Electric power resource adjusting method, device and equipment for coping with extreme weather

Abstract

The invention discloses an electric power resource adjusting method, device and equipment for coping with extreme weather, and the method comprises the steps: obtaining the duration time when the extreme weather occurs and the power supply adequacy parameter data corresponding to the duration time, thereby obtaining the time sequence of the power supply adequacy parameter; performing normalization processing to obtain a power supply adequacy parameter normalization value; calculating an influence coefficient of the extreme weather in the next time period on the power supply adequacy based on the normalized value and the power supply adequacy parameter data; calculating a predicted value of the extreme weather in the next time period on the power supply adequacy according to the influence coefficient of the extreme weather in the next time period on the power supply adequacy; and adjusting the resource configuration of the power system according to the predicted value of the power supply adequacy of the extreme weather in the next time period. The influence of the extreme weather condition on the adequacy of the power system can be predicted, the output condition of the thermal power generating unit is adjusted in time according to the adequacy index, the research is beneficial to guaranteeing the power supply after the extreme weather, and the stable operation of the power system is maintained.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular to a method, device and equipment for regulating power resources in response to extreme weather. Background Art

[0002] In the current power system, the proportion of renewable energy is constantly increasing, and the power system based on new energy will be more vulnerable to extreme weather. Common extreme weather includes lightning, typhoons, hurricanes, heavy rains, extreme high temperatures and extreme low temperatures, which will seriously affect the power supply security and power balance of the system. Although the probability of extreme weather is relatively low, the serious consequences it causes must be taken seriously. In recent years, due to the impact of climate change, the urgency of responding to climate change has increased, and the impact of extreme weather on the supply and demand situation has become increasingly prominent.

[0003] Weather conditions such as cold waves, high temperatures, and seasonal strong winds are sudden and complex, which have had a certain impact on power supply. The power system will face the dual challenges of ensuring power supply and "maintaining quantity and stabilizing rate" of new energy consumption. Summary of the invention

[0004] The present disclosure provides a method, device and equipment for regulating electric power resources in response to extreme weather, so as to at least solve the above technical problems existing in the prior art.

[0005] According to a first aspect of the present application, a method for regulating power resources in response to extreme weather is provided, the method comprising:

[0006] Obtaining the duration of extreme weather and power supply adequacy parameter data corresponding to the duration; the power supply adequacy parameter is used to characterize the impact of extreme weather on power supply adequacy;

[0007] Obtaining a time series of power supply adequacy parameters based on the power supply adequacy parameter data of the duration;

[0008] Normalizing the time series of the power supply margin parameter to obtain a normalized value of the power supply margin parameter;

[0009] Calculating the influence coefficient of extreme weather on power supply adequacy in the next period based on the normalized value and the power supply adequacy parameter data;

[0010] Calculate the predicted value of the power supply adequacy due to extreme weather in the next period according to the influence coefficient of extreme weather on power supply adequacy in the next period;

[0011] The resource allocation of the power system is adjusted according to the predicted value of power supply adequacy due to extreme weather in the next period.

[0012] In one possible implementation manner, the power supply margin parameter includes:

[0013] The average output power of wind turbines on the day when extreme weather occurs; the average output power of photovoltaic power on the day when extreme weather occurs; the total load of the affected area on the day when extreme weather occurs; the average output power of thermal power units on the day when extreme weather occurs; the adjustable energy storage capacity on the day when extreme weather occurs; the probability of extreme weather occurring in a year; the maximum and minimum output of wind turbines on the day when extreme weather occurs; the maximum and minimum output of photovoltaic power on the day when extreme weather occurs; the maximum and minimum load on the day when extreme weather occurs; the maximum and minimum output power of thermal power units on the day when extreme weather occurs.

[0014] In one possible implementation manner, obtaining the time series of the power supply margin parameter based on the power supply margin parameter data of the duration includes:

[0015] dividing the duration into a plurality of fixed time intervals;

[0016] Obtain power supply margin parameter data at multiple fixed time intervals.

[0017] In one possible implementation manner, the time series of the power supply margin parameter is:

[0018]

[0019] Among them, t1, t2, ..., t n ,...,t m is the duration of m extreme weather events, m is a natural number, m∈1,2,L,n∈{1,2,L,m}, and the time periods of m time intervals are t1,t2,...,t n ,...,t m ; is the test value of the average output power of the wind turbine during extreme weather events at m time intervals; is the test value of the average photovoltaic output power during extreme weather events at m time intervals; is the test value of the total load in the disaster-stricken area caused by extreme weather at m time intervals; is the test value of the average output power of thermal power units during extreme weather events at m time intervals; is the test value of load mutation caused by extreme weather at m time intervals; The test value of the adjustable energy storage capacity for extreme weather events at m time intervals.

