CN110348695A - Flexibility evaluation method, device, equipment and storage medium of power system - Google Patents
Flexibility evaluation method, device, equipment and storage medium of power system Download PDFInfo
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Abstract
The invention discloses a flexibility evaluation method, a flexibility evaluation device, flexibility evaluation equipment and a flexibility evaluation storage medium of a power system, wherein the flexibility evaluation method comprises the following steps: calculating to obtain the daily net load output data according to the historical load output of the power system and the output of the renewable energy; decomposing a plurality of fluctuation components of the daily net load output data according to a preset multi-scale decomposition algorithm of the WMMF filter; calculating the flexibility insufficiency probability and flexibility average cost of the power system according to the fluctuation component; and evaluating the power system and adjusting the power of the power system according to the flexibility shortage probability and the flexibility average cost. The method and the device solve the problems that the occupation ratio of the renewable energy sources and the influence of the flexibility cost on the flexibility regulation of the power system cannot be evaluated in the prior art.
Description
Technical Field
The present invention relates to the field of power system control technologies, and in particular, to a method, an apparatus, a device, and a storage medium for flexibility evaluation of a power system.
Background
With environmental pollution and gradual depletion of fossil energy, new energy has become one of the main power types in various countries. However, the access of a large amount of renewable energy (such as wind power generation, solar photovoltaic power generation, etc.) also causes problems for the safe and stable operation of the power system. Under the scene of high-proportion renewable energy grid connection, the renewable energy has output characteristics such as randomness, volatility and intermittence, and a power system needs to reserve a certain margin of flexibility adjusting capacity to serve as a flexibly-called rotary standby.
The existing flexibility evaluation index is focused on representing the situation that the flexibility of the whole power system is abundant in comparison of the flexibility resource of the power system and the flexibility requirement of the system, cannot reflect the influence of the occupation ratio of various renewable energy sources in the power system on flexibility regulation, neglects the limiting effect of flexibility cost on the flexibility regulation of the power system, and therefore the power fluctuation of the power system after the renewable energy sources are connected to the grid is difficult to deal with.
Disclosure of Invention
Embodiments of the present invention provide a method, an apparatus, a device, and a storage medium for flexibility evaluation of an electric power system, which can effectively solve the problem in the prior art that the occupation ratio of renewable energy sources cannot be evaluated and the flexibility cost affects the flexibility adjustment of the electric power system.
An embodiment of the present invention provides a flexibility evaluation method for an electric power system, including:
calculating to obtain the daily net load output data according to the historical load output of the power system and the output of the renewable energy;
decomposing the daily net load output data according to a preset multi-scale decomposition algorithm of the WMMF filter to obtain a plurality of fluctuation components;
calculating the flexibility insufficiency probability and flexibility average cost of the power system according to the fluctuation component;
and evaluating the power system and adjusting the power of the power system according to the flexibility shortage probability and the flexibility average cost.
As an improvement of the above scheme, decomposing the intra-day net load output data according to a preset multi-scale decomposition algorithm of the WMMF filter specifically includes:
decomposing the daily net load output data into a minute-level fluctuation component, a dozen and odd minute-level fluctuation component and an hour-level fluctuation component;
and respectively carrying out fluctuation identification on the minute-level fluctuation component, the dozen and odd minute-level fluctuation component and the hour-level fluctuation component, and respectively dividing the minute-level fluctuation component, the dozen and odd minute-level fluctuation component and the hour-level fluctuation component into an upward climbing section and a downward climbing section.
As an improvement of the above scheme, the establishing of the insufficient flexibility probability index and the flexibility average cost index of the power system according to the decomposed intra-day net load output data specifically includes:
acquiring the requirement of flexibility corresponding to each climbing section by counting the climbing amplitude of each climbing section;
acquiring corresponding available flexible resources according to the climbing time, the climbing operation point and the flexible resource output characteristics of each climbing section;
respectively comparing the available flexibility resources in each climbing section with the flexibility requirement to obtain the number of climbing sections with insufficient flexibility and the total number of climbing sections;
calculating to obtain the flexibility shortage probability according to the flexibility shortage climbing section and the total climbing section number;
and calculating the flexibility average cost according to the available flexibility resources in each climbing section and the flexibility requirement.
