Digitized water-light storage integrated energy management system

Abstract

The invention provides a digital water-light-storage integrated energy management system, and relates to the technical field of energy management, and the system comprises a historical power supply analysis module which is used for carrying out the historical power supply analysis of a target region, and obtaining the real-time power demand load of the region and the real-time power supply redundancy of the region; the real-time generating capacity prediction module is used for performing real-time generating capacity prediction based on the historical new energy power generation data to obtain renewable real-time generating capacity; the completion strategy acquisition module is used for acquiring a demand gap completion strategy according to the renewable real-time generating capacity, the regional real-time power demand load and the regional real-time power supply redundancy analysis; and the energy coordination module is used for executing water-light-storage integrated energy coordination in the energy network according to the demand gap completion strategy. The technical problems that a traditional energy management system is large in renewable energy source fluctuation, uncontrollable and the like, consequently, supply and demand imbalance is caused, a flexible energy scheduling strategy is lacked, energy losses are caused, and the system stability is poor are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management, and in particular to a digital water-photovoltaic-storage integrated energy management system. Background Art

[0002] Integrated water, photovoltaic and storage energy management mainly involves optimizing and managing energy supply using renewable energy sources such as hydropower and photovoltaic power generation. It is usually used in urban energy systems, microgrids, industrial parks and other scenarios that require stable and efficient energy supply. Traditional energy systems face problems such as large volatility and uncontrollability of renewable energy. They lack effective demand response and flexible energy scheduling strategies, making it difficult to cope with energy volatility and sudden demand. In addition, traditional energy storage and management methods make it difficult to achieve multi-energy coordination and efficient energy conversion, and there are problems of energy loss and high operating costs. Summary of the invention

[0003] This application provides a digital integrated water, photovoltaic and storage energy management system, which aims to solve the problems of large volatility and uncontrollable renewable energy in traditional energy management systems, resulting in imbalance between supply and demand, and lack of effective demand response and flexible energy scheduling strategies, leading to technical problems such as energy loss and poor system stability.

[0004] The first aspect disclosed in the present application provides a digital integrated water, photovoltaic and storage energy management system, the system comprising: a historical power supply analysis module, used to perform historical power supply analysis on a target area, and obtain the regional real-time power demand load and the regional real-time power supply redundancy; a real-time power generation prediction module, used to perform real-time power generation prediction on a target area based on historical new energy power generation data, and obtain renewable real-time power generation; a completion strategy acquisition module, used to obtain a demand gap completion strategy based on the renewable real-time power generation, the regional real-time power demand load and the regional real-time power supply redundancy analysis; an energy coordination module, used to perform integrated water, photovoltaic and storage energy coordination in the energy network of the target area according to the demand gap completion strategy.

[0005] Furthermore, the historical power supply analysis module also includes the following operating steps: presetting a power supply demand collection window; performing data collection with the power supply demand collection window as a constraint to obtain K historical power supply data of K power supply nodes in the target area; performing power supply fluctuation analysis on the K historical power supply data to obtain K standard power demand loads and K power supply redundancies; adding the K standard power demand loads to obtain the real-time power demand load of the area; adding the K power supply redundancies to obtain the real-time power supply redundancy of the area.

[0006] Furthermore, the historical power supply analysis module also includes the following operating steps: extracting the first historical power supply data of the first power supply node from the K historical power supply data, wherein the first historical power supply data includes multiple first instantaneous power demand loads; performing mean calculation on the multiple first instantaneous power demand loads to obtain a first standard power demand load; serializing the multiple first instantaneous power demand loads, and performing maximum value call based on the serialization result to obtain the historical highest load; calculating the difference between the historical highest load and the first standard power demand load as the first power supply redundancy; and so on, performing power supply fluctuation analysis on the K historical power supply data to obtain the K standard power demand loads and K power supply redundancies.

