Power system optimization method and device, storage medium and electronic equipment

Abstract

The invention discloses a power system optimization method and device, a storage medium and electronic equipment. The method relates to the technical field of data processing, and comprises the following steps: determining a load type and a power generation type corresponding to each piece of historical operation data according to multiple pieces of historical operation data of a target power system; determining a carbon emission calculation function corresponding to each piece of historical operation data according to the load type and the power generation type corresponding to each piece of historical operation data; determining a generating capacity optimization function corresponding to the target power system according to the carbon emission calculation function corresponding to each piece of historical operation data; and performing power supply optimization on the target power system according to the generating capacity optimization function. According to the method and the device, the problem of poor power supply optimization effect caused by performing power supply optimization on the power system through a manual adjustment mode in related technologies is solved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to an optimization method and device for an electric power system, a storage medium, and an electronic device. Background Art

[0002] With the rapid development of the economy, optimizing the power supply of the power system to reduce carbon emissions and promote the use of clean energy is of great significance and importance. In the global action to address climate change and reduce greenhouse gas emissions, optimizing the power supply of the power system provides an effective way to achieve low-carbon and sustainable energy supply, and has a far-reaching impact on promoting energy transformation and environmental protection. However, in the existing technology, the power supply of the power system is often optimized by manual adjustment to reduce carbon emissions. However, this method has the problem of poor power supply optimization effect.

[0003] Currently, no effective solution has been proposed to solve the problem that power supply optimization of the power system is performed through manual adjustment in related technologies, resulting in poor power supply optimization effect. Summary of the invention

[0004] The main purpose of the present application is to provide a method and device for optimizing an electric power system, a storage medium and an electronic device, so as to solve the problem in the related art that the power supply optimization of the electric power system is performed by manual adjustment, resulting in poor power supply optimization effect.

[0005] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a method for optimizing a power system is provided. The method comprises: determining the load type and power generation type corresponding to each historical operation data according to multiple historical operation data of the target power system; determining the carbon emission calculation function corresponding to each historical operation data according to the load type and power generation type corresponding to each historical operation data; determining the power generation optimization function corresponding to the target power system according to the carbon emission calculation function corresponding to each historical operation data; and optimizing the power supply of the target power system according to the power generation optimization function.

[0006] Furthermore, based on the load type and power generation type corresponding to each historical operating data, determining the carbon emission calculation function corresponding to each historical operating data includes: obtaining the load capacity rating of the target power system and the carbon emission factor corresponding to the power generation type, wherein the carbon emission factor is used to characterize the carbon emission value corresponding to the unit power generation under the power generation type; obtaining the target electricity price data based on the historical electricity price data in the multiple historical operating data and the load type; determining the carbon emission calculation function based on the load capacity rating, the carbon emission factor corresponding to the power generation type and the target electricity price data.

[0007] Furthermore, based on the carbon emission calculation function corresponding to each historical operating data, determining the power generation optimization function corresponding to the target power system includes: summing the carbon emission calculation functions corresponding to each historical operating data to obtain the target carbon emission calculation function; calculating based on the historical electricity load data on the user side to obtain the capacity electricity fee calculation function corresponding to the user side; determining the power generation optimization function based on the target carbon emission calculation function and the capacity electricity fee calculation function.

[0008] Furthermore, calculations are performed based on historical electricity load data on the user side to obtain a capacity electricity fee calculation function corresponding to the user side, including: determining a capacity electricity fee calculation parameter value based on the historical electricity load data and the historical capacity electricity fee corresponding to the historical electricity load data; and obtaining the capacity electricity fee calculation function based on the capacity electricity fee calculation parameter value.

[0009] Furthermore, based on multiple historical operating data of the target power system, determining the load type and power generation type corresponding to each historical operating data includes: classifying the multiple historical operating data to obtain a first initial load type and a first initial power generation type corresponding to each historical operating data; processing the multiple historical operating data through a target classification model to obtain a second initial load type and a second initial power generation type corresponding to each historical operating data; and obtaining the load type and power generation type corresponding to each historical operating data based on the first initial load type, the first initial power generation type, the second initial load type and the second initial power generation type.

[0010] Furthermore, based on the power generation optimization function, power supply optimization for the target power system includes: making predictions based on historical power load data on the user side to obtain a power load prediction value; obtaining an upper output limit value and a lower output limit value corresponding to the target power system; solving the power generation optimization function based on the power load prediction value, the upper output limit value and the lower output limit value to obtain a power supply optimization strategy; and optimizing the power supply for the target power system based on the power supply optimization strategy.

[0011] Furthermore, before determining the load type and power generation type corresponding to each historical operating data based on multiple historical operating data of the target power system, the method also includes: collecting multiple initial operating data in the target power system; filtering and filling in the multiple initial operating data to obtain multiple processed initial operating data; and normalizing the multiple processed initial operating data to obtain the multiple historical operating data.

[0012] In order to achieve the above-mentioned purpose, according to another aspect of the present application, a device for optimizing a power system is provided. The device comprises: a first determining unit, for determining the load type and power generation type corresponding to each historical operation data according to multiple historical operation data of the target power system; a second determining unit, for determining the carbon emission calculation function corresponding to each historical operation data according to the load type and power generation type corresponding to each historical operation data; a third determining unit, for determining the power generation optimization function corresponding to the target power system according to the carbon emission calculation function corresponding to each historical operation data; and an optimizing unit, for optimizing the power supply of the target power system according to the power generation optimization function.

