Probability power balance method and system based on flexible load flow calculation, and storage medium

Abstract

The invention discloses a probability power balance method and system based on flexible load flow calculation, and a storage medium, relates to the technical field of power systems, and solves the technical problem that the load flow calculation of an existing power system can increase unnecessary construction cost. The method comprises the following steps: analyzing source and load probability distribution conditions based on regional source and load current situations; analyzing probability density functions of the distributed power supply and the load, and performing coupling; based on the probability distribution condition after source-load coupling, flexible resource scheduling analysis is conducted, flexible resources are defined, on this basis, the equipment safety constraint probability is analyzed, the probability of reliable operation of the power equipment in the region is obtained, a constraint threshold value is set, and power grid flexible networking planning needs to be conducted when the probability is lower than the constraint threshold value; according to the invention, the power grid investment cost is saved, and the reliability and stability of power grid operation are ensured.

Description

Technical Field

[0001] The present invention belongs to the field of power systems, and in particular relates to a probabilistic power balance method, system and storage medium based on flexible power flow calculation. Background Art

[0002] With the large-scale access of renewable energy sources such as wind and solar energy to the power grid, the intermittent and random nature of these energy sources has brought new challenges to the planning and operation of power systems. In the face of increasingly complex supply and demand changes, enhancing the flexibility of power systems has become the key. One of the basic tasks of power systems is to ensure a balance between power generation and power demand. If supply exceeds demand, it may cause equipment damage or waste of resources; conversely, if demand exceeds supply, it will cause voltage drops or even power outages. By calculating the power balance, generator output can be reasonably dispatched and loads can be controlled to meet real-time needs.

[0003] Most of the current research is based on power system flow calculation. According to the calculation results, if the flow calculation results exceed the limit, the power grid architecture needs to be improved. Although the reliability of power grid operation is ensured, it also increases a lot of construction costs. But in fact, sometimes the limit may only be short-term, and short-term limit crossing generally does not need to be processed. Therefore, the present invention provides a probabilistic power balance method, system and storage medium based on flexible flow calculation. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a probabilistic power balance method, system and storage medium based on flexible power flow calculation, which are used to solve the technical problem that the power flow calculation of the existing power system will increase unnecessary construction costs.

[0005] To achieve the above object, a first aspect of the present invention provides a probabilistic power balance method based on flexible power flow calculation, comprising the following steps:

[0006] Step 1: Determine the source-load composition based on the regional power supply and load conditions;

[0007] Step 2: Calculate the equipment flow distribution; analyze the probability density functions of distributed power sources and loads, and couple the probability density functions of distributed power sources and loads to obtain the total probability distribution function of source-load coupling;

[0008] Step 3: Analysis of flexibility resource regulation; determine the flexible resources that can be regulated by the target equipment; analyze the total probability distribution function of the new source-load coupling through flexible resource scheduling; among them, the adjustable flexibility resources include the schedulable power and electricity of electric vehicles, the schedulable power and electricity of heating, and the schedulable power and electricity of energy storage;

[0009] Step 4: Analyze the equipment safety constraint probability; the equipment safety constraint probability includes the probability of not overloading the power supply, the probability of not overloading the transmission, and the probability of overall safe operation.

[0010] Preferably, the determining of source-load components based on regional power supply and load conditions includes:

[0011] Determine the region to which the target equipment belongs, and extract the annual maximum load of the target equipment and the operating data of the load within one year; the region to which the target equipment belongs includes the urban network and the rural network;

[0012] Determine the installed capacity of the distributed power station connected to the target device, and extract the operating data of each distributed power station within one year; among them, distributed power sources include distributed photovoltaic, wind power, hydropower and biomass power stations.

[0013] Preferably, the analysis of the probability density function of the distributed power source and the load comprises the following steps:

[0014] Step S1: Based on the annual operation data of the distributed power station and the annual operation data of the load, a box plot is made using Excel to form a statistical curve of the source and load probability distribution function, and a statistical curve of the source and load probability density function is obtained by derivation;

[0015] Step S2: performing curve fitting on the statistical curve of the source and load probability density function;

[0016] Step S3: Determine whether the degree of fit is greater than a threshold value of the degree of fit; if so, the fitting curve is consistent with the probability distribution characteristics of the original data, and the probability density functions of the load and the distributed generation are coupled and calculated; if not, it is not consistent.

