Optimized scheduling strategy generation method of intelligent park and related device

Abstract

The invention discloses an optimal scheduling strategy generation method for an intelligent park and a related device, and the method comprises the steps: obtaining the historical data of a wind turbine unit, a photovoltaic unit and a conventional unit in the intelligent park, and constructing a wind power output model and a photovoltaic output model in different output scenes according to the historical data, the method comprises the steps of obtaining output data under different output scenes through Monte Carlo simulation, determining conditional risk values of the corresponding output scenes according to the output data, calculating carbon emission cost by combining conventional unit output data, and finally screening out a target scheduling strategy under a preset constraint condition by comprehensively considering the conditional risk values and the carbon emission cost. The target scheduling strategy is an optimal scheduling strategy, optimal scheduling of intelligent park energy is realized, distributed resources are fully utilized, carbon emission of a power system is reduced, and economic benefits are improved.

Description

Technical Field

[0001] The present application relates to the technical field of optimization scheduling, and in particular to a method for generating an optimization scheduling strategy for a smart park and related devices. Background Art

[0002] Smart parks refer to areas that integrate information technology, energy technology, and modern management concepts. Through intelligent integration and collaborative management of various resources in smart parks, efficient energy utilization, convenient services, and sustainable development can be achieved. Distributed energy resources (DER), such as wind power generation and photovoltaic power generation, have gradually become a key part of the energy supply system of smart parks because of their clean, environmentally friendly, and decentralized layout.

[0003] In the context of reducing carbon emissions, smart parks need to carry out low-carbon optimization scheduling. At the same time, with the increase in energy costs, smart parks also need to make full use of distributed resources when carrying out low-carbon optimization scheduling, reduce the operating costs of smart parks, and improve economic benefits. Although distributed energy can reduce carbon emissions, it has problems such as unstable output, unpredictable and discontinuous production. These problems easily lead to increased risks in power market transactions and bring difficulties to cost control. The existing optimization scheduling methods are not accurate enough in quantifying the fluctuations in wind power and photovoltaic output, resulting in the inability of scheduling plans to fully consider the characteristics of new energy, affecting the reliability and stability of energy supply, and in terms of carbon emissions and carbon trading management, it is difficult to effectively adjust the use of carbon quotas, and it is impossible to effectively increase carbon trading revenue, which limits the development of smart parks under the low-carbon economy.

[0004] Therefore, how to obtain the optimal scheduling strategy to make full use of distributed resources, reduce the carbon emissions of the power system and improve economic benefits. Summary of the invention

[0005] The embodiment of the present application provides a method for generating an optimized scheduling strategy for a smart park and a related device. By constructing a wind power output model and a photovoltaic output model under different output scenarios of the smart park, the conditional risk value under different output scenarios is calculated, and the carbon emission cost is calculated according to the output data of conventional units. The optimal scheduling strategy is obtained by comprehensively considering the conditional risk value and the carbon emission cost. The optimal scheduling strategy effectively reduces the risks brought by fluctuations in wind power and photovoltaic output, reduces carbon emissions, improves energy utilization efficiency, and improves the economic benefits and overall scheduling efficiency of the smart park in a multi-market environment.

[0006] In a first aspect, an embodiment of the present application provides a method for generating an optimization scheduling strategy for a smart park, which is applied to a control device of a smart park, wherein the smart park includes: a wind turbine, a photovoltaic unit, and a conventional unit; the method includes: Acquire a first wind speed data set within a historical time period, a first wind power output data set corresponding to the wind turbine generator set, a first solar irradiance data set, and a first photovoltaic output data set corresponding to the photovoltaic generator set; The first wind speed data set and the first solar irradiance data set are classified to obtain m wind power categories and n photovoltaic categories, and the m wind power categories and the n photovoltaic categories are combined to obtain w output scenarios; w is equal to m multiplied by n, and n and m are both positive integers; Determine w wind power output models according to the first wind speed data set and the w output scenarios, and determine w photovoltaic output models according to the first solar irradiance data set and the w output scenarios; each output scenario corresponds to one wind power output model and one photovoltaic output model; Performing Monte Carlo simulation on the w wind power output models and the w photovoltaic output models under the w output scenarios to obtain k second wind power output data and k second photovoltaic output data; Determining w target condition risk values ​​according to the k second wind power output data and the k second photovoltaic output data; Obtaining conventional output data of the conventional unit in the w output scenarios to obtain w conventional output data; Determine the carbon emission costs under the w output scenarios according to the k second wind power output data, the k second photovoltaic output data and the w conventional output data, to obtain w carbon emission costs; A target scheduling strategy is determined according to the w target condition risk values ​​and the w carbon emission costs.

[0007] In a second aspect, an embodiment of the present application provides an optimized scheduling strategy generation device for a smart park, which is applied to a control device of a smart park, wherein the smart park includes: a wind turbine, a photovoltaic unit, and a conventional unit; the optimized scheduling strategy generation device for the smart park includes: a data acquisition module, a scene classification module, a model training module, a data simulation module, a data processing module, and a strategy generation module, wherein: The data acquisition module is used to obtain a first wind speed data set within a historical time period, a first wind power output data set corresponding to the wind turbine generator set, a first solar irradiance data set, and a first photovoltaic output data set corresponding to the photovoltaic generator set; The scene classification module is used to classify the first wind speed data set and the first solar irradiance data set to obtain m wind power categories and n photovoltaic categories, and combine the m wind power categories and the n photovoltaic categories to obtain w output scenes; w is equal to m multiplied by n, and n and m are both positive integers; The model training module is used to determine w wind power output models according to the first wind speed data set and the w output scenarios, and to determine w photovoltaic output models according to the first solar irradiance data set and the w output scenarios; each output scenario corresponds to one wind power output model and one photovoltaic output model; The data simulation module is used to perform Monte Carlo simulation under the w output scenarios through the w wind power output models and the w photovoltaic output models to obtain k second wind power output data and k second photovoltaic output data; The data processing module is used to determine w target condition risk values ​​according to the k second wind power output data and the k second photovoltaic output data; The data acquisition module is also used to obtain the conventional output data of the conventional unit in the w output scenarios to obtain w conventional output data; The data processing module is further used to determine the carbon emission costs under the w output scenarios according to the k second wind power output data, the k second photovoltaic output data and the w conventional output data, to obtain w carbon emission costs; The strategy generation module is used to determine the target scheduling strategy according to the w target condition risk values ​​and the w carbon emission costs.

[0008] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the program includes instructions for executing the steps in the first aspect of the embodiment of the present application.

[0009] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps described in the first aspect of the embodiment of the present application.

[0010] In a fifth aspect, an embodiment of the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps described in the first aspect of the embodiment of the present application. The computer program product may be a software installation package.

[0011] It can be seen that the following beneficial effects are achieved by using the embodiments of the present application: By implementing the embodiments of the present application, the historical data of wind turbines, photovoltaic units and conventional units in the smart park are obtained, and the wind power output model and photovoltaic output model under different output scenarios are constructed based on the historical data. The output data under different output scenarios are obtained through Monte Carlo simulation, and the conditional risk value of the corresponding output scenario is determined based on the output data. The carbon emission cost is calculated in combination with the output data of the conventional units. Finally, the conditional risk value and the carbon emission cost are comprehensively considered to select the target scheduling strategy under the preset constraints. It can be seen that the target scheduling strategy obtained by comprehensively considering the conditional risk value and the carbon emission cost under different wind power and photovoltaic output scenarios is the optimal scheduling strategy, which can effectively reduce the risks brought by wind power and photovoltaic output fluctuations, reduce carbon emissions, improve energy utilization efficiency, and improve the economic benefits and overall scheduling efficiency of the smart park in a multi-market environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the background technology, the drawings required for use in the embodiments of the present application or the background technology will be described below.

[0013] Figure 1 It is a flow chart of a method for generating an optimization scheduling strategy for a smart park provided in an embodiment of the present application; Figure 2 This is a schematic diagram of an energy structure of a smart park provided in an embodiment of the present application; Figure 3 It is a schematic diagram of a calculation process of a conditional risk value in an output scenario provided in an embodiment of the present application; Figure 4 This is an operational structure diagram of a smart park operator participating in a multi-time electricity market and a carbon market provided by an embodiment of the present application; Figure 5 This is an application scenario diagram of a method for generating an optimization scheduling strategy for a smart park provided in an embodiment of the present application; Figure 6 It is a structural diagram of an optimization scheduling strategy generation system for a smart park provided in an embodiment of the present application; Figure 7 It is a structural schematic diagram of an optimized scheduling strategy generation device for a smart park provided in an embodiment of the present application; Figure 8 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0014] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution 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 are within the scope of protection of this application.

[0015] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0016] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0017] The following is an explanation of the relevant contents, concepts, meanings, technical issues, technical solutions, beneficial effects, etc. involved in the embodiments of the present application.

[0018] See also Figure 1 , Figure 1 The present invention provides a flow chart of a method for generating an optimization scheduling strategy for a smart park provided in an embodiment of the present invention. The method is applied to a control device of a smart park. The smart park includes: a wind turbine, a photovoltaic unit, and a conventional unit. The method includes but is not limited to the following steps: S101, obtaining a first wind speed data set within a historical time period, a first wind power output data set corresponding to the wind turbine generator set, a first solar irradiance data set, and a first photovoltaic output data set corresponding to the photovoltaic generator set.

[0019] See also Figure 2 , Figure 2 is a schematic diagram of an energy structure of a smart park provided in an embodiment of the present application, such as Figure 2As shown in the figure, the smart park includes a variety of energy equipment to achieve comprehensive utilization and power supply of energy. The smart park includes x conventional units, x is a positive integer. Conventional units generally refer to traditional thermal power generating units, such as coal-fired and gas-fired units, which can generate electricity stably, but there are environmental problems such as carbon emissions. They can be used to adjust production energy in the energy supply of the smart park. The smart park includes y wind turbines, y is a positive integer. Wind turbines are equipment that converts wind energy into electrical energy. They are clean energy. The power generation is greatly affected by wind speed and wind direction and is intermittent. The smart park includes z photovoltaic units, z is a positive integer. Photovoltaic units convert solar energy into electrical energy through the photovoltaic effect. They are also clean energy. They are limited by light intensity and duration, and power generation is unstable. The smart park includes s energy storage devices. Energy storage devices are used to store electrical energy. They are charged when there is excess electricity and discharged when there is insufficient electricity. They can smooth the fluctuation of new energy power generation and adjust the supply and demand of electricity.

[0020] In the embodiment of the present application, a wind turbine refers to a device that converts wind energy into electrical energy. Wind energy is a clean and renewable energy source, so that the wind turbine does not produce greenhouse gas emissions during the power generation process, which helps to achieve low-carbon goals. However, the power generation output of the wind turbine is greatly affected by the wind speed. Unstable wind speed may cause significant fluctuations in the output power of the wind turbine. For example, when the wind speed is low, the wind turbine may not be able to reach the rated power generation. When the wind speed is too high, the wind turbine may stop operating for equipment safety considerations.

[0021] In the embodiments of the present application, a photovoltaic unit refers to a device that converts solar energy into electrical energy through the photoelectric effect. The solar energy used by the photovoltaic unit is also a clean and renewable energy. However, the power generation capacity of the photovoltaic unit depends on the solar irradiance and the illumination time. At night or on rainy days, when the solar irradiance is low, the output of the photovoltaic unit will drop significantly or even stop generating electricity. At the same time, factors such as changes in cloud cover will also cause unstable solar irradiance, resulting in fluctuations in the output power of the photovoltaic unit.

