Local power grid online simulation method and device based on prediction and scheduling

Abstract

The invention discloses a prediction and scheduling-based local power grid online simulation method and device, and the method comprises the steps: predicting the power utilization load power of a local power grid at a current time period according to the historical power utilization data information of a load side of the local power grid; scheduling the power supply side of the local power grid according to the predicted power utilization load power of the current time period, and determining the power generation power of each power supply device of the local power grid at the current time period; and performing online simulation of the local power grid by using a preset simulation model according to the power utilization load power of each time period and the power generation power of each power supply device.

Description

Technical Field

[0001] The present application relates to the technical field of power grid simulation, and in particular to a method and device for online simulation of a local power grid based on prediction and scheduling. Background Art

[0002] At present, simulation technology of local power grids has been widely used in the planning, design and operation of power systems. However, most existing simulation systems focus on the steady-state analysis of power grids. There are still many deficiencies in the dynamic change process of power grids, especially the real-time simulation of the power generation side and the load side. Traditional local power grid simulation systems usually use offline simulation to perform simulation analysis through preset parameters and scenarios. Although this method can reflect the basic characteristics of the power grid to a certain extent, its disadvantage is that it cannot reflect the actual operating status of the power grid in real time, nor can it accurately simulate the dynamic changes of the power grid.

[0003] The existing local power grid simulation methods have the problems of insufficient real-time performance and low dynamic simulation accuracy, and no effective solution has been proposed yet. Summary of the invention

[0004] The embodiments of the present disclosure provide a method and device for online simulation of a local power grid based on prediction and scheduling, so as to at least solve the technical problems of insufficient real-time performance and low dynamic simulation accuracy of the local power grid simulation method in the prior art.

[0005] According to one aspect of an embodiment of the present disclosure, there is provided a method for online simulation of a local power grid based on prediction and scheduling, comprising: predicting the power load power of the local power grid in the current period based on historical power consumption data information on the load side of the local power grid; scheduling the power supply side of the local power grid based on the predicted power load power in the current period to determine the power generation power of each power supply device in the local power grid in the current period; and performing online simulation of the local power grid using a preset simulation model based on the power load power in each period and the power generation power of each power supply device.

[0006] According to another aspect of the embodiments of the present disclosure, a storage medium is further provided. The storage medium includes a stored program, wherein the above method is executed by a processor when the program is running.

[0007] According to another aspect of the embodiment of the present disclosure, there is also provided an online simulation device for a local power grid based on prediction and scheduling, including: a prediction module, used to predict the power load power of the local power grid in the current time period based on the historical power consumption data information on the load side of the local power grid; a scheduling module, used to schedule the power supply side of the local power grid according to the predicted power load power in the current time period, and determine the power generation power of each power supply device in the local power grid in the current time period; and an online simulation module, used to perform online simulation of the local power grid according to the power load power in each time period and the power generation power of each power supply device using a preset simulation model.

[0008] According to another aspect of the embodiment of the present disclosure, there is also provided an online simulation device for a local power grid based on prediction and scheduling, including a processor; and a memory connected to the processor, for providing the processor with instructions for processing the following processing steps: predicting the power load power of the local power grid in the current period based on historical power consumption data information on the load side of the local power grid; scheduling the power supply side of the local power grid based on the predicted power load power in the current period, and determining the power generation power of each power supply device in the local power grid in the current period; and performing online simulation of the local power grid using a preset simulation model based on the power load power in each period and the power generation power of each power supply device.

[0009] In the present application, firstly, according to the historical power consumption data information of the load side of the local power grid, the power load power of the local power grid in the current period is predicted, so as to capture and analyze the power load power of the local power grid in the current period in real time, and provide timely and accurate data support for the subsequent real-time dispatch and simulation of the power grid. Then, according to the predicted power load power of the current period, the power supply side of the local power grid is dispatched to determine the power generation power of each power supply device of the local power grid in the current period. By performing dynamic dispatch based on the real-time prediction results, the power generation power of the power supply equipment of the local power grid is matched with the actual power demand, ensuring the real-time response and continuous optimization of the power grid operation state. Finally, using the data generated in the previous two steps, the online simulation of the local power grid is performed using the preset simulation model, realizing the effective combination of prediction, real-time power grid dispatch and simulation, and being able to feedback the real-time operation of the local power grid in real time, effectively improving the accuracy of dynamic simulation. Thereby solving the technical problems that the local power grid simulation method existing in the prior art is insufficient in real time and low in dynamic simulation accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The drawings described herein are used to provide a further understanding of the present disclosure and constitute a part of the present application. The illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation on the present disclosure. In the drawings:

[0011] Figure 1is a hardware structure block diagram of a computing device for implementing the method according to Embodiment 1 of the present application;

[0012] Figure 2a It is a schematic diagram of the association between the local power grid online simulation system based on prediction and scheduling and the local power grid according to Example 1 of the present application;

[0013] Figure 2b It is a schematic diagram of the framework of the online simulation system of the local power grid based on prediction and scheduling according to Example 1 of the present application;

[0014] Figure 3 It is a flow chart of the online simulation method of a local power grid based on prediction and scheduling according to Example 1 of the present application;

[0015] Figure 4 is a schematic diagram of a probability distribution curve related to civil and commercial power load power in the current period according to Example 1 of the present application;

[0016] Figure 5 is a schematic diagram of a local power grid online simulation device based on prediction and scheduling according to Example 2 of the present application; and

[0017] Figure 6 It is a schematic diagram of the online simulation device of the local power grid based on prediction and scheduling as described in Example 3 of the present application. DETAILED DESCRIPTION

[0018] In order to enable those skilled in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only embodiments of a part of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work should fall within the scope of protection of the present disclosure.

[0019] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. 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 necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0020] Example 1

[0021] According to this embodiment, a method embodiment of an online simulation method for a local power grid based on prediction and scheduling is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0022] The method embodiment provided in this embodiment can be executed in a mobile terminal, a computer terminal, a server or a similar computing device. Figure 1 The hardware structure block diagram of a computing device for implementing a local power grid online simulation method based on prediction and scheduling is shown. Figure 1 As shown, the computing device may include one or more processors (the processor may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory for storing data, a transmission device for communication functions, and an input / output interface. The memory, the transmission device, and the input / output interface are connected to the processor via a bus. In addition, it may also include: a display, a keyboard, and a cursor control device connected to the input / output interface. A person skilled in the art can understand that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0023] It should be noted that the one or more processors and / or other data processing circuits described above may generally be referred to herein as "data processing circuitry". The data processing circuitry may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuitry may be a single independent processing module, or may be incorporated in whole or in part into any of the other components of the computing device. As involved in the embodiments of the present disclosure, the data processing circuitry acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0024] The memory can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the online simulation method of the local power grid based on prediction and scheduling in the embodiment of the present disclosure. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, the online simulation method of the local power grid based on prediction and scheduling of the above-mentioned application program is realized. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the computing device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0025] The transmission device is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computing device. In one example, the transmission device includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device can be a radio frequency (Radio Frequency, RF) module, which is used to communicate with the Internet wirelessly.

