
a processor for implementing the power grid cooling load modeling method of any one of the above when executing the computer program.
The application of the power grid cold load modeling method is used for constructing a post-disaster power grid cold load recovery model.
Compared with the prior art, the invention has the following beneficial effects:
firstly, the reliability is high: according to the modeling method of the power grid cold load, the load characteristics needing to be recovered after a disaster are considered, and the influence factors such as the environment temperature, the power failure time, the temperature control load proportion and the like are added into the cold load model, so that the reliability of the cold load recovery model is ensured, and effective model support can be provided for subsequent load recovery research after the disaster.
Secondly, the safety is high, the stability is good: the power grid cold load model constructed based on the modeling method can be used for recovering the power grid cold load after disaster, and the model is high in safety and good in stability when used for recovering the load.
Drawings
FIG. 1 is a schematic flow chart of a power grid cold load modeling method according to the present invention;
FIG. 2 is a schematic diagram of the start-up characteristics of an NCL in an embodiment of the present invention;
FIG. 3 is a schematic diagram of the start-up characteristics of an MCL in an embodiment of the invention;
FIG. 4 is a characteristic curve of a cold load start model according to an embodiment of the present invention;
FIG. 5 is a simplified cold load start model characteristic curve according to an embodiment of the present invention.
Detailed Description
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, not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, shall fall within the scope of protection of the present invention.
A method for modeling grid cooling load, the flow of which is shown in FIG. 1, includes:
step 1: classifying loads in the post-disaster power grid recovery process into temperature control loads and non-temperature control loads;
step 2: establishing a preliminary model for the power grid cold load aiming at the temperature control load and the non-temperature control load;
and step 3: simplifying the model established in the step 2 to obtain a simplified model;
and 4, step 4: increasing influencing factors in the cold load recovery process in the simplified model;
step 4-1: adding an ambient temperature influence P to a simplified modeltem′；
Step 4-2: adding blackout time impact P to simplified modeltem″；
Step 4-3: adding temperature control load proportional influence P to simplified modeltem″′；
And 5: and (4) completing modeling of the cold load in the power grid recovery process after the disaster to obtain a cold load recovery model.
The cold load phenomenon can cause slow recovery time and even possibly cause secondary power failure, and particularly, in the current load recovery process of an urban distribution network or a micro-grid, the influence of cold load starting in the fault recovery process is larger due to the fact that the constant temperature control load proportion of the air conditioner load is continuously improved, so that a detailed cold load model is necessary to be established.
Due to the diversity of the loads, the establishment of a suitable cold load model should analyze the variation characteristics of the cold load according to its type. The degree and duration of the load impact of the cold load recovery process is determined by a number of factors, including: outage duration, connected load type, weather, recovery strategy, outage cause, distributed power supply, and time to failure and load level.
The load starting characteristic is classified into two types, namely a load with periodical temperature control in power change when power supply is recovered and a load with non-temperature control in which the power size before failure is basically unchanged.
(1) Temperature controlled load
The temperature control load can be divided into two parts, namely a cooling load or a heating load and a thermostat load. In power distribution systems, due to the high permeability of thermostatically/automatically controlled end loads, the power distribution system will exhibit higher load demands when the load is restored after as long as 10 minutes of down time. Under the influence of the environmental temperature, the phenomenon of centralized starting of the power equipment can occur so as to restore to the originally set constant temperature, and the diversity of the load states tends to be uniform. Among several reasons for CLPU, the loss of diversity of temperature control load is more noticeable, and the duration of tens of minutes or even hours may obstruct the recovery process of the network, which is a main reason for cold load start impact.
(2) Non-temperature controlled load
Both artificial-controlled loads (MCL) and Non-controlled loads (NCL) are Non-temperature-controlled loads, and in the normal system operation process, load distribution models of the MCL and the NCL can be established based on comprehensive load model characteristics. The MCL has a different start-up behavior at the instant of re-energization. Some appliances, such as washing machines, have a sudden response, while others, such as computers, are restarted in a standby mode. At the moment of system reactivation, the NCL and MCL are started, the NCL output is the same before and after the interruption, the MCL output then appears as a gradient climb from the moment of reactivation to the start of normal operation, this gradient is a random variable that can be considered as the MCL equipment, the start-up characteristics of the NCL and MCL are shown in fig. 2 and 3.
The load for manual restart is mainly a motor load, which trips when the voltage is reduced, and is manually restarted only after the voltage is restored. Motor loads are widely used in residential, commercial and industrial applications, and typically large motors used in industrial operations have a contactor mechanism for starting the motor, requiring voltage to keep the contactor closed. When the power supply is interrupted, the contactor is automatically opened, and the motor is prevented from being automatically started when the power supply is recovered. Throughout the restart process, manual intervention is typically required to restart the motor. The motor in the man-controlled load has little influence on the cold load starting transient process, so that only a static model of the motor needs to be introduced. Uncontrolled loads such as lighting, computer/electronics, and some mechanically driven loads are not changed before and after restoration. The analysis can obtain the expression of the non-temperature control load as follows:
in the formula: pNtemRepresenting the load of the non-temperature-controlled load, PMCLRepresenting the load of the human control load, TcIndicating the time at which power supply is resumed, TeIndicating the time to return to normal after power is restored, PNCLIndicating the load amount of the uncontrolled load.