[0013] Furthermore, the second determination unit includes: a first acquisition module, used to obtain the load capacity rating of the target power system and the carbon emission factor corresponding to the power generation type, wherein the carbon emission factor is used to characterize the carbon emission value corresponding to the unit power generation under the power generation type; a first processing module, used to obtain the target electricity price data based on the historical electricity price data in the multiple historical operation data and the load type; a determination module, used to determine the carbon emission calculation function based on the load capacity rating, the carbon emission factor corresponding to the power generation type and the target electricity price data.

[0014] Furthermore, the third determination unit includes: a summation module, used to sum the carbon emission calculation functions corresponding to each historical operation data to obtain a target carbon emission calculation function; a calculation module, used to calculate based on the historical electricity load data on the user side to obtain the capacity electricity fee calculation function corresponding to the user side; a second determination module, used to determine the power generation optimization function based on the target carbon emission calculation function and the capacity electricity fee calculation function.

[0015] Furthermore, the calculation module includes: a determination submodule, used to determine the capacity electricity fee calculation parameter value based on the historical electricity load data and the historical capacity electricity fee corresponding to the historical electricity load data; and a calculation submodule, used to obtain the capacity electricity fee calculation function based on the capacity electricity fee calculation parameter value.

[0016] Furthermore, the first determination unit includes: a first classification module, used to classify the multiple historical operating data to obtain a first initial load type and a first initial power generation type corresponding to each historical operating data; a second classification module, used to process the multiple historical operating data through a target classification model to obtain a second initial load type and a second initial power generation type corresponding to each historical operating data; a second processing module, used to obtain a load type and a power generation type corresponding to each historical operating data based on the first initial load type, the first initial power generation type, the second initial load type and the second initial power generation type.

[0017] Furthermore, the optimization unit includes: a prediction module, which is used to make predictions based on historical power load data on the user side to obtain a power load prediction value; a second acquisition module, which is used to obtain an upper output limit value and a lower output limit value corresponding to the target power system; a solution module, which is used to solve the power generation optimization function based on the power load prediction value, the upper output limit value and the lower output limit value to obtain a power supply optimization strategy; and an optimization module, which is used to optimize the power supply of the target power system based on the power supply optimization strategy.

[0018] Furthermore, the device also includes: a collection unit, which is used to collect multiple initial operating data in the target power system before determining the load type and power generation type corresponding to each historical operating data based on multiple historical operating data of the target power system; a first processing unit, which is used to filter and fill in the multiple initial operating data to obtain multiple processed initial operating data; and a second processing unit, which is used to normalize the multiple processed initial operating data to obtain the multiple historical operating data.

[0019] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a computer-readable storage medium is provided, wherein the storage medium stores a program, wherein when the program is running, the device where the storage medium is located is controlled to execute any one of the above-mentioned methods for optimizing the power system.

[0020] In order to achieve the above-mentioned purpose, according to another aspect of the present application, an electronic device is also provided, the electronic device includes one or more processors and a memory, and the memory is used to store one or more processors to implement any one of the above-mentioned power system optimization methods.

[0021] Through the present application, the following steps are adopted: based on multiple historical operating data of the target power system, determine the load type and power generation type corresponding to each historical operating data; based on the load type and power generation type corresponding to each historical operating data, determine the carbon emission calculation function corresponding to each historical operating data; based on the carbon emission calculation function corresponding to each historical operating data, determine the power generation optimization function corresponding to the target power system; based on the power generation optimization function, optimize the power supply of the target power system, which solves the problem of optimizing the power supply of the power system through manual adjustment in the related technology, resulting in poor power supply optimization effect. In this scheme, the load type and power generation type corresponding to the historical operation data are identified through multiple historical operation data of the target power system to obtain the corresponding load type and power generation type, and then the carbon emission calculation function corresponding to each historical operation data is determined according to the load type and power generation type corresponding to each historical operation data, and then the power generation optimization function corresponding to the target power system is determined according to the carbon emission calculation function. Finally, the power supply of the target power system is optimized through the power generation optimization function, different emission calculation functions are set according to different load types and power generation types, and the power generation optimization function is determined according to the emission calculation function. The power generation optimization function can effectively achieve the economy and low-carbon goals of the power system, thereby achieving the purpose of improving the power supply optimization effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0023] Figure 1 The process of the power system optimization method provided in the embodiment of the present application is as follows Figure 1 ;

[0024] Figure 2 The process of the power system optimization method provided in the embodiment of the present application is as follows Figure 2 ;

[0025] Figure 3 The process of the power system optimization method provided in the embodiment of the present application is as follows Figure 3 ;

[0026] Figure 4 The process of the power system optimization method provided in the embodiment of the present application is as follows Figure 4 ;

[0027] Figure 5 is a schematic diagram of an optimization device for a power system provided according to an embodiment of the present application;

[0028] Figure 6is a schematic diagram of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0029] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0030] In order to enable those skilled in the art to better understand the present application, 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 ordinary technicians in this field without creative work should fall within the scope of protection of this application.

[0031] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0032] It should be noted that the relevant information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for display, data for analysis, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties. For example, an interface is set between this system and the relevant user or organization. Before obtaining relevant information, it is necessary to send an acquisition request to the aforementioned user or organization through the interface, and obtain relevant information after receiving the consent information fed back by the aforementioned user or organization.

[0033] The present invention is described below in conjunction with preferred implementation steps. Figure 1 The process of the power system optimization method provided in the embodiment of the present application is as follows Figure 1 ,like Figure 1 As shown, the method comprises the following steps:

[0034] Step S101 : determining the load type and power generation type corresponding to each historical operation data according to a plurality of historical operation data of a target power system.