As an improvement of the above scheme, the calculating according to the available flexibility resources in each climbing section and the flexibility requirement to obtain the flexibility average cost specifically includes:
wherein,average cost for flexibility; diThe flexibility requirement in the ith climbing section is met; fi,j、ΔFi,Respectively the adjustable quantity and the actual quantity of the j resource in the ith climbing section; cjThe cost is invoked for the flexibility unit of the jth resource.
Another embodiment of the present invention correspondingly provides a flexibility evaluation apparatus for an electrical power system, including:
the data acquisition module is used for calculating the output of the daily net load according to the historical load output of the power system and the output of the renewable energy sources to obtain the output data of the daily net load;
the data decomposition module is used for decomposing the daily net load output data according to a preset multi-scale decomposition algorithm of the WMMF filter to obtain a plurality of fluctuation components;
the calculation module is used for calculating and obtaining the flexibility insufficiency probability and the flexibility average cost of the power system according to the fluctuation component;
and the evaluation module is used for evaluating the power system and adjusting the power of the power system according to the flexibility shortage probability and the flexibility average cost.
As an improvement of the above scheme, the data decomposition module includes:
the fluctuation component acquisition module is used for decomposing the daily net load output data into a minute-level fluctuation component, a dozen or so minute-level fluctuation component and an hour-level fluctuation component;
and the fluctuation identification module is used for respectively carrying out fluctuation identification on the minute-level fluctuation component, the dozen and odd minute-level fluctuation component and the hour-level fluctuation component, and respectively dividing the minute-level fluctuation component, the dozen and odd minute-level fluctuation component and the hour-level fluctuation component into an upward slope climbing section and a downward slope climbing section.
As an improvement of the above solution, the calculation module includes:
the flexible demand acquisition module is used for acquiring the flexible demand corresponding to each climbing section by counting the climbing amplitude of each climbing section;
the available flexibility resource acquisition module is used for acquiring corresponding available flexibility resources according to the climbing time, the climbing operation point and the flexibility resource output characteristics of each climbing section;
the statistical module is used for respectively comparing the available flexibility resources in each climbing section with the flexibility requirement to obtain the number of climbing sections with insufficient flexibility and the total number of climbing sections;
the insufficient flexibility probability calculation module is used for calculating and obtaining the insufficient flexibility probability according to the insufficient flexibility climbing section and the total climbing section number;
and the flexibility average cost calculation module is used for calculating the flexibility average cost according to the available flexibility resources in each climbing section and the flexibility requirement.
Another embodiment of the present invention correspondingly provides a flexibility evaluation apparatus for an electric power system, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and the processor implements the flexibility evaluation method for the electric power system when executing the computer program.
Another embodiment of the present invention correspondingly provides a computer-readable storage medium, where the computer-readable storage medium includes a stored computer program, and when the computer program runs, the apparatus where the computer-readable storage medium is located is controlled to execute the method for evaluating flexibility of a power system.
Compared with the prior art, the flexibility evaluation method, the device, the equipment and the storage medium of the power system disclosed by the embodiment of the invention decompose the daily net load output data through a preset multi-scale decomposition algorithm of the WMMF filter, calculate the flexibility shortage probability and the flexibility average cost according to the decomposed daily net load output data, and evaluate the flexibility of the power system through the flexibility shortage probability and the flexibility average cost, so that the power of the power system is regulated, and the power system can safely and stably run.
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Fig. 1 is a schematic flow chart of a flexibility evaluation method for an electric power system according to an embodiment of the present invention;
FIG. 2 is a flow chart illustrating multi-scale decomposition of a default WMMF filter according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a multi-time scale fluctuation decomposition of a payload curve in an embodiment of the present invention;
FIG. 4 is a schematic diagram of flexibility requirement determination according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a flexibility evaluation apparatus of an electric power system according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of a flexibility evaluation device of an electric power system 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 only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic flow chart of a flexibility evaluation method for an electric power system according to an embodiment of the present invention.
The embodiment of the invention provides a flexibility evaluation method of a power system, which comprises the following steps:
and S10, calculating the daytime net load output data according to the historical load output of the power system and the output of the renewable energy. Wherein, the output of the renewable energy can be as follows: wind power output, photovoltaic output and the like.