[0007] Furthermore, the real-time power generation prediction module also includes the following operating steps: presetting a hydropower generation indicator set and a photovoltaic power generation indicator set; performing associated data collection with the hydropower generation indicator set as a constraint, and synchronizing the collection results to a pre-built hydropower generation prediction model to obtain a hydropower generation prediction value; performing associated data collection with the photovoltaic power generation indicator set as a constraint, and synchronizing the collection results to a pre-built photovoltaic power generation prediction model to obtain a photovoltaic power generation prediction value; adding the hydropower generation prediction value and the photovoltaic power generation prediction value to obtain the renewable real-time power generation.

[0008] Furthermore, the photovoltaic power generation index set includes a humidity index, a wind speed index, a radiation index, an operating temperature index, an installation angle index, and an installation direction index.

[0009] Furthermore, the real-time power generation prediction module also includes the following operating steps: collecting historical photovoltaic power generation data with the photovoltaic power generation indicator set as a constraint to obtain multiple historical photovoltaic power generation indicators and multiple historical photovoltaic power generation; constructing the photovoltaic power generation prediction model based on the back propagation neural network, and using the multiple historical photovoltaic power generation indicators and multiple historical photovoltaic power generation as training data to optimize the power generation prediction accuracy of the photovoltaic power generation prediction model; collecting associated data with the photovoltaic power generation indicator set as a constraint to obtain a real-time associated indicator set; synchronizing the real-time associated indicator set to the photovoltaic power generation prediction model to perform power generation prediction analysis to obtain the photovoltaic power generation prediction value.

[0010] Furthermore, the completion strategy acquisition module also includes the following operating steps: performing numerical judgments on the renewable real-time power generation, the regional real-time power demand load and the regional real-time power supply redundancy; if the renewable real-time power generation is greater than the regional real-time power supply redundancy, generating an energy storage instruction, and using the energy storage instruction to replace the demand gap completion strategy; if the renewable real-time power generation is less than the regional real-time power demand load, calculating the power supply gap based on the renewable real-time power generation and the regional real-time power demand load, and generating a demand gap completion instruction based on the power supply gap; the demand gap completion instruction and the regional real-time power supply redundancy constitute the demand gap completion strategy.

[0011] Furthermore, the completion strategy acquisition module also includes the following operating steps: if the renewable real-time power generation is greater than the regional real-time power demand load and less than the regional real-time power supply redundancy, then the redundant gap is calculated based on the renewable real-time power generation and the regional real-time power supply redundancy, and a redundant gap completion instruction is generated based on the redundant gap.

[0012] The second aspect disclosed in the present application provides a computer device including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements a digital integrated water, photovoltaic and storage energy management system as described in any one of the first aspects disclosed in the present application.

[0013] The third aspect disclosed in the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, a digitalized integrated water, photovoltaic and storage energy management system as described in any one of the first aspects disclosed in the present application is implemented.

[0014] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0015] Perform historical power supply analysis on the target area to obtain the regional real-time power demand load and regional real-time power supply redundancy. By analyzing the historical power supply data, accurately determine the regional real-time power demand load and regional real-time power supply redundancy of the target area, ensure that the system can accurately obtain the real-time power demand and supply situation of the current area, and provide basic data for subsequent energy management decisions; predict the real-time power generation of the target area based on the historical new energy power generation data to obtain the real-time renewable power generation. Through the historical new energy power generation data, establish a prediction model to predict the real-time renewable energy power generation of the target area, improve the accuracy of the system's prediction of renewable energy power generation, and make energy scheduling more efficient and reliable; according to the real-time renewable power generation , regional real-time power demand load and regional real-time power supply redundancy analysis to obtain demand gap filling strategy, through comprehensive analysis of real-time data, formulate supplementary strategies to meet real-time power demand and maximize renewable energy utilization, optimize the utilization of energy resources, reduce dependence on traditional energy, and improve the stability and sustainability of energy supply; according to the demand gap filling strategy, perform water, photovoltaic and storage integrated energy coordination in the energy network of the target area, and implement the water, photovoltaic and storage integrated energy management strategy to achieve optimal configuration and coordinated operation of various energy resources, improve the flexibility and responsiveness of the energy system, reduce the operating cost of the energy system, and promote energy management to develop in a more intelligent, efficient and sustainable direction.