[0022] In the embodiments of this application, conventional units refer to traditional thermal power generation equipment, such as gas turbines, coal-fired units, etc. Conventional units generate relatively stable power. In the smart park energy supply system, when wind turbines and photovoltaic units are restricted by natural conditions, such as low wind speed and insufficient light, conventional units can provide stable power to ensure the stability of the park's power supply and meet the needs of electrical equipment. However, conventional units consume fossil energy and other energy sources to generate carbon emissions.

[0023] In smart parks, wind turbines and photovoltaic units provide clean energy to help achieve low-carbon goals, but there is a problem of unstable output. Conventional units can ensure a stable supply of electricity, but they will produce carbon emissions. Therefore, by generating optimized scheduling strategies, we can give full play to their respective advantages and achieve efficient energy utilization and low-carbon development.

[0024] In a specific embodiment, the first wind speed data set, the first wind power output data set corresponding to the wind turbine, the first solar irradiance data set, and the first photovoltaic output data set corresponding to the photovoltaic unit can be obtained within the historical time period. Since wind speed and solar irradiance directly affect the output of wind turbines and photovoltaic units, data that can reflect the historical operating conditions of wind turbines and photovoltaic units in the smart park can be collected for model construction, scenario analysis, and risk assessment.

[0025] In one possible embodiment, various monitoring devices will be installed in the smart park to measure data within a historical time period, such as a wind speed sensor used to measure wind speed to obtain a first wind speed data set, and a solar irradiance sensor to collect solar irradiance information to obtain a first solar irradiance data set. At the same time, the wind turbine's own monitoring system can record the output at different times to obtain a first wind power output data set, and the photovoltaic unit's own monitoring system can record photovoltaic output data at different times to obtain a first photovoltaic output data set. These data can be stored in the local database of the smart park, or transmitted to the data center of the smart park through a sensor network for storage and management.

[0026] S102: Classify the first wind speed data set and the first solar irradiance data set to obtain m wind power categories and n photovoltaic categories, and combine the m wind power categories and the n photovoltaic categories to obtain w output scenarios.

[0027] Among them, w is equal to m multiplied by n, and n and m are both positive integers. For example, according to the numerical range, change trend and other characteristics of wind speed and solar irradiance, the wind speed can be classified into three wind power categories: strong wind, normal wind, and weak wind, and the solar irradiance can be divided into three photovoltaic categories: strong light, normal light, and weak light. Then, the wind power category and the photovoltaic category are combined. Different combinations of wind speed and solar irradiance will produce different energy output situations, so 9 different output scenarios can be formed, such as the output scenario of strong wind and normal light.

[0028] In a specific embodiment, the first wind speed data set and the first solar irradiance data set can be classified according to historical experience and data distribution to obtain m wind power categories and n photovoltaic categories. The m wind power categories and n photovoltaic categories can be combined to obtain w output scenarios, that is, each wind power category can be combined with each photovoltaic category to form m times n output scenarios.

[0029] By classifying and combining multiple output scenarios, we can take into account various possible energy production conditions more comprehensively, so that in the subsequent optimization scheduling strategy generation process, we can conduct targeted analysis on different output scenarios and plan in advance how to reasonably arrange energy production and distribution under various circumstances. For example, in the output scenario of strong wind and strong light, how to make full use of the high output of wind turbines and photovoltaic units, and in the output scenario of weak wind and weak light, how to coordinate conventional units to supplement electricity, so as to ensure the stable power supply and economic benefits of the entire smart park.

[0030] S103: Determine w wind power output models according to the first wind speed data set and the w output scenarios, and determine w photovoltaic output models according to the first solar irradiance data set and the w output scenarios.

[0031] Among them, each output scenario corresponds to a wind power output model and a photovoltaic output model. The output of wind turbines and photovoltaic units in the smart park is affected by multiple factors such as wind speed and solar irradiance. These factors change at any time, resulting in diversity and uncertainty in energy production. Therefore, different wind speed and solar irradiance combinations form different output scenarios. The characteristics of energy production in each scenario are different. By constructing output models for different output scenarios, the energy production situation in different output scenarios can be simulated more accurately.

[0032] In a specific embodiment, w wind power output models under w output scenarios can be constructed based on the Weibull distribution according to the first wind speed data set, and w photovoltaic output models under w output scenarios can be constructed based on the Beta distribution according to the first solar irradiance data set. The wind power output model and photovoltaic output model constructed based on the Weibull distribution and the Beta distribution can more accurately describe the uncertainty of wind speed and solar irradiance, and thus more accurately predict the output of wind turbines and photovoltaic units under different output scenarios.

[0033] Optionally, the above step of determining w wind power output models according to the first wind speed data set and the w output scenarios may specifically include the following steps: A301. Select wind speed data corresponding to a first output scenario from the first wind speed data set to obtain a training wind speed data set; the first output scenario is any one of the w output scenarios; A302, obtaining a target likelihood function; A303, solving the target likelihood function by using a preset maximum likelihood estimation method and the training wind speed data set to obtain a first shape parameter and a first scale parameter; A304. Determine a first probability density function corresponding to the first output scenario according to the first shape parameter and the first scale parameter; the first probability density function is a Weibull probability density function; A305. Determine a wind power output model corresponding to the first output scenario according to a preset mapping relationship between wind speed and wind power output and the first probability density function.

[0034] Among them, the preset Maximum Likelihood Estimation (MLE) refers to a pre-set parameter estimation method, which is used to estimate model parameters through observed data when the probability distribution model form of the data is known but the parameters are unknown.

[0035] The preset mapping relationship between wind speed and wind power output refers to a preset corresponding relationship between wind speed and wind power output established according to the physical characteristics and operating principles of the wind turbine generator set.

[0036] In weak wind conditions, when the wind speed is lower than the cut-in wind speed, the wind turbine may not be able to start, and the output power is 0 at this time. When the wind speed is lower than the rated wind speed of the wind turbine and not lower than the cut-in wind speed, the wind turbine can operate to generate electricity, but due to the low wind speed, the power increases slowly and the output is low. For example, when the cut-in wind speed is 3m / s and the wind speed is 3-5m / s, the wind power output may only increase by 5%-10% of the rated power for every 1m / s increase in wind speed.

[0037] Under normal wind conditions, when the wind speed is not less than the rated wind speed, the relationship between wind power output and wind speed is relatively stable, and as the wind speed increases, the wind power output increases significantly. For example, when the wind speed is 8-12m / s, the wind power output may increase by 15%-20% of the rated power for every 1m / s increase in wind speed. However, in some cases, such as when the wind speed is close to the cut-out wind speed, in order to protect the safety of wind turbine equipment, the wind turbine will limit the output power by adjusting the blade angle and other methods to keep it near the rated power.

[0038] In strong wind conditions, when the wind speed is not lower than the cut-out wind speed, excessively high wind speeds may cause excessive pressure and wear on the blades, gearboxes and other components of the wind turbine. At this time, the power output needs to be limited to protect the equipment. The wind turbine will stop running and the output power will drop to 0.

[0039] In a specific embodiment, in order to construct a wind power output model under a specific output scenario, it is necessary to select the wind speed data under the scenario as training samples in a targeted manner to ensure that the model can accurately reflect the relationship between wind speed and wind power output in the scenario. Therefore, the wind speed data corresponding to the first output scenario can be selected from the first wind speed data set to obtain a training wind speed data set, wherein the first output scenario is any one of the w output scenarios.

[0040] Obtain the target likelihood function. For the Weibull distribution, its probability density function has a specific form. According to this form and the training wind speed data set, the target likelihood function can be constructed. The target likelihood function is as follows:

[0041] In the above formula, Represents the first shape parameter and the first scale parameter The likelihood function of ; Indicates the number of training wind speed data in the training wind speed dataset; Indicates Training wind speed data; Represents the quadrature formula.

[0042] Next, the target likelihood function is solved by using the preset maximum likelihood estimation method and the training wind speed data set to obtain the first shape parameter and the first scale parameter. Specifically, since directly maximizing the likelihood function is usually complicated, the logarithmic likelihood function can be maximized, and the maximization of the logarithmic likelihood function is as follows:

[0043] In the above formula, Represents the log-likelihood function. By solving the maximum value of the log-likelihood function, the first shape parameter can be obtained. and the first scale parameter .

[0044] A first probability density function corresponding to the first output scenario is determined according to the first shape parameter and the first scale parameter, wherein the first probability density function is a Weibull probability density function, and the Weibull probability density function is as follows:

[0045] In the above formula, express Real-time wind speed at the moment; Indicates wind speed probability; represents the first shape parameter; represents the first scale parameter.

[0046] The wind power output model corresponding to the first output scenario is determined according to the preset mapping relationship between wind speed and wind power output and the first probability density function, that is, the wind power output model for predicting the first output scenario is obtained by converting the probability distribution of wind speed into the probability distribution of wind power output. The wind power output model can accurately predict the wind power output in the first output scenario. For example, when the wind power category in the first output scenario is weak wind, the wind power output probability score under weak wind conditions can be determined through the preset mapping relationship between wind speed and wind power output.

[0047] Optionally, the above step of determining w photovoltaic output models according to the first solar irradiance data set and the w output scenarios may specifically include the following steps: B301. Selecting first solar irradiance data corresponding to a second output scene from the first solar irradiance data set to obtain a training solar irradiance data set; the second output scene is any one of the w output scenes; B302, determining the solar irradiance average value and solar irradiance standard deviation corresponding to the training solar irradiance data set; B303, determining a second shape parameter and a third shape parameter according to the solar irradiance average value and the solar irradiance standard deviation; B304. Determine a second probability density function corresponding to the second output scenario according to the second shape parameter and the third shape parameter; the second probability density function is a Beta distribution probability density function; B305. Determine a photovoltaic output model corresponding to the second output scenario according to a preset normalization coefficient and the second probability density function.

[0048] In a specific embodiment, the distribution of solar irradiance is different under different output scenarios. In order to accurately reflect the relationship between solar irradiance and photovoltaic output under the scenario, the solar irradiance data under a specific scenario can be selected in a targeted manner. Therefore, the first solar irradiance data corresponding to the second output scenario is selected from the first solar irradiance data set to obtain a training solar irradiance data set, wherein the second output scenario is any one of the w output scenarios.

[0049] Next, the average solar irradiance value and the standard deviation of solar irradiance corresponding to the training solar irradiance data set are determined, where the average solar irradiance value reflects the overall level of solar irradiance under the second output scenario, and the standard deviation reflects the degree of dispersion of the solar irradiance data relative to the average value.

[0050] The second shape parameter and the third shape parameter are determined according to the average solar irradiance and the standard deviation of the solar irradiance, wherein the calculation formula of the second shape parameter is as follows:

[0051] In the above formula, represents the second shape parameter; represents the average solar irradiance; Represents the standard deviation of solar irradiance.

[0052] The calculation formula of the third shape parameter is as follows;

[0053] In the above formula, represents the third shape parameter.

[0054] A second probability density function corresponding to the second output scenario is determined according to the second shape parameter and the third shape parameter, wherein the second probability density function is a beta distribution probability density function, and the beta distribution probability density function is as follows:

[0055] In the above formula, Indicates the solar irradiance at the current moment With the preset maximum irradiance The ratio of express The probability of represents the gamma function; represents the second shape parameter; Represents the third shape parameter.