[0017] Preferably, the coupling calculation of the probability density function of the load and the distributed power source to obtain the probability distribution function after source-load coupling includes:

[0018] The total probability distribution function F of source-charge coupling j-i (w) are as follows:

[0019]

[0020] Among them, i represents the time, j represents the device, and F j-i (w) represents the total probability distribution function of source-load coupling under device j at time i; f j-loadi (w-z1) represents the load probability density function of device j at time i, f j-sourcei (z1) represents the probability density function of distributed power under device j at time i; w-z1 represents the load, z1 represents the output of distributed power, and w represents the net load under source-load coupling.

[0021] Preferably, analyzing the total probability distribution function of the new source-load coupling through flexible resource scheduling includes:

[0022] Determine the flexible resources that can be adjusted by the target device j, and schedule the flexible resources;

[0023] Among them, the flexibility resource scheduling is based on the dispatchable amount of flexibility resources, shifting the electric vehicle load from the peak electricity consumption period to the low electricity consumption period, reducing the heating load during the peak electricity consumption period, discharging the energy storage during the peak electricity consumption period and charging during the low electricity consumption period, so that the source-load coupling curve fluctuates to the minimum.

[0024] After scheduling, based on the total probability distribution function of source-load coupling, the new total probability distribution function of source-load coupling F is obtained j-i (x) are as follows:

[0025]

[0026] Where z0 represents the flexibility resource; F j-i (w+z0) represents the total probability distribution function of the device j at time i after flexible resource scheduling, f j-load i (w-z1+z0) represents the load probability density function of device j at time i after flexible resource scheduling, f j-source i (z1) represents the probability density function of distributed power under device j at time i after flexible resource scheduling, and is the same as the probability density function of distributed power under device j at time i.

[0027] Preferably, based on the new total probability distribution function of source-load coupling, the analyzing device safety constraint probability includes:

[0028] Calculate the device security constraint probability of target device j at time i:

[0029] P1=F′ j-i [max(P j )]

[0030] P2=F′ j-i [min(P j )]

[0031] P3=F′ j-i [max(P j )]-F′ j-i [min(P j )]

[0032] Among them, P1 is the probability that the target device j is not overloaded when it is powered on; P2 is the probability that the target device j is not overloaded when it is powered on; P3 is the probability that the target device j can operate safely as a whole; max(P j) is the actual maximum power supply of device j; min(P j ) is the actual maximum transmission power of device j.

[0033] Preferably, constructing the safe operation parameter constraints of the target device j includes:

[0034] max(P j )<P j-limit

[0035] min(P j )>P j-limit′

[0036] Among them, P j-limit P is the power supply capacity limit of the device itself; j-limit′ Send the capability limit value for the j device itself.

[0037] Preferably, a constraint threshold of P3 is set to determine whether P3 is greater than the constraint threshold; if yes, the target device operates safely overall; if no, the target device needs to perform flexible networking planning.

[0038] A second aspect of the present invention provides a probabilistic power balance system based on flexible power flow calculation, comprising a data analysis module and a decision module;

[0039] The data analysis module is used to determine the source-load composition; based on the source-load situation, the probability density function of the distributed power source and the load is analyzed, and the probability density function of the distributed power source and the load is coupled and calculated to obtain the total probability distribution function of the source-load coupling;

[0040] Determine the flexible resources that can be controlled by the target equipment; analyze the total probability distribution function of the new source-load coupling through flexible resource scheduling;

[0041] And, analyze the probability of equipment safety constraints to obtain the probability of reliable operation of power equipment in the area;

[0042] The decision module analyzes whether flexible network planning is needed for the power grid based on the probability of reliable operation of power equipment.