Specifically, due to the addition of renewable energy (taking wind power generation and solar photovoltaic power generation as examples), the volatility required by the operation of the power system is enhanced, the wind power output and the photovoltaic output are considered as negative load outputs, and the historical load output curve of the power system is subtracted from the wind power output curve and the photovoltaic output curve to form an intraday net load output curve.
And S20, decomposing the daily net load output data according to a preset multi-scale decomposition algorithm of the WMMF filter to obtain a plurality of fluctuation components.
Specifically, the mathematical expression of the WMMF filter constructed based on the OCCO filter is as follows:
wherein f is an input signal; g is a structural element; degree represents open operation; represents a closed operation; Θ represents corrosion operation;representing a dilation operation;the weights of the results of the filtering are for each scale structuring element. In order to reduce the influence of noise in the filtering result of the small-scale structural element on the final filtering result, weight is addedThe value of (c) is determined by the variance value of the filtering noise of each scale.
In the formulaIs scale siThe variance of the difference is filtered down.
Specifically, referring to fig. 2, the preset multi-scale decomposition algorithm of the WMMF filter is to construct a first-stage WMMF filter according to an original payload time series (original flexibility demand time series) F; filtering the time sequence F based on a first-stage WMMF filter to output a time sequence Y, and then performing difference between the F and the Y to output a high-frequency component H; constructing a second-stage WMMF filter according to the time sequence Y; and filtering the time sequence Y based on a second-stage WMMF filter to output an intermediate frequency component M, and then performing difference between Y and M to output a low frequency component L.
In the present embodiment, the intra-day net load output data is decomposed into minute-scale fluctuation components, ten and several minute-scale fluctuation components, and hour-scale fluctuation components.
Specifically, referring to fig. 3, the payload contribution data is divided into a minute-order fluctuation component (corresponding to the high-frequency component in fig. 3), a dozen and several minute-order fluctuation component (corresponding to the medium-frequency component in fig. 3), and a small-order fluctuation component (corresponding to the low-frequency component in fig. 3) by the multi-scale decomposition algorithm of the WMMF filter, and a fluctuation component time-series curve in the small-order time scale, a fluctuation component time-series curve in the dozen and several minute-order time scale, and a fluctuation component time-series curve in the minute-order time scale are obtained according to the fluctuation components.
And respectively carrying out fluctuation identification on the minute-level fluctuation component, the dozen and odd minute-level fluctuation component and the hour-level fluctuation component, and respectively dividing the minute-level fluctuation component, the dozen and odd minute-level fluctuation component and the hour-level fluctuation component into an upward climbing section and a downward climbing section.
Specifically, the fluctuation component time sequence curves are subjected to fluctuation identification respectively, and the fluctuation component time sequence curves are divided into an upward-climbing section and a downward-climbing section.
And S30, calculating the flexibility insufficiency probability and flexibility average cost of the power system according to the fluctuation components.
Specifically, referring to fig. 4, the upward and downward flexibility requirements corresponding to each climbing section are obtained by counting the upward and downward climbing amplitudes of each climbing section.
And acquiring corresponding available flexible resources through the climbing time, the climbing operation point and the flexible resource output characteristics of each climbing section. Wherein, the resources which can adapt to the flexibility requirement through the output adjustment in the flexibility adjustment process can be all used as the flexibility resources. In the power system, the flexible resource can be a conventional unit with quick adjustment capability, a pumped storage power station, a power electronic energy storage device, a demand side response and a renewable energy unit with strong controllability. Each type of flexible resource has different output characteristics on different time scales.
Illustratively, taking a thermal power plant as an example, the flexible resource size provided by the output adjustment at the time scale of the minute scale and the dozen and odd minute scale is as follows:
FG，+＝min{PG，max-PG，0，Δt×rm，+}
FG，-＝min{PG，0-PG，min，Δt×rm，-}
in the formula, FG，+、FG，-Flexible resource sizes which are respectively adjusted upwards and downwards by the unit; pG,max、PG,minAnd PG,0Respectively the maximum running output, the minimum running output and the current output of the unit; r ism,+、rm,-The upward and downward climbing speeds of the unit are respectively; Δ t is the ramp-up time.