[0016] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic diagram of the structure of a digital water-photovoltaic-storage integrated energy management system is provided for the embodiment of the present application;

[0018] Figure 2 A schematic diagram of a process for obtaining regional real-time power supply redundancy in a digital water-photovoltaic-storage integrated energy management system is provided for an embodiment of the present application;

[0019] Figure 3 This is a diagram of the internal structure of a computer device provided in an embodiment of the present application.

[0020] Description of the accompanying drawings: historical power supply analysis module 10, real-time power generation prediction module 20, completion strategy acquisition module 30, energy coordination module 40. DETAILED DESCRIPTION

[0021] The embodiment of the present application provides a digital integrated water, photovoltaic and storage energy management system to solve the problems of large volatility and uncontrollable renewable energy in traditional energy management systems, which leads to imbalance between supply and demand, and lacks effective demand response and flexible energy scheduling strategies, resulting in technical problems such as energy loss and poor system stability.

[0022] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically described below in conjunction with the drawings in the specification.

[0023] like Figure 1 As shown, the embodiment of the present application provides a digital water-photovoltaic-storage integrated energy management system, the system comprising:

[0024] The historical power supply analysis module 10 is used to perform historical power supply analysis on the target area to obtain the real-time power demand load and the real-time power supply redundancy of the area;

[0025] Determine a time window for data collection, which may be, for example, hours, days, months and other time units, and collect historical power supply data of each power supply node in the target area within the time window. Assuming that there are K power supply nodes in the target area, obtain K historical power supply data of these K power supply nodes within a preset time window, perform power supply fluctuation analysis on the collected K historical power supply data, and respectively calculate and obtain K standard power demand loads and K power supply redundancies of the K power supply nodes, add up the standard power demand loads of the K power supply nodes to obtain the real-time power demand load of the entire target area, and add up the power supply redundancies of the K power supply nodes to obtain the real-time power supply redundancy of the entire target area.

[0026] Furthermore, if Figure 2 As shown, the historical power supply analysis module 10 also includes the following operation steps:

[0027] Preset power supply demand collection window;

[0028] Perform data collection with the power supply demand collection window as a constraint to obtain K historical power supply data of K power supply nodes in the target area;

[0029] Performing power supply fluctuation analysis on the K historical power supply data to obtain K standard power demand loads and K power supply redundancies;

[0030] Add the K standard power demand loads to obtain the real-time power demand load of the area;

[0031] The K power supply redundancies are added together to obtain the real-time power supply redundancy of the area.

[0032] Determine a time window for collecting power supply demand data in the target area. This time window can be hours, days, weeks, etc., depending on the needs of the application scenario. The size of the preset power supply demand collection window should be able to reflect the changing pattern of power supply demand in the target area.

[0033] Within a preset time window, data is collected from K power supply nodes in the target area to obtain K historical power supply data. The collected data include historical data such as power supply and power demand of each power supply node at different times. Data collection can be carried out through smart meters, data recording devices or energy management systems.

[0034] The K collected historical power supply data are analyzed to identify the fluctuation patterns of power supply and power demand, including statistical analysis of the historical power supply data of each power supply node, calculating its average power demand, using this as the standard power demand load, and calculating the difference between the historical power supply of each power supply node and the standard power demand load to identify excess power supply, i.e., power supply redundancy.

[0035] The standard power demand loads of the K power supply nodes are added together to obtain the real-time power demand load of the entire target area. The power supply redundancy of the K power supply nodes is added together to obtain the real-time power supply redundancy of the entire target area.