[0043] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a probabilistic power balancing method based on flexible power flow calculation.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] 1. The present invention can minimize costs while ensuring system stability, and can also effectively awaken various flexible resources on the user side; secondly, an effective power balance strategy can help smooth the impact of uncontrollable factors of distributed power sources; thirdly, it can maintain the stable operation of the power grid, improve economic benefits, and improve the flexibility of power grid operation, while providing a scientific basis for policy makers to formulate long-term development strategies;

[0046] 2. The present invention combines the source and load probability distribution characteristics, adjusts the flexibility resources, and further calculates the equipment safety constraint probability according to the adjusted source-load probability distribution function, and finally obtains the probability of safe operation of the equipment in the target year. The over-limit results are presented in a probabilistic manner. For the over-limit of the current under low probability, it can be further solved by flexible load adjustment and peak load regulation of energy storage equipment. This situation mainly corresponds to the over-limit in a short time; for the over-limit of the current under higher probability, the power grid itself needs to expand its capacity to achieve long-term safe operation. In this way, the investment cost can be effectively saved, and the safe and stable operation of the power grid can be ensured. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0048] Figure 1 The figure is a flowchart of the method of the present invention. DETAILED DESCRIPTION

[0049] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] See also Figure 1 The first aspect of the present invention provides a probabilistic power balance method based on flexible power flow calculation, comprising the following steps:

[0051] Step 1: Determine the source-load composition based on the regional power supply and load conditions;

[0052] Specifically, it is necessary to clarify whether the target equipment belongs to an urban grid or a rural grid, the target equipment's annual maximum load and the load's operating data within one year; as well as clarify the installed capacity of the distributed photovoltaic, wind power, hydropower and biomass power stations connected to the target equipment, and their corresponding operating data within one year.

[0053] Among them, the operating data is collected every 15 minutes, and there are a total of 15*24*365 data in a year.

[0054] Step 2: Analyze the probability density functions of distributed power sources and loads, and couple the probability density functions of distributed power sources and loads to obtain the total probability distribution function of source-load coupling;

[0055] Among them, distributed power sources include photovoltaic, wind power, hydropower and biomass energy;

[0056] Specifically, step S1: according to the annual operation data of the distributed power station and the annual operation data of the load, a box plot is made using Excel to form a statistical curve of the source and load probability distribution function, and a statistical curve of the source and load probability density function is obtained by derivation;

[0057] Step S2: curve fitting the source and load probability density function statistical curves by SPSS simulation software;

[0058] Step S3: Determine whether the degree of fit is greater than the degree of fit threshold; if yes, the fitting curve is consistent with the probability distribution characteristics of the original data, and the simulation software outputs the fitted load probability density function and distributed power probability density function, and enters step S4; if no, it is not consistent; wherein the degree of fit threshold can be set to 0.9;

[0059] Step S4: Couple the probability density functions of the load and the distributed generation to obtain the total probability distribution function F of the source-load coupling j-i (w) are as follows:

[0060]

[0061] Among them, i represents the time, j represents the device, and F j-i (w) represents the total probability distribution function of source-load coupling under device j at time i; f j-load i (w-z1) represents the load probability density function of device j at time i, f j-source i (z1) represents the probability density function of distributed power under device j at time i; w-z1 represents the load, z1 represents the output of distributed power, and w represents the net load under source-load coupling.

[0062] It should be noted that in actual calculations, the upper and lower limits of the integral of the above formula can be implemented according to the domain (the starting and ending intervals required for the calculation of the source-charge coupling probability density function), and the integral value of the part exceeding the domain is 0.

[0063] Step 3: Determine the adjustable flexibility resources of the target equipment; analyze the total probability distribution function of the new source-load coupling through the flexible resource scheduling; among them, the adjustable flexibility resources include the adjustable power and electricity of electric vehicles, the adjustable power and electricity of heating, and the adjustable power and electricity of energy storage;

[0064] Specifically, determining the flexible resources that can be adjusted by the target device j, and scheduling the flexible resources;

[0065] Among them, the flexibility resource scheduling is based on the dispatchable amount of flexibility resources, shifting the electric vehicle load from the peak electricity consumption period to the low electricity consumption period, reducing the heating load during the peak electricity consumption period, discharging the energy storage during the peak electricity consumption period and charging during the low electricity consumption period, so that the source-load coupling curve fluctuates to the minimum.