[0026] The display may be, for example, a touch screen liquid crystal display (LCD) that may enable a user to interact with a user interface of the computing device.

[0027] It should be noted that, in some optional embodiments, the above Figure 1 The computing device shown may include hardware elements (including circuits), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware elements and software elements. Figure 1 This is merely one example of a particular embodiment and is intended to illustrate the types of components that may be present in the computing devices described above.

[0028] The technical solution can be used to realize online simulation of a local power grid (regional power grid). Specifically, the local power grid can be, for example, a power grid in a city area. Figure 2a is a schematic diagram of the association between the local power grid online simulation system based on prediction and scheduling and the local power grid according to this embodiment, Figure 2b This is a schematic diagram of the structure of the local power grid online simulation system based on prediction and scheduling. Figure 2a and Figure 2bAs shown, the local power grid can be powered by power supply equipment of different energy types, such as photovoltaic equipment, wind turbine equipment, and thermal power equipment. Among them, photovoltaic equipment can be associated with multiple photovoltaic units (such as photovoltaic units 1-I), wind power equipment can be associated with multiple wind turbines (such as wind turbines 1-J), thermal power equipment can be associated with multiple thermal power units (such as thermal power units 1-L), energy storage equipment can be associated with multiple energy storage units (such as energy storage units 1-M), and power load equipment can be associated with smart meters 1-U, and residential power consumption data, industrial power consumption data, commercial power consumption data, etc. are collected through smart meters 1-U. In addition, the energy storage equipment can obtain and store electricity from the local power grid during the off-peak period, and supply power to the local power grid during the peak period to balance the power supply load of the local power grid. The power grid purchasing equipment can purchase electricity from the State Grid Corporation when the local power grid's spontaneous power output is insufficient to achieve power supplement.

[0029] In this embodiment, the local power grid online simulation system can, for example, communicate and connect with photovoltaic equipment, wind turbine equipment, thermal power equipment and power load equipment through the Internet of Things, so as to obtain the power supply power of photovoltaic equipment, the power supply power of wind turbine equipment and the power supply power of thermal power equipment, as well as the power consumption of power load equipment. Thus, through power dispatching, the balance between the power consumption of power load equipment in the local power grid and the total power supply power of each power supply equipment can be achieved (wherein the total power supply power includes the power supply power of photovoltaic equipment, the power supply power of wind turbine equipment and the power supply power of thermal power equipment).

[0030] Among them, the equipment involved in the dispatching includes photovoltaic equipment, wind power equipment, thermal power equipment, energy storage equipment and power grid purchasing equipment. The dispatching of thermal power equipment is mainly reflected in the dispatching of the power generation power of multiple thermal power units 1~L associated with the thermal power equipment. The dispatching of energy storage equipment is mainly reflected in the dispatching of the charging power and discharging power of multiple energy storage units 1~M associated with the energy storage equipment. The dispatching of photovoltaic equipment is mainly reflected in the dispatching of the power supply power of multiple photovoltaic units 1~I associated with the photovoltaic equipment. The dispatching of wind power equipment is mainly reflected in the dispatching of the power supply power of multiple wind turbines 1~J associated with the wind power equipment. The dispatching of power grid purchasing equipment is mainly reflected in the dispatching of the power purchased by the power grid purchasing equipment from the national power grid.

[0031] It should be noted that the above-mentioned hardware structure can be applied to the local power grid online simulation system, various power supply equipment, local power grid and national power grid.

[0032] Under the above operating environment, according to the first aspect of this embodiment, a local power grid online simulation method based on prediction and scheduling is provided. The method comprises Figure 2a and Figure 2b The local power grid online simulation system shown in is implemented. Figure 3A schematic diagram showing the process of the method is shown in FIG. Figure 3 As shown, the method includes:

[0033] S302: predicting the power load power of the local power grid in the current period based on the historical power consumption data information on the load side of the local power grid;

[0034] S304: scheduling the power supply side of the local power grid according to the predicted power load power of the current period, and determining the power generation power of each power supply device of the local power grid in the current period; and

[0035] S306: Perform online simulation of the local power grid using a preset simulation model according to the power load power in each time period and the power generation power of each power supply device.

[0036] Specifically, refer to Figure 2a and Figure 2b As shown, the local power grid online simulation system includes a load-side prediction subsystem, an overall dispatching subsystem, and a simulation subsystem. First, the load-side prediction subsystem predicts the power load power of the local power grid in the current period based on the historical power consumption data information on the load side of the local power grid. This embodiment does not rely solely on preset parameters and scenarios, but rather uses the historical power consumption data information on the load side for prediction, thereby obtaining and analyzing the power load power of the local power grid in the current period in real time, and providing timely and accurate data support for subsequent real-time dispatching and simulation of the power grid.

[0037] Then, the overall dispatching subsystem dispatches the power supply side of the local power grid according to the power load power of the current period predicted by the load-side prediction subsystem, and determines the power generation power of each power supply device in the local power grid in the current period. In this way, real-time dispatching is achieved, which effectively overcomes the post-analysis limitations of traditional simulation technology and improves the response speed and dispatching flexibility of the simulation system. In addition, by dynamically dispatching based on real-time prediction results, the power generation power of the power supply equipment in the local power grid is matched with the actual power demand, ensuring real-time response and continuous optimization of the power grid operation status.

[0038] Finally, the simulation subsystem uses the data generated in the previous two steps and the preset simulation model to perform online simulation of the local power grid, realizing the effective combination of prediction, real-time power grid dispatching and simulation, and can provide real-time feedback on the real-time operation of the local power grid, effectively improving the accuracy of dynamic simulation. Among them, a simulation model can be pre-set so that the behavior of the local power grid under different conditions can be quickly and effectively simulated. That is, the simulation model is used to simulate the operating status of the local power grid based on the real-time power load power of each time period and the power generation power of each power supply device. Through real-time simulation, potential problems can be discovered in time, measures can be taken in advance to avoid the occurrence of failures, and it is also convenient for the optimal allocation of resources.