[0020] In one possible implementation manner, the time series of the power supply margin parameter is normalized in the following manner:

[0021]

[0022] in, The duration of the nth time is t n The normalized value of the average wind power output on the day when the extreme weather occurs; The duration of the nth time is t n The average output power of wind turbines on the day when the extreme weather occurs; P nd,max and P nd,min The duration of the nth time is t n The maximum and minimum wind turbine output on the day of extreme weather; The duration of the nth time is t n Normalized value of average photovoltaic output on the day when extreme weather occurs; The duration of the nth time is t n The average photovoltaic output power on the day when the extreme weather occurs; P G,max and P G,min The duration of the nth time is t n The maximum and minimum photovoltaic output on the day of extreme weather; The duration of the nth time is t n Normalized value of load on the day when extreme weather occurs; The duration of the nth extreme weather event is t n The total load of the disaster area on the day when extreme weather occurs; L tf,max and L tf,min The duration of the nth time is t n The maximum and minimum load values ​​on the day when extreme weather occurs; The duration of the nth extreme weather event is t n The normalized value of the average output power of thermal power units on the day when extreme weather occurs; The duration of the nth time is t n The average output power of thermal power units on the day when the extreme weather occurs; gn,max and F gn,min The duration of the nth time is t n The maximum and minimum output power of thermal power units on the day when extreme weather occurs; The duration of the nth extreme weather event is t n The normalized value of the load mutation on the day when extreme weather occurs, The duration of the nth time is t n The average load mutation on the day of extreme weather; The duration of the nth extreme weather event is t n Normalized value of adjustable energy storage capacity on the day when extreme weather occurs; The duration of the nth extreme weather event is t n Average adjustable energy storage capacity on the day when extreme weather occurs; Δ L,max and ΔL,min is the maximum and minimum value of the load mutation, C rs,max and C rs,min It is the maximum and minimum value of adjustable energy storage capacity in a certain area.

[0023] In one possible implementation, the influence coefficient of extreme weather on power supply adequacy in the next period is calculated in the following manner:

[0024]

[0025] Among them, O iw is the impact coefficient of extreme weather on power supply adequacy in the next period; δ is the probability of extreme weather occurring in a year.

[0026] In one possible implementation, the following method is used to calculate the predicted value of power supply adequacy due to extreme weather in the next period:

[0027]

[0028] Among them, W sy It is the predicted value of the power supply adequacy due to extreme weather in the next period.

[0029] In one possible implementation manner, adjusting the resource configuration of the power system according to the predicted value of power supply adequacy due to extreme weather in the next period includes:

[0030] Comparing the predicted value of power supply adequacy due to extreme weather in the next period with a preset threshold;

[0031] If the predicted value of the power supply margin due to extreme weather in the next period is less than the preset threshold, the output of the thermal power units in the current power system is increased or the thermal power units that were originally stopped are started; otherwise, the thermal power units continue to output stably or stop.

[0032] According to a second aspect of the present application, there is provided a power resource regulation device for coping with extreme weather, the device comprising:

[0033] An acquisition module is used to acquire the duration of extreme weather and power supply adequacy parameter data corresponding to the duration; the power supply adequacy parameter is used to characterize the impact of extreme weather on power supply adequacy;

[0034] A sequence module, used for obtaining a time series of power supply margin parameters based on power supply margin parameter data of duration;

[0035] A processing module, used for normalizing the time series of the power supply margin parameter to obtain a normalized value of the power supply margin parameter;

[0036] A first calculation module is used to calculate the influence coefficient of extreme weather on power supply margin in the next period based on the normalized value and the power supply margin parameter data;

[0037] A second calculation module is used to calculate the predicted value of the power supply adequacy due to extreme weather in the next period according to the influence coefficient of extreme weather on the power supply adequacy in the next period;

[0038] The adjustment module is used to adjust the resource configuration of the power system according to the predicted value of the power supply margin due to extreme weather in the next period.

[0039] According to a third aspect of the present application, an electronic device is provided, including:

[0040] at least one processor; and

[0041] a memory communicatively connected to the at least one processor; wherein,

[0042] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in the present application.