[0056] The photovoltaic output model corresponding to the second output scenario is determined according to the preset normalization coefficient and the second probability density function, and the photovoltaic output model indicates that the photovoltaic output power also obeys the Beta distribution. Among them, the preset normalization coefficient refers to a pre-set coefficient used to ensure the correct normalization of the probability density function. Among them, the photovoltaic output model is as follows:

[0057] In the above formula, Indicates the output power of photovoltaic, that is, photovoltaic output; Indicates photovoltaic output The probability of It is a preset normalization coefficient, which is usually a coefficient determined according to the number of photovoltaic panels in a photovoltaic unit. The more panels there are, the stronger the power generation capacity of the entire photovoltaic system will be under the same lighting conditions, and the larger the coefficient will be. It is a preset normalization coefficient, which usually indicates a coefficient determined according to the area of ​​the photovoltaic panels in the photovoltaic unit. The larger the area of ​​the photovoltaic panels, the more solar radiation can be received, thus generating more electricity. It is a preset normalization coefficient, which usually refers to a coefficient determined according to the photoelectric conversion efficiency of the photovoltaic panels in the photovoltaic unit. It reflects the ability of the photovoltaic panels to convert solar energy into electrical energy. The higher the conversion efficiency, the more electrical energy is generated under the same light. Indicates the maximum output power of photovoltaic.

[0058] The photovoltaic output model constructed by combining the hardware parameters of the photovoltaic unit, such as the number, area, and conversion efficiency of solar panels, and the statistical characteristics of the Beta distribution, can accurately describe the probability distribution of photovoltaic output and provide accurate information for the energy scheduling of the smart park. In different output scenarios, the output of the photovoltaic unit can be predicted based on the model, and energy production, storage, and distribution can be reasonably arranged to improve energy utilization efficiency and reduce energy costs. It also helps to ensure the stability and reliability of the park's power supply.

[0059] S104 . Performing Monte Carlo simulation under the w output scenarios using the w wind power output models and the w photovoltaic output models to obtain k second wind power output data and k second photovoltaic output data.

[0060] In the embodiment of the present application, Monte Carlo simulation is a numerical method for solving mathematical, physical, engineering and other problems through random sampling. By performing multiple random simulation calculations on the selected wind power output model and photovoltaic output model, multiple second wind power output data and multiple second photovoltaic output data can be obtained.

[0061] In a specific embodiment, for each output scenario, a wind power output model corresponding to the scenario is selected from w wind power output models, and a photovoltaic output model corresponding to the scenario is selected from w photovoltaic output models, and Monte Carlo simulation is performed through the selected wind power output model and photovoltaic output model to obtain k second wind power output data and k second photovoltaic output data. The larger k is, the closer the simulation result is to the actual situation, but the amount of calculation will also increase accordingly.

[0062] Wind power output and photovoltaic output are affected by natural factors and have great uncertainty. Through Monte Carlo simulation and multiple random simulations based on probability distribution, we can simulate the output conditions under different output scenarios, thereby more comprehensively showing the range and possibility of changes in wind power output and photovoltaic output under different output scenarios, so as to accurately simulate the energy production conditions corresponding to the output scenarios.

[0063] Among them, k is equal to w multiplied by e, e is an integer greater than or equal to 1, and each output scenario corresponds to e second wind power output data and e second photovoltaic output data.

[0064] Optionally, the above step of performing Monte Carlo simulation on the w wind power output models and the w photovoltaic output models under the w output scenarios to obtain k second wind power output data and k second photovoltaic output data may specifically include the following steps: A401. Selecting a wind power output model corresponding to a target output scenario from the w wind power output models to obtain a target wind power output model, and selecting a photovoltaic output model corresponding to the target output scenario from the w photovoltaic output models to obtain a target photovoltaic output model; the target output scenario is any one of the w output scenarios; A402. Performing Monte Carlo simulation on the target wind power output model and the target photovoltaic output model to obtain x second wind power output data and x second photovoltaic output data; x is an integer greater than or equal to e; A403, removing abnormal values ​​from the x pieces of second wind power output data to obtain e pieces of second wind power output data; A404. Remove abnormal values ​​from the x pieces of second photovoltaic output data to obtain e pieces of second photovoltaic output data.

[0065] In a specific embodiment, a wind power output model corresponding to the target output scenario is selected from w wind power output models to obtain a target wind power output model, and a photovoltaic output model corresponding to the target output scenario is selected from w photovoltaic output models to obtain a target photovoltaic output model, wherein the target output scenario is any one of the w output scenarios. For example, if the target output scenario is a strong wind or strong light scenario, a wind power output model and a photovoltaic output model constructed based on strong wind speed data and strong light solar irradiance data are selected. By selecting a suitable target output model, the Monte Carlo simulation can be made more in line with the actual situation and the accuracy of the simulation results can be improved.

[0066] According to the Monte Carlo simulation process, in the target wind power output model, the wind speed value is randomly generated according to the Weibull probability density function on which the target wind power output model is based, and then the wind power output is calculated according to the preset mapping relationship between wind speed and wind power output. In the target photovoltaic output model, the solar irradiance value is randomly generated according to the Beta distribution probability density function, and the photovoltaic output is calculated in combination with the preset normalization coefficient. The simulation can obtain x second wind power output data and x second photovoltaic output data, where x is an integer greater than or equal to e.

[0067] During the simulation process, due to the randomness of random sampling, some outliers that deviate from the normal range may be generated, which may affect the accuracy of subsequent data analysis, so they need to be removed to obtain more reliable wind power output data. By removing the outliers in the x second wind power output data, e second wind power output data can be obtained. At the same time, by removing the outliers in the x second photovoltaic output data, e second photovoltaic output data can be obtained. After removing the outliers, the e second wind power output data and the e second photovoltaic output data can better represent the actual distribution of wind power output and photovoltaic output under the target output scenario, which can improve the accuracy of subsequent risk value assessment and scheduling strategy formulation.

[0068] Optionally, the above step of removing abnormal values ​​from the x second wind power output data to obtain e second wind power output data may specifically include the following steps: B401. Selecting x target wind power output data corresponding to the target output scenario from the first wind power output data set; B402. Determine a target Pearson correlation coefficient and a target mean square error according to the x second wind power output data and the x target wind power output data; B403, determining a first weight coefficient corresponding to the target Pearson correlation coefficient and a second weight coefficient corresponding to the target mean square error; the sum of the first weight coefficient and the second weight coefficient is 1; B404, determining a target comprehensive score according to the target Pearson correlation coefficient, the first weight coefficient, the target mean square error and the second weight coefficient; B405. If the target comprehensive score is less than or equal to a preset comprehensive score threshold, determine the wind power output difference between the x second wind power output data and the x target wind power output data to obtain x wind power output difference values; B406. Selecting a wind power output difference value that is less than a preset difference threshold value from the x wind power output difference values ​​to obtain e wind power output difference values; B407. Selecting the second wind power output data corresponding to the e wind power output differences from the x second wind power output data to obtain the e second wind power output data; B408. If the target comprehensive score is greater than the preset comprehensive score threshold, obtain the average value of the x second wind power output data, and select e second wind power output data with the smallest absolute value of the difference between the average value and the x second wind power output data.

[0069] Among them, the preset comprehensive score threshold refers to a limit value set when evaluating the degree of matching between the second wind power output data and the target wind power output data. It is used to determine whether the similarity between the simulated data and the actual data meets acceptable standards. It can be set specifically based on historical data experience and actual application requirements.

[0070] In a specific embodiment, the first wind power output data set is actually recorded wind power output data. X data matching the target output scenario are screened out from the first wind power output data set to obtain x target wind power output data. The target wind power output data is used to evaluate and compare with the second wind power output data obtained by Monte Carlo simulation to determine whether the simulation results are reasonable.

[0071] The target Pearson correlation coefficient and the target mean square error are determined according to the x second wind power output data and the x target wind power output data. The target Pearson correlation coefficient is used to measure the linear correlation between the simulation data and the actual data, and the mean square error is an indicator to measure the difference between the simulation data and the actual data. In order to comprehensively consider the impact of the target Pearson correlation coefficient and the target mean square error on the simulation data evaluation, the first weight coefficient corresponding to the target Pearson correlation coefficient and the second weight coefficient corresponding to the target mean square error can be determined, wherein the sum of the first weight coefficient and the second weight coefficient is 1.

[0072] The target Pearson correlation coefficient and the target mean square error are weighted and summed according to their respective weights to obtain the target comprehensive score. The target comprehensive score is used to evaluate the matching degree between the second wind power output data and the target wind power output data. The calculation formula is as follows:

[0073] In the above formula, represents the target comprehensive score; represents the first weight coefficient, which represents the weight of the Pearson correlation coefficient in the comprehensive score; represents the target Pearson correlation coefficient; represents the second weight coefficient, which represents the weight of the mean square error in the comprehensive score; represents the target mean square error.

[0074] If the target comprehensive score is less than or equal to the preset comprehensive score threshold, it means that the second wind power output data and the target wind power output data have a poor match, and outliers can be further screened out. Determine the wind power output difference between the x second wind power output data and the x target wind power output data to obtain x wind power output differences, select the wind power output difference less than the preset difference threshold from the x wind power output differences, and obtain e wind power output differences, and select the second wind power output data corresponding to the e wind power output differences from the x second wind power output data to obtain e second wind power output data, wherein the larger the wind power output difference, the greater the deviation between the second wind power output data and the target wind power output data. The preset difference threshold is a value pre-set to determine whether the wind power output difference is within an acceptable range. By setting the difference threshold for screening, data that differs too much from the actual data, i.e., outliers, can be removed.

[0075] If the target comprehensive score is greater than the preset comprehensive score threshold, it means that the second wind power output data matches the target wind power output data well. At this time, the average value of the second wind power output data can be taken as the center, and the data with the smallest difference from the average value can be directly selected as the second wind power output data. Specifically, the average value of x second wind power output data can be obtained, and e second wind power output data with the smallest absolute value of the difference from the average value can be selected from the x second wind power output data.

[0076] See also Figure 3 , Figure 3 This is a schematic diagram of the calculation process of the conditional risk value under an output scenario provided by an embodiment of the present application. As shown in the figure, by collecting historical data of the smart park, including wind speed and solar irradiance data, and classifying them according to intensity, multiple output scenarios are obtained. Then, the Monte Carlo simulation method is used to perform random simulation for each output scenario, considering the uncertainty of the output of new energy (wind power, photovoltaics), and obtain new energy output data under multiple output scenarios. For each simulated output scenario, the comprehensive score of wind power and photovoltaic output is calculated according to specific rules to comprehensively reflect the performance of new energy power generation in the scenario. According to the comprehensive score, the abnormal simulation values ​​are identified and removed to make the simulation data more representative, and the final simulation data is obtained.

[0077] Check whether the number of simulations is less than the set number, where the set number represents the predetermined number of simulations. The more times the simulation is performed, the closer the simulation results are to the actual distribution of new energy output, and the more accurately the uncertainty characteristics of wind power and photovoltaic output can be captured, thus improving the simulation accuracy. If the number of simulations is less than the set number, return to continue the simulation, otherwise, based on the final simulation data, calculate the conditional risk values ​​corresponding to different output scenarios to measure the potential risks of the smart park under the uncertainty of new energy output.

[0078] S105. Determine w target condition risk values ​​according to the k second wind power output data and the k second photovoltaic output data.