[0039] As described in the background technology, most existing simulation systems focus on steady-state analysis of power grids. There are still many deficiencies in the dynamic change process of power grids, especially the real-time simulation of the power generation side and the load side. Traditional local power grid simulation systems usually use offline simulation methods to perform simulation analysis through preset parameters and scenarios. Although this method can reflect the basic characteristics of the power grid to a certain extent, its disadvantage is that it cannot reflect the actual operating status of the power grid in real time, nor can it accurately simulate the dynamic changes of the power grid.

[0040] In view of this, according to this embodiment, firstly, according to the historical power consumption data information of the load side of the local power grid, the power load power of the local power grid in the current period is predicted, so as to capture and analyze the power load power of the local power grid in the current period in real time, and provide timely and accurate data support for the subsequent real-time scheduling and simulation of the power grid. Then, according to the predicted power load power of the current period, the power supply side of the local power grid is scheduled to determine the power generation power of each power supply device of the local power grid in the current period. By performing dynamic scheduling based on the real-time prediction results, the power generation power of the power supply equipment of the local power grid is matched with the actual power demand, ensuring real-time response and continuous optimization of the power grid operation state. Finally, using the data generated in the previous two steps, the online simulation of the local power grid is performed using the preset simulation model, realizing the effective combination of prediction, real-time power grid scheduling and simulation, and being able to feedback the real-time operation of the local power grid in real time, effectively improving the accuracy of dynamic simulation. Thereby solving the technical problems of insufficient real-time performance and low dynamic simulation accuracy of the local power grid simulation method existing in the prior art.

[0041] Optionally, based on the power load power in each time period and the power generation power of each power supply device, an online simulation of the local power grid is performed using a preset simulation model, including: based on the power load power in each time period and the power generation power of each power supply device, using a preset simulation model to construct a time-related power load power curve and a power generation power curve; and displaying the power load power curve and the power generation power curve.

[0042] Specifically, according to the power load power in each time period and the power generation power of each power supply device, a preset simulation model is used to comprehensively consider various factors, such as line loss, load distribution, power type, etc., to generate a time-related power load power curve and power generation power curve. Afterwards, the power load power curve and power generation power curve obtained above can be displayed in a visual way. For example, but not limited to, through charts, graphical interfaces, etc., so that relevant personnel can intuitively understand the operating status of the power grid and expected changes in the future. In this way, it not only helps monitoring and management, but also assists decision-making, such as whether it is necessary to increase backup power supply, adjust electricity price strategies, etc.

[0043] Optionally, based on the power load power in each time period and the power generation power of each power supply device, an online simulation of the local power grid is performed using a preset simulation model, including: performing a differential operation on the power generation power of each power supply device in each time period to obtain a first differential operation result; based on the first differential operation result, using a preset simulation model to simulate the fluctuation of the power generation power of each power supply device in the local power grid.

[0044] Specifically, the power generation data of each power supply device in each time period is differentially operated to calculate the rate of change of power generation over time, that is, how the power generation increases or decreases over time, so as to obtain the power generation change rate of each power supply device in each time period, that is, the first differential operation result, to reflect the instantaneous change of power generation. Afterwards, the power generation change rate of each power supply device in each time period is input into the preset simulation model, and the fluctuation of power generation of each power supply device is simulated by the simulation model, that is, how the power generation of each power supply device fluctuates with the change of time and external conditions. Thus, the changing trend of power generation is revealed through differential operation, and the impact of these changes on the power grid is predicted and analyzed with the help of simulation model, which provides a data basis for evaluating the overall operation status and safety of the local power grid.

[0045] Optionally, based on the power load power in each time period and the power generation power of each power supply device, an online simulation of the local power grid is performed using a preset simulation model, including: performing a differential operation on the power load power in each time period to obtain a second differential operation result; based on the second differential operation result, using a preset simulation model to simulate the fluctuation of the power load power of the local power grid.

[0046] Specifically, the power load power of each time period is differentially operated, and the growth or decrease rate of the power load power in different time periods is calculated to obtain the second differential operation result. Afterwards, the second differential operation result is input into a preset simulation model, and the simulation model is used to simulate the fluctuation of the power load power with the change of time and external conditions in different time periods. Thus, understanding the changing trend of the power load power through differential operation and using the simulation model to predict and analyze the specific impact of these changes on the local power grid can help relevant personnel foresee potential problems in advance.

[0047] Optionally, based on the historical electricity consumption data information on the load side of the local power grid, the power load power of the local power grid in the current time period is predicted, including: predicting the industrial power load power in the current time period based on the historical electricity consumption data information of the industrial production plan and the industrial production line; predicting the civil and commercial power load power in the current time period based on the historical electricity consumption data information of the civil and commercial power loads; and determining the power load power of the local power grid in the current time period based on the industrial power load power and the civil and commercial power load power in the current time period.

[0048] Specifically, the industrial production plan includes the production arrangements of the factory in the future, such as the products produced, the expected output, the number of industrial production lines, etc. The historical electricity consumption data information of the industrial production line refers to the actual electricity consumption of the industrial production line in the same or similar industrial production plans in the past. The historical electricity consumption data information of the industrial production line is obtained from the smart meters 1~U through the factory production line power load module, and then the industrial electricity load in the current period is predicted by statistical methods or machine learning algorithms based on the industrial production plan and the historical electricity consumption data information of the industrial production line. Thus, based on the past electricity consumption pattern and the current production plan, the electricity demand for industrial production in a certain period of time in the future is estimated.

[0049] Then, the household and commercial electricity load module collects historical electricity consumption data in the civil and commercial fields from the smart meters 1 to U. These data can come from various places such as homes, shops, office buildings, etc., reflecting the electricity consumption habits in different time periods in daily life. For the civil and commercial fields, statistical methods or machine learning algorithms are used to predict the electricity load in the current period. Finally, the predicted industrial power load power and the civil and commercial power load power are combined to obtain the total power load power of the entire local power grid in the current period, laying the foundation for subsequent power grid scheduling and resource allocation.

[0050] Optionally, based on the historical electricity consumption data information of the civil and commercial electricity loads, the civil and commercial electricity load power in the current time period is predicted, including: based on the historical electricity consumption data information of the civil and commercial electricity loads, using a preset neural network model to determine a probability distribution vector related to the civil and commercial electricity load power in the current time period; wherein each element of the probability distribution vector represents the probability of different electricity load power intervals; based on the probability distribution vector, determining a probability distribution curve related to the civil and commercial electricity load power in the current time period; and based on a preset confidence level, determining the civil and commercial electricity load power in the current time period based on the probability distribution curve.