[0043] According to a fourth aspect of the present application, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute the method described in the present application.

[0044] According to a fifth aspect of the present application, a computer program product is provided, comprising a computer program or instructions, which implement the method described in the present application when executed by a processor.

[0045] Utilizing the technical solution of the present application, the technical solution provided by the present application can be quantified into a "sufficiency index", which represents the power supply sufficiency of the power system in extreme weather conditions within a certain period of time. The staff can adjust the thermal power units according to the power supply sufficiency index to ensure the power supply of the power system.

[0046] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] By reading the detailed description below with reference to the accompanying drawings, the above and other purposes, features and advantages of the exemplary embodiments of the present application will become easily understood. In the accompanying drawings, several embodiments of the present application are shown in an exemplary and non-limiting manner, wherein:

[0048] In the drawings, the same or corresponding reference numerals represent the same or corresponding parts.

[0049] Figure 1 A flow chart of a method for regulating power resources in response to extreme weather in an embodiment is shown;

[0050] Figure 2 A schematic diagram of the structure of a power resource adjustment device for dealing with extreme weather in an embodiment is shown;

[0051] Figure 3 A schematic diagram of the structure of an electronic device in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0052] In order to make the purpose, features, and advantages of the present application more obvious and easy to understand, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0053] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0054] In the following description, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it can be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0055] In the following description, the terms "first\second" involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first\second" can be interchanged with a specific order or sequence where permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0056] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0057] It should be understood that in the various embodiments of the present application, the size of the serial number of each implementation process does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0058] The following describes a method, device and equipment for regulating power resources in response to extreme weather provided by the present application in conjunction with the accompanying drawings.

[0059] like Figure 1 As shown, the present application provides a method for regulating power resources in response to extreme weather, the method comprising:

[0060] S101, obtaining the duration of extreme weather and power supply adequacy parameter data corresponding to the duration; the power supply adequacy parameter is used to characterize the impact of extreme weather on power supply adequacy;

[0061] It is understandable that extreme weather including cold waves, high temperatures, seasonal strong winds and other weather conditions are sudden and complex, which has had a certain impact on power supply security.

[0062] In some embodiments, the power supply adequacy parameter includes:

[0063] The average output power of wind turbines on the day when extreme weather occurs; the average output power of photovoltaic power on the day when extreme weather occurs; the total load of the affected area on the day when extreme weather occurs; the average output power of thermal power units on the day when extreme weather occurs; the adjustable energy storage capacity on the day when extreme weather occurs; the probability of extreme weather occurring in a year; the maximum and minimum output of wind turbines on the day when extreme weather occurs; the maximum and minimum output of photovoltaic power on the day when extreme weather occurs; the maximum and minimum load on the day when extreme weather occurs; the maximum and minimum output power of thermal power units on the day when extreme weather occurs.

[0064] In addition, to measure the probability of extreme weather, a certain area is selected, and based on its meteorological data in the past five years, the number of extreme weather events such as tornadoes and severe cold in the meteorological data is selected to calculate the probability of extreme weather occurrence in a year δ.

[0065] S102, obtaining a time series of power supply adequacy parameters based on the power supply adequacy parameter data of the duration;

[0066] In some embodiments, obtaining the time series of the power supply margin parameter based on the power supply margin parameter data of the duration includes:

[0067] dividing the duration into a plurality of fixed time intervals;

[0068] Obtain power supply margin parameter data at multiple fixed time intervals.

[0069] For example, t1, t2, ... t n ,...,t m is the duration of m extreme weather events, t n is the duration of the nth extreme weather event, where m and n are natural numbers, m∈1,2,L, and n∈{1,2,L,m}.

[0070] In some embodiments, the time series of the power supply margin parameter is

[0071]

[0072] Among them, t1, t2, ..., t n ,...,t m is the duration of m extreme weather events, m is a natural number, m∈1,2,L,n∈{1,2,L,m}, and the time periods of m time intervals are t1,t2,...,t n ,...,t m ; is the test value of the average output power of the wind turbine during extreme weather events at m time intervals; is the test value of the average photovoltaic output power during extreme weather events at m time intervals; is the test value of the total load in the disaster-stricken area caused by extreme weather at m time intervals; is the test value of the average output power of thermal power units during extreme weather events at m time intervals; is the test value of load mutation caused by extreme weather at m time intervals; The test value of the adjustable energy storage capacity for extreme weather events at m time intervals.