[0036] Furthermore, the historical power supply analysis module 10 further includes the following operation steps:

[0037] Extracting first historical power supply data of a first power supply node from the K historical power supply data, wherein the first historical power supply data includes a plurality of first instantaneous power demand loads;

[0038] Calculating the average of the multiple first instantaneous power demand loads to obtain a first standard power demand load;

[0039] Serializing the plurality of first instantaneous power demand loads, and performing maximum value call based on the serialization result to obtain the historical highest load;

[0040] Calculating the difference between the historical maximum load and the first standard power demand load as the first power supply redundancy;

[0041] By analogy, the K historical power supply data are subjected to power supply fluctuation analysis to obtain the K standard power demand loads and K power supply redundancies.

[0042] Data of the first power supply node is extracted from the historical data sets of K power supply nodes, and the data includes multiple first instantaneous power demand loads at multiple time points within a preset collection window. For example, the preset power demand collection window is one month, and the hourly power demand load data of the first power supply node within the month is extracted, and the data can be stored in the form of a time series.

[0043] The extracted multiple instantaneous power demand load data are averaged, that is, the instantaneous power demand load data at all time points are added together and then divided by the total number of data points to calculate and obtain the first standard power demand load.

[0044] The extracted instantaneous power demand load data is serialized, that is, arranged in descending order, and the maximum value is found in the serialized data as the historical maximum load. The difference between the historical maximum load and the first standard power demand load is calculated to obtain the first power supply redundancy.

[0045] By analogy, K historical power supply data are traversed to perform power supply fluctuation analysis to obtain the K standard power demand loads and K power supply redundancies.

[0046] A real-time power generation prediction module 20 is used to predict the real-time power generation of a target area based on historical new energy power generation data to obtain real-time renewable power generation;

[0047] Determine the key indicators for predicting hydropower and photovoltaic power generation, and obtain the hydropower indicator set and the photovoltaic power generation indicator set. According to the preset hydropower indicator set, collect relevant data, such as real-time reservoir water level, precipitation, etc., and input the collected data into the pre-built hydropower generation prediction model. The model calculates according to the input data and outputs the predicted hydropower generation. According to the preset photovoltaic power generation indicator set, collect relevant data, such as real-time humidity, wind speed, radiation, etc., and input the collected data into the pre-built photovoltaic power generation prediction model. The model calculates according to the input data and outputs the predicted photovoltaic power generation. Add the hydropower generation prediction value and the photovoltaic power generation prediction value to obtain the real-time renewable power generation of the entire target area.

[0048] Furthermore, the real-time power generation prediction module 20 also includes the following operation steps:

[0049] Preset hydropower generation indicator set and photovoltaic power generation indicator set;

[0050] Collecting associated data with the hydropower generation indicator set as a constraint, and synchronizing the collected results to a pre-built hydropower generation prediction model to obtain a hydropower generation prediction value;

[0051] Collecting related data based on the photovoltaic power generation indicator set as a constraint, and synchronizing the collected results to a pre-built photovoltaic power generation prediction model to obtain a photovoltaic power generation prediction value;

[0052] The hydroelectric power generation prediction value and the photovoltaic power generation prediction value are added to obtain the renewable real-time power generation.

[0053] A hydropower generation indicator set and a photovoltaic power generation indicator set are preset, wherein the hydropower generation indicator set includes water level height, flow velocity, reservoir capacity, rainfall, water temperature, etc.; the photovoltaic power generation indicator set includes humidity, wind speed, radiation, working temperature, installation angle, installation direction, etc.

[0054] According to the preset set of hydropower indicators, data related to these indicators are collected. For example, real-time water level, flow rate, rainfall and other data are obtained through sensors, weather stations and other equipment. The collected data is input into a pre-built and trained hydropower generation prediction model. The prediction model performs calculations based on the input data and outputs the predicted hydropower generation.