[0035] Optionally, the initial historical operation data of the target power system is collected. For example, the initial historical operation data includes but is not limited to photovoltaic power generation, hydropower generation, coal-fired power generation, and user-side load data. After obtaining a plurality of initial historical operation data, preprocessing can be performed to obtain the above-mentioned plurality of historical operation data. The purpose of preprocessing is to standardize all data so that they fall on the same scale for easy comparison and analysis.

[0036] After obtaining the above-mentioned multiple historical operation data, the load type and power generation type of each historical operation data are identified to obtain the load type and power generation type corresponding to each historical operation data. For example, based on historical data, several typical load types can be pre-set, such as peak period load type, valley period load type and flat peak period load type. Then, the load characteristics corresponding to the typical load type are matched with multiple historical operation data to obtain the load type corresponding to each historical operation data. For example, the matching formula is: K=argmin1(LK-L0), where argmin1() is a function for extracting the load mode number in the typical load type that minimizes the difference between the current load characteristic and the typical load characteristic, LK is the load characteristic value corresponding to the typical load type, K is the type number, and L0 is the current load characteristic value corresponding to the historical operation data.

[0037] For example, several typical power generation types can be pre-set based on historical data, such as photovoltaic power generation type, hydropower generation type and coal-fired power generation type, and then the power generation characteristics corresponding to the typical power generation type are matched with multiple historical operation data to obtain the power generation type corresponding to each historical operation data. For example, the matching formula is: P = argmin2 (SK-S0), where argmin2 () is a function for extracting the power mode number of all typical power generation types that minimizes the difference between the current power supply characteristics and the power supply characteristics corresponding to the typical power generation type, SP is the power supply characteristics corresponding to the typical power generation type, P is the type number, and S0 is the current power supply characteristics. This formula helps find the typical power supply characteristics that are closest to the current power supply characteristics, so that the current power supply can be classified into the corresponding power supply scenario. Through the above steps, the historical operation data can be classified into the closest typical mode, thereby obtaining a variety of load types and power generation types.

[0038] Step S102: determining a carbon emission calculation function corresponding to each historical operation data according to the load type and power generation type corresponding to each historical operation data.

[0039] Optionally, according to the load type and power generation type corresponding to each historical operation data, the carbon emission calculation function corresponding to each historical operation data is determined. For example, the electricity price information of each load type is obtained. This can be determined by analyzing the historical electricity price data. At the same time, the carbon emission factors of each power generation type are obtained, which reflect the total carbon emissions corresponding to the unit power generation of different energy sources (such as coal, natural gas, wind power, solar energy, etc.). The carbon emission factor can be obtained according to the environmental protection standards and energy reports of the region. In an optional embodiment, the carbon emission calculation function corresponding to each historical operation data is: CB = (SPi × FDi ÷ CPj ÷ EP), where CB is the carbon emission, CPj is the electricity price information of the jth load type, EP is the load capacity rating of the power system (for example, in kilowatts), SPi is the carbon emission factor of the i-th power generation type, and FDi is the power generation of the i-th power generation type (for example, in kilowatt-hours).

[0040] Step S103, determining a power generation optimization function corresponding to the target power system according to the carbon emission calculation function corresponding to each historical operation data.

[0041] Optionally, the power generation optimization function corresponding to the target power system is determined based on the carbon emission calculation function corresponding to each historical operating data. For example, the power generation optimization function is zcb=(FR)×(1+CB), where FR is the capacity electricity charge calculation function, which can be obtained based on the historical electricity consumption on the user side.

[0042] Step S104: optimizing the power supply of the target power system according to the power generation optimization function.

[0043] Optionally, the power supply of the target power system is optimized through the power generation optimization function. For example, the target power system is optimized. If an energy storage device is also provided in the target power system, the charging and discharging of the energy storage device can also be optimized.

[0044] To sum up, in this scheme, through multiple historical operating data of the target power system, the load type and power generation type corresponding to the historical operating data are identified to obtain the corresponding load type and power generation type, and then the carbon emission calculation function corresponding to each historical operating data is determined according to the load type and power generation type corresponding to each historical operating data, and then the power generation optimization function corresponding to the target power system is determined according to the carbon emission calculation function, and finally the power supply of the target power system is optimized through the power generation optimization function, different emission calculation functions are set according to different load types and power generation types, and the power generation optimization function is determined according to the emission calculation function, and the economy and low-carbon goals of the power system can be effectively achieved through the power generation optimization function, thereby achieving the purpose of improving the power supply optimization effect.

[0045] Optionally, in the power system optimization method provided in the embodiment of the present application, determining the carbon emission calculation function corresponding to each historical operating data based on the load type and power generation type corresponding to each historical operating data includes: obtaining the load capacity rating of the target power system and the carbon emission factor corresponding to the power generation type, wherein the carbon emission factor is used to characterize the carbon emission value corresponding to the unit power generation under the power generation type; obtaining the target electricity price data based on the historical electricity price data and load type in multiple historical operating data; determining the carbon emission calculation function based on the load capacity rating, the carbon emission factor corresponding to the power generation type and the target electricity price data.

