Power dispatching optimization method and device based on energy peak shifting complementation, terminal equipment and computer readable storage medium

Abstract

The invention discloses a power dispatching optimization method and device based on energy peak shifting complementation, terminal equipment and a computer readable storage medium. The method comprises the steps of obtaining clean energy power, traditional energy power and load power of a power system; determining a daily scheduling power value at the current moment and a monthly scheduling parameter of the current month according to the obtained power value; comparing the daily scheduling power value at the current moment with different preset thresholds, and if the daily scheduling power value is greater than a first preset threshold, storing electric energy according to the value of the monthly scheduling parameter; if the daily dispatching power value is smaller than a second preset threshold value and larger than or equal to a third preset threshold value, electric energy is output according to the value of the monthly dispatching parameter and a first preset influence factor; and if the daily scheduling power value is smaller than a third preset threshold value, outputting electric energy according to the value of the monthly scheduling parameter and a second preset influence factor. According to the invention, the utilization efficiency of energy and the operation efficiency of the virtual power plant can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of virtual power plants, and in particular to a method, device, terminal equipment and computer-readable storage medium for optimizing power dispatching based on energy peak shifting and complementation. Background Art

[0002] A virtual power plant is a coordinated management system that uses advanced information and communication technologies and software systems to aggregate, coordinate and optimize the management of various distributed energy resources such as distributed power sources, energy storage systems, controllable loads, microgrids and electric vehicles, so as to participate in the power market and power grid operation as a special power plant. Through technologies such as the Internet of Things and cloud computing, virtual power plants can adjust power in real time and respond quickly to market demand. They use 5G networks to accurately collect power generation information from distributed power sources and the operating conditions of various energy storage devices in order to accurately distribute energy and provide effective decision-making basis for power market transactions. The advantages of virtual power plants include improving energy utilization efficiency, reducing energy costs, enhancing the stability of energy supply, promoting the development of clean energy, and bringing economic benefits. It is of great significance in promoting the balance of supply and demand in the power grid, realizing the low-cost grid connection of distributed energy, and fully absorbing clean energy.

[0003] However, due to the volatility and intermittency of renewable energy, the lack of complementarity with other energy resources may result in virtual power plants being unable to fully utilize renewable energy. For example, when renewable energy supply is sufficient, if other energy resources (such as coal-fired power generation) are not properly or flexibly dispatched, virtual power plants may not be able to maximize the use of renewable energy, thereby limiting the energy utilization efficiency of virtual power plants. Summary of the invention

[0004] The embodiments of the present invention provide a method, apparatus, terminal device and computer-readable storage medium for optimizing power dispatching based on energy peak shifting and complementation, which can improve the operation efficiency and energy utilization efficiency of virtual power plants.

[0005] An embodiment of the present invention provides a method for optimizing power dispatch based on energy peak shifting and complementation, comprising:

[0006] Obtain clean energy power, traditional energy power and load power of the power system;

[0007] According to the clean energy power, traditional energy power and load power, determine the daily dispatch power value at the current moment and the monthly dispatch parameters for the current month;

[0008] Compare the current daily dispatch power value with different preset thresholds, and if the daily dispatch power value is greater than the first preset threshold, store electric energy according to the value of the monthly dispatch parameter;

[0009] If the daily dispatch power value is less than the second preset threshold value, and the daily dispatch power value is greater than or equal to the third preset threshold value, output electric energy according to the value of the monthly dispatch parameter and the first preset influencing factor;

[0010] If the daily dispatch power value is less than the third preset threshold, electric energy is output according to the value of the monthly dispatch parameter and the second preset influencing factor.

[0011] Furthermore, according to the clean energy power, traditional energy power and load power, the daily dispatching power value at the current moment and the monthly dispatching parameters of the current month are determined, including:

[0012] Subtract the clean energy power at the current moment from the load power at the current moment to obtain the net load value;

[0013] Subtract the net load value from the current traditional energy power to get the daily dispatch power value;

[0014] For the daily dispatch power value of the same day, the daily dispatch curve is obtained according to the daily dispatch power value at each moment;

[0015] According to the net area of ​​the daily dispatch curve, the daily dispatch parameters are obtained;

[0016] For the daily scheduling parameters of the same month, the daily scheduling parameters are averaged to obtain the monthly scheduling parameters.