[0079] In the present application, the value at risk (VaR) refers to the The maximum possible loss caused by the uncertainty of wind and solar power output in a specific period of time in the future. For example, at a 95% confidence level, VaR is 100, then there is a 95% probability that the loss will not exceed 100, that is, there is a 5% probability that the loss will exceed 100. However, VaR cannot well reflect the impact of extreme loss events because it ignores tail losses. In contrast, Conditional Value at Risk (CVaR) indicates that when the confidence level is greater than Therefore, CVaR can better measure the tail loss. For example, at a 95% confidence level, in addition to knowing that there is a 95% probability that the loss will not exceed 100, it can also determine the average loss when the loss exceeds 100. It pays more attention to the tail of the loss distribution and reflects the risks under extreme events.

[0080] In a specific embodiment, the uncertainty of wind and solar power output can be quantified based on k second wind power output data and k second photovoltaic output data, and w target conditional risk values ​​corresponding to w output scenarios can be obtained. By quantifying the energy supply risk under different output scenarios, the target conditional risk value can be used to understand the potential loss caused by insufficient power supply under various possible output conditions, so as to better assess the risk status of the energy system, so that a more reasonable optimization scheduling strategy can be formulated according to the conditional risk value, improve the stability and reliability of the energy system, and reduce operating costs and risks.

[0081] Optionally, the above step of determining w target condition risk values ​​according to the k second wind power output data and the k second photovoltaic output data may specifically include the following steps: A501. Obtain e second wind power output data and e second photovoltaic output data corresponding to the target output scenario; A502. Selecting actual wind power output data corresponding to the target output scenario from the first wind power output data set, and selecting actual photovoltaic output data corresponding to the target output scenario from the first photovoltaic output data set; A503, determining e penalty costs according to the e second wind power output data, the e second photovoltaic output data, the actual wind power output data and the actual photovoltaic output data; A504. Determine a first risk value according to the e penalty costs and a preset confidence level; A505. Determine y first penalty costs according to the e penalty costs and the first risk value; y is a positive integer less than e; A506, determining the y first penalty costs and performing calculations on the first risk value to obtain y risk differences; A507. Determine a target conditional risk value based on the first risk value and the y risk difference values.

[0082] In a specific embodiment, e second wind power output data and e second photovoltaic output data corresponding to the target output scenario can be obtained from k second wind power output data and k second photovoltaic output data, and actual wind power output data corresponding to the target output scenario can be selected from the first wind power output data set, and actual photovoltaic output data corresponding to the target output scenario can be selected from the first photovoltaic output data set.

[0083] Next, e penalty costs are determined based on the e second wind power output data, e second photovoltaic output data, the actual wind power output data and the actual photovoltaic output data. The penalty cost is used to measure the degree of deviation between the simulated wind power and photovoltaic output data and the actual data. The greater the deviation, the higher the penalty cost, so as to reflect the potential risks brought about by the discrepancy between the simulation results and the actual situation.

[0084] The first risk value is determined based on e penalty costs and the preset confidence level, and its calculation formula is as follows:

[0085] In the above formula, Indicates the first risk value corresponding to the target output scenario; It means that after sorting all penalty costs from small to large, The penalty cost value, To round up, therefore, Indicates the total number of samples with e penalty costs Multiply by the pre-set confidence level , and round it up to get the index value, and select the penalty cost from the sorted e penalty costs according to the index value.

[0086] Among the e penalty costs, the penalty costs exceeding the first risk value are screened out to obtain y first penalty costs, where y is a positive integer less than e. The first penalty cost represents an extreme situation outside the preset confidence level. The difference between each first penalty cost and the first risk value among the y first penalty costs is calculated to obtain y risk differences. The risk difference reflects the additional loss exceeding the first risk value. Next, the average value of the y risk differences is calculated first, and then the average value is added to the first risk value to obtain the target conditional risk value. The calculation formula of the target conditional risk value is as follows:

[0087] In the above formula, Indicates the target condition risk value corresponding to the target output scenario; represents the first risk value; represents the total number of samples with e penalty costs; Indicates the confidence level; Indicates The first penalty cost.

[0088] Optionally, the above step of determining e penalty costs according to the e second wind power output data, the e second photovoltaic output data, the actual wind power output data and the actual photovoltaic output data may specifically include the following steps: B501. Determine the total wind power and photovoltaic output value according to the e second wind power output data and the e second photovoltaic output data, and obtain e total wind power and photovoltaic output values; B502. Determine a target wind power and photovoltaic power output value according to the actual wind power output data and the actual photovoltaic power output data; B503, determining the difference between each wind power photovoltaic total output value of the e wind power photovoltaic total output values ​​and the target wind power photovoltaic total output value, to obtain e total output value differences; B504. Determine the e penalty costs based on the e total output differences, a preset first penalty coefficient, and a preset second penalty coefficient; wherein, if the total output difference i is greater than or equal to 0, determine the penalty cost i based on the total output difference i and the preset first penalty coefficient; if the total output difference i is less than 0, determine the penalty cost i based on the total output difference i and the preset second penalty coefficient; the total output difference i is any one of the e total output differences.

[0089] Among them, the preset first penalty coefficient is used to calculate the penalty cost when the total simulated wind power and photovoltaic output is greater than or equal to the actual value. In the energy management scenario of the smart park, although the excess power generation of new energy can meet the power demand, there will also be some problems. For example, if the excess power cannot be stored or effectively used in time, it may need to be sold at a low price, resulting in economic losses, and the storage of excess power will incur storage costs, including the purchase and maintenance of energy storage equipment and the loss during the energy storage process. The preset first penalty coefficient represents a comprehensive quantification of potential economic losses. Its specific value setting can be based on the actual situation of the smart park, such as the cost of energy storage equipment, price fluctuations in the electricity market, and energy storage efficiency. For example, if the cost of energy storage equipment in the smart park is high, and the electricity price is low when the local electricity market is in excess of supply, then the preset first penalty coefficient should be adjusted accordingly to more accurately reflect the economic impact of excess power generation.

[0090] The preset second penalty coefficient is used to calculate the penalty cost when the total simulated wind power and photovoltaic output is less than the actual value. When the power generation of new energy is insufficient, there is a gap in the power supply of the smart park. In order to ensure the stable supply of electricity, it is necessary to purchase electricity from the external power grid at a high price, thereby increasing the cost of electricity. At the same time, the shortage of power supply may affect the normal production and operation of enterprises in the park, causing indirect economic losses such as production interruption and equipment damage. The preset second penalty coefficient represents a comprehensive quantification of the direct and indirect economic losses caused by insufficient power generation. Its specific numerical setting can consider factors such as the external power purchase price and the average economic loss caused by power outages to enterprises in the park. For example, if the power purchase price of the power grid around the smart park is high, and the smart park is sensitive to power outages and the power outage losses are large, the preset second penalty coefficient can be set higher to accurately reflect the losses caused by insufficient power generation.

[0091] In a specific embodiment, the total wind power and photovoltaic output values ​​are determined based on e second wind power output data and e second photovoltaic output data, and e total wind power and photovoltaic output values ​​are obtained. In the smart park energy supply scenario, wind power and photovoltaic jointly provide power for the smart park, so the wind power output and photovoltaic output are combined and calculated to obtain the total new energy power generation output under each simulation sample. The target wind power and photovoltaic output value is determined based on the actual wind power output data and the actual photovoltaic output data.

[0092] Determine the difference between each of the e wind power photovoltaic total output values ​​and the target wind power photovoltaic total output value to obtain e total output value differences. The total output value difference reflects the degree of difference between the simulated total new energy power generation and the actual total power generation. The larger the difference, the greater the deviation between the simulated value and the actual value.

[0093] e penalty costs are determined according to e total output value differences, a preset first penalty coefficient, and a preset second penalty coefficient, wherein if the total output value difference i is greater than or equal to 0, the penalty cost i is determined according to the total output value difference i and the preset first penalty coefficient; if the total output value difference i is less than 0, the penalty cost i is determined according to the total output value difference i and the preset second penalty coefficient; the total output value difference i is any total output value difference among the e total output value differences, and the penalty cost is calculated as follows:

[0094] In the above formula, Output scene The output of wind power and photovoltaic simulation value is as follows; Output scene The actual output of wind and photovoltaic power; Indicates The penalty cost is the difference in total output value. The corresponding penalty cost; Indicates the preset first penalty coefficient, which specifically indicates the penalty coefficient for high output; It indicates the preset second penalty coefficient, which specifically indicates the penalty coefficient for low output.

[0095] In actual energy management, the impacts and costs of excess and insufficient power generation from renewable energy are different. For example, excess power generation may involve electricity storage costs or losses from selling excess electricity at a low price, while insufficient power generation may lead to a shortage of power supply in the park and the need to purchase electricity from outside at a high price. By determining the penalty cost and converting the deviation between the simulated value and the actual value into a quantifiable cost indicator, the potential economic impact caused by the discrepancy between the simulation results and the actual situation can be intuitively reflected.

[0096] S106. Obtain conventional output data of the conventional unit in the w output scenarios to obtain w conventional output data.

[0097] The energy supply of smart parks is usually jointly provided by wind turbines, photovoltaic units and conventional units. The output of wind turbines and photovoltaic units is greatly affected by natural conditions and has uncertainty and volatility. The power generation of conventional units is relatively stable and can play a supplementary role when the power generation of new energy is insufficient. By obtaining the conventional output data of conventional units under different output scenarios, we can fully understand the energy supply capacity of smart parks under different output scenarios, so as to more reasonably formulate optimized scheduling strategies and ensure the stability and reliability of power supply.

[0098] In a specific embodiment, the data of the unit can be recorded in real time by collecting the monitoring equipment equipped by the conventional unit itself, and the monitoring equipment can record the operating data such as the power generation power of the unit in real time. By obtaining the conventional output data of the conventional unit in w output scenarios, w conventional output data can be obtained. When calculating the carbon emission cost, evaluating the overall performance of the energy system, and formulating an optimized scheduling strategy, the power generation of the conventional unit needs to be considered. For example, when evaluating the carbon emission cost, the carbon emissions generated by different conventional units are different. The conventional output data can be used to accurately calculate the carbon emissions of the conventional units in each scenario, and then the carbon emission cost of the entire park can be obtained.

[0099] S107. Determine the carbon emission costs under the w output scenarios according to the k second wind power output data, the k second photovoltaic output data and the w conventional output data to obtain w carbon emission costs.

[0100] In the energy supply system of the smart park, carbon emissions mainly come from the power generation process of conventional units, because wind turbines and photovoltaic units produce almost no carbon emissions during the power generation process. However, changes in wind power and photovoltaic output will affect the power generation capacity of conventional units, and thus affect the total carbon emissions. For example, when the wind power and photovoltaic output are high, the power generation capacity of conventional units may be reduced, thereby reducing carbon emissions. Otherwise, when the wind power and photovoltaic output are insufficient, conventional units need to increase their power generation capacity to meet the power demand of the park, and carbon emissions will increase accordingly.

[0101] In a specific embodiment, the total amount of carbon emissions under w output scenarios can be determined based on k second wind power output data, k second photovoltaic output data, and w conventional output data, and the carbon emission cost is calculated based on the total carbon emissions to obtain w carbon emission costs. When calculating the total amount of carbon emissions, since the increase in wind power and photovoltaic output will reduce the power generation demand of conventional units, thereby reducing carbon emissions, the k second wind power output data and the k second photovoltaic output data can be analyzed and the total amount of carbon emissions can be adjusted.