[0046] In an optional embodiment, determining the carbon emission calculation function includes: obtaining the load capacity rating of the target power system and the carbon emission factor corresponding to the power generation type, and then obtaining the target electricity price data corresponding to each load type according to the historical electricity price data and the load type in the multiple historical operation data. For example, the target electricity price data corresponding to each load type can be obtained by fitting the historical electricity price data corresponding to each load type, and the target electricity price data corresponding to each load type can be obtained by predicting the electricity price data at the current time according to the historical electricity price data. Finally, the carbon emission calculation function is determined according to the load capacity rating, the carbon emission factor corresponding to the power generation type, and the target electricity price data.

[0047] For example, the carbon emissions calculation function corresponding to each historical operating data is: CB = (SPi×FDi÷CPj÷EP), where CB is the carbon emissions, CPj is the electricity price information of the j-th load type, EP is the load capacity rating of the power system (for example, in kilowatts), SPi is the carbon emission factor of the i-th power generation type, and FDi is the power generation of the i-th power generation type (for example, in kilowatt-hours).

[0048] In an optional embodiment, the Figure 2 The flowchart shown obtains the above-mentioned carbon emission calculation function: obtain the electricity price of each load type; obtain the carbon emission factor of each power generation type, wherein the carbon emission factor is the total carbon emission corresponding to unit power generation; determine the carbon emission calculation function.

[0049] The carbon emission calculation function can be used to effectively evaluate the carbon emission cost corresponding to the current carbon emissions of the target power system, and the current carbon emissions of the target power system can be effectively optimized and controlled.

[0050] Optionally, in the power system optimization method provided in the embodiment of the present application, determining the power generation optimization function corresponding to the target power system based on the carbon emission calculation function corresponding to each historical operating data includes: summing the carbon emission calculation functions corresponding to each historical operating data to obtain a target carbon emission calculation function; calculating based on the historical electricity load data on the user side to obtain the capacity electricity fee calculation function corresponding to the user side; determining the power generation optimization function based on the target carbon emission calculation function and the capacity electricity fee calculation function.

[0051] In an optional embodiment, the following steps are used to obtain the power generation optimization function: the carbon emission calculation function corresponding to each historical operation data is summed to obtain the target carbon emission calculation function, and then the historical power load data on the user side is calculated to obtain the capacity electricity fee calculation function corresponding to the user side. For example, the capacity electricity fee calculation function is:

[0052] FR=GD×-0.2P≤A

[0053] FR=0A<P<B

[0054] FR=GD×0.2B≤P

[0055] Among them, A is the first capacity comparison margin, B is the second capacity comparison margin, P is the annual power capacity on the user side, GD is the preset power purchase price, and FR is the capacity electricity fee. The first capacity comparison margin and the second capacity comparison margin can be obtained by fitting based on the historical capacity electricity fees of multiple users.

[0056] The power generation optimization function is determined according to the target carbon emission calculation function and the capacity electricity charge calculation function. For example, the power generation optimization function is zcb=(FR)×(1+CB). By solving the power generation optimization function, zcb is minimized, thereby achieving the purpose of optimizing the power supply of the target power system.

[0057] In an optional embodiment, the power generation optimization function can also be zcb = (FR + JG) × (1 + CB), where JG is the basic power purchase cost, JG = CPj × P, where JG is the basic power purchase cost, CPj is the real-time electricity price of the j-th load scenario, and P is the user's annual real-time power consumption capacity. zcb is equivalent to the total cost. This formula integrates capacity electricity charges, basic power purchase costs, and carbon emission costs, and achieves the goal of minimizing costs by adjusting different parameters.

[0058] Optionally, in the power system optimization method provided in the embodiment of the present application, calculation is performed based on the historical electricity load data on the user side to obtain the capacity electricity fee calculation function corresponding to the user side, including: determining the capacity electricity fee calculation parameter value based on the historical electricity load data and the historical capacity electricity fee corresponding to the historical electricity load data; and obtaining the capacity electricity fee calculation function based on the capacity electricity fee calculation parameter value.

[0059] In an optional embodiment, the following steps are used to obtain the capacity electricity fee calculation function corresponding to the user side: obtain the historical electricity load data and the historical capacity electricity fee corresponding to the historical electricity load data, and fit the historical electricity load data and the historical capacity electricity fee corresponding to the historical electricity load data to obtain the capacity electricity fee calculation parameter value, and finally obtain the capacity electricity fee calculation function according to the capacity electricity fee calculation parameter value. For example, the capacity electricity fee calculation function is:

[0060] FR=GD×-0.2P≤A

[0061] FR=0A<P<B

[0062] FR=GD×0.2B≤P

[0063] Among them, the first capacity comparison margin and the second capacity comparison margin of A and B, that is, the above-mentioned capacity electricity fee calculation parameter value, P is the annual electricity capacity on the user side, GD is the preset electricity purchase price, and FR is the capacity electricity fee.

[0064] Optionally, in the power system optimization method provided in the embodiment of the present application, determining the load type and power generation type corresponding to each historical operating data based on multiple historical operating data of the target power system includes: classifying multiple historical operating data to obtain a first initial load type and a first initial power generation type corresponding to each historical operating data; processing multiple historical operating data through a target classification model to obtain a second initial load type and a second initial power generation type corresponding to each historical operating data; obtaining the load type and power generation type corresponding to each historical operating data based on the first initial load type, the first initial power generation type, the second initial load type and the second initial power generation type.