[0051] Specifically, a large amount of historical data on civil and commercial electricity loads is collected. These historical data contain electricity consumption in different time periods in the past, such as daily peak and valley values, and other related information. Then, these historical data are used to train a pre-designed neural network model, so that the model can learn useful trends and rules from historical data, thereby improving the accuracy of future electricity load forecasts.

[0052] When the model training is completed, it is used to predict the power load in the current period, and a probability distribution vector is obtained accordingly. Each item in the vector corresponds to a specific power load interval and its probability of occurrence. For example, the first item of the vector represents the probability of "the power load power is between 0 and 1500 kilowatts", the second item represents the probability of "the power load power is between 1500-1700 kilowatts", and so on. After obtaining the probability distribution vector, the probability distribution curve of the power load in the corresponding time period is further drawn. The probability distribution curve shows the probability of different power load intervals being selected, such as Figure 4 As shown in the figure, the horizontal axis represents different power load intervals, and the vertical axis corresponds to the respective probability of occurrence. By analyzing this curve, we can intuitively understand which power load intervals have a higher probability.

[0053] Then, based on the preset confidence level, a sliding window can be used, starting from a starting point of the probability distribution curve, to gradually move the position and size of the window until a window that meets the confidence level requirements and maximizes the high probability interval is found. Finally, the average value of the power load interval corresponding to the highest probability value in the window can be selected as the civil and commercial power load power of the current period. In this way, it is ensured that the final civil and commercial power load is within a highly confident range, while also avoiding the risk of relying only on a single maximum value.

[0054] Optionally, according to the predicted power load power of the current time period, the power supply side of the local power grid is scheduled to determine the power generation power of each power supply device in the local power grid in the current time period, including: obtaining corresponding power supply prediction data from multiple preset prediction and scheduling subsystems; wherein the multiple prediction and scheduling subsystems include a power purchase prediction and scheduling subsystem, a photovoltaic prediction and scheduling subsystem, a wind power prediction and scheduling subsystem, a thermal power prediction and scheduling subsystem, and an energy storage prediction and scheduling subsystem; the power supply prediction data is data obtained by each prediction and scheduling subsystem using its own pre-trained prediction model to predict the power generation power of each power supply device in the local power grid in the current time period; according to the predicted power load power of the current time period and the power supply prediction data, the scheduling data of each power supply device in the local power grid in the current time period is determined; according to the scheduling data, the power supply side of the local power grid is scheduled; according to the scheduling result, the power generation power of each power supply device in the local power grid in the current time period is determined.

[0055] Specifically, refer to Figure 2b As shown in the figure, the local power grid online simulation system includes multiple forecasting and scheduling subsystems, namely photovoltaic forecasting and scheduling subsystem, wind power forecasting and scheduling subsystem, thermal power forecasting and scheduling subsystem, energy storage forecasting and scheduling subsystem and power purchase forecasting and scheduling subsystem. These subsystems can use their own forecasting models and combine historical data to accurately predict the power generation power of each power supply equipment in the local power grid in the current period. Among them, taking the power purchase forecasting and scheduling subsystem as an example, the power purchase forecasting and scheduling subsystem uses its own forecasting model to predict the power supply forecast data of photovoltaic equipment, wind power equipment, thermal power equipment, energy storage equipment and power purchase equipment in the current period, which are: photovoltaic power supply power x a1 、Wind power supply power x b1 、Thermal power supply power x c1 , energy storage charging and discharging power x d1 And the grid transmission power x e1 .

[0056] Similarly, the wind power forecasting and dispatching subsystem uses its own forecasting model to predict the power supply forecast data of photovoltaic equipment, wind power equipment, thermal power equipment, energy storage equipment and power grid purchasing equipment in the current period, which are x a2 、x b2 、x c2 、x d2 and x e2 The thermal power forecasting and dispatching subsystem uses its own forecasting model to predict the power supply forecast data of photovoltaic equipment, wind power equipment, thermal power equipment, energy storage equipment and power grid purchasing equipment in the current period, which are x a3 、x b3 、x c3 、x d3 and x e3The energy storage prediction and dispatch subsystem uses its own prediction model to predict the power supply prediction data of photovoltaic equipment, wind power equipment, thermal power equipment, energy storage equipment and power grid purchasing equipment in the current period, which are x a4 、x b4 、x c4 、x d4 and x e4 The power purchase forecasting and dispatching subsystem uses its own forecasting model to predict the power supply forecast data of photovoltaic equipment, wind power equipment, thermal power equipment, energy storage equipment and power grid power purchase equipment in the current period, which are x a5 、x b5 、x c5 、x d5 and x e5 .

[0057] Then, the overall dispatching subsystem combines the power supply forecast data obtained by each forecasting and dispatching subsystem with the power load power of the current period predicted by the load-side forecasting subsystem, and calculates the dispatching data of each power supply device in the local power grid in the current period based on the integrated data. For example, but not limited to, first determine the weight values ​​of the photovoltaic forecasting and dispatching subsystem, the wind power forecasting and dispatching subsystem, the thermal power forecasting and dispatching subsystem, the energy storage forecasting and dispatching subsystem, and the power purchase forecasting and dispatching subsystem based on the power supply forecasting data of multiple historical periods. Afterwards, based on the weight value, the power supply forecasting data of each forecasting and dispatching subsystem is weighted and summed to obtain the dispatching data (x a 、x b 、x c 、x d 、x e ).

[0058] Specifically, based on the power supply forecast data of multiple historical periods, the weight values ​​of the photovoltaic forecast dispatch subsystem, wind power forecast dispatch subsystem, thermal power forecast dispatch subsystem, energy storage forecast dispatch subsystem and power purchase forecast dispatch subsystem are determined to be ω1, ω2, ω3, ω4 and ω5 respectively. Then, the dispatch data (x a 、x b 、x c 、x d 、x e ):

[0059] (x a 、x b 、x c 、x d 、x e )=ω1(x a1 、x b1 、x c1 、x d1 、xe11 )+ω2(x a2 、x b2 、x c2 、x d2 、x e2 )+ω3(x a3 、x b3 、x c3 、x d3 、x e3 )+ω4(x a4 、x b4 、x c4 、x d4 、x e4 )+ω5(x a5 、x b5 、x c5 、x d5 、x e5 )

[0060] Through weighted summation, more emphasis is placed on the prediction results of the forecasting and scheduling subsystems with excellent historical performance, which reduces the uncertainty in scheduling decisions and improves the scientificity and rationality of decisions.