[0017] Furthermore, the power dispatch optimization method based on energy peak shifting and complementation also includes:

[0018] For the net load value of the same day, the net load daily curve is obtained according to the net load value at each moment;

[0019] According to the area of ​​the net load daily curve, the daily net load parameter is obtained;

[0020] Count the number of warnings in each month when the daily dispatch power value is less than the second preset threshold and greater than or equal to the third preset threshold;

[0021] Determine the gaming parameters according to the daily net load parameter, the number of warnings, and the third preset influencing factor;

[0022] Determine the price adjustment parameter according to the game parameter and the fourth preset influencing factor;

[0023] The game parameter is compared with the fourth preset threshold. If the game parameter is greater than or equal to the fourth preset threshold, the selling price of the virtual power plant is increased according to the price adjustment parameter. If the game parameter is less than the fourth preset threshold, the selling price of the virtual power plant is reduced according to the price adjustment parameter.

[0024] Further, according to the daily net load parameter, the number of warnings, and the third preset influencing factor, the game parameters are determined, including:

[0025] The daily net load parameter, the number of warnings and the third preset impact factor are multiplied to obtain the game parameter.

[0026] Furthermore, according to the game parameters and the fourth preset influencing factor, the price adjustment parameters are determined, including:

[0027] The price adjustment parameter is obtained by square-raising the product of the game parameter and the fourth preset influencing factor.

[0028] Based on the above method embodiment, the present invention provides a corresponding device embodiment, including: a power system power acquisition module, a comprehensive analysis module, and an execution module;

[0029] The power system power acquisition module is used to obtain the clean energy power, traditional energy power and load power of the power system;

[0030] Comprehensive analysis module, used to determine the current daily dispatch power value and the monthly dispatch parameters according to clean energy power, traditional energy power and load power;

[0031] An execution module, comprising: an energy storage submodule, a first electric energy scheduling submodule, and a second electric energy scheduling submodule;

[0032] The energy storage submodule is used to compare the daily dispatch power value at the current moment with different preset thresholds, and if the daily dispatch power value is greater than the first preset threshold, store electric energy according to the value of the monthly dispatch parameter;

[0033] A first electric energy scheduling submodule, configured to output electric energy according to the value of the monthly scheduling parameter and the first preset influencing factor if the daily scheduling power value is less than the second preset threshold value and the daily scheduling power value is greater than or equal to the third preset threshold value;

[0034] The second electric energy scheduling submodule is used to output electric energy according to the value of the monthly scheduling parameter and the second preset influencing factor if the daily scheduling power value is less than the third preset threshold value.

[0035] Further, the comprehensive analysis module includes: a net load calculation submodule, a daily dispatch power calculation submodule, a daily dispatch curve generation submodule, a daily dispatch parameter calculation submodule, and a monthly dispatch parameter calculation submodule;

[0036] The net load calculation submodule is used to obtain the net load value by subtracting the clean energy power at the current moment from the load power at the current moment;

[0037] The daily dispatch power calculation submodule is used to subtract the net load value from the current traditional energy power to obtain the daily dispatch power value;

[0038] A daily dispatch curve generation submodule is used to obtain a daily dispatch curve for the daily dispatch power value of the same day according to the daily dispatch power value at each moment;

[0039] The daily dispatch parameter calculation submodule is used to obtain the daily dispatch parameters according to the net area of ​​the daily dispatch curve;

[0040] The monthly scheduling parameter calculation submodule is used to calculate the average value of the daily scheduling parameters of the same month to obtain the monthly scheduling parameters.

[0041] Furthermore, the power dispatch optimization device based on energy peak shifting and complementation also includes: an electricity price game module;

[0042] The electricity price game module includes: a net load daily curve generation submodule, a daily net load parameter calculation submodule, a warning number statistics submodule, a game parameter calculation submodule, a price adjustment parameter calculation submodule, and an electricity price adjustment submodule;

[0043] The net load daily curve generation submodule is used to obtain the net load daily curve according to the net load value at each moment for the net load value of the same day;

[0044] The daily net load parameter calculation submodule is used to obtain the daily net load parameter according to the area of ​​the net load daily curve;

[0045] A warning number statistics submodule, used to count the number of warnings for which the daily dispatch power value of each month is less than the second preset threshold and greater than or equal to the third preset threshold;

[0046] A game parameter calculation submodule, used to determine the game parameter according to the daily net load parameter, the number of warnings, and the third preset influencing factor;

[0047] A price adjustment parameter calculation submodule, used to determine the price adjustment parameter according to the game parameter and the fourth preset influencing factor;

[0048] The electricity price adjustment submodule is used to compare the game parameter with the fourth preset threshold. If the game parameter is greater than or equal to the fourth preset threshold, the electricity price sold by the virtual power plant is increased according to the price adjustment parameter; if the game parameter is less than the fourth preset threshold, the electricity price sold by the virtual power plant is reduced according to the price adjustment parameter.