[0073] S103, normalizing the time series of the power supply margin parameter to obtain a normalized value of the power supply margin parameter;

[0074] The time series of the power supply margin parameter is normalized in the following manner:

[0075]

[0076] in, The duration of the nth time is t n The normalized value of the average wind power output on the day when the extreme weather occurs; The duration of the nth time is t n The average output power of wind turbines on the day when the extreme weather occurs; P nd,max and P nd,min The duration of the nth time is t n The maximum and minimum wind turbine output on the day of extreme weather; The duration of the nth time is t n Normalized value of average photovoltaic output on the day when extreme weather occurs; The duration of the nth time is t n The average photovoltaic output power on the day when the extreme weather occurs; P G,max and P G,min The duration of the nth time is t n The maximum and minimum photovoltaic output on the day of extreme weather; The duration of the nth time is t n Normalized value of load on the day when extreme weather occurs; The duration of the nth extreme weather event is t n The total load of the disaster area on the day when extreme weather occurs; L tf,max and L tf,min The duration of the nth time is t n The maximum and minimum load values ​​on the day when extreme weather occurs; The duration of the nth extreme weather event is t n The normalized value of the average output power of thermal power units on the day when extreme weather occurs; The duration of the nth time is t n The average output power of thermal power units on the day when the extreme weather occurs; gn,max and F gn,min The duration of the nth time is t n The maximum and minimum output power of thermal power units on the day when extreme weather occurs; The duration of the nth extreme weather event is t n Normalized value of load mutation on the day when extreme weather occurs; The duration of the nth time is t n The load mutation on the day of extreme weather; The duration of the nth extreme weather event is t n Normalized value of adjustable energy storage capacity on the day when extreme weather occurs; The duration of the nth time is t n The energy storage capacity can be adjusted on the day of extreme weather; Δ L,max and Δ L,min is the maximum and minimum value of the load mutation, C rs,max and C rs,min It is the maximum and minimum value of adjustable energy storage capacity in a certain area.

[0077] S104, calculating the influence coefficient of extreme weather on power supply adequacy in the next period based on the normalized value and the power supply adequacy parameter data;

[0078] In some embodiments, the influence coefficient of extreme weather on power supply adequacy in the next period is calculated in the following manner:

[0079]

[0080] Among them, O iw is the impact coefficient of extreme weather on power supply adequacy in the next period; δ is the probability of extreme weather occurring in a year.

[0081] S105, calculating a predicted value of the power supply adequacy due to extreme weather in the next period of time according to the influence coefficient of the extreme weather in the next period of time on the power supply adequacy;

[0082] The following method is used to calculate the predicted value of power supply adequacy due to extreme weather in the next period:

[0083]

[0084] Deformation obtained,

[0085] Among them, W sy It is the predicted value of the power supply adequacy due to extreme weather in the next period.

[0086] S106, adjusting the resource allocation of the power system according to the predicted value of the power supply adequacy due to extreme weather in the next period.

[0087] In some embodiments, adjusting the resource configuration of the power system according to the predicted value of power supply adequacy due to extreme weather in the next period includes:

[0088] Comparing the predicted value of power supply adequacy due to extreme weather in the next period with a preset threshold;

[0089] If the predicted value of the power supply margin due to extreme weather in the next period is less than the preset threshold, the output of the thermal power units in the current power system is increased or the thermal power units that were originally stopped are started; otherwise, the thermal power units continue to output stably or stop.

[0090] In this application, resource allocation is adjusted according to the predicted value of the power supply margin of the power system in extreme weather in the next period to meet the restoration of power balance and smooth operation of the power system after extreme weather.

[0091] The present application provides a method for regulating electric power resources in response to extreme weather, which predicts the extreme weather power supply adequacy of the electric power system during the next extreme weather based on historical data, and adjusts the resource allocation before the next extreme weather occurs according to the prediction results, thereby reducing the impact of extreme weather on the power supply of the electric power system, and helping to quickly restore power supply after extreme weather occurs, thereby ensuring the power balance of the electric power system.