[0061] Then, according to the scheduling data (x a 、x b 、x c 、x d 、x e ), and perform actual dispatching operations on the power supply side of the local power grid. Specifically, dispatching the photovoltaic power supply power of the photovoltaic equipment is x a , the wind power supply power of the dispatching wind power equipment is x b , the thermal power supply power of the dispatching thermal power equipment is x c , the energy storage charging and discharging power of the energy storage equipment is x d , the grid transmission power of the dispatching grid power purchasing equipment is x e Finally, according to the scheduling results, the power generation of each power supply device in the local power grid in the current period is checked and determined again. In this way, the power generation of each power supply device in the local power grid in the current period can be accurately determined, providing timely and accurate data support for the subsequent online real-time simulation of the local power grid.

[0062] Optionally, based on the power supply forecast data of multiple historical time periods, the weight values ​​of the photovoltaic forecast scheduling subsystem, the wind power forecast scheduling subsystem, the thermal power forecast scheduling subsystem, the energy storage forecast scheduling subsystem and the power purchase forecast scheduling subsystem can be determined in the following manner:

[0063] (1) Obtain the power supply forecast data determined by each forecast dispatch subsystem in previous time periods, and obtain the dispatch data determined by the overall dispatch subsystem in previous dispatches;

[0064] (2) For each dispatching subsystem, calculate the error between the power supply forecast data predicted by the dispatching subsystem in each historical period and the dispatching data determined by the overall dispatching subsystem;

[0065]

[0066] In the formula, e ij is the error between the power supply forecast data predicted by the ith dispatching subsystem in the jth historical period and the dispatching data determined by the overall dispatching subsystem; (x iaj 、x ibj 、x icj 、x idj 、x iej 、x ifj ) is the power supply forecast data of the jth historical period predicted by the i-th dispatching subsystem, x iaj is the photovoltaic power supply power in the jth historical period predicted by the i-th scheduling subsystem, x ibj is the wind power supply power in the jth historical period predicted by the i-th dispatching subsystem, x icj is the thermal power supply power in the jth historical period predicted by the i-th dispatching subsystem, x idj is the energy storage charging and discharging power predicted by the i-th dispatching subsystem in the j-th historical period, x iej is the grid transmission power predicted by the i-th dispatching subsystem in the j-th historical period, where i = a~e, a represents photovoltaic equipment, b represents wind power equipment, c represents thermal power equipment, d represents energy storage equipment, and e represents grid power purchasing equipment; the dispatching data for the j-th historical period is (x aj 、x bj 、x cj 、x dj 、x ej ), x aj is the photovoltaic power supply power in the jth historical period, x bj is the wind power supply power in the jth historical period, x cj is the thermal power supply power in the jth historical period, x dj is the energy storage charging and discharging power in the jth historical period, x ej is the power transmission power of the power grid in the jth historical period;

[0067] (3) For each dispatching subsystem, calculate the average error between the power supply forecast data predicted by the subsystem in all historical periods and the dispatching data determined by the overall dispatching subsystem.

[0068]

[0069] (4) Use the softmax function to map the average error of all scheduling subsystems to a value in the interval [0,1], and determine the mapped value as the weight ω of the corresponding scheduling subsystem i .

[0070] Therefore, the prediction performance of each prediction and scheduling subsystem can be quantified by calculating the error, providing an objective basis for the subsequent determination of the weight value. By averaging the error, the prediction performance of different prediction and scheduling subsystems can be compared fairly, avoiding deviations caused by abnormal performance in individual time periods. The softmax function helps to smooth the error, avoid uneven weight distribution caused by extreme error values, improve the stability and rationality of the weight value, and make the prediction and scheduling subsystem with better prediction performance obtain a higher weight value, reflecting the relative importance and contribution of each prediction and scheduling subsystem in coordinated scheduling.

[0071] Optionally, before the overall scheduling subsystem determines the scheduling data of each power supply equipment in the local power grid in the current time period based on the predicted power load power and power supply prediction data of the current time period, it also includes: each prediction and scheduling subsystem obtains the power supply prediction data of the current time period based on the prediction, and performs internal pre-scheduling for each unit or group, so as to optimize the respective predicted power supply prediction data.

[0072] Specifically, refer to Figure 2b As shown in the figure, photovoltaic equipment, wind power equipment, thermal power equipment and energy storage equipment are all associated with multiple units or units. Therefore, these forecasting and scheduling subsystems can obtain the power supply forecast data of the current period based on the forecast, and perform internal pre-scheduling for each unit or unit, thereby optimizing the respective predicted power supply forecast data. However, due to the poor controllability of photovoltaic power generation equipment and wind power equipment, the photovoltaic forecasting and scheduling subsystem and the wind power forecasting and scheduling subsystem only perform forecasting without performing actual internal pre-scheduling operations. The internal pre-scheduling operation is performed by the thermal power forecasting and scheduling subsystem, the energy storage forecasting and scheduling subsystem and the power purchase forecasting and scheduling subsystem. Therefore, on this basis, the thermal power forecasting and scheduling subsystem, the energy storage forecasting and scheduling subsystem and the power purchase forecasting and scheduling subsystem respectively optimize the various units or units associated with the thermal power equipment, energy storage equipment and power grid power purchase equipment, thereby performing internal pre-scheduling between the equipment.

[0073] Optionally, each prediction and dispatching subsystem obtains power supply prediction data for the current time period based on the prediction, and performs internal pre-dispatching operations for each unit or generator set, including: the thermal power prediction and dispatching subsystem, the energy storage prediction and dispatching subsystem, and the power purchase prediction and dispatching subsystem respectively determine the constraints and objective functions of each unit or unit associated with the thermal power equipment, energy storage equipment, and power grid power purchase equipment according to the actual conditions; the thermal power prediction and dispatching subsystem, the energy storage prediction and dispatching subsystem, and the power purchase prediction and dispatching subsystem determine the power supply power of each unit or unit associated with each unit in the next time period using a particle swarm optimization algorithm based on the determined constraints and objective functions; the thermal power prediction and dispatching subsystem, the energy storage prediction and dispatching subsystem, and the power purchase prediction and dispatching subsystem determine the total power supply power of each device in the current time period based on the power supply power of each unit or unit associated with each unit in the next time period; the thermal power prediction and dispatching subsystem, the energy storage prediction and dispatching subsystem, and the power purchase prediction and dispatching subsystem replace the power supply power of the corresponding device in the initial pre-dispatching data predicted by each device with the total power supply power of each device in the current time period to obtain optimized power supply prediction data.