[0065] In an optional embodiment, determining the load type and power generation type corresponding to each historical operation data includes the following steps:

[0066] Classify multiple historical operation data to obtain the first initial load type and the first initial power generation type corresponding to each historical operation data. For example, several typical load types can be pre-set based on historical data, such as peak period load type, valley period load type and flat peak period load type. Then, match the load characteristics corresponding to the typical load type with multiple historical operation data to obtain the load type corresponding to each historical operation data. For example, the matching formula is: K = argmin1 (LK-L0), where argmin1() is a function for extracting the load mode number in the typical load type that minimizes the difference between the current load characteristic and the typical load characteristic, LK is the load characteristic value corresponding to the typical load type, K is the type number, and L0 is the current load characteristic value corresponding to the historical operation data.

[0067] For example, based on historical data, several typical power generation types can be pre-set, such as photovoltaic power generation type, hydropower generation type and coal-fired power generation type, and then, based on the power generation characteristics corresponding to the typical power generation type, the power generation type corresponding to each historical operation data is matched. For example, the matching formula is: P = argmin2 (SK-S0), where argmin2() is a function for extracting the power mode number that minimizes the difference between the current power supply characteristics and the power supply characteristics corresponding to the typical power generation type from all typical power generation types, SP is the power supply characteristic corresponding to the typical power generation type, P is the type number, and S0 is the current power supply characteristic. This formula helps find the typical power supply characteristics that are closest to the current power supply characteristics, so that the current power supply can be classified into the corresponding power supply scenario. Through the above steps, the historical operation data can be classified into the closest typical mode, so as to obtain the first load type and the first power generation type. In an optional embodiment, it can be achieved by Figure 3 The flowchart shown implements type identification, pre-setting several typical load types, matching the load characteristics corresponding to the typical load types with multiple historical operating data, and then obtaining the load type corresponding to each historical operating data; pre-setting several typical power generation types, matching the power generation characteristics corresponding to the typical power generation types with multiple historical operating data, and then obtaining the power generation type corresponding to each historical operating data.

[0068] A plurality of historical operating data are processed through a target classification model to obtain a second initial load type and a second initial power generation type corresponding to each historical operating data. For example, a training sample set can be constructed based on sample operating data and sample load types and sample power generation types corresponding to the sample operating data. Then, a target classification model is obtained by training the training sample set. Finally, the second initial load type and the second initial power generation type corresponding to each historical operating data are obtained through the target classification model.

[0069] Finally, according to the first initial load type, the first initial power generation type, the second initial load type and the second initial power generation type, the load type and power generation type corresponding to each historical operation data are obtained.

[0070] Optionally, in the power system optimization method provided in the embodiment of the present application, power supply optimization of the target power system is performed based on the power generation optimization function, including: making predictions based on historical power load data on the user side to obtain a power load prediction value; obtaining an upper output limit value and a lower output limit value corresponding to the target power system; solving the power generation optimization function based on the power load prediction value, the upper output limit value and the lower output limit value to obtain a power supply optimization strategy; and optimizing the power supply of the target power system based on the power supply optimization strategy.

[0071] In an optional embodiment, a prediction is made based on the historical power load data on the user side to obtain a power load prediction value, and then the output upper limit and output lower limit of each power generation source in the target power system are obtained. Finally, the power generation optimization function is solved based on the power load prediction value, the output upper limit and the output lower limit to obtain a power supply optimization strategy. For example, the power supply optimization strategy may include optimizing the operating time of the generator set, adjusting the utilization ratio of renewable energy, and controlling the energy storage device to charge when the electricity price is low and discharge when the electricity price is high. By optimizing the target power system, the power generation optimization function is minimized, thereby achieving the purpose of controlling carbon emissions.

[0072] Optionally, in the power system optimization method provided in the embodiment of the present application, before determining the load type and power generation type corresponding to each historical operating data based on multiple historical operating data of the target power system, the method also includes: collecting multiple initial operating data in the target power system; filtering and filling in the multiple initial operating data to obtain multiple processed initial operating data; normalizing the processed multiple initial operating data to obtain multiple historical operating data.

[0073] In an optional embodiment, the following steps are adopted to obtain multiple historical operating data of the target power system: collecting multiple initial operating data in the target power system; filtering and filling in the gaps of the multiple initial operating data, that is, screening necessary data and filling in the gaps of the data, and then normalizing the processed multiple initial operating data for subsequent analysis and processing to obtain multiple historical operating data.

[0074] For example, one can Figure 4The flowchart shown obtains the above-mentioned multiple historical operation data: a database is set up to collect historical data, and the historical data at least includes photovoltaic power generation, load demand and electricity price information; the historical data is preprocessed to form multiple historical operation data. Through preprocessing, each data point is converted into a value between 0 and 1, where 0 corresponds to the minimum value and 1 corresponds to the maximum value. This conversion ensures that data of different scales and units can be effectively compared and analyzed.

[0075] The power system optimization method provided in the embodiment of the present application determines the load type and power generation type corresponding to each historical operating data based on multiple historical operating data of the target power system; determines the carbon emission calculation function corresponding to each historical operating data based on the load type and power generation type corresponding to each historical operating data; determines the power generation optimization function corresponding to the target power system based on the carbon emission calculation function corresponding to each historical operating data; and optimizes the power supply of the target power system based on the power generation optimization function, thereby solving the problem in the related art of optimizing the power supply of the power system through manual adjustment, resulting in poor power supply optimization effect. In this scheme, the load type and power generation type corresponding to the historical operation data are identified through multiple historical operation data of the target power system to obtain the corresponding load type and power generation type, and then the carbon emission calculation function corresponding to each historical operation data is determined according to the load type and power generation type corresponding to each historical operation data, and then the power generation optimization function corresponding to the target power system is determined according to the carbon emission calculation function. Finally, the power supply of the target power system is optimized through the power generation optimization function, different emission calculation functions are set according to different load types and power generation types, and the power generation optimization function is determined according to the emission calculation function. The power generation optimization function can effectively achieve the economy and low-carbon goals of the power system, thereby achieving the purpose of improving the power supply optimization effect.