[0092] As a specific embodiment, the probability of extreme weather is measured, a certain area is selected, and according to its meteorological data in the past five years, the probability of extreme weather occurrence δ is calculated according to the number of extreme weather such as tornadoes and severe cold in the meteorological data, and other related parameters of the extreme weather power supply adequacy index of the power system are measured, and the time series of the influencing parameters of the extreme weather power supply adequacy index of the power system is established according to the obtained measured values, where

[0093] In a specific embodiment, the fixed time interval in the measurement process is set to 5 hours, and 5 measurement time periods are selected, namely t1, t2, t3, t4, and t5 are 5 fixed time intervals; the load mutation measurement value is measured in the fixed time intervals t1, t2, t3, t4, and t5. The average wind power generation is measured in fixed time intervals t1, t2, t3, t4, t5. The output power of the photovoltaic power plant is measured at fixed time intervals t1, t2, t3, t4, t5. The total load is measured at fixed time intervals t1, t2, t3, t4, t5 The measured value of the average output power of the thermal power unit is measured in the fixed time interval time period t1, t2, t3, t4, t5 The measured values ​​of the adjustable energy storage capacity are measured in the time periods t1, t2, t3, t4, t5 at fixed time intervals According to all the above measurements, the time series of parameters related to the extreme weather adequacy index of the power system is established as follows:

[0094]

[0095] in, It is the load mutation amount when extreme weather occurs.

[0096] The measured data of the parameters related to the extreme weather power supply adequacy index of the power system are normalized.

[0097]

[0098] in, is the normalized value of wind turbine output when extreme weather occurs in time period t6; is the normalized value of the PV output under extreme weather conditions during time period t6; is the normalized value of the load, is the normalized value of the average output power of the thermal power unit in the t6 period, is the normalized value of the extreme weather load mutation during the t6 period, is the normalized value of the adjustable energy storage capacity during extreme weather in time period t6, Δ L,max and Δ L,min is the maximum and minimum value of the load mutation, C rs,max and C rs,min It is the maximum and minimum value of adjustable energy storage capacity in a certain area.

[0099] According to the historical data of the parameters related to the extreme weather power supply adequacy index of the power system, the influence coefficient of the extreme weather of the power system on the power supply adequacy index in the next period is calculated.

[0100]

[0101] Among them, O iw It is the impact coefficient of extreme weather in the power system on the power supply adequacy index in the next period.

[0102] The power supply adequacy index prediction value of the power system extreme weather is calculated by using the power supply adequacy index prediction value of the time period t6, so as to realize the prediction of the power supply adequacy index of the power system extreme weather;

[0103] Calculate t n+1 The predicted value of the power system extreme weather power supply adequacy index,

[0104]

[0105] Finally, the resource allocation is adjusted according to the predicted value of the extreme weather power supply adequacy index of the power system in the next period to meet the restoration of power balance and smooth operation of the power system after extreme weather.

[0106] In this embodiment, the predicted value of the extreme weather power supply adequacy index of the power system in the time period t6 is calculated as follows:

[0107]

[0108] If the predicted value of the power system's extreme weather power supply margin is greater than or equal to 0.5, it is considered that the power system has a large power supply margin in the next time period, that is, the new energy site is less affected by extreme weather, and the thermal power units can continue to output stably or stop at this time; if the predicted value of the power system's extreme weather power supply margin index is less than 0.5, it is considered that the power system has a small power supply margin in the next time period, and the new energy site is greatly affected by extreme weather. At this time, the output of the current power system's thermal power units can be increased or the thermal power units that were originally stopped can be started.

[0109] like Figure 2 As shown, the present application provides a power resource regulation device for dealing with extreme weather, the device comprising:

[0110] An acquisition module 201 is used to acquire the duration of extreme weather and power supply adequacy parameter data corresponding to the duration; the power supply adequacy parameter is used to characterize the impact of extreme weather on power supply adequacy;

[0111] A sequence module 202, configured to obtain a time series of power supply margin parameters based on power supply margin parameter data of duration;

[0112] The processing module 203 is used to perform normalization processing on the time series of the power supply margin parameter to obtain a normalized value of the power supply margin parameter;

[0113] A first calculation module 204 is used to calculate the influence coefficient of extreme weather on power supply adequacy in the next period based on the normalized value and the power supply adequacy parameter data;

[0114] The second calculation module 205 is used to calculate the predicted value of the power supply adequacy due to the extreme weather in the next period according to the influence coefficient of the extreme weather in the next period on the power supply adequacy;

[0115] The adjustment module 206 is used to adjust the resource configuration of the power system according to the predicted value of the power supply margin due to extreme weather in the next period.