The output regulating capacity of the thermal power plant under the small-scale time scale is as follows:
FG,+＝PSG,max
FG，-＝PSG，0
in the formula, PSG,0、PSG,maxRespectively the current output and the maximum regulation capacity of the peak shaving unit.
Specifically, the power system starting mode is determined according to the daily net load output, and the flexibility resources under each time scale are determined by combining the running state and the output characteristic of each type of power supply. And respectively selecting flexible units (renewable energy units and the like) under corresponding scales to participate in flexibility adjustment for the fluctuation components under each time scale, so as to determine the size of the available flexible resources under each time scale.
And respectively comparing the available flexibility resources in each climbing section with the flexibility requirement to obtain the number of climbing sections with insufficient flexibility and the total number of climbing sections. Wherein, the climbing section that the available flexibility resource is lower than the flexibility demand is called the insufficient flexibility climbing section.
And calculating to obtain the flexibility shortage probability according to the flexibility shortage climbing section and the total climbing section number.
In particular, the amount of the solvent to be used,in the formula, N is the number of the climbing sections with insufficient flexibility, and N is the total number of the climbing sections of the power system.
And calculating the flexibility average cost according to the available flexibility resources in each climbing section and the flexibility requirement. And in each climbing section, the lowest total flexibility calling cost is realized by economically calling the flexibility resources. Thereby defining the average call cost over all ramp segments as the flexibility cost.
In particular, the amount of the solvent to be used,
wherein,average cost for flexibility; diThe flexibility requirement in the ith climbing section is met; fi,j、ΔFi,Respectively the adjustable quantity and the actual quantity of the j resource in the ith climbing section; cjThe cost is invoked for the flexibility unit of the jth resource.
And S40, evaluating the power system according to the flexibility shortage probability and the flexibility average cost and adjusting the power of the power system.
Specifically, the smaller the flexibility shortage probability E is, the greater the probability that the system meets the system flexibility requirement is; cost of flexibilitySmaller means that the system can meet system flexibility requirements at a smaller adjustment cost.
Compared with the prior art, the flexibility evaluation method of the power system disclosed by the embodiment of the invention decomposes the daily net load output data through the preset multi-scale decomposition algorithm of the WMMF filter, calculates the flexibility shortage probability and the flexibility average cost according to the decomposed daily net load output data, and evaluates the flexibility of the power system through the flexibility shortage probability and the flexibility average cost, so that the power of the power system is regulated, and the power system can safely and stably run.
For ease of understanding, the Hainan grid is used as an example:
the calling of various flexible resources is carried out under a certain time scale, and the system flexible resource output participating in regulation under each time scale is related to the output characteristic of the system flexible resource output. The thermal power generating unit participates in flexible adjustment under different time scales according to the characteristics of the thermal power generating unit, a gas generating unit with high adjusting capacity can participate in flexible adjustment of a minute-scale time scale, a part of gas generating units and a part of coal-fired units with low output power participate in flexible adjustment within a time scale of tens of minutes to tens of minutes, and in addition, a thermal power standby unit can participate in flexible adjustment above the hour-scale time scale; the hydroelectric generating set has higher adjusting speed and can be used as an important peak-shaving resource to participate in the flexible adjustment within the time scale from the system minute level to dozens of minute levels in the dry season; the pumped storage power station as a peak regulation power supply with excellent performance can participate in flexibility regulation under a minute-scale time scale, and the size of the provided flexibility resource is mainly influenced by the capacity of the pumped storage power station; the power electronic energy storage device can participate in flexibility adjustment within the time scale of the order of minutes to tens of minutes, and the flexibility resource provided by the power electronic energy storage device depends on a plurality of factors, including the release time, the efficiency, the storage capacity and the like of the energy storage battery; the interrupt load can be used as a main demand side response adjusting means to participate in the flexibility adjustment on the small-scale time scale; the renewable energy with strong controllability can be considered as a controllable unit with certain confidence capacity, and can participate in flexibility adjustment on the time scale of tens of minutes, and can also participate in flexibility adjustment on the time scale of hours by abandoning wind and light.