[0076] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0077] The embodiment of the present application also provides an optimization device for a power system. It should be noted that the optimization device for a power system in the embodiment of the present application can be used to execute the optimization method for a power system provided in the embodiment of the present application. The optimization device for a power system provided in the embodiment of the present application is introduced below.

[0078] Figure 5 Schematic diagram of an optimization device for a power system according to an embodiment of the present application. Figure 5As shown, the device includes: a first determining unit 501, a second determining unit 502, a third determining unit 503 and an optimizing unit 504.

[0079] A first determining unit 501 is used to determine the load type and the power generation type corresponding to each historical operation data according to multiple historical operation data of the target power system;

[0080] A second determining unit 502 is used to determine a carbon emission calculation function corresponding to each historical operation data according to a load type and a power generation type corresponding to each historical operation data;

[0081] The third determination unit 503 is used to determine the power generation optimization function corresponding to the target power system according to the carbon emission calculation function corresponding to each historical operation data;

[0082] The optimization unit 504 is used to optimize the power supply of the target power system according to the power generation optimization function.

[0083] The power system optimization device provided in the embodiment of the present application determines the load type and power generation type corresponding to each historical operation data according to multiple historical operation data of the target power system through the first determination unit 501; the second determination unit 502 determines the carbon emission calculation function corresponding to each historical operation data according to the load type and power generation type corresponding to each historical operation data; the third determination unit 503 determines the power generation optimization function corresponding to the target power system according to the carbon emission calculation function corresponding to each historical operation data; the optimization unit 504 optimizes the power supply of the target power system according to the power generation optimization function, which solves the problem of optimizing the power supply of the power system through manual adjustment in the related art, resulting in poor power supply optimization effect. In this scheme, the load type and power generation type corresponding to the historical operation data are identified through multiple historical operation data of the target power system to obtain the corresponding load type and power generation type, and then the carbon emission calculation function corresponding to each historical operation data is determined according to the load type and power generation type corresponding to each historical operation data, and then the power generation optimization function corresponding to the target power system is determined according to the carbon emission calculation function. Finally, the power supply of the target power system is optimized through the power generation optimization function, different emission calculation functions are set according to different load types and power generation types, and the power generation optimization function is determined according to the emission calculation function. The power generation optimization function can effectively achieve the economy and low-carbon goals of the power system, thereby achieving the purpose of improving the power supply optimization effect.

[0084] Optionally, in the power system optimization device provided in the embodiment of the present application, the second determination unit includes: a first acquisition module, used to obtain the load capacity rating of the target power system and the carbon emission factor corresponding to the power generation type, wherein the carbon emission factor is used to characterize the carbon emission value corresponding to the unit power generation under the power generation type; a first processing module, used to obtain target electricity price data based on historical electricity price data and load types in multiple historical operation data; a determination module, used to determine the carbon emission calculation function based on the load capacity rating, the carbon emission factor corresponding to the power generation type and the target electricity price data.

[0085] Optionally, in the power system optimization device provided in the embodiment of the present application, the third determination unit includes: a summation module, used to sum the carbon emission calculation functions corresponding to each historical operation data to obtain a target carbon emission calculation function; a calculation module, used to calculate based on the historical power load data on the user side to obtain the capacity electricity fee calculation function corresponding to the user side; a second determination module, used to determine the power generation optimization function based on the target carbon emission calculation function and the capacity electricity fee calculation function.

[0086] Optionally, in the power system optimization device provided in the embodiment of the present application, the calculation module includes: a determination submodule, used to determine the capacity electricity charge calculation parameter value based on historical electricity load data and the historical capacity electricity charge corresponding to the historical electricity load data; and a calculation submodule, used to obtain the capacity electricity charge calculation function based on the capacity electricity charge calculation parameter value.

[0087] Optionally, in the optimization device of the power system provided in the embodiment of the present application, the first determination unit includes: a first classification module, used to classify multiple historical operation data to obtain a first initial load type and a first initial power generation type corresponding to each historical operation data; a second classification module, used to process multiple historical operation data through a target classification model to obtain a second initial load type and a second initial power generation type corresponding to each historical operation data; a second processing module, used to obtain the load type and power generation type corresponding to each historical operation data based on the first initial load type, the first initial power generation type, the second initial load type and the second initial power generation type.

[0088] Optionally, in the optimization device of the power system provided in the embodiment of the present application, the optimization unit includes: a prediction module, which is used to make predictions based on historical power load data on the user side to obtain a power load prediction value; a second acquisition module, which is used to obtain an upper output limit value and a lower output limit value corresponding to the target power system; a solution module, which is used to solve the power generation optimization function based on the power load prediction value, the upper output limit value and the lower output limit value to obtain a power supply optimization strategy; and an optimization module, which is used to optimize the power supply of the target power system based on the power supply optimization strategy.

[0089] Optionally, in the power system optimization device provided in the embodiment of the present application, the device also includes: a collection unit, which is used to collect multiple initial operating data in the target power system before determining the load type and power generation type corresponding to each historical operating data based on multiple historical operating data of the target power system; a first processing unit, which is used to filter and fill in the multiple initial operating data to obtain multiple processed initial operating data; and a second processing unit, which is used to normalize the processed multiple initial operating data to obtain multiple historical operating data.