[0055] According to the preset set of photovoltaic power generation indicators, data related to these indicators are collected. For example, real-time data such as humidity, wind speed, and radiation are obtained through meteorological stations and photovoltaic power station monitoring systems. The collected data is input into a pre-built and trained photovoltaic power generation prediction model. The prediction model performs calculations based on the input data and outputs the predicted photovoltaic power generation.

[0056] The predicted values ​​of hydropower generation and photovoltaic power generation are added together to obtain the real-time renewable power generation, providing key data support for subsequent energy management and scheduling.

[0057] Furthermore, the photovoltaic power generation index set includes a humidity index, a wind speed index, a radiation index, an operating temperature index, an installation angle index, and an installation direction index.

[0058] The humidity index is the water vapor content in the air, usually expressed as a percentage. High humidity will cause condensation or stains on the surface of photovoltaic modules, affecting the efficiency of light absorption. The wind speed index refers to the speed of air flow. Appropriate wind speed can help photovoltaic modules dissipate heat and improve power generation efficiency; too high wind speed may cause damage to the equipment. The radiation index is the amount of solar radiation. The radiation directly determines the amount of solar energy that photovoltaic modules can absorb and is one of the most important factors affecting power generation. The operating temperature index is the operating temperature of the photovoltaic module. Excessive temperature will reduce the efficiency and service life of the photovoltaic module. Temperature changes will affect power generation performance. The installation angle index is the inclination angle of the photovoltaic module relative to the horizontal plane. The appropriate installation angle can maximize the amount of solar radiation received by the photovoltaic module, thereby improving power generation efficiency. The installation direction index is the direction of the photovoltaic module. The best direction is usually due south, which can ensure that the photovoltaic module receives the maximum amount of sunlight and increase power generation.

[0059] Furthermore, the real-time power generation prediction module 20 also includes the following operation steps:

[0060] Collecting historical photovoltaic power generation data with the photovoltaic power generation index set as a constraint to obtain multiple historical photovoltaic power generation index sets and multiple historical photovoltaic power generation amounts;

[0061] Constructing the photovoltaic power generation prediction model based on a back propagation neural network, and using the multiple historical photovoltaic power generation indicator sets and multiple historical photovoltaic power generation as training data to optimize the power generation prediction accuracy of the photovoltaic power generation prediction model;

[0062] Collecting relevant data based on the photovoltaic power generation indicator set as a constraint to obtain a real-time relevant indicator set;

[0063] The real-time associated indicator set is synchronized to the photovoltaic power generation prediction model to perform power generation prediction analysis to obtain the photovoltaic power generation prediction value.

[0064] Determine the time range and data source for data collection, including meteorological data stations, photovoltaic power station monitoring systems, etc., and use the photovoltaic power generation indicator set as a constraint to collect relevant historical data, including historical humidity, historical wind speed, historical radiation, historical operating temperature, historical installation angle, and historical installation direction, to obtain multiple historical photovoltaic power generation indicator sets. At the same time, record the photovoltaic power generation data in the corresponding time period to obtain multiple historical photovoltaic power generation.

[0065] The photovoltaic power generation prediction model is constructed based on a back propagation neural network (BPNN), and the power generation prediction accuracy of the model is optimized using the collected historical data. Specifically, a back propagation neural network (BPNN) is selected as the prediction model, and the collected historical data is divided into a training set, a validation set, and a test set. For example, the ratio is set to 8:1:1, and the model is trained using the training set data. The model weights and biases are adjusted through a back propagation algorithm to minimize the prediction error. The model is verified using the validation set data, the model parameters and structure are adjusted to improve the prediction accuracy, and the model is tested using the test set data to evaluate the generalization ability and prediction performance of the model, so as to finally obtain the photovoltaic power generation prediction model that meets the power generation prediction accuracy requirements.

[0066] Select a real-time data source, such as a weather station, photovoltaic power station monitoring system, etc., to obtain real-time data at the current moment. According to the photovoltaic power generation indicator set, including humidity, wind speed, radiation, working temperature, installation angle, and installation direction, obtain the real-time data at the current moment to form a real-time associated indicator set.