In practical application, after the flexibility indexes (namely, the flexibility insufficiency probability and the flexibility average cost) under each time scale are calculated, the flexibility indexes under each time scale can be weighted and summed according to the attention preference of an evaluator to each time scale, and the overall flexibility insufficiency probability index and the flexibility average cost index of the power system comprehensively considering each time scale are obtained. The output flexibility evaluation results are shown in the following table:
observing the above-mentioned not enough index of flexibility probability then can adjust electric power system, upwards not enough probability of flexibility is all less than the not enough probability of flexibility downwards on each time scale, this is because lack the regulation of water and electricity in the south of the sea electric wire netting, and most flexibility resources are provided by thermal power plant. The upward climbing rate of the thermal power generating unit is greater than the downward climbing rate, the thermal power generating unit is limited by the minimum stable operation power, and the downward regulating capacity of the thermal power generating unit is limited, so that the downward flexibility shortage probability is greater than the upward flexibility shortage probability in the flexibility shortage probability index. The average cost of upward flexibility on the order of minutes and tens to tens of minutes is higher than the average cost of downward flexibility, because the participation of the confidence capacity and the wind abandoning light abandoning capacity under the access of the wind power and photovoltaic power stations in downward flexibility adjustment is considered in the flexibility resources, and the adjustment cost is far lower than that of thermal power, so the average cost of downward adjustment of the flexibility of the system is reduced due to the participation of the wind power and the photovoltaic power stations. On the small-scale time scale, the average upward flexibility cost is slightly lower than that of the average downward flexibility cost, because the flexibility requirement of adjustment on the time scale is far greater than that on the short-scale time scale, the adjustment task is mainly borne by the spare power unit of the thermal power plant, and the addition of controllable load is considered on the upward flexibility resource, so that the average upward flexibility adjustment cost is reduced. The flexibility average cost of each time scale is compared transversely, the flexibility average cost of the small-scale time scale is far higher than the flexibility average cost of the other two time scales, and the fact that the start-stop peak shaving of the thermal power generating unit as a main flexibility resource of the small-scale time scale has higher adjusting cost is reflected. The flexibility of the power system is evaluated through the flexibility shortage probability and the flexibility average cost, so that the power system is adjusted, and the power system runs more stably and safely.
Fig. 5 is a schematic structural diagram of a flexibility evaluation apparatus of an electric power system according to an embodiment of the present invention.
The embodiment of the invention correspondingly provides a flexibility evaluation device of an electric power system, which comprises:
the data acquisition module 10 is configured to calculate, according to the historical load output of the power system and the output of the renewable energy, daily net load output data.
And the data decomposition module 20 is configured to decompose the intra-day net load output data according to a preset multi-scale decomposition algorithm of the WMMF filter to obtain a plurality of fluctuation components.
And the calculating module 30 is used for calculating the flexibility insufficiency probability and the flexibility average cost of the power system according to the fluctuation component.
And the evaluation module 40 is used for evaluating the power system and adjusting the power of the power system according to the flexibility shortage probability and the flexibility average cost.
As an improvement of the above scheme, the data decomposition module includes:
and the fluctuation component acquisition module is used for decomposing the daily net load output data into a minute-level fluctuation component, a dozen and odd minute-level fluctuation component and an hour-level fluctuation component.
And the fluctuation identification module is used for respectively carrying out fluctuation identification on the minute-level fluctuation component, the dozen and odd minute-level fluctuation component and the hour-level fluctuation component, and respectively dividing the minute-level fluctuation component, the dozen and odd minute-level fluctuation component and the hour-level fluctuation component into an upward slope climbing section and a downward slope climbing section.
As an improvement of the above solution, the calculation module includes:
and the flexible demand acquisition module is used for acquiring the flexible demand corresponding to the climbing section by counting the climbing amplitude of each climbing section.
And the available flexibility resource acquisition module is used for acquiring corresponding available flexibility resources according to the climbing time, the climbing operation point and the flexibility resource output characteristics of each climbing section.
And the statistical module is used for comparing the available flexibility resources with the flexibility requirements in each climbing section respectively to obtain the number of climbing sections with insufficient flexibility and the total number of climbing sections.