[0116] The present application provides an electric power resource regulation device for coping with extreme weather, wherein an acquisition module 201 acquires the duration of extreme weather and power supply margin parameter data corresponding to the duration; the power supply margin parameter is used to characterize the effect of extreme weather on power supply margin; a sequence module 202 obtains a time series of power supply margin parameters based on the power supply margin parameter data of the duration; a processing module 203 normalizes the time series of the power supply margin parameters to obtain a normalized value of the power supply margin parameters; a first calculation module 204 calculates an influence coefficient of extreme weather on power supply margin in the next period based on the normalized value and the power supply margin parameter data; a second calculation module 205 calculates a predicted value of extreme weather on power supply margin in the next period based on the influence coefficient of extreme weather on power supply margin in the next period; and an adjustment module 206 adjusts the resource configuration of the electric power system based on the predicted value of extreme weather on power supply margin in the next period.

[0117] According to an embodiment of the present application, the present application also provides an electronic device and a readable storage medium.

[0118] The electronic device includes at least one processor; and a memory in communication with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the power resource adjustment method for dealing with extreme weather described in the present application. The computer instructions are used to enable the computer to execute the power resource adjustment method for dealing with extreme weather described in the present application.

[0119] The present application also provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the method of regulating electric power resources in response to extreme weather of the present application.

[0120] Figure 3 A schematic block diagram of an example electronic device 300 that can be used to implement an embodiment of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0121] like Figure 3 As shown, the device 300 includes a computing unit 301, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 302 or a computer program loaded from a storage unit 308 into a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the device 300 can also be stored. The computing unit 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0122] A number of components in the device 300 are connected to the I / O interface 305, including: an input unit 306, such as a keyboard, a mouse, etc.; an output unit 307, such as various types of displays, speakers, etc.; a storage unit 308, such as a disk, an optical disk, etc.; and a communication unit 309, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 309 allows the device 300 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0123] The computing unit 301 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 301 performs the various methods and processes described above, such as the power resource adjustment method for extreme weather. For example, in some embodiments, the power resource adjustment method for extreme weather may be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit 308. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 300 via the ROM 302 and / or the communication unit 309. When the computer program is loaded into the RAM 303 and executed by the computing unit 301, one or more steps of the power resource adjustment method for extreme weather described above may be performed. Alternatively, in other embodiments, the computing unit 301 may be configured to execute the power resource adjustment method for coping with extreme weather in any other appropriate manner (eg, by means of firmware).

[0124] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0125] The program code for implementing the method of the present application can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, implements the functions / operations specified in the flow chart and / or block diagram. The program code can be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0126] In the context of the present application, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0127] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0128] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0129] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0130] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for regulating power resources in response to extreme weather, characterized in that: The method comprises: Obtaining the duration of extreme weather and power supply adequacy parameter data corresponding to the duration; the power supply adequacy parameter is used to characterize the impact of extreme weather on power supply adequacy; Obtaining a time series of power supply adequacy parameters based on the power supply adequacy parameter data of the duration; Normalizing the time series of the power supply margin parameter to obtain a normalized value of the power supply margin parameter; Calculating the influence coefficient of extreme weather on power supply adequacy in the next period based on the normalized value and the power supply adequacy parameter data; Calculate the predicted value of the power supply adequacy due to extreme weather in the next period according to the influence coefficient of extreme weather on power supply adequacy in the next period; The resource allocation of the power system is adjusted according to the predicted value of power supply adequacy due to extreme weather in the next period.

2. The method according to claim 1, characterized in that The power supply margin parameters include: The average output power of wind turbines on the day when extreme weather occurs; the average output power of photovoltaic power on the day when extreme weather occurs; the total load of the affected area on the day when extreme weather occurs; the average output power of thermal power units on the day when extreme weather occurs; the adjustable energy storage capacity on the day when extreme weather occurs; the probability of extreme weather occurring in a year; the maximum and minimum output of wind turbines on the day when extreme weather occurs; the maximum and minimum output of photovoltaic power on the day when extreme weather occurs; the maximum and minimum load on the day when extreme weather occurs; the maximum and minimum output power of thermal power units on the day when extreme weather occurs.

3. The method according to claim 1, characterized in that The time series of power supply margin parameters is obtained based on the power supply margin parameter data of the duration, including: dividing the duration into a plurality of fixed time intervals; Obtain power supply margin parameter data at multiple fixed time intervals.