[0067] The real-time associated indicator set is input into the pre-built photovoltaic power generation prediction model, and the real-time associated indicator set is predicted using the trained BP neural network model. The model predicts the power generation of the photovoltaic power station at the current moment based on real-time humidity, wind speed, radiation, working temperature, installation angle, installation direction and other indicators, and outputs the predicted photovoltaic power generation value, i.e., the photovoltaic power generation prediction value.

[0068] A filling strategy acquisition module 30, used to obtain a demand gap filling strategy according to the real-time renewable power generation, the real-time regional power demand load and the real-time regional power supply redundancy analysis;

[0069] Determine the values ​​of the current renewable real-time power generation, regional real-time power demand load, and regional real-time power supply redundancy. If the renewable real-time power generation is greater than the regional real-time power supply redundancy, it means that the current power generation exceeds the demand, and an electric energy storage instruction is generated to store the excess electric energy first for future use. If the renewable real-time power generation is less than the regional real-time power demand load, it means that the current power generation is insufficient to meet the demand, and the power supply gap is calculated. According to the power supply gap, a demand gap filling instruction is generated, indicating that additional electric energy is needed. The demand gap filling instruction is combined with the regional real-time power supply redundancy to form a complete demand gap filling strategy to ensure the balance of regional power supply and demand.

[0070] Furthermore, the completion strategy acquisition module 30 further includes the following operation steps:

[0071] Making numerical judgments on the real-time renewable power generation, the real-time regional power demand load, and the real-time regional power supply redundancy;

[0072] If the real-time renewable power generation is greater than the real-time regional power supply redundancy, an electric energy storage instruction is generated, and the electric energy storage instruction is used to replace the demand gap filling strategy;

[0073] If the real-time renewable power generation is less than the real-time regional power demand load, the power supply gap is calculated based on the real-time renewable power generation and the real-time regional power demand load, and a demand gap filling instruction is generated based on the power supply gap;

[0074] The demand gap filling instruction and regional real-time power supply redundancy constitute the demand gap filling strategy.

[0075] The current values ​​of renewable real-time power generation, regional real-time power demand load and regional real-time power supply redundancy are obtained, and the magnitude relationship between renewable real-time power generation and regional real-time power supply redundancy is compared, and the magnitude relationship between renewable real-time power generation and regional real-time power demand load is compared.

[0076] If the real-time renewable power generation is greater than the regional real-time power supply redundancy, an energy storage instruction is generated to store the excess renewable electricity. The energy storage instruction is used to replace the original demand gap filling strategy to ensure that the system can reasonably utilize excess renewable energy when there is power supply redundancy.

[0077] If the real-time renewable power generation is less than the real-time regional power demand, the power supply gap is calculated. The power supply gap is the value of the real-time regional power demand minus the real-time renewable power generation, that is, the power demand that cannot be met by renewable energy. Based on the calculated power supply gap, a demand gap filling instruction is generated to ensure that the system can provide stable power supply.

[0078] The generated demand gap filling instructions are combined with the regional real-time power supply redundancy to form a complete demand gap filling strategy, which is used in the energy management system to guide the system on how to adjust and optimize power supply in real-time operation, thereby ensuring the reliability and efficiency of the energy system.

[0079] Furthermore, the completion strategy acquisition module 30 further includes the following operation steps:

[0080] If the renewable real-time power generation is greater than the regional real-time power demand load and less than the regional real-time power supply redundancy, a redundant gap is calculated based on the renewable real-time power generation and the regional real-time power supply redundancy, and a redundant gap filling instruction is generated based on the redundant gap.

[0081] If the real-time renewable power generation is greater than the real-time regional power demand and less than the real-time regional power supply redundancy, the redundant gap is calculated. The redundant gap is the value of the real-time renewable power generation minus the real-time regional power supply redundancy, that is, the part of renewable energy that exceeds the power demand but is not fully utilized. Based on the calculated redundant gap, redundant gap filling instructions are generated to optimize the operation and utilization efficiency of the energy system.