And the insufficient flexibility probability calculation module is used for calculating the insufficient flexibility probability according to the insufficient flexibility climbing section and the total climbing section number.
And the flexibility average cost calculation module is used for calculating the flexibility average cost according to the available flexibility resources in each climbing section and the flexibility requirement.
Compared with the prior art, the flexibility evaluation device for the electric power system disclosed by the embodiment of the invention decomposes the daily net load output data through the preset multi-scale decomposition algorithm of the WMMF filter, calculates the flexibility shortage probability and the flexibility average cost according to the decomposed daily net load output data, evaluates the flexibility of the electric power system through the flexibility shortage probability and the flexibility average cost, and accordingly adjusts the power of the electric power system, so that the electric power system can safely and stably operate.
Fig. 6 is a schematic diagram of a flexibility evaluation terminal device of an electric power system according to an embodiment of the present invention. The flexibility evaluation terminal device of the electric power system of the embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. The processor, when executing the computer program, implements the steps in the above-described embodiments of the flexibility assessment method for each power system. Alternatively, the processor implements the functions of the modules/units in the above device embodiments when executing the computer program.
Illustratively, the computer program may be partitioned into one or more modules/units that are stored in the memory and executed by the processor to implement the invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used for describing the execution process of the computer program in the flexibility evaluation terminal device of the power system.
The flexibility evaluation terminal device of the power system can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing devices. The flexibility evaluation terminal device of the power system can include, but is not limited to, a processor and a memory. It will be understood by those skilled in the art that the schematic diagram is merely an example of the flexibility evaluation terminal device of the power system, and does not constitute a limitation of the flexibility evaluation terminal device of the power system, and may include more or less components than those shown, or combine some components, or different components, for example, the flexibility evaluation terminal device of the power system may further include an input-output device, a network access device, a bus, etc.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, said processor being the control center of the flexibility assessment terminal device of the power system, connected to the various parts of the entire apparatus/terminal device by means of various interfaces and lines.
The memory may be used to store the computer programs and/or modules, and the processor may implement various functions of the flexibility evaluation terminal device of the power system by executing or executing the computer programs and/or modules stored in the memory and calling data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
Wherein, the flexibility evaluation terminal device integrated module/unit of the power system can be stored in a computer readable storage medium if it is implemented in the form of software functional unit and sold or used as an independent product. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like.
It should be noted that the above-described device embodiments are merely illustrative, where the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. In addition, in the drawings of the embodiment of the apparatus provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, and may be specifically implemented as one or more communication buses or signal lines. One of ordinary skill in the art can understand and implement it without inventive effort.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (9)
1. A flexibility assessment method for an electric power system is characterized by comprising the following steps:
calculating to obtain the daily net load output data according to the historical load output of the power system and the output of the renewable energy;
decomposing the daily net load output data according to a preset multi-scale decomposition algorithm of the WMMF filter to obtain a plurality of fluctuation components;
calculating the flexibility insufficiency probability and flexibility average cost of the power system according to the fluctuation component;
and evaluating the power system and adjusting the power of the power system according to the flexibility shortage probability and the flexibility average cost.
2. The method according to claim 1, wherein decomposing the intra-day net load output data according to a multi-scale decomposition algorithm of a preset WMMF filter to obtain a plurality of fluctuation components comprises:
decomposing the daily net load output data into a minute-level fluctuation component, a dozen and odd minute-level fluctuation component and an hour-level fluctuation component;
and respectively carrying out fluctuation identification on the minute-level fluctuation component, the dozen and odd minute-level fluctuation component and the hour-level fluctuation component, and respectively dividing the minute-level fluctuation component, the dozen and odd minute-level fluctuation component and the hour-level fluctuation component into an upward climbing section and a downward climbing section.
3. The method according to claim 2, wherein the calculating a flexibility shortage probability index and a flexibility average cost index of the power system according to the fluctuation component includes:
acquiring the requirement of flexibility corresponding to each climbing section by counting the climbing amplitude of each climbing section;
acquiring corresponding available flexible resources according to the climbing time, the climbing operation point and the flexible resource output characteristics of each climbing section;
respectively comparing the available flexibility resources in each climbing section with the flexibility requirement to obtain the number of climbing sections with insufficient flexibility and the total number of climbing sections;
calculating to obtain the flexibility shortage probability according to the flexibility shortage climbing section and the total climbing section number;
and calculating the flexibility average cost according to the available flexibility resources in each climbing section and the flexibility requirement.
