Power generation dispatching system based on cloud computing

Abstract

The present invention relates to the field of power dispatching technology, specifically to a power generation dispatching system based on cloud computing, which includes a power demand prediction module, a resource allocation optimization module, a data monitoring and feedback module, and a regional power dispatching coordination module. In the present invention, the accuracy of power demand forecasting is optimized by comprehensively applying in-depth analysis of historical and real-time data, enhancing the adaptability to load changes in the power system, realizing dynamic adjustment of the energy output ratio, effectively balancing the use of renewable energy and traditional energy according to real-time load changes, optimizing energy distribution efficiency, and utilizing continuous monitoring and instant feedback mechanisms to adjust power configuration in real time, reduce resource waste, and improve the response speed and adaptive adjustment capability of the power grid. Through dynamic management of inter-regional power flows, more sophisticated load balancing is achieved, improving inter-regional power collaboration efficiency, and enhancing the overall stability and efficiency of the power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power dispatching, and in particular to a power generation dispatching system based on cloud computing. Background Art

[0002] The field of power dispatch technology involves the process of managing and optimizing the power system to ensure that electricity is efficiently and safely transmitted from power stations to users, including demand forecasting, real-time power monitoring, power load management, and optimized distribution among different power sources. The goal is to balance the supply and demand of the power grid, reduce power losses, and improve the stability and reliability of the power grid. Technological developments include the use of advanced algorithms and computing technologies such as machine learning and artificial intelligence to predict power demand and optimize resource allocation, as well as improving power grid operation efficiency through real-time data collection and analysis.

[0003] Among them, the cloud computing power generation dispatching system refers to a system that uses cloud computing platforms and technologies to achieve power dispatching. The system can centrally process and store a large amount of power usage data on the cloud platform, making power dispatching more flexible and efficient. Its main uses include real-time power monitoring, load forecasting, automatic control, and power resource optimization. Through cloud computing, the system can better cope with the complexity of large-scale power grids, improve the ability to respond to emergencies, and reduce the system's maintenance costs and operational difficulties.

[0004] Existing technologies often rely on relatively static data models when processing power demand forecasts, and lack accurate responses to rapid changes in power demand, resulting in insufficient power supply or power waste due to grid dispatch when demand suddenly increases or decreases. In addition, existing technologies lack high automation and real-time optimization capabilities in resource allocation, making it impossible for the grid to make optimal adjustments when faced with rapidly changing loads. For example, under the influence of extreme weather or other emergencies, the existing grid may not be able to maintain power supply stability due to inflexible dispatch, affecting the reliability of the grid and the safety of users' electricity use, highlighting the need for automation and data-driven decision-making, and highlighting the challenges faced by existing technologies in adapting to modern power demand. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and propose a power generation scheduling system based on cloud computing.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: A power generation dispatching system based on cloud computing includes:

[0007] The power demand forecasting module collects historical power usage data, calculates the daily peak and valley values ​​of power usage, compares and analyzes them with real-time grid load data, and generates power load comparison results;

[0008] The resource allocation optimization module analyzes and evaluates the power supply and demand relationship based on the power load comparison result, adjusts the output ratio of renewable energy and traditional energy, synchronously adjusts the charging and discharging plan according to the output ratio adjustment result, matches the power load change, and generates a power resource optimization configuration plan;

[0009] The data monitoring and feedback module connects to the real-time data stream of the power grid, monitors the real-time status of the current power grid load and energy distribution, compares the real-time data with the power resource optimization configuration plan, identifies the configuration deviation, and adjusts the power configuration in real time according to the configuration deviation identification result to match the actual power usage and obtain real-time power adjustment feedback data;

[0010] The regional power dispatch coordination module uses the real-time power adjustment feedback data to evaluate the power supply and demand situation between regions, dynamically adjust the power flow between regions, optimize the load balance between regions based on the dynamic adjustment results of the power flow between regions, and formulate a power exchange plan based on the renewable energy power generation and consumption patterns between regions to maximize the power grid operation efficiency and stability and generate regional power generation coordinated dispatch results.

[0011] As a further solution of the present invention, the steps for obtaining the power load comparison result are:

[0012] Collect historical power data and real-time grid load data, extract the maximum and minimum values ​​of daily power usage from the historical data, and record the real-time load on the corresponding date to generate historical and real-time power data records;

[0013] Performing time series alignment on the historical and real-time power data records, confirming the consistency of the time label of each data point, comparing the difference between the historical power peak and valley values ​​and the real-time load on the same day, and obtaining a daily comparison difference record;

[0014] Based on the daily comparison difference record, the formula is used:

[0015] ;

[0016] Calculate the sum of squares of the normalized differences for each day , generate power load comparison analysis results, where, Indicates The difference between the peak and valley values ​​of electricity usage during the day, Indicates The real-time load value of the day, Represents the total number of days.