[0049] Based on the above-mentioned method embodiment, the present invention provides a corresponding terminal device embodiment, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the steps of the power dispatch optimization method based on energy peak shifting and complementarity as described in the present invention are implemented.

[0050] Based on the above-mentioned method embodiment, the present invention provides a corresponding computer-readable storage medium embodiment, including: a stored computer program, which controls the device where the computer-readable storage medium is located to execute the steps of the power dispatch optimization method based on energy peak shifting and complementarity as described in the present invention when the computer program is running.

[0051] Compared with the prior art, the beneficial effects of the embodiment of this solution are:

[0052] The present invention obtains the clean energy power, traditional energy power and load power of the power system, and determines the daily dispatching power value at the current moment and the monthly dispatching parameters of the current month according to the clean energy power, traditional energy power and load power. The daily dispatching power value reflects the balance between energy generation and load in the power system. The positive value of the daily dispatching power value indicates that the traditional energy generation and the clean energy generation exceed the load demand, which means that there is an excess energy supply in the system, while the negative value indicates that the load demand exceeds the traditional energy generation and the clean energy generation, which means that there is a situation where the system is in short supply of energy. By analyzing the daily dispatching power value, the balance between power generation and load in the power system can be understood, thereby providing a reference for the optimization of system operation and dispatching; finally, the daily dispatching power value is compared with different preset thresholds. When the daily dispatching power value is greater than the first preset threshold, the excess electric energy is stored; when it is less than the second threshold, the stored electric energy is output. By storing the excess electric energy and outputting the stored electric energy when needed, the peak-shifting utilization of energy is realized, and the utilization efficiency of energy is improved.

[0053] To sum up, the present invention obtains real-time data of each power equipment and load in the power system, so that the virtual power plant can accurately understand the supply of clean energy and traditional energy in the system, as well as the real-time changes of the load, and calculates the daily scheduling power value based on these energy data to achieve peak-shifting complementarity of energy, thereby improving the operating efficiency and energy utilization efficiency of the virtual power plant. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a flow chart of a method for optimizing power dispatching based on energy peak shifting and complementation provided by an embodiment of the present invention;

[0055] Figure 2 is a function graph of clean energy power changing with time provided by an embodiment of the present invention;

[0056] Figure 3 It is a function graph of the traditional energy power changing with time provided by an embodiment of the present invention;

[0057] Figure 4 is a function graph of load power changing with time provided by an embodiment of the present invention;

[0058] Figure 5 is an image of a net load daily curve provided by an embodiment of the present invention;

[0059] Figure 6 is an image of a daily scheduling curve provided by an embodiment of the present invention;

[0060] Figure 7 is a function graph of carbon dioxide concentration changing with time provided by an embodiment of the present invention;

[0061] Figure 8 It is a structural schematic diagram of an electric power dispatching optimization device based on energy peak shifting and complementarity provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0063] In the description of the present invention, it should be understood that the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features.

[0064] like Figure 1 As shown, an embodiment of the present invention provides a method for optimizing power dispatch based on energy peak shifting and complementation, the method comprising at least the following steps:

[0065] Step S1: Obtain clean energy power, traditional energy power and load power of the power system;

[0066] For step S1, the sum of the power output of all photovoltaic power stations, hydropower stations and wind power stations in the power grid at the current time i is obtained at preset time intervals, and recorded as the clean energy power P 清洁 , whose vector is represented as (i, P 清洁 );

[0067] Similarly, the sum of the power output of all thermal power stations in the power grid at the current time i is obtained at preset intervals and recorded as the traditional energy power P 传统 , whose vector is represented as (i, P 传统 );

[0068] Similarly, the load power output of the power grid at the current time i is obtained every preset time, and recorded as load power P 负荷 , whose vector is represented as (i, P 负荷 ).

[0069] The vector data (i, P 清洁 )、(i,P 传统 ) and (i, P 负荷 ) are grouped by time. All vectors obtained on the same day are recorded as a daily detection group, and all vectors obtained in the same month are recorded as a monthly detection group.

[0070] Step S2: Determine the daily dispatch power value at the current time and the monthly dispatch parameters for the current month according to the clean energy power, traditional energy power and load power;

[0071] In a preferred embodiment, the daily dispatching power value at the current moment and the monthly dispatching parameters of the current month are determined according to the clean energy power, the traditional energy power and the load power, including:

[0072] Subtract the clean energy power at the current moment from the load power at the current moment to obtain the net load value;

[0073] Subtract the net load value from the current traditional energy power to get the daily dispatch power value;

[0074] For the daily dispatch power value of the same day, the daily dispatch curve is obtained according to the daily dispatch power value at each moment;

[0075] According to the net area of ​​the daily dispatch curve, the daily dispatch parameters are obtained;

[0076] For the daily scheduling parameters of the same month, the daily scheduling parameters are averaged to obtain the monthly scheduling parameters.