[0074] Specifically, the initial power supply forecast data obtained by thermal power equipment prediction is (x a3 、x b3 、x c3 、x d3 、x e3 ), refer to Figure 2b As shown, since the thermal power equipment is associated with thermal power units 1 to L, the thermal power equipment is based on the initial power supply forecast data (x a3 、x b3 、x c3 、x d3 、x e3 ), and perform internal pre-dispatch for each thermal power unit 1 to L, specifically:

[0075] (1) Thermal power equipment determines the constraints related to the power supply of each thermal power unit 1 to L.

[0076] First, assume that the power supply of each thermal power unit is x c31 ~x c3L , according to the preset maximum power x of each thermal power unit c31Max ~x c3LMax and minimum power x c31Min ~x c3LMin Set the first constraint, that is, x c31Min ≦x c31 ≦x c31Max , x c32Min ≦x c32 ≦x c32Max , x c3LMin ≦x c3L≦x c3LMax ;

[0077] Next, set the second constraint, x c31 +x c32 +x c33 +...+x c3L ≥x f -x a3 -x b3 -x d3 -x e3 ; where x f The power load power predicted by the load-side prediction subsystem;

[0078] (2) According to the principle of minimum cost, determine the objective function f(x c31 ~x c3L ):f(x c31 ~x c3L )=s c31 (x c31 )+s c32 (x c32 )+...+s c3L (x c3L );

[0079] Among them, s c31 (),s c32 (),...s c3L () is the cost function corresponding to each thermal power unit;

[0080] (3) Based on the above constraints and objective function, the particle swarm optimization algorithm is used to determine the power supply x of each thermal power unit. c31 ~x c3L , x c31 +x c32 +x c33 +...+x c3L =x' c3 ;

[0081] (4) The initial power supply forecast data (x a3 、x b3 、x c3 、x d3 、x e3 ) c3 Replace with x' c3 , thus obtaining the optimized power supply forecast data (x a3 、x b3 、x' c3 、x d3 、x e3 ).

[0082] In this embodiment, the energy storage device also uses the same method as the thermal power device to perform internal pre-scheduling to determine the optimized power supply forecast data (x a4 、x b4 、x c4 、x' d4 , X e4 =).

[0083] In this embodiment, the power grid purchasing device calculates the power supply forecast data (x a5 、x b5 、x c5 、x d5 、x e5 ), power transmission to the grid x e5 Perform internal pre-scheduling, specifically:

[0084] (1) Assume that the power transmission power of the power grid is x e5 , we can first calculate the maximum power x of the power transmission power of the pre-set grid e5Max and the minimum power 0 sets the constraint, 0≦x e5 ≦x e5Max ; Secondly, set constraints, x e5 ≥x f -x a5 -x b5 -x c5 -x d5 ; where x f The power load power predicted by the load-side prediction subsystem;

[0085] (2) According to the principle of minimum cost, determine the objective function f(x e5 )=s e5 (x e5 );

[0086] Among them, s e5 () is the cost function corresponding to the power transmission power of the power grid.

[0087] (3) Based on the above constraints and objective function, the genetic algorithm is used to optimize and determine the power transmission power x' e5 ;

[0088] (4) The initial power supply forecast data (x a5 、x b5 、x c5 、x d5 、x e5 ) e5 Replace with x' e5 , thus obtaining the optimized power supply forecast data (x a5 、x b5 、x c5 、xd5 、x' e5 ).

[0089] Therefore, the thermal power forecasting and dispatching subsystem, energy storage forecasting and dispatching subsystem, and power purchase forecasting and dispatching subsystem can set constraints and objective functions according to the characteristics of the thermal power equipment, energy storage equipment, and power grid power purchase equipment units or units, ensuring that the optimization process fully considers the physical limitations of the equipment and operational goals, such as cost minimization, emission control, etc. In addition, the optimized power supply forecasting data is closer to the actual operation situation, which improves the accuracy and reliability of the forecast.

[0090] In addition, according to this embodiment, a storage medium is also provided, wherein the storage medium includes a stored program, wherein when the program is run, a processor executes any one of the above methods.

[0091] In the present application, firstly, according to the historical power consumption data information of the load side of the local power grid, the power load power of the local power grid in the current period is predicted, so as to capture and analyze the power load power of the local power grid in the current period in real time, and provide timely and accurate data support for the subsequent real-time dispatch and simulation of the power grid. Then, according to the predicted power load power of the current period, the power supply side of the local power grid is dispatched to determine the power generation power of each power supply device of the local power grid in the current period. By performing dynamic dispatch based on the real-time prediction results, the power generation power of the power supply equipment of the local power grid is matched with the actual power demand, ensuring the real-time response and continuous optimization of the power grid operation state. Finally, using the data generated in the previous two steps, the online simulation of the local power grid is performed using the preset simulation model, realizing the effective combination of prediction, real-time power grid dispatch and simulation, and being able to feedback the real-time operation of the local power grid in real time, effectively improving the accuracy of dynamic simulation. Thereby solving the technical problems that the local power grid simulation method existing in the prior art is insufficient in real time and low in dynamic simulation accuracy.

[0092] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0093] Example 2

[0094] Figure 5The schematic diagram of the structure of the local power grid online simulation device 500 based on prediction and scheduling according to the present embodiment is shown. The local power grid online simulation device 500 comprises: a prediction module 510, which is used to predict the power load power of the local power grid in the current period according to the historical power consumption data information of the load side of the local power grid; a scheduling module 520, which is used to schedule the power supply side of the local power grid according to the predicted power load power of the current period, and determine the power generation power of each power supply device of the local power grid in the current period; and an online simulation module 530, which is used to perform online simulation of the local power grid using a preset simulation model according to the power load power of each period and the power generation power of each power supply device.

[0095] Optionally, based on the power load power in each time period and the power generation power of each power supply device, an online simulation of the local power grid is performed using a preset simulation model, including: based on the power load power in each time period and the power generation power of each power supply device, using a preset simulation model to construct a time-related power load power curve and a power generation power curve; and displaying the power load power curve and the power generation power curve.

[0096] Optionally, based on the power load power in each time period and the power generation power of each power supply device, an online simulation of the local power grid is performed using a preset simulation model, including: performing a differential operation on the power generation power of each power supply device in each time period to obtain a first differential operation result; based on the first differential operation result, using a preset simulation model to simulate the fluctuation of the power generation power of each power supply device in the local power grid.