[0090] The optimization device of the power system includes a processor and a memory. The above-mentioned first determination unit 501, second determination unit 502, third determination unit 503 and optimization unit 504 are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize corresponding functions.

[0091] The processor includes a kernel, which calls the corresponding program unit from the memory. One or more kernels can be set, and the power supply of the power system can be optimized by adjusting the kernel parameters.

[0092] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0093] An embodiment of the present invention provides a computer-readable storage medium having a program stored thereon, and the program implements a method for optimizing a power system when executed by a processor.

[0094] An embodiment of the present invention provides a processor, which is used to run a program, wherein the program executes an optimization method for a power system when it is run.

[0095] like Figure 6 As shown, an embodiment of the present invention provides an electronic device, the device includes a processor, a memory, and a program stored in the memory and executable on the processor, and the processor implements the following steps when executing the program: based on multiple historical operating data of the target power system, determine the load type and power generation type corresponding to each historical operating data; based on the load type and power generation type corresponding to each historical operating data, determine the carbon emission calculation function corresponding to each historical operating data; based on the carbon emission calculation function corresponding to each historical operating data, determine the power generation optimization function corresponding to the target power system; based on the power generation optimization function, optimize the power supply of the target power system.

[0096] Optionally, based on the load type and power generation type corresponding to each historical operating data, determining the carbon emission calculation function corresponding to each historical operating data includes: obtaining the load capacity rating of the target power system and the carbon emission factor corresponding to the power generation type, wherein the carbon emission factor is used to characterize the carbon emission value corresponding to the unit power generation under the power generation type; obtaining the target electricity price data based on the historical electricity price data and load type in multiple historical operating data; determining the carbon emission calculation function based on the load capacity rating, the carbon emission factor corresponding to the power generation type and the target electricity price data.

[0097] Optionally, based on the carbon emission calculation function corresponding to each historical operating data, determining the power generation optimization function corresponding to the target power system includes: summing the carbon emission calculation functions corresponding to each historical operating data to obtain a target carbon emission calculation function; calculating based on the historical electricity load data on the user side to obtain the capacity electricity fee calculation function corresponding to the user side; determining the power generation optimization function based on the target carbon emission calculation function and the capacity electricity fee calculation function.

[0098] Optionally, calculation is performed based on historical electricity load data on the user side to obtain a capacity electricity fee calculation function corresponding to the user side, including: determining a capacity electricity fee calculation parameter value based on the historical electricity load data and the historical capacity electricity fee corresponding to the historical electricity load data; and obtaining a capacity electricity fee calculation function based on the capacity electricity fee calculation parameter value.

[0099] Optionally, based on multiple historical operating data of the target power system, determining the load type and power generation type corresponding to each historical operating data includes: classifying the multiple historical operating data to obtain a first initial load type and a first initial power generation type corresponding to each historical operating data; processing the multiple historical operating data through a target classification model to obtain a second initial load type and a second initial power generation type corresponding to each historical operating data; and obtaining the load type and power generation type corresponding to each historical operating data based on the first initial load type, the first initial power generation type, the second initial load type and the second initial power generation type.

[0100] Optionally, based on the power generation optimization function, power supply optimization for the target power system includes: making predictions based on historical power load data on the user side to obtain a power load prediction value; obtaining an upper output limit value and a lower output limit value corresponding to the target power system; solving the power generation optimization function based on the power load prediction value, the upper output limit value and the lower output limit value to obtain a power supply optimization strategy; and optimizing the power supply for the target power system based on the power supply optimization strategy.

[0101] Optionally, before determining the load type and power generation type corresponding to each historical operating data based on multiple historical operating data of the target power system, the method also includes: collecting multiple initial operating data in the target power system; filtering and filling in the multiple initial operating data to obtain multiple processed initial operating data; normalizing the processed multiple initial operating data to obtain multiple historical operating data.

[0102] The devices in this article can be servers, PCs, PADs, mobile phones, etc.

[0103] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program having the following method steps: determining the load type and power generation type corresponding to each historical operating data based on multiple historical operating data of the target power system; determining the carbon emission calculation function corresponding to each historical operating data based on the load type and power generation type corresponding to each historical operating data; determining the power generation optimization function corresponding to the target power system based on the carbon emission calculation function corresponding to each historical operating data; and optimizing the power supply of the target power system based on the power generation optimization function.

[0104] Optionally, based on the load type and power generation type corresponding to each historical operating data, determining the carbon emission calculation function corresponding to each historical operating data includes: obtaining the load capacity rating of the target power system and the carbon emission factor corresponding to the power generation type, wherein the carbon emission factor is used to characterize the carbon emission value corresponding to the unit power generation under the power generation type; obtaining the target electricity price data based on the historical electricity price data and load type in multiple historical operating data; determining the carbon emission calculation function based on the load capacity rating, the carbon emission factor corresponding to the power generation type and the target electricity price data.

[0105] Optionally, based on the carbon emission calculation function corresponding to each historical operating data, determining the power generation optimization function corresponding to the target power system includes: summing the carbon emission calculation functions corresponding to each historical operating data to obtain a target carbon emission calculation function; calculating based on the historical electricity load data on the user side to obtain the capacity electricity fee calculation function corresponding to the user side; determining the power generation optimization function based on the target carbon emission calculation function and the capacity electricity fee calculation function.

[0106] Optionally, calculation is performed based on historical electricity load data on the user side to obtain a capacity electricity fee calculation function corresponding to the user side, including: determining a capacity electricity fee calculation parameter value based on the historical electricity load data and the historical capacity electricity fee corresponding to the historical electricity load data; and obtaining a capacity electricity fee calculation function based on the capacity electricity fee calculation parameter value.