[0077] For step S2, a two-dimensional rectangular coordinate system is established with time as the horizontal coordinate and clean energy power as the vertical coordinate. The data (i, P) in the same daily detection group are 清洁 ) is filled into the coordinate system to generate a two-dimensional discrete random variable discrete distribution diagram with time as the independent variable and clean energy power as the dependent variable. A smooth curve is used to connect the points in the diagram to obtain the function image of clean energy power changing with time (such as Figure 2 );

[0078] A two-dimensional rectangular coordinate system is established with time as the horizontal axis and traditional energy power as the vertical axis. The data (i, P 传统 ) is filled into the coordinate system to generate a two-dimensional discrete random variable distribution diagram with time as the independent variable and traditional energy power as the dependent variable. A smooth curve is used to connect the points in the diagram to generate a function graph of traditional energy power changing with time (such as Figure 3 );

[0079] A two-dimensional rectangular coordinate system is established with time as the horizontal axis and load power as the vertical axis. The data (i, P 负荷 ) is filled into the coordinate system to generate a two-dimensional discrete random variable distribution diagram with time as the independent variable and load power as the dependent variable. A smooth curve is used to connect the points in the diagram to generate a function image of load power changing with time (such as Figure 4 ).

[0080] The function graph of clean energy power changing with time and the function graph of load power changing with time are overlapped, and the clean energy power is subtracted from the load power in the overlapped image to obtain the net load daily curve (such as Figure 5 ), the net load daily curve reflects the difference between the peak load and the renewable energy generation in a day, and comprehensively reflects the demand of the power system for traditional energy generation. The net load daily curve and the function graph of the traditional energy power changing over time are overlapped, and the power of the net load daily curve is subtracted from the traditional energy power to obtain the daily dispatch curve (such as Figure 6 ), when the daily dispatch curve is greater than 0, it means that the output power of the thermal power station at the current moment is greater than the power demand on the load side. When the daily dispatch curve is less than 0, it means that the output power of the thermal power station is insufficient to meet the power demand on the load side. 负荷 Subtract the clean energy power P at the current moment j 清洁 , get the net load value; the traditional energy power P at the current time j 传统 Subtract the net load value P at the current time j 负荷 , and get the daily dispatching power value.

[0081] The daily scheduling parameters are obtained by subtracting the area below the horizontal axis from the area above the horizontal axis of the scheduling curve. The daily scheduling parameters of each daily detection group data in the monthly detection group are counted and the average value is obtained to obtain the monthly scheduling parameters.

[0082] Step S3: Compare the current daily dispatch power value with different preset thresholds. If the daily dispatch power value is greater than the first preset threshold, store electric energy according to the value of the monthly dispatch parameter.

[0083] For step S3, the daily scheduling power value at the current moment is compared with different preset thresholds. In this embodiment, the value of the first preset threshold is greater than 0. If the daily scheduling power value at the current moment is greater than the first preset threshold, it means that the sum of the clean energy power and the traditional energy power currently emitted exceeds the load power, indicating that the power system is operating with excess energy. When this happens, the value of the monthly scheduling parameter is obtained, and the thermal power station in the power system is controlled based on the Internet of Things technology to transfer the electric energy of the monthly scheduling parameter value to the energy storage system for use in the future when needed, thereby ensuring the stable operation of the system and improving energy utilization.

[0084] Step S4: if the daily dispatch power value is less than the second preset threshold value, and the daily dispatch power value is greater than or equal to the third preset threshold value, output electric energy according to the value of the monthly dispatch parameter and the first preset influencing factor;

[0085] For step S4, in this embodiment, the values ​​of the second preset threshold and the third preset threshold are both less than 0, and the second preset threshold is greater than the third preset threshold, that is, when the daily dispatch power value is less than the second preset threshold, and greater than or equal to the third preset threshold, it indicates that the sum of the clean energy power and the traditional energy power emitted by the power system is insufficient to support the load power, that is, there is a shortage of electric energy. In this case, a secondary warning signal is issued, and the energy storage system is controlled to output the transferred electric energy to the power system based on the Internet of Things technology. The output electric energy value is the monthly dispatch parameter value multiplied by the first preset impact factor. At the same time, the number of times the secondary warning signal is generated in each month is recorded and recorded as the number of warnings.