[0102] By calculating the carbon emission costs under different output scenarios, the environmental costs generated by the energy supply of the smart park under different output scenarios can be intuitively reflected. By comparing the carbon emission costs under different output scenarios, the impact of different energy scheduling strategies on the environment can be evaluated, so as to select strategies with lower carbon emission costs and achieve the goal of energy conservation and emission reduction. At the same time, the calculation of carbon emission costs can also help park managers understand the operating costs of the energy system, reasonably arrange energy production and consumption, improve energy utilization efficiency, and reduce operating costs.

[0103] Optionally, the above step of determining the carbon emission costs under the w output scenarios according to the k second wind power output data, the k second photovoltaic output data and the w conventional output data to obtain w carbon emission costs may specifically include the following steps: A701. Obtain w total load values ​​corresponding to the smart park; A702. Determine w first carbon emissions according to the preset power supply carbon emission quota reference value and the w total load values; A703, determining w second carbon emissions according to the w conventional output data; A704. Determine w third carbon emissions according to the k second wind power output data and the k second photovoltaic output data; A705. Determine w target carbon emissions according to the w first carbon emissions, the w second carbon emissions, and the w third carbon emissions; A706. Determine the w carbon emission costs according to the mapping relationship between the w target carbon emissions and preset carbon emissions and costs.

[0104] In a specific embodiment, the total power load of the smart park will be different under different output scenarios. Specifically, the power consumption data under different output scenarios can be collected through the power monitoring equipment and related data recording systems in the smart park, and the total load value corresponding to each output scenario can be obtained through sorting and statistics. Determine w first carbon emissions based on the preset power supply carbon emission rights quota reference value and w total load values, wherein the preset power supply carbon emission rights quota reference value is a pre-set standard for measuring the carbon emissions allowed per unit power supply. Specifically, the total load value of each output scenario can be multiplied by the preset power supply carbon emission rights quota reference value to obtain the corresponding first carbon emission. The calculation formula for the first carbon emission is as follows:

[0105] In the above formula, represents the first carbon emission; It indicates the preset power supply carbon emission quota benchmark value, usually in tons per megawatt-hour; Output scene The total load value under .

[0106] Calculating the first carbon emissions can be used to determine whether the carbon emission quota requirements are met under the current output scenario. If the actual carbon emissions exceed the first carbon emissions, it may be necessary to purchase additional carbon emission rights or take measures to reduce carbon emissions, which will increase costs.

[0107] According to w conventional output data, w second carbon emissions are determined. The calculation formula of the second carbon emissions is as follows:

[0108] In the above formula, represents the second carbon emission; Indicates the number of conventional units in the smart park; , , are the carbon emission coefficients of conventional power generation units, among which, Indicates conventional unit The fixed carbon emission coefficient is related to the type, manufacturing process, initial installation and other factors of the conventional unit. It does not change with the power generation of the conventional unit. It represents a part of the carbon emissions generated when the conventional unit is not generating electricity. Indicates conventional unit The carbon emission coefficient related to the first-order term of power generation represents the part where carbon emissions are linearly related to power generation. Indicates conventional unit The carbon emission coefficient related to the quadratic term of power generation represents the part of carbon emissions related to the square of power generation, which means that as power generation increases, this part of carbon emissions will grow faster than the linear part; Indicates conventional unit In the output scene The output is as follows.

[0109] When the wind turbines and photovoltaic units in the smart park transmit electricity to the upper power grid through the interconnection line, the operation of thermal power in the region and the upper power grid area is actually reduced, thereby reducing carbon emissions. Therefore, w third carbon emissions are determined based on k second wind power output data and k second photovoltaic output data. The calculation formula for the third carbon emissions is as follows:

[0110] In the above formula, represents the third carbon emission; , They represent the number of wind turbines and photovoltaic units in the smart park respectively; , Respectively represent wind turbines and photovoltaic units In the output scene The output of the next , Respectively represent wind turbines and photovoltaic units The corresponding carbon emission coefficient indicates the amount of carbon emissions that can be reduced by wind turbines and photovoltaic units for every unit of electricity delivered.

[0111] According to w first carbon emissions, w second carbon emissions and w third carbon emissions, w target carbon emissions are determined. The calculation formula of the target carbon emissions is as follows:

[0112] In the above formula, represents the target carbon emissions; represents the first carbon emission; represents the second carbon emission; Represents the third carbon emission.

[0113] When the target carbon emission is greater than 0, w carbon emission costs can be determined according to the w target carbon emissions and the mapping relationship between the preset carbon emissions and the costs, wherein the mapping relationship between the preset carbon emissions and the costs is a pre-set rule for converting the total carbon emissions into the corresponding costs. The mapping relationship between the preset carbon emissions and the costs is as follows:

[0114] In the above formula, represents the cost of carbon emissions; Indicates the base price of carbon emission rights trading; represents the target carbon emissions; Indicates the unit quota step interval of carbon emissions, which is used to divide different intervals of carbon emissions. Its value is pre-set according to the rules and actual needs of the carbon emission trading market; Represents the price growth rate.

[0115] When the target carbon emissions are not greater than 0, it means that the smart park has remaining carbon emission rights. At this time, the smart park can sell these excess carbon emission rights in the carbon emission rights trading market to obtain economic benefits, while further reducing carbon emissions.

[0116] Determining the carbon emission costs corresponding to different output scenarios can provide an economic reference for the energy management of smart parks. At the same time, the energy scheduling strategy can be adjusted according to the carbon emission costs under different output scenarios, so that scenarios and energy combinations with lower carbon emission costs are given priority.

[0117] S108. Determine a target scheduling strategy according to the w target condition risk values ​​and the w carbon emission costs.

[0118] In the embodiment of the present application, the optimal optimization scheduling strategy is determined by comprehensively considering the target condition risk value and carbon emission cost under different output scenarios to balance risks and costs and achieve efficient, stable and sustainable supply of energy for smart parks.

[0119] In a specific embodiment, multiple scheduling strategies can be pre-generated. Different scheduling strategies include operating parameters of various energy equipment, charging and discharging plans of energy storage equipment, interaction strategies with the power grid, etc. in different output scenarios. For example, in a strong wind and strong light output scenario, the output of wind turbines and photovoltaic units is higher, and excess electricity can be charged into energy storage devices. In a weak wind and weak light scenario, conventional units generate electricity according to load demand, and at the same time, energy storage devices discharge to supplement the power gap. Different output scenarios have different corresponding risks and carbon emissions. Therefore, by determining the target scheduling strategy according to the target condition risk values ​​and carbon emission costs corresponding to different scheduling strategies in multiple scheduling strategies, the target scheduling strategy is the optimal scheduling strategy among multiple scheduling strategies. It can control energy costs while reducing risks and improve the economic benefits of the smart park.

[0120] Optionally, the above step of determining the target scheduling strategy according to the w target condition risk values ​​and the w carbon emission costs may specifically include the following steps: A801. Generate s scheduling strategies according to preset constraints; A802. Determine the output scenario corresponding to each of the s scheduling strategies to obtain s output scenarios; A803. Select the target condition risk values ​​and carbon emission costs corresponding to the s output scenarios from the w target condition risk values ​​and the w carbon emission costs to obtain s target condition risk values ​​and s carbon emission costs; A804. Determine s strategy scores according to the s target condition risk values, the s carbon emission costs and a preset smart park optimization scheduling model; A805. Select the highest strategy score from the s strategy scores to obtain the highest strategy score; A806. Use the scheduling strategy corresponding to the highest strategy score as the target scheduling strategy.

[0121] See also Figure 4 , Figure 4This is an operation structure diagram of a smart park operator participating in a multi-time electricity market and a carbon market provided by an embodiment of the present application. As shown in the figure, the operation structure includes main equipment such as conventional units, wind turbines, photovoltaic units, and energy storage equipment. Among them, conventional units can use natural gas as fuel, and can generate electricity through combustion. They can flexibly start and stop and adjust the power generation according to the power demand of the park. As a supplementary power source when new energy power generation is insufficient, it ensures a stable supply of electricity, but the power generation process will produce carbon emissions. Wind turbines are equipment that uses wind power to generate electricity, converting wind energy into electrical energy to provide a clean energy source for the park power supply. Photovoltaic units are equipment that converts solar energy into electrical energy through the solar photovoltaic effect. They depend on light intensity and sunshine duration. Wind turbines and photovoltaic units are greatly affected by the environment and have intermittent and fluctuating characteristics. Energy storage equipment is a device for storing excess electrical energy. It can be charged when there is an excess of electricity (such as wind power and photovoltaic power generation periods), and discharged when there is a peak in electricity demand or insufficient new energy generation, which plays a role in smoothing power fluctuations, regulating power supply and demand, and improving the stability and reliability of power supply.

[0122] In terms of electricity market participation, smart park operators can sign electricity trading contracts in the futures market in advance, lock in electricity trading prices and electricity volume for a certain period in the future, plan electricity sales or procurement strategies in advance, and avoid market price fluctuation risks. At the same time, smart park operators can bid for the purchase and sale of electricity in the day-ahead market based on information such as the next day's electricity demand and new energy power generation forecasts, determine the electricity trading plan for each period of the next day, and optimize the allocation of electricity resources. In the real-time operation stage of electricity, smart park operators can make real-time purchase and sale adjustments of electricity in the real-time market based on actual electricity supply and demand deviations, such as actual new energy power generation not in line with forecasts, sudden changes in park load, etc., to balance electricity supply and demand and ensure real-time balance and stable supply of electricity.

[0123] During the operation process, smart park operators generate carbon emissions due to conventional power generation and other means, and need to participate in the carbon market. They can meet their emission needs by purchasing carbon emission rights, or they can reduce carbon emissions by optimizing the energy structure, such as increasing the proportion of wind power and photovoltaics, and improving energy utilization efficiency. They can also sell excess carbon emission rights for profit, thereby achieving a balance between economic and environmental benefits.

[0124] The operating status of conventional units, wind turbines, photovoltaic units and energy storage equipment, such as power generation, energy storage capacity and other information can be fed back to the park operator. Based on this information and market price signals from futures, day-ahead and real-time markets, the operator can formulate a dispatch strategy for each equipment and participate in electricity market transactions. At the same time, the operator can participate in carbon market transactions considering carbon emissions, realize multi-market coordinated optimization of operations, and ensure stable power supply and cost optimization in the park.

[0125] Among them, the preset constraints include energy storage equipment charging and discharging constraints, futures market output constraints, day-ahead market bidding constraints, smart park time-sharing power balance constraints, conditional risk value constraints and other constraints.

[0126] Among them, the energy storage device charging and discharging constraints of the energy storage device include the following constraints:

[0127] The above formula represents the equipment capacity constraint in the energy storage equipment charging and discharging constraint, which is expressed in Energy storage equipment before the moment Energy state Need to meet a certain range, that is, the energy state Need to be greater than or equal to the energy storage device Discharge power and the discharge efficiency constant A lower limit value is calculated, and the energy state Less than or equal to energy storage equipment Maximum capacity .

[0128]

[0129] The above formula represents the SOC constraint in the energy storage device charge and discharge constraint, which means that the energy storage device In the output scene Down State of charge at the moment Must be at minimum and maximum value Ensure that the charge state of the energy storage device is within a reasonable range to avoid damage to the device due to overcharging or discharging.

[0130] The calculation formula of state of charge is as follows:

[0131] In the above formula, energy storage device exist Moment The corresponding energy state and energy storage devices Maximum capacity Calculated.