(3) Cold load model
Several models have been proposed in prior studies to estimate the loading behavior under CLPU conditions, including physical, exponential, probabilistic, and regression-based models. The peak and duration are essential to correctly quantify the thermal impact on the electrical equipment and to develop a recovery plan. The CLPU characteristic is closer to the delay exponential model of the mathematical expression, as shown in fig. 4, which is as follows:
wherein, Ptem(t) is a power function of the temperature controlled load over time; t is0To restore the start time; t is1Is the decay start time; alpha is a cold load attenuation factor; u (t) is a unit step function.
The temperature control load when the system is not in failure is PtemThe power-off time after the fault is Tout，T0At the moment, the cold load begins to recover and is kept at PpeakConstant and continuing at until the load begins to recover diversity, cold load power from PpeakGradually decreases according to the cold load attenuation factor alpha and returns to the normal power P after hourstem。
The present embodiment mainly considers the steady-state process of load recovery, so the model can be simplified as follows:
in the formula: p (t) is the active load required by the load in the fault recovery process; pbaseA load value that is a normal condition; lambda [ alpha ]CLPUAnd τ are the initial growth factor and duration, respectively, of the cold load phenomenon. The parameters lambda are influenced by factors such as the duration of power failure, the reason of power failure, the type of load, weather, the recovery mode and the time of failureCLPUAnd τ, and thus, these two parameters have not been quantitatively defined in the literature, and are typically obtained by computing historical data in a data acquisition and monitoring (SCADA) system or by multiple simulations and then compiled into a book to facilitate reference to decisions. The characteristic curve of the simplified model is shown in fig. 5.
Discretizing the simplified model:
wherein, Δ tiFor each time step, i 1.. n, n is the number of discrete time intervals.
Because actual data acquisition when the fault happens is very difficult, the embodiment only selects 3 main influence factors of the environmental temperature, the power failure time and the temperature control load proportion to analyze, researches the influence of the main influence factors on the power amplitude of cold load starting, and adds three influence factors into the model.
(1) Influence of ambient temperature
Due to the influence of the ambient temperature, the temperature control equipment will be invested or cut off in large quantities, and the demand of the load will vary greatly. Ambient temperature has an effect on cold load recovery from two aspects. On the one hand, since the temperature of the air is severe in some seasons, temperature control devices such as air conditioners and the like are used in large quantities, and the load demand of the entire power grid increases. On the other hand, the ambient temperature directly affects the load amount at the time of cold load recovery. Therefore, when considering the effect of the ambient temperature, two factors need to be considered together. The power versus ambient temperature relationship at the time of cold load recovery can be expressed as:
Ptem′＝ηtemPtem
in the formula: etatemIs an ambient temperature influencing factor; ptem' is the temperature controlled load power taking into account the ambient temperature factor. The power amplitude when the cold load is recovered and the ambient temperature are in a quadratic function relation:
ηtem＝0.0024T2-0.06147T+1.375
the power amplitude takes a minimum value when the ambient temperature is 18.3 deg.c.
2) Influence of blackout time
The power failure time of the load is an important index for predicting the load power amplitude when the cold load is recovered. As the outage time increases, the loss of load diversity is more severe and the load recovery requirements escalate. For different temperature control devices, the prior research provides corresponding load variation curves. From the perspective of a power grid, the relation between the power failure time and the load change is found, and the power amplitude during cold load recovery can be successfully estimated.
Ptem″＝Ptemηtout
In the formula: ptem"temperature controlled load power considering blackout time factor; etatoutThe power failure time influence factor; t is toutThe unit is the power failure time which is hour; and a and b are time coefficients, wherein a is 0.908, and b is 9.346.
(3) Influence of temperature-controlled load ratio
The temperature control equipment in the normal working state is in a periodic working state, the non-temperature control load is in a random state, and the system load has strong diversity. During cold load recovery, a large number of temperature control devices may be simultaneously activated to maintain a set temperature, resulting in a loss of system load diversity. The load increase during the cold load recovery period is primarily related to the temperature controlled load portion, while the non-temperature controlled load remains substantially at its original size. Therefore, the influence of the temperature-controlled load ratio on the load recovery peak needs to be considered. The temperature-controlled load ratio is generally determined by the ratio mu of the power value of all temperature-controlled loads before the system fault occurs to the total loadtemAnd (4) showing.