[0082] The energy coordination module 40 is used to perform water-solar-storage integrated energy coordination in the energy network of the target area according to the demand gap filling strategy.

[0083] Based on the generated demand gap filling strategy, specific operational steps are determined, including electric energy storage and release, hydropower generation scheduling, photovoltaic power generation scheduling, etc. Through the integrated energy management system, the operating status of hydropower generation, photovoltaic power generation and energy storage systems is monitored in real time. According to the demand gap filling strategy, comprehensive coordination and optimization are carried out to ensure the balance of energy supply and demand in the target area.

[0084] In summary, the digital integrated water, photovoltaic and storage energy management system provided by the embodiment of the present application has the following technical effects:

[0085] Perform historical power supply analysis on the target area to obtain the regional real-time power demand load and regional real-time power supply redundancy. By analyzing the historical power supply data, accurately determine the regional real-time power demand load and regional real-time power supply redundancy of the target area, ensure that the system can accurately obtain the real-time power demand and supply situation of the current area, and provide basic data for subsequent energy management decisions; predict the real-time power generation of the target area based on the historical new energy power generation data to obtain the real-time renewable power generation. Through the historical new energy power generation data, establish a prediction model to predict the real-time renewable energy power generation of the target area, improve the accuracy of the system's prediction of renewable energy power generation, and make energy scheduling more efficient and reliable; according to the real-time renewable power generation , regional real-time power demand load and regional real-time power supply redundancy analysis to obtain demand gap filling strategy, through comprehensive analysis of real-time data, formulate supplementary strategies to meet real-time power demand and maximize renewable energy utilization, optimize the utilization of energy resources, reduce dependence on traditional energy, and improve the stability and sustainability of energy supply; according to the demand gap filling strategy, perform water, photovoltaic and storage integrated energy coordination in the energy network of the target area, and implement the water, photovoltaic and storage integrated energy management strategy to achieve optimal configuration and coordinated operation of various energy resources, improve the flexibility and responsiveness of the energy system, reduce the operating cost of the energy system, and promote energy management to develop in a more intelligent, efficient and sustainable direction.

[0086] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus, wherein the processor of the computer device is used to provide computing and control capabilities; the memory of the computer device includes a non-volatile storage medium and an internal memory, wherein the non-volatile storage medium stores an operating system, a computer program and a database, and the internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium; the network interface of the computer device is used to communicate with an external terminal through a network connection. The computer program is executed by the processor to execute a digital water-photovoltaic-storage integrated energy management system.

[0087] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0088] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0089] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A digital integrated water, photovoltaic and storage energy management system, characterized in that: The system comprises: The historical power supply analysis module is used to analyze the historical power supply of the target area and obtain the real-time power demand load and real-time power supply redundancy of the area; The real-time power generation prediction module is used to predict the real-time power generation of the target area based on the historical new energy power generation data to obtain the real-time renewable power generation; A filling strategy acquisition module, used for obtaining a demand gap filling strategy according to the real-time renewable power generation, the real-time regional power demand load and the real-time regional power supply redundancy analysis; The energy coordination module is used to perform integrated water, photovoltaic and storage energy coordination in the energy network of the target area according to the demand gap filling strategy.

2. A digital water-photovoltaic-storage integrated energy management system as claimed in claim 1, characterized in that: The historical power supply analysis module also includes the following operation steps: Preset power supply demand collection window; Perform data collection with the power supply demand collection window as a constraint to obtain K historical power supply data of K power supply nodes in the target area; Performing power supply fluctuation analysis on the K historical power supply data to obtain K standard power demand loads and K power supply redundancies; Add the K standard power demand loads to obtain the real-time power demand load of the area; The K power supply redundancies are added together to obtain the real-time power supply redundancy of the area.