[0086] It should be noted that when the daily dispatch power value is less than or equal to 0, but greater than or equal to the first preset threshold, it means that the sum of the clean energy power and the traditional energy power generated in the power system is not enough to support the load power, but the normal operation of the power system can still be maintained. There is no need to store or release electric energy, but a flexible early warning signal is issued to remind relevant personnel to pay attention to the energy supply problem of the power system.

[0087] Step S5: if the daily dispatch power value is less than the third preset threshold, output electric energy according to the value of the monthly dispatch parameter and the second preset influencing factor.

[0088] For step S5, when the daily dispatching power value is less than the third preset threshold, it indicates that the power generation capacity of clean energy and traditional energy in the power system seriously fails to meet the current load demand, and the power system is facing a serious energy shortage problem. In this case, a first-level warning signal is issued, and the power output power of the energy storage system is increased based on the Internet of Things technology. The power output power value is the monthly dispatching parameter multiplied by the second preset impact factor, wherein the second preset impact factor is greater than the first preset impact factor. By adjusting the impact factor, the amount of electric energy transferred from the energy storage system to the power system is increased to make up for the energy shortage.

[0089] In addition, when issuing the first-level warning signal, a text message notification should also be sent to load-side grid users stating that "electricity is tight, please turn off unnecessary electrical appliances."

[0090] In a preferred embodiment, the power dispatch optimization method based on energy peak shifting and complementation also includes:

[0091] For the net load value of the same day, the net load daily curve is obtained according to the net load value at each moment;

[0092] According to the area of ​​the net load daily curve, the daily net load parameter is obtained;

[0093] Count the number of warnings in each month when the daily dispatch power value is less than the second preset threshold and greater than or equal to the third preset threshold;

[0094] Determine the gaming parameters according to the daily net load parameter, the number of warnings, and the third preset influencing factor;

[0095] Determine the price adjustment parameter according to the game parameter and the fourth preset influencing factor;

[0096] The game parameter is compared with the fourth preset threshold. If the game parameter is greater than or equal to the fourth preset threshold, the selling price of the virtual power plant is increased according to the price adjustment parameter. If the game parameter is less than the fourth preset threshold, the selling price of the virtual power plant is reduced according to the price adjustment parameter.

[0097] In a preferred embodiment, the game parameters are determined according to the daily net load parameter, the number of warnings, and the third preset influencing factor, including:

[0098] The daily net load parameter, the number of warnings and the third preset impact factor are multiplied to obtain the game parameter.

[0099] In a preferred embodiment, determining the price adjustment parameter according to the game parameter and the fourth preset influencing factor includes:

[0100] The price adjustment parameter is obtained by square-raising the product of the game parameter and the fourth preset influencing factor.

[0101] In one embodiment of the present invention, a function graph of clean energy power changing with time and a function graph of load power changing with time are overlapped, and the clean energy power is subtracted from the load power in the overlapped image to obtain a net load daily curve; then, the area of ​​the net load daily curve image is extracted and recorded as the daily net load parameter, and at the same time, the number of warnings for each monthly daily dispatching power value that is less than the second preset threshold and greater than or equal to the third preset threshold is counted, that is, the number of times a second-level flexible warning is issued. The number of warnings can be used to evaluate the frequency of energy shortages in the power system within the month, and reflect the stability and reliability of the power system operation.

[0102] The daily net load parameter is multiplied by the number of warnings, and then multiplied by the third preset influencing factor to obtain the game parameter. This game parameter will be used to quantify the coordination and competition relationship between various factors within the system, thereby affecting subsequent decision-making and regulation strategies; then, the obtained game parameter is multiplied by the fourth preset influencing factor, and then the quadratic operation is performed to obtain the price adjustment parameter, which is used to adjust the electricity price sold by the virtual power plant. The game parameter is compared with the fourth preset threshold. If the game parameter is greater than or equal to the fourth preset threshold, the electricity price sold by the virtual power plant is increased according to the price adjustment parameter to cope with the risk of energy shortage; if the game parameter is less than the fourth preset threshold, the electricity price sold by the virtual power plant is reduced according to the price adjustment parameter to promote the rational use of energy and market competition.

[0103] Through such a price adjustment strategy, the electricity consumption habits of load-side users can be improved and unnecessary electricity waste can be reduced. Excessive electricity waste will lead to an increase in game parameters, thereby driving up electricity prices. In order to reduce electricity prices, load-side users will be encouraged to reduce electricity consumption during peak hours to reduce fluctuations in the net load daily curve. Such behavior can not only reduce the maintenance cost of virtual power plants, but also improve the operating efficiency of the power system.