Therefore, according to the above three influencing factors, the load power peak value of the cold load recovery under different scenes can be expressed as:
Ppeak＝Pbaseηtemηtoutμtem
the embodiment also relates to a modeling device for the grid cold load, which comprises:
a memory for storing a computer program;
a processor for implementing the power grid cooling load modeling method of any one of the above when executing the computer program.
The power grid cold load modeling method is used for constructing a post-disaster power grid cold load recovery model.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A modeling method for grid cooling load is characterized by comprising the following steps:
step 1: classifying loads in the post-disaster power grid recovery process into temperature control loads and non-temperature control loads;
step 2: establishing a preliminary model for the power grid cold load aiming at the temperature control load and the non-temperature control load;
and step 3: simplifying the model established in the step 2 to obtain a simplified model;
and 4, step 4: increasing influencing factors in the cold load recovery process in the simplified model;
and 5: and (5) completing modeling of the cold load in the recovery process of the power grid after the disaster to obtain a cold load model.
2. The modeling method of grid cooling load according to claim 1, wherein the temperature control load comprises a cooling load or a heating load and an automatic cooling equipment load; the non-temperature control load comprises a manual control load and a non-control load.
3. The modeling method for grid cooling load according to claim 1, wherein the step 2 specifically comprises:
the following model was established:
wherein, Ptem(t) is a power function of the temperature controlled load over time; t is0To restore the start time; t is1Is the decay start time; alpha is a cold load attenuation factor; u (t) is a unit step function.
4. The modeling method for grid cooling load according to claim 1, wherein the step 3 specifically comprises:
considering only the steady-state process of load recovery, the model is simplified as follows:
wherein, P (t) is the active load required by the load in the fault recovery process; pbaseA load value that is a normal condition; lambda [ alpha ]CLPUAnd τ are the initial growth factor and duration of the cold load phenomenon, respectively;
discretizing the simplified model:
wherein, Δ tiFor each time step, i 1.. n, n is the number of discrete time intervals.
5. The modeling method for grid cooling load according to claim 1, wherein the step 4 specifically comprises:
step 4-1: adding an ambient temperature influence P to a simplified modeltem′；
Step 4-2: adding blackout time impact P to simplified modeltem″；
Step 4-3: adding temperature control load proportional influence P to simplified modeltem″′。
6. A modeling method of grid cooling load according to claim 5, characterized in that said ambient temperature influence is specifically:
the relationship between the power and the ambient temperature when the cold load is recovered is as follows:
Ptem′＝ηtemPtem
ηtem＝0.0024T2-0.06147T+1.375
wherein eta istemIs an ambient temperature influencing factor; ptem' is the temperature controlled load power taking into account the ambient temperature factor.
7. The method according to claim 5, wherein the blackout time influence is specifically:
Ptem″＝Ptemηtout
in the formula: ptem"temperature controlled load power considering blackout time factor; etatoutThe power failure time influence factor; t is toutThe unit is the power failure time which is hour; a and b are both time coefficients.
8. The modeling method of grid cooling load according to claim 5, wherein the temperature-controlled load proportional effect is specifically:
from the ratio mu of the power value of all temperature-controlled loads before the system fault occurs to the total loadtemIs shown, i.e.
Ptem″′＝μtemPtem。
9. A modeling apparatus for grid cooling load, the modeling apparatus comprising:
a memory for storing a computer program;
a processor for implementing the power grid cooling load modeling method as claimed in any one of claims 1 to 8 when executing the computer program.
10. The application of the power grid cold load modeling method according to any one of claims 1-8 is characterized in that the power grid cold load modeling method is used for building a post-disaster power grid cold load recovery model.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20200153273A1 (en) * | 2018-11-13 | 2020-05-14 | Mitsubishi Electric Research Laboratories, Inc. | Methods and Systems for Post-Disaster Resilient Restoration of Power Distribution System |
| CN112054520A (en) * | 2020-09-08 | 2020-12-08 | 国网上海市电力公司电力科学研究院 | Urban power grid load recovery method considering cold load starting characteristic |
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20200153273A1 (en) * | 2018-11-13 | 2020-05-14 | Mitsubishi Electric Research Laboratories, Inc. | Methods and Systems for Post-Disaster Resilient Restoration of Power Distribution System |
| CN112054520A (en) * | 2020-09-08 | 2020-12-08 | 国网上海市电力公司电力科学研究院 | Urban power grid load recovery method considering cold load starting characteristic |
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| Title |
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| DAVID P. CHASSIN: "Electrical Load Modeling and Simulation", HIGH PERFORMANCE COMPUTING IN POWER AND ENERGY SYSTEMS, 31 December 2013 (2013-12-31), pages 281 - 334 * |
| 姚梦 等: "考虑冷负荷冲击影响的电动汽车辅助孤岛复电方法", 电力系统自动化, vol. 43, no. 7, 27 February 2019 (2019-02-27), pages 144 - 150 * |
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