[0017] As a further solution of the present invention, the steps for adjusting the output ratio of renewable energy to traditional energy are:

[0018] Based on the power load comparison results, trend analysis and pattern recognition are performed to determine the key factors of power supply and demand changes and generate a comprehensive supply and demand relationship evaluation record;

[0019] Based on the comprehensive evaluation records of the supply and demand relationship, the formula is used:

[0020] ;

[0021] Calculate the output ratio of renewable energy to traditional energy , output energy output ratio scheme, where, Representative The value of renewable energy supply per day, Represents the total energy demand value for the same day, is the difference between demand and supply on that day, Represents the total number of days;

[0022] The energy output ratio scheme is applied to adjust the power generation plan and load response strategy to ensure a dynamic balance between supply and load, and form a record of the adjusted energy output ratio.

[0023] As a further solution of the present invention, the steps for obtaining the power resource optimization configuration solution are:

[0024] Based on the adjusted energy output ratio record, according to the new ratio, the required energy usage data and charging and discharging cycle are collected, and a preliminary charging and discharging plan is re-formulated and output;

[0025] Based on the preliminary charge and discharge plan, an environmental simulation is performed to simulate the performance under different power load conditions, using the formula,

[0026] ;

[0027] Calculate the overall efficiency index of the charging and discharging plan , and get the simulation test results, where Indicates Daily charging capacity, is the amount of electricity consumed that day, and Represent the predicted and actual loads, respectively. Indicates the total number of days;

[0028] According to the simulation test results, the charging and discharging parameters, including charging time and discharging rate, are adjusted in real time to determine the final charging and discharging plan, maximize the use of renewable energy, simultaneously meet the stable operation requirements of the power grid, and obtain an optimal configuration plan for power resources.

[0029] As a further solution of the present invention, the step of identifying the configuration deviation is:

[0030] Establish a communication connection with the power grid data center through a secure network protocol, receive the load and energy distribution data of the power grid in real time, and generate a real-time power grid data stream;

[0031] By using the real-time grid data stream, the current values ​​of grid load and energy distribution are compared with the preset values ​​of the power resource optimization configuration scheme, and the formula is used:

[0032] ;

[0033] Calculate the average of the weighted absolute deviations of the parameters , generate configuration deviation analysis results; where, Indicates the real-time parameter value. Indicates the parameter value of the configuration scheme. Represents the parameter weight, which adjusts the influence of each parameter deviation. Indicates the total number of parameters;

[0034] According to the configuration deviation analysis result, a threshold is set and deviation items exceeding the threshold are identified and classified as key configuration deviations, and a key deviation identification result is generated.

[0035] As a further solution of the present invention, the step of acquiring the real-time power adjustment feedback data is:

[0036] Analyze the key deviation identification results, check the areas exceeding the threshold in the power configuration, determine the power configuration items that need to be adjusted and prioritize them, and generate a power adjustment priority list;

[0037] According to the power adjustment priority list, real-time adjustment of power configuration is performed to increase power supply in key areas and reduce power supply in non-key areas, optimize power resource allocation, and generate power adjustment execution records;

[0038] Based on the power adjustment execution record, the usage data after the power adjustment is collected, and by comparing the power usage and user feedback data before and after the adjustment, the effectiveness of the adjustment measures is evaluated to generate real-time power adjustment feedback data.

[0039] As a further solution of the present invention, the steps of dynamically adjusting the inter-regional power flow are:

[0040] Based on the real-time power adjustment feedback data, record the power usage of each area after adjustment, including power consumption and power supply, and generate a regional power usage record;

[0041] Using the regional electricity usage records, the formula is adopted,

[0042] ;

[0043] Calculate regional power supply and demand ratio , analyze and output the power balance state identification results, where, and Respectively represent The power supply and demand in the region, is the total number of regions, It is an adjustment factor set according to regional priorities, adjusted according to the intensity of regional economic activity or population density;

[0044] According to the power balance state identification result, the inter-regional power is dynamically adjusted to achieve the optimal allocation of inter-regional power supply and demand, and the inter-regional power flow dynamic adjustment result is output.

[0045] As a further solution of the present invention, the steps for obtaining the regional power generation coordinated dispatch result are:

[0046] According to the inter-regional power flow dynamic adjustment result, extract the current power supply and demand status of the difference region and the adjusted power flow information to generate a preliminary regional power load balance record;

[0047] Based on the preliminary regional power load balance records, combined with regional renewable energy generation data and power consumption patterns, the formula is used:

[0048] ;

[0049] Calculate the efficiency improvement ratio of power flow , and the power flow adjustment result is obtained, where Indicates Renewable energy production in the region, Indicates The regional electricity demand, is the weight coefficient adjusted according to the regional priority, Indicates the total number of regions;

[0050] According to the power flow adjustment results, inter-regional power generation coordinated scheduling is performed to ensure that the region receives or supplies the corresponding power as planned, monitor the power flow and status in real time, dynamically match sudden changes, and output regional power generation coordinated scheduling results.