[0104] In addition, the price adjustment strategy can also encourage the use of clean energy. By increasing the power generation of clean energy in virtual power plants and reducing dependence on fossil fuels, the game parameters can be reduced, thereby driving down electricity prices. At the same time, this will also reduce the operating costs of virtual power plants. By reducing electricity prices, more users can be attracted to join virtual power plants and increase the income of operators. With the increase in income, operators will invest more in clean energy construction, achieving a win-win situation of reducing carbon emissions and economic benefits.

[0105] In one embodiment of the present invention, the carbon dioxide concentration C around all thermal power plants at the current time i is obtained at preset intervals, and its vector is represented as (i, C). Then, a two-dimensional rectangular coordinate system is established with time as the horizontal coordinate and carbon dioxide concentration as the vertical coordinate, and the data (i, C) in the same daily detection group are filled into the coordinate system to generate a two-dimensional discrete random variable discrete distribution diagram with time as the independent variable and carbon dioxide concentration as the dependent variable, and a smooth curve is used to connect the various points in the diagram to obtain a function graph of carbon dioxide concentration changing with time (such as Figure 7 ), and this image is recorded as the daily carbon emission curve. The area enclosed by the function image is extracted from the daily carbon emission curve and recorded as the daily carbon emission parameter. Subsequently, the daily carbon emission parameters of each daily detection group data in the monthly detection group are counted and averaged to obtain the monthly carbon emission parameters. The monthly carbon emission parameters of the previous month are subtracted from the monthly carbon emission parameters of the current month to obtain the emission reduction index. At the same time, the power value corresponding to the minimum point of the net load daily curve is recorded as the daily difference parameter, and the daily difference parameters of each daily detection group data in the monthly detection group are counted and averaged to obtain the monthly difference parameter. The emission reduction index and the monthly difference parameter are compared. The larger parameter value is recorded as a, and the smaller parameter value is recorded as b. An ellipse is generated with a as the major axis length and b as the minor axis length, and the area of ​​the ellipse is extracted and recorded as the energy carbon index. The size of the energy carbon index can be used to evaluate the carbon dioxide emissions and the energy efficiency of the power system. By monitoring and calculating these parameters, we can better understand the changing trend of carbon dioxide emissions and provide an important reference for reducing carbon emissions and improving energy efficiency.

[0106] like Figure 8 As shown, based on the above method embodiment, a corresponding device embodiment is provided;

[0107] An embodiment of the present invention provides a power dispatch optimization device based on energy peak shifting and complementation, comprising: a power system power acquisition module, a comprehensive analysis module, and an execution module;

[0108] The power system power acquisition module is used to obtain the clean energy power, traditional energy power and load power of the power system;

[0109] Comprehensive analysis module, used to determine the current daily dispatch power value and the monthly dispatch parameters according to clean energy power, traditional energy power and load power;

[0110] An execution module, comprising: an energy storage submodule, a first electric energy scheduling submodule, and a second electric energy scheduling submodule;

[0111] The energy storage submodule is used to compare the daily dispatch power value at the current moment with different preset thresholds, and if the daily dispatch power value is greater than the first preset threshold, store electric energy according to the value of the monthly dispatch parameter;

[0112] A first electric energy scheduling submodule, configured to output electric energy according to the value of the monthly scheduling parameter and the first preset influencing factor if the daily scheduling power value is less than the second preset threshold value and the daily scheduling power value is greater than or equal to the third preset threshold value;

[0113] The second electric energy scheduling submodule is used to output electric energy according to the value of the monthly scheduling parameter and the second preset influencing factor if the daily scheduling power value is less than the third preset threshold value.

[0114] In a preferred embodiment, the comprehensive analysis module includes: a net load calculation submodule, a daily dispatch power calculation submodule, a daily dispatch curve generation submodule, a daily dispatch parameter calculation submodule, and a monthly dispatch parameter calculation submodule;

[0115] The net load calculation submodule is used to obtain the net load value by subtracting the clean energy power at the current moment from the load power at the current moment;

[0116] The daily dispatch power calculation submodule is used to subtract the net load value from the current traditional energy power to obtain the daily dispatch power value;

[0117] A daily dispatch curve generation submodule is used to obtain a daily dispatch curve for the daily dispatch power value of the same day according to the daily dispatch power value at each moment;

[0118] The daily dispatch parameter calculation submodule is used to obtain the daily dispatch parameters according to the net area of ​​the daily dispatch curve;

[0119] The monthly scheduling parameter calculation submodule is used to calculate the average value of the daily scheduling parameters of the same month to obtain the monthly scheduling parameters.