[0132]

[0133] The above formula represents the charge and discharge power constraint in the charge and discharge constraint of the energy storage device, which limits the energy storage device exist Charging power at the moment and discharge power The value range of charging and discharging power must be greater than or equal to 0, and cannot exceed the maximum charging power allowed by the device and maximum discharge power .

[0134]

[0135] The above formula represents the energy state update formula in the energy storage device charging and discharging constraints, which represents the energy storage device exist Energy status at all times It is based on the energy state at the previous moment. ,as well as Charging power at the moment and discharge capacity To be updated.

[0136]

[0137] The above formula represents the charging power calculation formula in the charging and discharging constraints of the energy storage device, which represents the energy storage device exist Charging power at the moment The charging power Multiply by the efficiency constant for energy loss during charging , which takes into account the energy loss during the charging process.

[0138]

[0139] The above formula represents the discharge power calculation in the charge and discharge constraints of the energy storage device, which represents the energy storage device exist Discharge capacity at the moment The discharge power Divided by the efficiency constant of the energy loss during discharge , which takes into account the energy loss during the discharge process.

[0140] Among them, the futures market output constraints include the following constraints:

[0141] The above formula represents the output range constraint in the futures market output constraint, which limits the output scenario Down The actual contribution of Shike Intelligent Park in the futures market The value range of For CFD electricity, The actual contract time-sharing electricity. The deviation tolerance between the actual demand of the futures market and the time-sharing power of the CFD contract, that is, the actual output To be within the range of the sum of the CFD electricity and the actual contract time-of-use electricity after adjustment based on the deviation tolerance.

[0142]

[0143] The above formula represents the total power balance constraint in the futures market output constraint, which means that in the output scenario Throughout the entire time period The actual total output of the smart park in the futures market must be equal to the sum of the CFD electricity and the actual contract time-sharing electricity during the entire cycle to ensure the balance of electricity in the futures market.

[0144] Among them, the day-ahead market bidding constraints include the following constraints:

[0145] The above formula represents the output scenario Down The power purchase and sale of the smart park in the day-ahead market , is the result of this scenario The simulated output of each wind turbine in and The simulated output of each photovoltaic unit The sum of The power of each load It is obtained by summing up, which represents the power supply and demand relationship of the park in the day-ahead market.

[0146] Among them, the time-sharing power balance constraints of the smart park include the following constraints:

[0147] The above formula indicates that in the output scenario Down At this moment, the power balance relationship of the smart park, Indicates the power purchased and sold from the main power grid. express Among the conventional units Conventional units, express Among the wind turbines The actual output of each wind turbine is express Among the photovoltaic units The actual output of each photovoltaic unit, Indicates the discharge power of the energy storage device, It indicates the power purchased and sold in the market today. Indicates the actual output of the futures market. express In the non-flexible load A non-flexible load actual load, Represents the charging power of the energy storage device. The time-divided power balance constraint of the smart park indicates the need to ensure that the power supply and demand of the smart park are balanced at every moment.

[0148]

[0149] The above formula restricts Smart Park purchases and sells electricity from the main grid at all times range, if If it is greater than 0, it means selling electricity from the main grid. Otherwise, it means purchasing electricity from the main grid. The absolute value of the purchased and sold power cannot exceed the maximum exchange power between the smart park and the main grid. , to prevent the power exchange between the park and the main network from exceeding the allowable range.

[0150] Among them, the conditional risk value constraints include the following constraints:

[0151] The above formula is based on the constraint of conditional risk value. Output scene Next The penalty cost of each case, represents the risk value, represents the conditional value at risk, represents the confidence level, Indicates the number of penalty costs. The constraint of this conditional risk value indicates that the sum of the risk values ​​of the tail losses must be greater than or equal to the sum of the conditional risk values ​​of the tail losses to ensure that the risk is controlled at a certain confidence level. The sum of the risk values ​​of the tail losses needs to be greater than or equal to 0.

[0152] Based on the above constraints, an intelligent park optimization scheduling model is constructed. The objective function of the intelligent park optimization scheduling model can maximize the overall benefits of the intelligent park in a multi-market environment by comprehensively considering multiple factors. Its objective function is as follows:

[0153] In the above formula, Output scene Maximizing the overall benefits of smart parks , Indicates the output scenario The calculation time period is Output scene Down Shike Intelligent Park has made practical contributions in the futures market. represents the futures market contract electricity price, Output scene Down The smart park buys and sells power in the day-ahead market. If it is positive, it means selling electricity, which brings profit. If it is negative, it means purchasing electricity, which is the cost. Output scene Down The electricity price in the market at the time of day, , For output scene Down Real-time market sales and purchase of electricity power at all times, , For output scene Down Real-time market electricity prices at all times, Output scene The total cost of operating conventional units and energy storage equipment is: Output scene Down The carbon emission cost at each moment, Output scene The conditional value at risk.

[0154] The total cost of operating conventional units and energy storage equipment is calculated as follows:

[0155] In the above formula, Output scene The total cost of operating conventional units and energy storage equipment is: Indicated in Among the conventional units The operating cost of a conventional unit is express Energy storage device The operating cost of an energy storage device.

[0156] The operating cost of the energy storage equipment is calculated as follows:

[0157] In the above formula, Output scene Down Energy storage device The operating cost of an energy storage device is represents the unit operating cost of energy storage equipment, , Indicates the charging and discharging power of the energy storage device.

[0158] The objective function provides a clear economic goal for the energy optimization and dispatching of the park by comprehensively considering the revenue and various costs of the smart park in different markets. When making energy dispatch decisions, such as determining the power purchase and sale strategies in different markets, the power generation of distributed gas turbines, and the charging and discharging plans of energy storage equipment, all are guided by maximizing the objective function, so as to achieve the optimal economic benefits of the smart park in a multi-market environment. At the same time, it also encourages the park to consider cost factors in the process of energy use, reasonably arrange energy production and consumption, and improve energy utilization efficiency.

[0159] In the specific implementation, the preset constraints include equipment capacity limitations, power balance requirements, energy market rules, etc., which constitute the boundaries of the generation of scheduling strategies. Different energy scheduling strategies can be generated according to the preset constraints through algorithms or manual experience, and finally s strategies are obtained.

[0160] Each scheduling strategy corresponds to a specific output scenario. By analyzing each scheduling strategy, the output scenario corresponding to each of the s scheduling strategies can be determined, and s output scenarios can be obtained. Then, the target condition risk values ​​and carbon emission costs corresponding to the s output scenarios are selected from the w target condition risk values ​​and w carbon emission costs, and s target condition risk values ​​and s carbon emission costs are obtained.

[0161] The preset smart park optimization scheduling model includes an objective function and constraints. The objective function is a function that comprehensively considers risks, costs, and benefits. Using this model, each scheduling strategy is evaluated based on s target condition risk values ​​and s carbon emission costs, thereby obtaining s strategy scores. The strategy score reflects the comprehensive performance of each scheduling strategy in terms of risk control, cost control, and meeting the energy needs of the park.

[0162] The highest strategy score is selected from the s strategy scores to obtain the highest strategy score, and the scheduling strategy corresponding to the highest strategy score is used as the target scheduling strategy. The target scheduling strategy performs best in terms of risk control, cost control, and meeting the energy needs of the park. Therefore, it can be used to guide the energy scheduling work of the smart park.

[0163] See also Figure 5 , Figure 5This is an application scenario diagram of a method for generating an optimized scheduling strategy for a smart park provided in an embodiment of the present application. As shown in the figure, the current time is 12:00, the current wind speed is in a strong wind state, and the current light intensity is normal light. At the same time, a prediction graph of power generation output is given, in which the solid line represents the actual output, the dotted line represents the predicted output, and the horizontal axis is time. The prediction graph of power generation output can provide a reference for scheduling decisions.

[0164] Real-time dynamic information on the electricity market and the carbon market may include real-time data on electricity prices and trading conditions in various electricity markets, such as the futures market, day-ahead market, and real-time market, and carbon prices and quota trading in the carbon market.

[0165] According to meteorological conditions, power generation output and market information, multiple dispatching strategies are calculated through the preset smart park optimization dispatching model, namely dispatching strategy 1, dispatching strategy 2 and dispatching strategy 3. Among them, dispatching strategy 1 scores 85, dispatching strategy 2 scores 86, and dispatching strategy 3 scores 90. Based on the scores, the optimal dispatching strategy can be determined, and detailed information of the optimal dispatching strategy can be given to guide the current energy dispatch of the smart park to achieve the optimal balance between economy and risk.

[0166] It can be seen that by comprehensively considering the target condition risk value and carbon emission cost, and based on the preset constraints, an optimal scheduling strategy that conforms to the actual situation can be generated, providing accurate decision-making basis for park energy management.

[0167] In summary, by implementing the embodiments of the present application, the historical data of wind turbines, photovoltaic units and conventional units in the smart park are obtained, and the wind power output model and photovoltaic output model under different output scenarios are constructed based on the historical data. The output data under different output scenarios are obtained through Monte Carlo simulation, and the conditional risk value of the corresponding output scenario is determined based on the output data. The carbon emission cost is calculated in combination with the output data of the conventional units. Finally, the conditional risk value and the carbon emission cost are comprehensively considered to select the target scheduling strategy under the preset constraints. It can be seen that the target scheduling strategy obtained by comprehensively considering the conditional risk value and carbon emission cost under different wind power and photovoltaic output scenarios is the optimal scheduling strategy, which can effectively reduce the risks brought by wind power and photovoltaic output fluctuations, reduce carbon emissions, improve energy utilization efficiency, and improve the economic benefits and overall scheduling efficiency of the smart park in a multi-market environment.

[0168] See also Figure 6 , Figure 6 It is a structural diagram of an optimization scheduling strategy generation system for a smart park provided in an embodiment of the present application. The optimization scheduling strategy generation system for the smart park includes a data acquisition unit, a data simulation unit, and a strategy generation unit.

[0169] Among them, the data acquisition unit is responsible for collecting various energy-related data in the smart park, such as the real-time power generation of wind turbines and photovoltaic units, the operating parameters of conventional units, the power and charging and discharging status of energy storage equipment, the load demand of the park in different periods, as well as electricity market prices and carbon market trading information.

[0170] The data simulation unit uses the data obtained by the data acquisition unit and adopts methods such as Monte Carlo simulation to simulate the uncertainty of renewable energy output and generate multiple output scenarios and corresponding data. It can also simulate electricity supply and demand, carbon emissions and other situations in different scenarios based on historical data and preset models, providing diversified scenario analysis for strategy generation.

[0171] The strategy generation unit generates multiple scheduling strategies based on the output results of the data simulation unit, combined with preset constraints and optimization goals, such as reducing costs, controlling risks, and reducing carbon emissions, through the preset smart park optimization scheduling model. These strategies are evaluated and scored, and finally the optimal scheduling strategy is determined to guide the energy scheduling operation of the smart park.