4. The flexibility evaluation method for an electric power system according to claim 3, wherein the flexibility average cost is calculated according to the available flexibility resources in each uphill segment and the flexibility requirement, and specifically:
wherein,average cost for flexibility; diThe flexibility requirement in the ith climbing section is met; fi,j、ΔFi,jRespectively the adjustable quantity and the actual quantity of the j resource in the ith climbing section; cjThe cost is invoked for the flexibility unit of the jth resource.
5. An apparatus for assessing flexibility of an electric power system, comprising:
the data acquisition module is used for acquiring daily net load output data according to the historical load output of the power system and the output of the renewable energy;
the data decomposition module is used for decomposing the daily net load output data according to a preset multi-scale decomposition algorithm of the WMMF filter;
the calculation module is used for calculating the flexibility insufficiency probability and the flexibility average cost of the power system according to the decomposed daily net load output data;
and the evaluation module is used for evaluating the power system and adjusting the power of the power system according to the flexibility shortage probability and the flexibility average cost.
6. The flexibility evaluation apparatus of an electric power system according to claim 5, wherein the data decomposition module includes:
the fluctuation component acquisition module is used for decomposing the daily net load output data into a minute-level fluctuation component, a dozen or so minute-level fluctuation component and an hour-level fluctuation component;
and the fluctuation identification module is used for respectively carrying out fluctuation identification on the minute-level fluctuation component, the dozen and odd minute-level fluctuation component and the hour-level fluctuation component, and respectively dividing the minute-level fluctuation component, the dozen and odd minute-level fluctuation component and the hour-level fluctuation component into an upward slope climbing section and a downward slope climbing section.
7. The flexibility evaluation apparatus of an electric power system according to claim 6, wherein the calculation module includes:
the flexible demand acquisition module is used for acquiring the flexible demand corresponding to each climbing section by counting the climbing amplitude of each climbing section;
the available flexibility resource acquisition module is used for acquiring corresponding available flexibility resources according to the climbing time, the climbing operation point and the flexibility resource output characteristics of each climbing section;
the statistical module is used for respectively comparing the available flexibility resources in each climbing section with the flexibility requirement to obtain the number of climbing sections with insufficient flexibility and the total number of climbing sections;
the insufficient flexibility probability calculation module is used for calculating and obtaining the insufficient flexibility probability according to the insufficient flexibility climbing section and the total climbing section number;
and the flexibility average cost calculation module is used for calculating the flexibility average cost according to the available flexibility resources in each climbing section and the flexibility requirement.
8. An flexibility evaluation apparatus of an electric power system, characterized by comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the flexibility evaluation method of the electric power system according to any one of claims 1 to 4 when executing the computer program.
9. A computer-readable storage medium, comprising a stored computer program, wherein the computer program, when executed, controls an apparatus in which the computer-readable storage medium is located to perform the method for assessing flexibility of an electric power system according to any one of claims 1 to 4.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111340335A (en) * | 2020-02-13 | 2020-06-26 | 国网青海省电力公司经济技术研究院 | Method and system for evaluating flexibility supply capacity of thermal power generating unit |
| CN112491093A (en) * | 2020-11-16 | 2021-03-12 | 国网山东省电力公司电力科学研究院 | Method for quantizing flexible safety margin of wide-area controllable resources in day |