[0120] In a preferred embodiment, the power dispatch optimization device based on energy peak shifting and complementation further includes: an electricity price game module;

[0121] The electricity price game module includes: a net load daily curve generation submodule, a daily net load parameter calculation submodule, a warning number statistics submodule, a game parameter calculation submodule, a price adjustment parameter calculation submodule, and an electricity price adjustment submodule;

[0122] The net load daily curve generation submodule is used to obtain the net load daily curve according to the net load value at each moment for the net load value of the same day;

[0123] The daily net load parameter calculation submodule is used to obtain the daily net load parameter according to the area of ​​the net load daily curve;

[0124] A warning number statistics submodule, used to count the number of warnings for which the daily dispatch power value of each month is less than the second preset threshold and greater than or equal to the third preset threshold;

[0125] A game parameter calculation submodule, used to determine the game parameter according to the daily net load parameter, the number of warnings, and the third preset influencing factor;

[0126] A price adjustment parameter calculation submodule, used to determine the price adjustment parameter according to the game parameter and the fourth preset influencing factor;

[0127] The electricity price adjustment submodule is used to compare the game parameter with the fourth preset threshold. If the game parameter is greater than or equal to the fourth preset threshold, the electricity price sold by the virtual power plant is increased according to the price adjustment parameter; if the game parameter is less than the fourth preset threshold, the electricity price sold by the virtual power plant is reduced according to the price adjustment parameter.

[0128] It can be understood that the above-mentioned device item embodiments correspond to the method item embodiments of the present invention, which can implement the power dispatch optimization method based on energy peak shifting and complementarity provided by any one of the above-mentioned method item embodiments of the present invention.

[0129] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art may understand and implement the present invention without creative work.

[0130] Based on the above-mentioned embodiment of the power dispatch optimization method based on energy peak-shaving and complementarity, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the power dispatch optimization method based on energy peak-shaving and complementarity according to any embodiment of the present invention is implemented.

[0131] Exemplarily, in this embodiment, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more module elements may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the terminal device.

[0132] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0133] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire terminal device.

[0134] Based on the above method embodiment, another embodiment is provided: another embodiment of the present invention provides a computer-readable storage medium, including a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the power dispatch optimization method based on energy peak shifting and complementarity as described in any one of the above method embodiments of the present invention.

[0135] Wherein, the module / unit integrated in the power dispatch optimization device / terminal equipment based on energy peak shifting and complementation, if implemented in the form of a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, 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, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0136] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for optimizing power dispatch based on energy peak shifting and complementation, characterized in that: include: Obtain clean energy power, traditional energy power and load power of the power system; Determine the daily dispatch power value at the current time and the monthly dispatch parameters for the current month according to the clean energy power, traditional energy power and load power; Compare the current daily dispatch power value with different preset thresholds, and if the daily dispatch power value is greater than the first preset threshold, store electric energy according to the value of the monthly dispatch parameter; If the daily dispatch power value is less than the second preset threshold value, and the daily dispatch power value is greater than or equal to the third preset threshold value, output electric energy according to the value of the monthly dispatch parameter and the first preset influencing factor; If the daily dispatch power value is less than the third preset threshold, electric energy is output according to the value of the monthly dispatch parameter and the second preset influencing factor.

2. The power dispatch optimization method based on energy peak shifting and complementarity according to claim 1 is characterized in that: According to the clean energy power, traditional energy power and load power, the daily dispatching power value at the current moment and the monthly dispatching parameters of the current month are determined, including: Subtract the clean energy power at the current moment from the load power at the current moment to obtain the net load value; Subtract the net load value from the current traditional energy power to obtain the daily dispatch power value; For the daily dispatch power value of the same day, the daily dispatch curve is obtained according to the daily dispatch power value at each moment; According to the net area of ​​the daily dispatch curve, the daily dispatch parameters are obtained; For the daily scheduling parameters of the same month, the daily scheduling parameters are averaged to obtain the monthly scheduling parameters.

3. The power dispatch optimization method based on energy peak shifting and complementation according to claim 2 is characterized in that: Also includes: For the net load value of the same day, the net load daily curve is obtained according to the net load value at each moment; Obtaining a daily net load parameter according to the area of ​​the net load daily curve; Count the number of warnings in each month when the daily dispatch power value is less than the second preset threshold and greater than or equal to the third preset threshold; Determine the gaming parameters according to the daily net load parameter, the number of warnings, and the third preset influencing factor; Determine the price adjustment parameter according to the game parameter and the fourth preset influencing factor; The game parameter is compared with a fourth preset threshold value. If the game parameter is greater than or equal to the fourth preset threshold value, the selling price of the virtual power plant is increased according to the price adjustment parameter. If the game parameter is less than the fourth preset threshold value, the selling price of the virtual power plant is reduced according to the price adjustment parameter.