[0172] See also Figure 7 , Figure 7 : is a structural schematic diagram of an optimized scheduling strategy generation device for a smart park provided in an embodiment of the present application. The optimized scheduling strategy generation device 700 for the smart park is applied to a control device of a smart park. The smart park includes: a wind turbine, a photovoltaic unit, and a conventional unit; the optimized scheduling strategy generation device 700 for the smart park includes: a data acquisition module 701, a scene classification module 702, a model training module 703, a data simulation module 704, a data processing module 705, and a strategy generation module 706, wherein: The data acquisition module 701 is used to obtain a first wind speed data set within a historical time period, a first wind power output data set corresponding to the wind turbine, a first solar irradiance data set, and a first photovoltaic output data set corresponding to the photovoltaic unit; The scene classification module 702 is used to classify the first wind speed data set and the first solar irradiance data set to obtain m wind power categories and n photovoltaic categories, and combine the m wind power categories and the n photovoltaic categories to obtain w output scenes; w is equal to m multiplied by n, and n and m are both positive integers; The model training module 703 is used to determine w wind power output models according to the first wind speed data set and the w output scenarios, and to determine w photovoltaic output models according to the first solar irradiance data set and the w output scenarios; each output scenario corresponds to one wind power output model and one photovoltaic output model; The data simulation module 704 is used to perform Monte Carlo simulation under the w output scenarios through the w wind power output models and the w photovoltaic output models to obtain k second wind power output data and k second photovoltaic output data; The data processing module 705 is used to determine w target condition risk values ​​according to the k second wind power output data and the k second photovoltaic output data; The data acquisition module 701 is also used to obtain the conventional output data of the conventional unit in the w output scenarios, and obtain w conventional output data; The data processing module 705 is further used to determine the carbon emission costs under the w output scenarios according to the k second wind power output data, the k second photovoltaic output data and the w conventional output data, to obtain w carbon emission costs; The strategy generation module 706 is used to determine a target scheduling strategy according to the w target condition risk values ​​and the w carbon emission costs.

[0173] Optionally, in determining w wind power output models according to the first wind speed data set and the w output scenarios, the model training module 703 is further specifically used for: Select wind speed data corresponding to a first output scenario from the first wind speed data set to obtain a training wind speed data set; the first output scenario is any one of the w output scenarios; Get the target likelihood function; Solving the target likelihood function by using a preset maximum likelihood estimation method and the training wind speed data set to obtain a first shape parameter and a first scale parameter; Determine a first probability density function corresponding to the first output scenario according to the first shape parameter and the first scale parameter; the first probability density function is a Weibull probability density function; A wind power output model corresponding to the first output scenario is determined according to a preset mapping relationship between wind speed and wind power output and the first probability density function.

[0174] Optionally, in determining w photovoltaic output models according to the first solar irradiance data set and the w output scenarios, the model training module 703 is further specifically used to: Selecting first solar irradiance data corresponding to a second output scene from the first solar irradiance data set to obtain a training solar irradiance data set; the second output scene is any one of the w output scenes; Determine the solar irradiance mean value and the solar irradiance standard deviation corresponding to the training solar irradiance data set; Determine a second shape parameter and a third shape parameter according to the solar irradiance average value and the solar irradiance standard deviation; Determine a second probability density function corresponding to the second output scenario according to the second shape parameter and the third shape parameter; the second probability density function is a Beta distribution probability density function; A photovoltaic output model corresponding to the second output scenario is determined according to a preset normalization coefficient and the second probability density function.

[0175] Optionally, k is equal to w multiplied by e, where e is an integer greater than or equal to 1; each output scenario corresponds to e second wind power output data and e second photovoltaic output data; in terms of performing Monte Carlo simulation under the w output scenarios by using the w wind power output models and the w photovoltaic output models to obtain k second wind power output data and k second photovoltaic output data, the data simulation module 704 is further specifically used for: Selecting a wind power output model corresponding to a target output scenario from the w wind power output models to obtain a target wind power output model, and selecting a photovoltaic output model corresponding to the target output scenario from the w photovoltaic output models to obtain a target photovoltaic output model; the target output scenario is any one of the w output scenarios; Performing Monte Carlo simulation through the target wind power output model and the target photovoltaic output model to obtain x second wind power output data and x second photovoltaic output data; x is an integer greater than or equal to e; removing abnormal values ​​from the x pieces of second wind power output data to obtain e pieces of second wind power output data; Abnormal values ​​in the x second photovoltaic output data are removed to obtain e second photovoltaic output data.

[0176] Optionally, in terms of removing abnormal values ​​from the x second wind power output data to obtain e second wind power output data, the data simulation module 704 is further specifically used for: Selecting x target wind power output data corresponding to the target output scenario from the first wind power output data set; Determine a target Pearson correlation coefficient and a target mean square error according to the x second wind power output data and the x target wind power output data; Determine a first weight coefficient corresponding to the target Pearson correlation coefficient and a second weight coefficient corresponding to the target mean square error; the sum of the first weight coefficient and the second weight coefficient is 1; Determine a target comprehensive score according to the target Pearson correlation coefficient, the first weight coefficient, the target mean square error and the second weight coefficient; If the target comprehensive score is less than or equal to the preset comprehensive score threshold, determining the wind power output difference between the x second wind power output data and the x target wind power output data to obtain x wind power output difference values; Selecting a wind power output difference value that is less than a preset difference threshold value from the x wind power output difference values ​​to obtain e wind power output difference values; Selecting the second wind power output data corresponding to the e wind power output differences from the x second wind power output data to obtain the e second wind power output data; If the target comprehensive score is greater than the preset comprehensive score threshold, an average value of the x second wind power output data is obtained, and e second wind power output data with the smallest absolute value of the difference with the average value are selected from the x second wind power output data.

[0177] Optionally, in determining w target condition risk values ​​according to the k second wind power output data and the k second photovoltaic output data, the data processing module 705 is further specifically used for: Obtaining e second wind power output data and e second photovoltaic output data corresponding to the target output scenario; Selecting actual wind power output data corresponding to the target output scenario from the first wind power output data set, and selecting actual photovoltaic output data corresponding to the target output scenario from the first photovoltaic output data set; determining e penalty costs according to the e second wind power output data, the e second photovoltaic output data, the actual wind power output data and the actual photovoltaic output data; Determine a first risk value according to the e penalty costs and a preset confidence level; Determine y first penalty costs according to the e penalty costs and the first risk value; y is a positive integer less than e; Determine the y first penalty costs and the first risk value to perform operations to obtain y risk difference values; A target conditional risk value is determined according to the first risk value and the y risk difference values.

[0178] Optionally, in determining e penalty costs according to the e second wind power output data, the e second photovoltaic output data, the actual wind power output data and the actual photovoltaic output data, the data processing module 705 is further specifically used to: Determine the total wind power and photovoltaic output value according to the e second wind power output data and the e second photovoltaic output data, and obtain e total wind power and photovoltaic output values; Determine a target wind power and photovoltaic power total output value according to the actual wind power output data and the actual photovoltaic power output data; Determine the difference between each of the e wind power photovoltaic total output values ​​and the target wind power photovoltaic total output value to obtain e total output value differences; The e penalty costs are determined according to the e total output value differences, a preset first penalty coefficient, and a preset second penalty coefficient; wherein, if the total output value difference i is greater than or equal to 0, the penalty cost i is determined according to the total output value difference i and the preset first penalty coefficient; if the total output value difference i is less than 0, the penalty cost i is determined according to the total output value difference i and the preset second penalty coefficient; the total output value difference i is any total output value difference among the e total output value differences.

[0179] Optionally, in determining the carbon emission costs under the w output scenarios according to the k second wind power output data, the k second photovoltaic output data and the w conventional output data, and obtaining the w carbon emission costs, the data processing module 705 is further specifically used to: Obtain w total load values ​​corresponding to the smart park; Determine w first carbon emissions according to a preset power supply carbon emission quota reference value and the w total load values; Determining w second carbon emissions according to the w conventional output data; Determining w third carbon emissions according to the k second wind power output data and the k second photovoltaic output data; Determining w target carbon emissions according to the w first carbon emissions, the w second carbon emissions, and the w third carbon emissions; The w carbon emission costs are determined according to the w target carbon emissions and a mapping relationship between preset carbon emissions and costs.

[0180] Optionally, in determining the target scheduling strategy according to the w target condition risk values ​​and the w carbon emission costs, the strategy generation module 706 is further specifically used to: Generate s scheduling strategies according to the preset constraints; Determine the output scenario corresponding to each of the s scheduling strategies to obtain s output scenarios; Selecting the target condition risk values ​​and carbon emission costs corresponding to the s output scenarios from the w target condition risk values ​​and the w carbon emission costs to obtain s target condition risk values ​​and s carbon emission costs; Determine s strategy scores according to the s target condition risk values, the s carbon emission costs and a preset smart park optimization scheduling model; Select the highest strategy score from the s strategy scores to obtain the highest strategy score; The scheduling strategy corresponding to the highest strategy score is used as the target scheduling strategy.

[0181] The optimized dispatching strategy generating device 700 of the smart park described in the present application can obtain the historical data of the wind turbines, photovoltaic units and conventional units in the smart park, and construct the wind power output model and photovoltaic output model under different output scenarios based on the historical data, obtain the output data under different output scenarios through Monte Carlo simulation, determine the conditional risk value of the corresponding output scenario based on the output data, calculate the carbon emission cost in combination with the output data of the conventional units, and finally comprehensively consider the conditional risk value and the carbon emission cost to screen out the target dispatching strategy under the preset constraints. It can be seen that the target dispatching strategy obtained by comprehensively considering the conditional risk value and the carbon emission cost under different wind power and photovoltaic output scenarios is the optimal dispatching strategy, which can effectively reduce the risks brought by the fluctuations of wind power and photovoltaic output, reduce carbon emissions, improve energy utilization efficiency, and improve the economic benefits and overall dispatching efficiency of the smart park in a multi-market environment.

[0182] See also Figure 8 , Figure 8 : is a structural diagram of an electronic device provided in an embodiment of the present application, the electronic device may include a processor, a memory, a communication interface and one or more programs, the processor, the memory and the communication interface may be interconnected through a bus; the one or more programs are stored in the memory and configured to be executed by the processor; in the embodiment of the present application, the program includes instructions for executing the following steps: Acquire a first wind speed data set within a historical time period, a first wind power output data set corresponding to the wind turbine generator set, a first solar irradiance data set, and a first photovoltaic output data set corresponding to the photovoltaic generator set; The first wind speed data set and the first solar irradiance data set are classified to obtain m wind power categories and n photovoltaic categories, and the m wind power categories and the n photovoltaic categories are combined to obtain w output scenarios; w is equal to m multiplied by n, and n and m are both positive integers; Determine w wind power output models according to the first wind speed data set and the w output scenarios, and determine w photovoltaic output models according to the first solar irradiance data set and the w output scenarios; each output scenario corresponds to one wind power output model and one photovoltaic output model; Performing Monte Carlo simulation on the w wind power output models and the w photovoltaic output models under the w output scenarios to obtain k second wind power output data and k second photovoltaic output data; Determining w target condition risk values ​​according to the k second wind power output data and the k second photovoltaic output data; Obtaining conventional output data of the conventional unit in the w output scenarios to obtain w conventional output data; Determine the carbon emission costs under the w output scenarios according to the k second wind power output data, the k second photovoltaic output data and the w conventional output data, to obtain w carbon emission costs; A target scheduling strategy is determined according to the w target condition risk values ​​and the w carbon emission costs.