[0107] Optionally, based on multiple historical operating data of the target power system, determining the load type and power generation type corresponding to each historical operating data includes: classifying the multiple historical operating data to obtain a first initial load type and a first initial power generation type corresponding to each historical operating data; processing the multiple historical operating data through a target classification model to obtain a second initial load type and a second initial power generation type corresponding to each historical operating data; and obtaining the load type and power generation type corresponding to each historical operating data based on the first initial load type, the first initial power generation type, the second initial load type and the second initial power generation type.

[0108] Optionally, based on the power generation optimization function, power supply optimization for the target power system includes: making predictions based on historical power load data on the user side to obtain a power load prediction value; obtaining an upper output limit value and a lower output limit value corresponding to the target power system; solving the power generation optimization function based on the power load prediction value, the upper output limit value and the lower output limit value to obtain a power supply optimization strategy; and optimizing the power supply for the target power system based on the power supply optimization strategy.

[0109] Optionally, before determining the load type and power generation type corresponding to each historical operating data based on multiple historical operating data of the target power system, the method also includes: collecting multiple initial operating data in the target power system; filtering and filling in the multiple initial operating data to obtain multiple processed initial operating data; normalizing the processed multiple initial operating data to obtain multiple historical operating data.

[0110] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0111] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1A device that provides the functions specified in a block or multiple blocks.

[0112] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0113] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0114] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0115] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0116] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0117] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0118] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0119] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for optimizing a power system, characterized in that: include: According to a plurality of historical operation data of the target power system, determining a load type and a power generation type corresponding to each historical operation data; Determine the carbon emission calculation function corresponding to each historical operation data according to the load type and power generation type corresponding to each historical operation data; Determine a power generation optimization function corresponding to the target power system according to a carbon emission calculation function corresponding to each historical operation data; The target power system is powered by electricity according to the power generation optimization function.

2. The method according to claim 1, characterized in that According to the load type and power generation type corresponding to each historical operation data, the carbon emission calculation function corresponding to each historical operation data is determined to include: Acquire the load capacity rating of the target power system and the carbon emission factor corresponding to the power generation type, wherein the carbon emission factor is used to characterize the carbon emission value corresponding to the unit power generation under the power generation type; Obtaining target electricity price data according to the historical electricity price data in the plurality of historical operation data and the load type; The carbon emission calculation function is determined according to the load capacity rating, the carbon emission factor corresponding to the power generation type and the target electricity price data.

3. The method according to claim 1, characterized in that According to the carbon emission calculation function corresponding to each historical operation data, determining the power generation optimization function corresponding to the target power system includes: The carbon emission calculation function corresponding to each historical operation data is summed to obtain the target carbon emission calculation function; Calculate based on the historical electricity load data on the user side to obtain the corresponding capacity electricity fee calculation function on the user side; The power generation optimization function is determined according to the target carbon emission calculation function and the capacity electricity fee calculation function.

4. The method according to claim 3, characterized in that The calculation function of the capacity electricity fee corresponding to the user side is obtained based on the historical electricity load data of the user side, including: Determining a capacity electricity fee calculation parameter value based on the historical electricity load data and the historical capacity electricity fee corresponding to the historical electricity load data; The capacity electricity fee calculation function is obtained according to the capacity electricity fee calculation parameter value.

5. The method according to claim 1, characterized in that According to multiple historical operation data of the target power system, the load type and generation type corresponding to each historical operation data are determined to include: Classifying the plurality of historical operation data to obtain a first initial load type and a first initial power generation type corresponding to each historical operation data; Processing the plurality of historical operation data by using a target classification model to obtain a second initial load type and a second initial power generation type corresponding to each historical operation data; Based on the first initial load type, the first initial power generation type, the second initial load type and the second initial power generation type, the load type and power generation type corresponding to each historical operation data are obtained.

6. The method according to claim 1, characterized in that According to the power generation optimization function, optimizing the power supply of the target power system includes: Make a prediction based on the historical power load data on the user side to obtain the power load forecast value; Obtaining an output upper limit value and an output lower limit value corresponding to the target power system; Solving the power generation optimization function according to the power load forecast value, the output upper limit value and the output lower limit value to obtain a power supply optimization strategy; According to the power supply optimization strategy, power supply optimization is performed on the target power system.

7. The method according to claim 1, characterized in that Before determining the load type and the power generation type corresponding to each historical operation data according to the plurality of historical operation data of the target power system, the method further includes: Collecting a plurality of initial operation data in the target power system; Filtering and filling in gaps on the multiple initial operation data to obtain multiple processed initial operation data; The processed multiple initial operation data are normalized to obtain the multiple historical operation data.

8. An optimization device for a power system, characterized in that: include: A first determining unit, configured to determine a load type and a power generation type corresponding to each historical operation data according to a plurality of historical operation data of a target power system; A second determination unit is used to determine a carbon emission calculation function corresponding to each historical operation data according to a load type and a power generation type corresponding to each historical operation data; A third determination unit is used to determine a power generation optimization function corresponding to the target power system according to a carbon emission calculation function corresponding to each historical operation data; The optimization unit is used to optimize the power supply of the target power system according to the power generation optimization function.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the storage medium is controlled to execute the power system optimization method according to any one of claims 1 to 7 on a device.

10. An electronic device, characterized in that: It includes one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the power system optimization method described in any one of claims 1 to 7.

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