[0097] Optionally, based on the power load power in each time period and the power generation power of each power supply device, an online simulation of the local power grid is performed using a preset simulation model, including: performing a differential operation on the power load power in each time period to obtain a second differential operation result; based on the second differential operation result, using a preset simulation model to simulate the fluctuation of the power load power of the local power grid.

[0098] Optionally, based on the historical electricity consumption data information on the load side of the local power grid, the power load power of the local power grid in the current time period is predicted, including: predicting the industrial power load power in the current time period based on the historical electricity consumption data information of the industrial production plan and the industrial production line; predicting the civil and commercial power load power in the current time period based on the historical electricity consumption data information of the civil and commercial power loads; and determining the power load power of the local power grid in the current time period based on the industrial power load power and the civil and commercial power load power in the current time period.

[0099] Optionally, based on the historical electricity consumption data information of the civil and commercial electricity loads, the civil and commercial electricity load power in the current time period is predicted, including: based on the historical electricity consumption data information of the civil and commercial electricity loads, using a preset neural network model to determine a probability distribution vector related to the civil and commercial electricity load power in the current time period; wherein each element of the probability distribution vector represents the probability of different electricity load power intervals; based on the probability distribution vector, determining a probability distribution curve related to the civil and commercial electricity load power in the current time period; and based on a preset confidence level, determining the civil and commercial electricity load power in the current time period based on the probability distribution curve.

[0100] Optionally, according to the predicted power load power of the current time period, the power supply side of the local power grid is scheduled to determine the power generation power of each power supply device in the local power grid in the current time period, including: obtaining corresponding power supply prediction data from multiple preset prediction and scheduling subsystems; wherein the multiple prediction and scheduling subsystems include a power purchase prediction and scheduling subsystem, a photovoltaic prediction and scheduling subsystem, a wind power prediction and scheduling subsystem, a thermal power prediction and scheduling subsystem, and an energy storage prediction and scheduling subsystem; the power supply prediction data is data obtained by each prediction and scheduling subsystem using its own pre-trained prediction model to predict the power generation power of each power supply device in the local power grid in the current time period; according to the predicted power load power of the current time period and the power supply prediction data, the scheduling data of each power supply device in the local power grid in the current time period is determined; according to the scheduling data, the power supply side of the local power grid is scheduled; according to the scheduling result, the power generation power of each power supply device in the local power grid in the current time period is determined.

[0101] Thus, according to this embodiment, firstly, according to the historical power consumption data information on the load side of the local power grid, the power load power of the local power grid in the current period is predicted, so as to capture and analyze the power load power of the local power grid in the current period in real time, and provide timely and accurate data support for the subsequent real-time scheduling and simulation of the power grid. Then, according to the predicted power load power of the current period, the power supply side of the local power grid is scheduled to determine the power generation power of each power supply device of the local power grid in the current period. By performing dynamic scheduling based on the real-time prediction results, the power generation power of the power supply equipment of the local power grid is matched with the actual power demand, ensuring real-time response and continuous optimization of the power grid operation state. Finally, using the data generated in the previous two steps, the online simulation of the local power grid is performed using the preset simulation model, realizing the effective combination of prediction, real-time power grid scheduling and simulation, and being able to feedback the real-time operation of the local power grid in real time, effectively improving the accuracy of dynamic simulation. Thus, the technical problems of insufficient real-time performance and low dynamic simulation accuracy of the local power grid simulation method existing in the prior art are solved.

[0102] Example 3

[0103] Figure 6 An online simulation device 600 for a local power grid based on prediction and scheduling according to the present embodiment is shown, comprising: a processor 610; and a memory 620, connected to the processor 610, for providing instructions for the processor 610 to process the following processing steps: predicting the power load power of the local power grid in the current period based on historical power consumption data information on the load side of the local power grid; scheduling the power supply side of the local power grid based on the predicted power load power in the current period, and determining the power generation power of each power supply device in the local power grid in the current period; and performing online simulation of the local power grid using a preset simulation model based on the power load power in each period and the power generation power of each power supply device.

[0104] Optionally, based on the power load power in each time period and the power generation power of each power supply device, an online simulation of the local power grid is performed using a preset simulation model, including: based on the power load power in each time period and the power generation power of each power supply device, using a preset simulation model to construct a time-related power load power curve and a power generation power curve; and displaying the power load power curve and the power generation power curve.

[0105] Optionally, based on the power load power in each time period and the power generation power of each power supply device, an online simulation of the local power grid is performed using a preset simulation model, including: performing a differential operation on the power generation power of each power supply device in each time period to obtain a first differential operation result; based on the first differential operation result, using a preset simulation model to simulate the fluctuation of the power generation power of each power supply device in the local power grid.

[0106] Optionally, based on the power load power in each time period and the power generation power of each power supply device, an online simulation of the local power grid is performed using a preset simulation model, including: performing a differential operation on the power load power in each time period to obtain a second differential operation result; based on the second differential operation result, using a preset simulation model to simulate the fluctuation of the power load power of the local power grid.

[0107] Optionally, based on the historical electricity consumption data information on the load side of the local power grid, the power load power of the local power grid in the current time period is predicted, including: predicting the industrial power load power in the current time period based on the historical electricity consumption data information of the industrial production plan and the industrial production line; predicting the civil and commercial power load power in the current time period based on the historical electricity consumption data information of the civil and commercial power loads; and determining the power load power of the local power grid in the current time period based on the industrial power load power and the civil and commercial power load power in the current time period.

[0108] Optionally, based on the historical electricity consumption data information of the civil and commercial electricity loads, the civil and commercial electricity load power in the current time period is predicted, including: based on the historical electricity consumption data information of the civil and commercial electricity loads, using a preset neural network model to determine a probability distribution vector related to the civil and commercial electricity load power in the current time period; wherein each element of the probability distribution vector represents the probability of different electricity load power intervals; based on the probability distribution vector, determining a probability distribution curve related to the civil and commercial electricity load power in the current time period; and based on a preset confidence level, determining the civil and commercial electricity load power in the current time period based on the probability distribution curve.