[0183] The electronic device described in this application can obtain the historical data of wind turbines, photovoltaic units and conventional units in the smart park, and construct wind power output models and photovoltaic output models under different output scenarios based on the historical data, obtain the output data under different output scenarios through Monte Carlo simulation, determine the conditional risk value of the corresponding output scenario based on the output data, calculate the carbon emission cost in combination with the output data of conventional units, and finally comprehensively consider the conditional risk value and carbon emission cost to screen out the target scheduling strategy under the preset constraints. It can be seen that the target scheduling strategy obtained by comprehensively considering the conditional risk value and carbon emission cost under different wind power and photovoltaic output scenarios is the optimal scheduling strategy, which can effectively reduce the risks brought by wind power and photovoltaic output fluctuations, reduce carbon emissions, improve energy utilization efficiency, and improve the economic benefits and overall scheduling efficiency of smart parks in a multi-market environment.

[0184] An embodiment of the present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps of any method described in the above method embodiments, and the above computer includes an electronic device.

[0185] The embodiment of the present application also provides a computer program product, the computer program product includes a non-transitory computer-readable storage medium storing a computer program, the computer program is operable to cause a computer to execute some or all of the steps of any method described in the method embodiment. The computer program product may be a software installation package, and the computer includes an electronic device.

[0186] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

[0187] The specific implementation methods described above further illustrate the purpose, technical solutions and beneficial effects of the embodiments of the present application. It should be understood that the above description is only the specific implementation method of the embodiments of the present application and is not intended to limit the protection scope of the embodiments of the present application. Any modifications, equivalent substitutions, improvements, etc. made on the basis of the technical solutions of the embodiments of the present application should be included in the protection scope of the embodiments of the present application.

Claims

1. A method for generating an optimization scheduling strategy for a smart park, characterized in that: A control device applied to an intelligent park, wherein the intelligent park includes: a wind turbine unit, a photovoltaic unit, and a conventional unit; the method includes: Acquire a first wind speed data set within a historical time period, a first wind power output data set corresponding to the wind turbine generator set, a first solar irradiance data set, and a first photovoltaic output data set corresponding to the photovoltaic generator set; The first wind speed data set and the first solar irradiance data set are classified to obtain m wind power categories and n photovoltaic categories, and the m wind power categories and the n photovoltaic categories are combined to obtain w output scenarios; w is equal to m multiplied by n, and n and m are both positive integers; Determine w wind power output models according to the first wind speed data set and the w output scenarios, and determine w photovoltaic output models according to the first solar irradiance data set and the w output scenarios; each output scenario corresponds to one wind power output model and one photovoltaic output model; Performing Monte Carlo simulation on the w wind power output models and the w photovoltaic output models under the w output scenarios to obtain k second wind power output data and k second photovoltaic output data; Determining w target condition risk values ​​according to the k second wind power output data and the k second photovoltaic output data; Obtaining conventional output data of the conventional unit in the w output scenarios to obtain w conventional output data; Determine the carbon emission costs under the w output scenarios according to the k second wind power output data, the k second photovoltaic output data and the w conventional output data, to obtain w carbon emission costs; A target scheduling strategy is determined according to the w target condition risk values ​​and the w carbon emission costs.

2. The method according to claim 1, characterized in that The determining w wind power output models according to the first wind speed data set and the w output scenarios includes: Select wind speed data corresponding to a first output scenario from the first wind speed data set to obtain a training wind speed data set; the first output scenario is any one of the w output scenarios; Get the target likelihood function; Solving the target likelihood function by using a preset maximum likelihood estimation method and the training wind speed data set to obtain a first shape parameter and a first scale parameter; Determine a first probability density function corresponding to the first output scenario according to the first shape parameter and the first scale parameter; the first probability density function is a Weibull probability density function; A wind power output model corresponding to the first output scenario is determined according to a preset mapping relationship between wind speed and wind power output and the first probability density function.

3. The method according to claim 1, characterized in that The determining w photovoltaic output models according to the first solar irradiance data set and the w output scenarios includes: Selecting first solar irradiance data corresponding to a second output scene from the first solar irradiance data set to obtain a training solar irradiance data set; the second output scene is any one of the w output scenes; Determine the solar irradiance mean value and the solar irradiance standard deviation corresponding to the training solar irradiance data set; Determine a second shape parameter and a third shape parameter according to the solar irradiance average value and the solar irradiance standard deviation; Determine a second probability density function corresponding to the second output scenario according to the second shape parameter and the third shape parameter; the second probability density function is a Beta distribution probability density function; A photovoltaic output model corresponding to the second output scenario is determined according to a preset normalization coefficient and the second probability density function.

4. The method according to any one of claims 1 to 3, characterized in that: k is equal to w multiplied by e, where e is an integer greater than or equal to 1; each output scenario corresponds to e second wind power output data and e second photovoltaic output data; the Monte Carlo simulation is performed under the w output scenarios using the w wind power output models and the w photovoltaic output models to obtain k second wind power output data and k second photovoltaic output data, including: Selecting a wind power output model corresponding to a target output scenario from the w wind power output models to obtain a target wind power output model, and selecting a photovoltaic output model corresponding to the target output scenario from the w photovoltaic output models to obtain a target photovoltaic output model; the target output scenario is any one of the w output scenarios; Performing Monte Carlo simulation through the target wind power output model and the target photovoltaic output model to obtain x second wind power output data and x second photovoltaic output data; x is an integer greater than or equal to e; removing abnormal values ​​from the x pieces of second wind power output data to obtain e pieces of second wind power output data; Abnormal values ​​in the x second photovoltaic output data are removed to obtain e second photovoltaic output data.

5. The method according to claim 4, characterized in that The removing of abnormal values ​​from the x second wind power output data to obtain e second wind power output data includes: Selecting x target wind power output data corresponding to the target output scenario from the first wind power output data set; Determine a target Pearson correlation coefficient and a target mean square error according to the x second wind power output data and the x target wind power output data; Determine a first weight coefficient corresponding to the target Pearson correlation coefficient and a second weight coefficient corresponding to the target mean square error; the sum of the first weight coefficient and the second weight coefficient is 1; Determine a target comprehensive score according to the target Pearson correlation coefficient, the first weight coefficient, the target mean square error and the second weight coefficient; If the target comprehensive score is less than or equal to the preset comprehensive score threshold, determining the wind power output difference between the x second wind power output data and the x target wind power output data to obtain x wind power output difference values; Selecting a wind power output difference value that is less than a preset difference threshold value from the x wind power output difference values ​​to obtain e wind power output difference values; Selecting the second wind power output data corresponding to the e wind power output differences from the x second wind power output data to obtain the e second wind power output data; If the target comprehensive score is greater than the preset comprehensive score threshold, an average value of the x second wind power output data is obtained, and e second wind power output data with the smallest absolute value of the difference with the average value are selected from the x second wind power output data.

6. The method according to claim 4, characterized in that The determining w target condition risk values ​​according to the k second wind power output data and the k second photovoltaic output data comprises: Obtaining e second wind power output data and e second photovoltaic output data corresponding to the target output scenario; Selecting actual wind power output data corresponding to the target output scenario from the first wind power output data set, and selecting actual photovoltaic output data corresponding to the target output scenario from the first photovoltaic output data set; determining e penalty costs according to the e second wind power output data, the e second photovoltaic output data, the actual wind power output data and the actual photovoltaic output data; Determine a first risk value according to the e penalty costs and a preset confidence level; Determine y first penalty costs according to the e penalty costs and the first risk value; y is a positive integer less than e; Determine the y first penalty costs and the first risk value to perform operations to obtain y risk difference values; A target conditional risk value is determined according to the first risk value and the y risk difference values.

7. The method according to claim 6, characterized in that The determining e penalty costs according to the e second wind power output data, the e second photovoltaic output data, the actual wind power output data and the actual photovoltaic output data comprises: Determine the total wind power and photovoltaic output value according to the e second wind power output data and the e second photovoltaic output data, and obtain e total wind power and photovoltaic output values; Determine a target wind power and photovoltaic power total output value according to the actual wind power output data and the actual photovoltaic power output data; Determine the difference between each of the e wind power photovoltaic total output values ​​and the target wind power photovoltaic total output value to obtain e total output value differences; The e penalty costs are determined according to the e total output value differences, a preset first penalty coefficient, and a preset second penalty coefficient; wherein, if the total output value difference i is greater than or equal to 0, the penalty cost i is determined according to the total output value difference i and the preset first penalty coefficient; if the total output value difference i is less than 0, the penalty cost i is determined according to the total output value difference i and the preset second penalty coefficient; the total output value difference i is any total output value difference among the e total output value differences.

8. The method according to claim 1, characterized in that The step of determining the carbon emission costs under the w output scenarios according to the k second wind power output data, the k second photovoltaic output data and the w conventional output data to obtain the w carbon emission costs includes: Obtain w total load values ​​corresponding to the smart park; Determine w first carbon emissions according to a preset power supply carbon emission quota reference value and the w total load values; Determining w second carbon emissions according to the w conventional output data; Determining w third carbon emissions according to the k second wind power output data and the k second photovoltaic output data; Determining w target carbon emissions according to the w first carbon emissions, the w second carbon emissions, and the w third carbon emissions; The w carbon emission costs are determined according to the w target carbon emissions and a mapping relationship between preset carbon emissions and costs.

9. The method according to claim 1, characterized in that The determining of the target scheduling strategy according to the w target condition risk values ​​and the w carbon emission costs includes: Generate s scheduling strategies according to the preset constraints; Determine the output scenario corresponding to each of the s scheduling strategies to obtain s output scenarios; Selecting the target condition risk values ​​and carbon emission costs corresponding to the s output scenarios from the w target condition risk values ​​and the w carbon emission costs to obtain s target condition risk values ​​and s carbon emission costs; Determine s strategy scores according to the s target condition risk values, the s carbon emission costs and a preset smart park optimization scheduling model; Select the highest strategy score from the s strategy scores to obtain the highest strategy score; The scheduling strategy corresponding to the highest strategy score is used as the target scheduling strategy.

10. A device for generating an optimization scheduling strategy for a smart park, characterized in that: A control device applied to a smart park, wherein the smart park includes: a wind turbine, a photovoltaic unit, and a conventional unit; the optimization scheduling strategy generation device of the smart park includes: a data acquisition module, a scene classification module, a model training module, a data simulation module, a data processing module, and a strategy generation module, wherein: The data acquisition module is used to obtain a first wind speed data set within a historical time period, a first wind power output data set corresponding to the wind turbine generator set, a first solar irradiance data set, and a first photovoltaic output data set corresponding to the photovoltaic generator set; The scene classification module is used to classify the first wind speed data set and the first solar irradiance data set to obtain m wind power categories and n photovoltaic categories, and combine the m wind power categories and the n photovoltaic categories to obtain w output scenes; w is equal to m multiplied by n, and n and m are both positive integers; The model training module is used to determine w wind power output models according to the first wind speed data set and the w output scenarios, and to determine w photovoltaic output models according to the first solar irradiance data set and the w output scenarios; each output scenario corresponds to one wind power output model and one photovoltaic output model; The data simulation module is used to perform Monte Carlo simulation under the w output scenarios through the w wind power output models and the w photovoltaic output models to obtain k second wind power output data and k second photovoltaic output data; The data processing module is used to determine w target condition risk values ​​according to the k second wind power output data and the k second photovoltaic output data; The data acquisition module is also used to obtain the conventional output data of the conventional unit in the w output scenarios to obtain w conventional output data; The data processing module is further used to determine the carbon emission costs under the w output scenarios according to the k second wind power output data, the k second photovoltaic output data and the w conventional output data, to obtain w carbon emission costs; The strategy generation module is used to determine the target scheduling strategy according to the w target condition risk values ​​and the w carbon emission costs.

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