4. The method according to claim 1, characterized in that: The time series of the power supply margin parameter is: Among them, t1, t2, ..., t n ,...,t m is the duration of m extreme weather events, m is a natural number, m∈1,2,L,n∈{1,2,L,m}, and the time periods of m time intervals are t1,t2,...,t n ,...,t m ; is the test value of the average output power of the wind turbine during extreme weather events at m time intervals; is the test value of the average photovoltaic output power during extreme weather events at m time intervals; is the test value of the total load in the disaster-stricken area caused by extreme weather at m time intervals; is the test value of the average output power of thermal power units during extreme weather events at m time intervals; is the test value of load mutation caused by extreme weather at m time intervals; The test value of the adjustable energy storage capacity for extreme weather events at m time intervals.

5. The method according to claim 1, characterized in that The time series of the power supply margin parameter is normalized in the following manner: in, The duration of the nth time is t n The normalized value of the average wind power output on the day when the extreme weather occurs; The duration of the nth time is t n The average output power of wind turbines on the day when the extreme weather occurs; P nd,max and P nd,min The duration of the nth time is t n The maximum and minimum wind turbine output on the day of extreme weather; The duration of the nth time is t n Normalized value of average photovoltaic output on the day when extreme weather occurs; The duration of the nth time is t n The average photovoltaic output power on the day when the extreme weather occurs; P G,max and P G,min The duration of the nth time is t n The maximum and minimum photovoltaic output on the day of extreme weather; The duration of the nth time is t n Normalized value of load on the day when extreme weather occurs; The duration of the nth extreme weather event is t n The total load of the disaster area on the day when extreme weather occurs; L tf,max and L tf,min The duration of the nth time is t n The maximum and minimum load values ​​on the day when extreme weather occurs; The duration of the nth extreme weather event is t n The normalized value of the average output power of thermal power units on the day when extreme weather occurs; The duration of the nth time is t n The average output power of thermal power units on the day when the extreme weather occurs; gn,max and F gn,min The duration of the nth time is t n The maximum and minimum output power of thermal power units on the day when extreme weather occurs; The duration of the nth extreme weather event is t n Normalized value of load mutation on the day when extreme weather occurs; The duration of the nth time is t n The load mutation on the day of extreme weather; The duration of the nth extreme weather event is t n Normalized value of adjustable energy storage capacity on the day when extreme weather occurs; The duration of the nth time is t n The energy storage capacity can be adjusted on the day of extreme weather; Δ L,max and Δ L,min is the maximum and minimum value of the load mutation, C rs,max and C rs,min It is the maximum and minimum value of adjustable energy storage capacity in a certain area.

6. The method according to claim 1, characterized in that The influence coefficient of extreme weather on power supply adequacy in the next period is calculated in the following way: Among them, O iw is the impact coefficient of extreme weather on power supply adequacy in the next period; δ is the probability of extreme weather occurring in a year.

7. The method according to claim 1, characterized in that The following method is used to calculate the predicted value of power supply adequacy due to extreme weather in the next period: Among them, W sy It is the predicted value of the power supply adequacy due to extreme weather in the next period.

8. The method according to claim 1, characterized in that The adjusting the resource allocation of the power system according to the predicted value of power supply adequacy due to extreme weather in the next period includes: Comparing the predicted value of power supply adequacy due to extreme weather in the next period with a preset threshold; If the predicted value of the power supply margin due to extreme weather in the next period is less than the preset threshold, the output of the thermal power units in the current power system is increased or the thermal power units that were originally stopped are started; otherwise, the thermal power units continue to output stably or stop.

9. A power resource regulation device for dealing with extreme weather, characterized in that: The device comprises: An acquisition module is used to acquire the duration of extreme weather and power supply adequacy parameter data corresponding to the duration; the power supply adequacy parameter is used to characterize the impact of extreme weather on power supply adequacy; A sequence module, used for obtaining a time series of power supply margin parameters based on power supply margin parameter data of duration; A processing module, used for normalizing the time series of the power supply margin parameter to obtain a normalized value of the power supply margin parameter; A first calculation module, used for calculating the influence coefficient of extreme weather on power supply adequacy in the next period based on the normalized value and the power supply adequacy parameter data; A second calculation module is used to calculate the predicted value of the power supply adequacy due to extreme weather in the next period according to the influence coefficient of extreme weather on the power supply adequacy in the next period; The adjustment module is used to adjust the resource configuration of the power system according to the predicted value of the power supply margin due to extreme weather in the next period.

10. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 8.

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