[0066] After scheduling, based on the total probability distribution function of source-load coupling, the new total probability distribution function of source-load coupling F is obtained j-i (x) are as follows:

[0067]

[0068] Where z0 represents the flexibility resource; F j-i (w+z0) represents the total probability distribution function of the device j at time i after flexible resource scheduling, f j-load i (w-z1+z0) represents the load probability density function of device j at time i after flexible resource scheduling, f j-source i (z1) represents the probability density function of distributed power under device j at time i after flexible resource scheduling, and is the same as the probability density function of distributed power under device j at time i.

[0069] Step 4: Based on the total probability distribution function of the new source-load coupling, analyze the equipment safety constraint probability; the equipment safety constraint probability includes the probability of line power supply not being overloaded, the probability of transmission not being overloaded, and the probability of overall safe operation.

[0070] Specifically, construct the safe operation parameter constraints of the target device j, including:

[0071] max(P j )<P j-limit

[0072] min(P j )>P j-limit′

[0073] Among them, max(P j ) is the actual maximum power supply of device j; min(P j ) is the actual maximum transmission power of device j; P j-limit P is the power supply capacity limit of the target device j itself; j-limit′ Send the capability limit for the target device j itself.

[0074] Based on the safe operation parameter constraints of target device j, the device safety constraint probability of target device j at time i is calculated:

[0075] P1=F′ j-i [max(P j )]

[0076] P2=F′ j-i [min(P j )]

[0077] P3=F′ j-i [max(P j )]-F′ j-i [min(P j )]

[0078] Among them, P1 is the probability that the power supply capacity of the target device j meets the load transfer demand, that is, the probability that the power supply is not overloaded; P2 is the probability that the distributed power supply carrying capacity of the target device j meets the distributed power supply consumption demand, that is, the probability that the transmission is not overloaded; P3 is the probability that the target device j meets the source and load carrying capacity requirements at the same time, that is, the overall probability of safe operation.

[0079] Set the constraint threshold of P3 to determine whether P3 is greater than the constraint threshold; if yes, the target device operates safely overall; if no, the target device needs to perform flexible networking planning; wherein the constraint threshold of P3 is set to 95%.

[0080] A second aspect of the present invention provides a probabilistic power balance system based on flexible power flow calculation, comprising a data analysis module and a decision module;

[0081] The data analysis module is used to determine the source-load composition; based on the source-load situation, the probability density function of the distributed power source and the load is analyzed, and the probability density function of the distributed power source and the load is coupled and calculated to obtain the total probability distribution function of the source-load coupling;

[0082] Determine the flexible resources that can be controlled by the target equipment; analyze the total probability distribution function of the new source-load coupling through flexible resource scheduling;

[0083] And, analyze the probability of equipment safety constraints to obtain the probability of reliable operation of power equipment in the area;

[0084] The decision module analyzes whether flexible network planning is needed for the power grid based on the probability of reliable operation of power equipment.

[0085] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a probabilistic power balancing method based on flexible power flow calculation.

[0086] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and fitting thresholds in the formula are set by technicians in this field according to actual conditions or obtained through simulation of a large amount of data.

[0087] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A probabilistic power balance method based on flexible power flow calculation, characterized in that: The following steps are involved: Step 1: Determine the source-load composition based on the regional power supply and load conditions; Step 2: Analyze the probability density functions of distributed power sources and loads, and couple the probability density functions of distributed power sources and loads to obtain the total probability distribution function of source-load coupling; Step 3: Determine the adjustable flexibility resources of the target equipment; analyze the total probability distribution function of the new source-load coupling through the flexible resource scheduling; among them, the adjustable flexibility resources include the adjustable power and electricity of electric vehicles, the adjustable power and electricity of heating, and the adjustable power and electricity of energy storage; Step 4: Analyze the equipment safety constraint probability; the equipment safety constraint probability includes the probability of not overloading the power supply, the probability of not overloading the transmission, and the probability of overall safe operation.

2. The probabilistic power balance method based on flexible power flow calculation according to claim 1 is characterized in that: Determining the source-load composition based on the regional power supply and load conditions includes: Determine the region to which the target equipment belongs, and extract the annual maximum load of the target equipment and the operating data of the load within one year; the region to which the target equipment belongs includes the urban network and the rural network; Determine the installed capacity of the distributed power station connected to the target device, and extract the operating data of each distributed power station within one year; among them, distributed power sources include distributed photovoltaic, wind power, hydropower and biomass power stations.