| CN112633642A (en) * | 2020-12-09 | 2021-04-09 | 中国南方电网有限责任公司 | Method, system, device and storage medium for predicting standby demand of power system |
| CN116911695A (en) * | 2023-09-12 | 2023-10-20 | 北京工业大学 | Flexible resource adequacy evaluation method and device |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104659798A (en) * | 2013-11-20 | 2015-05-27 | 沈阳工业大学 | Hybrid wind power energy storage system based on fuzzy neural network and optimization method of system |
| CN107965356A (en) * | 2017-12-25 | 2018-04-27 | 东北电力大学 | A kind of fired power generating unit flexibility lifting control method |
| CN108388968A (en) * | 2018-03-20 | 2018-08-10 | 云南电网有限责任公司玉溪供电局 | Generation schedule based on pre- bid deviation electric quantity balancing mechanism rolls method of adjustment |
| CN108462165A (en) * | 2018-01-19 | 2018-08-28 | 长沙理工大学 | A kind of part throttle characteristics appraisal procedure of new energy access electric system |
| CN109038675A (en) * | 2018-08-31 | 2018-12-18 | 中国南方电网有限责任公司电网技术研究中心 | Modeling method based on wind power fluctuation multi-scale decomposition |
| US20190041886A1 (en) * | 2007-05-09 | 2019-02-07 | GridPoint | Method and system for scheduling the discharge of distributed power storage devices and for levelizing dispatch participation |
| US10223751B1 (en) * | 2012-02-02 | 2019-03-05 | Progressive Casualty Insurance Company | Multivariate predictive system |
| CN109523060A (en) * | 2018-10-22 | 2019-03-26 | 上海交通大学 | Ratio optimization method of the high proportion renewable energy under transmission and distribution network collaboration access |
| CN109559012A (en) * | 2018-10-18 | 2019-04-02 | 国网山东省电力公司经济技术研究院 | Power network planning scheme integrated evaluating method under the conditions of a kind of wind-electricity integration |
-
2019
- 2019-06-18 CN CN201910524376.5A patent/CN110348695B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190041886A1 (en) * | 2007-05-09 | 2019-02-07 | GridPoint | Method and system for scheduling the discharge of distributed power storage devices and for levelizing dispatch participation |
| US10223751B1 (en) * | 2012-02-02 | 2019-03-05 | Progressive Casualty Insurance Company | Multivariate predictive system |
| CN104659798A (en) * | 2013-11-20 | 2015-05-27 | 沈阳工业大学 | Hybrid wind power energy storage system based on fuzzy neural network and optimization method of system |
| CN107965356A (en) * | 2017-12-25 | 2018-04-27 | 东北电力大学 | A kind of fired power generating unit flexibility lifting control method |
| CN108462165A (en) * | 2018-01-19 | 2018-08-28 | 长沙理工大学 | A kind of part throttle characteristics appraisal procedure of new energy access electric system |
| CN108388968A (en) * | 2018-03-20 | 2018-08-10 | 云南电网有限责任公司玉溪供电局 | Generation schedule based on pre- bid deviation electric quantity balancing mechanism rolls method of adjustment |
| CN109038675A (en) * | 2018-08-31 | 2018-12-18 | 中国南方电网有限责任公司电网技术研究中心 | Modeling method based on wind power fluctuation multi-scale decomposition |
| CN109559012A (en) * | 2018-10-18 | 2019-04-02 | 国网山东省电力公司经济技术研究院 | Power network planning scheme integrated evaluating method under the conditions of a kind of wind-electricity integration |
| CN109523060A (en) * | 2018-10-22 | 2019-03-26 | 上海交通大学 | Ratio optimization method of the high proportion renewable energy under transmission and distribution network collaboration access |
Non-Patent Citations (2)
| Title |
|---|
| 卢斯煜: "高比例光伏发电并网条件下中国远景电源结构探讨", 《中国电机工程学报》 * |
| 李海波: "大规模风电并网的电力系统运行灵活性评估", 《电网技术》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111340335A (en) * | 2020-02-13 | 2020-06-26 | 国网青海省电力公司经济技术研究院 | Method and system for evaluating flexibility supply capacity of thermal power generating unit |
| CN112491093A (en) * | 2020-11-16 | 2021-03-12 | 国网山东省电力公司电力科学研究院 | Method for quantizing flexible safety margin of wide-area controllable resources in day |
| CN112633642A (en) * | 2020-12-09 | 2021-04-09 | 中国南方电网有限责任公司 | Method, system, device and storage medium for predicting standby demand of power system |
| CN116911695A (en) * | 2023-09-12 | 2023-10-20 | 北京工业大学 | Flexible resource adequacy evaluation method and device |
| CN116911695B (en) * | 2023-09-12 | 2023-12-01 | 北京工业大学 | Flexible resource adequacy evaluation method and device |
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