4. The power dispatch optimization method based on energy peak shifting and complementation according to claim 3 is characterized in that: According to the daily net load parameter, the number of warnings, and the third preset influencing factor, the game parameters are determined, including: The daily net load parameter, the number of warnings and the third preset impact factor are multiplied to obtain a game parameter.

5. The power dispatch optimization method based on energy peak shifting and complementation according to claim 3 is characterized in that: According to the game parameters and the fourth preset influencing factor, the price adjustment parameters are determined, including: The price adjustment parameter is obtained by square-raising the product of the game parameter and the fourth preset influencing factor.

6. A power dispatch optimization device based on energy peak shifting and complementation, characterized in that: include: Power system power acquisition module, comprehensive analysis module, and execution module; The power system power acquisition module is used to acquire the clean energy power, traditional energy power and load power of the power system; The comprehensive analysis module is used to determine the daily scheduling power value at the current moment and the monthly scheduling parameters of the current month according to the clean energy power, traditional energy power and load power; The execution module includes: an energy storage submodule, a first electric energy scheduling submodule, and a second electric energy scheduling submodule; The energy storage submodule is used to compare the daily dispatch power value at the current moment with different preset thresholds, and if the daily dispatch power value is greater than the first preset threshold, store electric energy according to the value of the monthly dispatch parameter; The first electric energy scheduling submodule is used to output electric energy according to the value of the monthly scheduling parameter and the first preset influencing factor if the daily scheduling power value is less than the second preset threshold value and the daily scheduling power value is greater than or equal to the third preset threshold value; The second electric energy scheduling submodule is used to output electric energy according to the value of the monthly scheduling parameter and the second preset influencing factor if the daily scheduling power value is less than the third preset threshold value.

7. The power dispatch optimization device based on energy peak shifting and complementation according to claim 6 is characterized in that: The comprehensive analysis module includes: a net load calculation submodule, a daily dispatch power calculation submodule, a daily dispatch curve generation submodule, a daily dispatch parameter calculation submodule, and a monthly dispatch parameter calculation submodule; The net load calculation submodule is used to obtain a net load value by subtracting the clean energy power at the current moment from the load power at the current moment; The daily dispatch power calculation submodule is used to subtract the net load value from the current conventional energy power to obtain the daily dispatch power value; The daily dispatch curve generating submodule is used to obtain the daily dispatch curve according to the daily dispatch power value at each moment for the daily dispatch power value of the same day; The daily scheduling parameter calculation submodule is used to obtain the daily scheduling parameters according to the net area of ​​the daily scheduling curve; The monthly scheduling parameter calculation submodule is used to calculate the average value of the daily scheduling parameters of the same month to obtain the monthly scheduling parameters.

8. The power dispatch optimization device based on energy peak shifting and complementation according to claim 7 is characterized in that: Also includes: Electricity price game module; The electricity price game module includes: a net load daily curve generation submodule, a daily net load parameter calculation submodule, a warning number statistics submodule, a game parameter calculation submodule, a price adjustment parameter calculation submodule, and an electricity price adjustment submodule; The net load daily curve generating submodule is used to obtain a net load daily curve according to the net load value at each moment for the net load value of the same day; The daily net load parameter calculation submodule is used to obtain the daily net load parameter according to the area of ​​the net load daily curve; The warning number counting submodule is used to count the number of warnings for each monthly daily scheduling power value that is less than the second preset threshold and greater than or equal to the third preset threshold; The game parameter calculation submodule is used to determine the game parameters according to the daily net load parameter, the number of warnings, and the third preset influencing factor; The price adjustment parameter calculation submodule is used to determine the price adjustment parameter according to the game parameter and the fourth preset influencing factor; The electricity price adjustment submodule is used to compare the game parameter with a fourth preset threshold value. If the game parameter is greater than or equal to the fourth preset threshold value, the electricity price sold by the virtual power plant is increased according to the price adjustment parameter; if the game parameter is less than the fourth preset threshold value, the electricity price sold by the virtual power plant is reduced according to the price adjustment parameter.

9. A terminal device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the method for optimizing power dispatching based on energy peak shifting and complementarity as described in any one of claims 1 to 5 is implemented.

10. A computer-readable storage medium, characterized in that: include: A stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the power dispatch optimization method based on energy peak shifting and complementarity as described in any one of claims 1 to 5.

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