3. The probabilistic power balance method based on flexible power flow calculation according to claim 1 is characterized in that: The analysis of the probability density function of distributed power sources and loads includes the following steps: Step S1: Based on the annual operation data of the distributed power station and the annual operation data of the load, a box plot is made using Excel to form a statistical curve of the source and load probability distribution function, and a statistical curve of the source and load probability density function is obtained by derivation; Step S2: performing curve fitting on the statistical curve of the source and load probability density function; Step S3: Determine whether the degree of fit is greater than a threshold value of the degree of fit; if so, the fitting curve is consistent with the probability distribution characteristics of the original data, and the probability density functions of the load and the distributed generation are coupled and calculated; if not, it is not consistent.

4. The probabilistic power balance method based on flexible power flow calculation according to claim 3 is characterized in that: The coupling calculation of the probability density function of the load and the distributed power source to obtain the probability distribution function after source-load coupling includes: The total probability distribution function F of source-charge coupling j-i (w) are as follows: Among them, i represents the time, j represents the device, and F j-i (w) represents the total probability distribution function of source-load coupling under device j at time i; f j-load i (w-z1) represents the load probability density function of device j at time i, f j-source i (z1) represents the probability density function of distributed power under device j at time i; w-z1 represents the load, z1 represents the output of distributed power, and w represents the net load under source-load coupling.

5. The probabilistic power balance method based on flexible power flow calculation according to claim 4 is characterized in that: The total probability distribution function of the new source-load coupling is analyzed through flexible resource scheduling, including: Determine the flexible resources that can be adjusted by the target device j, and schedule the flexible resources; After scheduling, based on the total probability distribution function of source-load coupling, the new total probability distribution function of source-load coupling F′ is obtained j-i (x) are as follows: Where z0 represents the flexibility resource; F j-i (w+z0) represents the total probability distribution function of the device j at time i after flexible resource scheduling, f j-load i (w-z1+z0) represents the load probability density function of device j at time i after flexible resource scheduling, f j-source i (z1) represents the probability density function of distributed power under device j at time i after flexible resource scheduling, and is the same as the probability density function of distributed power under device j at time i.

6. The probabilistic power balance method based on flexible power flow calculation according to claim 5 is characterized in that: Based on the new total probability distribution function of source-load coupling, the analysis of equipment safety constraint probability includes: Calculate the device security constraint probability of target device j at time i: P1=F′ j-i [max(P j )] P2=F′ j-i [min(P j )] P3=F′ j-i [max(P j )]-F′ j-i [min(P j )] Among them, P1 is the probability that the target device j is not overloaded when it is powered on; P2 is the probability that the target device j is not overloaded when it is powered on; P3 is the probability that the target device j can operate safely as a whole; max(P j ) is the actual maximum power supply of device j; min(P j ) is the actual maximum transmission power of device j.

7. The probabilistic power balance method based on flexible power flow calculation according to claim 6 is characterized in that: Construct the safe operation parameter constraints of the target device j, including: max(P j )<P j-limit my(P j )>P j-limit′ Among them, P j-limit P is the power supply capacity limit of the device itself; j-limit′ Send the capability limit value for the j device itself.

8. The probabilistic power balance method based on flexible power flow calculation according to claim 6, characterized in that: Set the constraint threshold of P3 to determine whether P3 is greater than the constraint threshold; if yes, the target device operates safely overall; if no, the target device needs to perform flexible networking planning.

9. A probabilistic power balance system based on flexible power flow calculation, operating based on the probabilistic power balance method based on flexible power flow calculation according to any one of claims 1 to 8, characterized in that: Includes data analysis module and decision-making module; The data analysis module is used to determine the source-load composition; based on the source-load situation, the probability density function of the distributed power source and the load is analyzed, and the probability density function of the distributed power source and the load is coupled and calculated to obtain the total probability distribution function of the source-load coupling; Determine the flexible resources that can be controlled by the target equipment; analyze the total probability distribution function of the new source-load coupling through flexible resource scheduling; And, analyze the probability of equipment safety constraints to obtain the probability of reliable operation of power equipment in the area; The decision-making module analyzes whether flexible networking planning of the power grid is necessary based on the probability of reliable operation of power equipment.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

Images