[0109] Optionally, according to the predicted power load power of the current time period, the power supply side of the local power grid is scheduled to determine the power generation power of each power supply device in the local power grid in the current time period, including: obtaining corresponding power supply prediction data from multiple preset prediction and scheduling subsystems; wherein the multiple prediction and scheduling subsystems include a power purchase prediction and scheduling subsystem, a photovoltaic prediction and scheduling subsystem, a wind power prediction and scheduling subsystem, a thermal power prediction and scheduling subsystem, and an energy storage prediction and scheduling subsystem; the power supply prediction data is data obtained by each prediction and scheduling subsystem using its own pre-trained prediction model to predict the power generation power of each power supply device in the local power grid in the current time period; according to the predicted power load power of the current time period and the power supply prediction data, the scheduling data of each power supply device in the local power grid in the current time period is determined; according to the scheduling data, the power supply side of the local power grid is scheduled; according to the scheduling result, the power generation power of each power supply device in the local power grid in the current time period is determined.

[0110] Thus, according to this embodiment, firstly, according to the historical power consumption data information on the load side of the local power grid, the power load power of the local power grid in the current period is predicted, so as to capture and analyze the power load power of the local power grid in the current period in real time, and provide timely and accurate data support for the subsequent real-time scheduling and simulation of the power grid. Then, according to the predicted power load power of the current period, the power supply side of the local power grid is scheduled to determine the power generation power of each power supply device of the local power grid in the current period. By performing dynamic scheduling based on the real-time prediction results, the power generation power of the power supply equipment of the local power grid is matched with the actual power demand, ensuring real-time response and continuous optimization of the power grid operation state. Finally, using the data generated in the previous two steps, the online simulation of the local power grid is performed using the preset simulation model, realizing the effective combination of prediction, real-time power grid scheduling and simulation, and being able to feedback the real-time operation of the local power grid in real time, effectively improving the accuracy of dynamic simulation. Thus, the technical problems of insufficient real-time performance and low dynamic simulation accuracy of the local power grid simulation method existing in the prior art are solved.

[0111] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0112] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0113] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0114] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0115] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0116] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0117] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A local power grid online simulation method based on prediction and scheduling, characterized in that: include: Predicting the load power of the local power grid in the current period based on historical power consumption data information on the load side of the local power grid; According to the predicted power load power of the current period, the power supply side of the local power grid is dispatched to determine the power generation power of each power supply device of the local power grid in the current period; as well as According to the power load power in each time period and the power generation power of each power supply device, the online simulation of the local power grid is performed using a preset simulation model.

2. The method according to claim 1, characterized in that The online simulation of the local power grid is performed using a preset simulation model according to the power load power of each time period and the power generation power of each power supply device, including: According to the power load power in each time period and the power generation power of each power supply device, a preset simulation model is used to construct a time-related power load power curve and a power generation power curve; and The power load power curve and the power generation power curve are displayed.

3. The method according to claim 1, characterized in that The online simulation of the local power grid is performed using a preset simulation model according to the power load power of each time period and the power generation power of each power supply device, including: Performing a differential operation on the power generation of each power supply device in each time period to obtain a first differential operation result; According to the first differential operation result, a preset simulation model is used to simulate the fluctuation of the power generation power of each power supply device in the local power grid.

4. The method according to claim 1, characterized in that The online simulation of the local power grid is performed using a preset simulation model according to the power load power of each time period and the power generation power of each power supply device, including: Performing differential operation on the power load power in each time period to obtain a second differential operation result; According to the second differential operation result, a preset simulation model is used to simulate the fluctuation of the power load of the local power grid.

5. The method according to claim 1, characterized in that The method of predicting the power load power of the local power grid in the current period based on the historical power consumption data information on the load side of the local power grid includes: According to the industrial production plan and the historical power consumption data of the industrial production line, the industrial power load power of the current period is predicted; Based on the historical electricity consumption data information of civil and commercial electricity loads, predict the civil and commercial electricity load power in the current period; and The power load power of the local power grid in the current period is determined according to the industrial power load power and the civil and commercial power load power in the current period.

6. The method according to claim 5, characterized in that The method of predicting the power of civil and commercial power loads in the current period based on the historical power consumption data information of civil and commercial power loads includes: According to the historical electricity consumption data information of civil and commercial electricity loads, a probability distribution vector related to the power of civil and commercial electricity loads in the current period is determined by using a preset neural network model; wherein each element of the probability distribution vector represents the probability of different electricity load power intervals; Determining a probability distribution curve related to civil and commercial electrical load power in the current period according to the probability distribution vector; and According to a preset confidence level and based on the probability distribution curve, the civil and commercial electricity load powers in the current period are determined.

7. The method according to claim 1, characterized in that The step of scheduling the power supply side of the local power grid according to the predicted power load power of the current time period to determine the power generation power of each power supply device of the local power grid in the current time period includes: Obtain corresponding power supply forecast data from a plurality of preset forecasting and scheduling subsystems; wherein the plurality of forecasting and scheduling subsystems include a power purchase forecasting and scheduling subsystem, a photovoltaic forecasting and scheduling subsystem, a wind power forecasting and scheduling subsystem, a thermal power forecasting and scheduling subsystem, and an energy storage forecasting and scheduling subsystem; the power supply forecast data is data obtained by each forecasting and scheduling subsystem using its own pre-trained forecasting model to predict the power generation of each power supply device in the local power grid in the current period; Determine the dispatching data of each power supply device of the local power grid in the current period according to the predicted power load power in the current period and the power supply prediction data; According to the scheduling data, scheduling the power supply side of the local power grid; According to the scheduling result, the power generation power of each power supply device of the local power grid in the current period is determined.

8. A storage medium, characterized in that: The storage medium includes a stored program, wherein when the program is run, the processor executes the method according to any one of claims 1 to 7.

9. A local power grid online simulation device based on prediction and scheduling, characterized in that: include: A prediction module, used to predict the power load power of the local power grid in the current period based on the historical power consumption data information on the load side of the local power grid; A scheduling module, used to schedule the power supply side of the local power grid according to the predicted power load power of the current period, and determine the power generation power of each power supply device of the local power grid in the current period; as well as The online simulation module is used to perform online simulation of the local power grid using a preset simulation model according to the power load power in each time period and the power generation power of each power supply device.

10. A local power grid online simulation device based on prediction and scheduling, characterized in that: include: processor; as well as A memory, connected to the processor, configured to provide the processor with instructions for processing the following processing steps: Predicting the load power of the local power grid in the current period based on historical power consumption data information on the load side of the local power grid; According to the predicted power load power of the current period, the power supply side of the local power grid is dispatched to determine the power generation power of each power supply device of the local power grid in the current period; as well as According to the power load power in each time period and the power generation power of each power supply device, the online simulation of the local power grid is performed using a preset simulation model.

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