3. A digital water-photovoltaic-storage integrated energy management system as claimed in claim 2, characterized in that: The historical power supply analysis module also includes the following operation steps: Extracting first historical power supply data of a first power supply node from the K historical power supply data, wherein the first historical power supply data includes a plurality of first instantaneous power demand loads; Calculating the average of the multiple first instantaneous power demand loads to obtain a first standard power demand load; Serializing the plurality of first instantaneous power demand loads, and performing maximum value call based on the serialization result to obtain the historical highest load; Calculating the difference between the historical maximum load and the first standard power demand load as the first power supply redundancy; By analogy, power supply fluctuation analysis is performed on the K historical power supply data to obtain the K standard power demand loads and K power supply redundancies.

4. A digital water-photovoltaic-storage integrated energy management system as claimed in claim 1, characterized in that: The real-time power generation prediction module also includes the following operation steps: Preset hydropower generation indicator set and photovoltaic power generation indicator set; Collecting associated data with the hydropower generation indicator set as a constraint, and synchronizing the collected results to a pre-built hydropower generation prediction model to obtain a hydropower generation prediction value; Collecting related data based on the photovoltaic power generation indicator set as a constraint, and synchronizing the collected results to a pre-built photovoltaic power generation prediction model to obtain a photovoltaic power generation prediction value; The hydroelectric power generation prediction value and the photovoltaic power generation prediction value are added to obtain the renewable real-time power generation.

5. A digital water-photovoltaic-storage integrated energy management system as claimed in claim 4, characterized in that: The photovoltaic power generation index set includes a humidity index, a wind speed index, a radiation index, an operating temperature index, an installation angle index, and an installation direction index.

6. A digital water-photovoltaic-storage integrated energy management system as claimed in claim 5, characterized in that: The real-time power generation prediction module also includes the following operation steps: Collecting historical photovoltaic power generation data with the photovoltaic power generation index set as a constraint to obtain multiple historical photovoltaic power generation index sets and multiple historical photovoltaic power generation amounts; Constructing the photovoltaic power generation prediction model based on a back propagation neural network, and using the multiple historical photovoltaic power generation indicator sets and multiple historical photovoltaic power generation as training data to optimize the power generation prediction accuracy of the photovoltaic power generation prediction model; Collecting relevant data based on the photovoltaic power generation indicator set as a constraint to obtain a real-time relevant indicator set; The real-time associated indicator set is synchronized to the photovoltaic power generation prediction model to perform power generation prediction analysis to obtain the photovoltaic power generation prediction value.

7. A digital water-photovoltaic-storage integrated energy management system as claimed in claim 1, characterized in that: The completion strategy acquisition module also includes the following steps: Making numerical judgments on the real-time renewable power generation, the real-time regional power demand load, and the real-time regional power supply redundancy; If the real-time renewable power generation is greater than the real-time regional power supply redundancy, an electric energy storage instruction is generated, and the electric energy storage instruction is used to replace the demand gap filling strategy; If the real-time renewable power generation is less than the real-time regional power demand load, the power supply gap is calculated based on the real-time renewable power generation and the real-time regional power demand load, and a demand gap filling instruction is generated based on the power supply gap; The demand gap filling instruction and regional real-time power supply redundancy constitute the demand gap filling strategy.

8. A digital water-photovoltaic-storage integrated energy management system as claimed in claim 7, characterized in that: The completion strategy acquisition module also includes the following steps: If the renewable real-time power generation is greater than the regional real-time power demand load and less than the regional real-time power supply redundancy, a redundant gap is calculated based on the renewable real-time power generation and the regional real-time power supply redundancy, and a redundant gap filling instruction is generated based on the redundant gap.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, a digital water-photovoltaic-storage integrated energy management system according to any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, a digitalized integrated water, photovoltaic and storage energy management system according to any one of claims 1 to 8 is implemented.

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