[0051] Compared with the prior art, the advantages and positive effects of the present invention are:

[0052] In the present invention, the accuracy of power demand forecasting is optimized through the comprehensive application of in-depth analysis of historical and real-time data, the adaptability to changes in the power system load is enhanced, and dynamic adjustment of the energy output ratio is achieved. According to real-time load changes, the use of renewable energy and traditional energy is effectively balanced, and the energy distribution efficiency is optimized. Utilizing continuous monitoring and instant feedback mechanisms, real-time adjustments are made to power configuration, reducing resource waste, improving the response speed and adaptive adjustment capabilities of the power grid, and through dynamic management of inter-regional power flows, more sophisticated load balancing is achieved, improving inter-regional power collaboration efficiency, and enhancing the overall stability and efficiency of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 is a system flow chart of the present invention;

[0054] Figure 2 A flowchart for obtaining the power load comparison result of the present invention;

[0055] Figure 3 The flowchart of adjusting the output ratio of renewable energy and traditional energy of the present invention;

[0056] Figure 4 A flowchart for obtaining the power resource optimization configuration scheme of the present invention;

[0057] Figure 5 A flow chart for identifying deviations in configuration of the present invention;

[0058] Figure 6 A flowchart of obtaining real-time power adjustment feedback data according to the present invention;

[0059] Figure 7 The flowchart of the dynamic adjustment of the inter-regional power flow of the present invention is as follows;

[0060] Figure 8 This is a flow chart for obtaining the results of regional power generation coordinated scheduling according to the present invention. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0062] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0063] See also Figure 1 , the power generation dispatching system based on cloud computing includes:

[0064] The power demand forecasting module collects historical power usage data, calculates the daily peak and valley values ​​of power usage, compares and analyzes them with real-time grid load data, and generates power load comparison results;

[0065] The resource allocation optimization module analyzes and evaluates the power supply and demand relationship based on the power load comparison results, adjusts the output ratio of renewable energy and traditional energy, and adjusts the charging and discharging plan according to the output ratio adjustment results to match the power load changes and generate an optimal power resource allocation plan;

[0066] The data monitoring and feedback module connects to the real-time data stream of the power grid, monitors the real-time status of the current power grid load and energy distribution, compares the real-time data with the power resource optimization configuration plan, identifies the configuration deviation, and adjusts the power configuration in real time according to the configuration deviation identification results to match the actual power usage and obtain real-time power adjustment feedback data;

[0067] The regional power dispatch coordination module uses real-time power adjustment feedback data to evaluate the power supply and demand situation between regions, dynamically adjust the power flow between regions, optimize the load balance between regions based on the dynamic adjustment results of the power flow between regions, and formulate a power exchange plan based on the renewable energy generation and consumption patterns between regions to maximize the efficiency and stability of the power grid operation and generate regional power generation coordinated dispatch results.

[0068] The power load comparison results include power peak difference analysis results, power valley difference analysis results and load fluctuation analysis results. The power resource optimization configuration plan includes energy output optimization results, power allocation strategy and load response adjustment records. The real-time power adjustment feedback data includes configuration deviation identification results, adjustment strategy effectiveness analysis results and power consumption response records. The regional power generation coordinated scheduling results include regional load balancing records, energy exchange efficiency and power supply stability analysis records.

[0069] See also Figure 2, the steps to obtain the power load comparison results are:

[0070] Collect historical power data and real-time grid load data, extract the maximum and minimum values ​​of daily power usage from the historical data, and record the real-time load on the corresponding date to generate historical and real-time power data records;

[0071] In the stage of collecting historical power data and real-time grid load data, the core task is to ensure the integrity and accuracy of the data. First, define the data source, including the historical records of the power company and the real-time monitoring data of the power grid, and collect data from the data source at a regular interval. The data includes the daily maximum and minimum power usage values ​​and the real-time load of the power grid. Then perform data quality detection to filter out complete and error-free records, and eliminate data with incomplete timestamps or abnormal values. For example, values ​​that exceed the normal power usage range are considered to be caused by instrument failure or data transmission errors. Finally, a preliminary processed historical and real-time power data record is generated for subsequent analysis and comparison, including not only power usage data but also the corresponding real-time load data to ensure effective time series comparison in subsequent steps.

[0072] Perform time series alignment on historical and real-time power data records, confirm the consistency of the time label of each data point, compare the difference between historical power peak and valley values ​​and the real-time load on the same day, and obtain daily comparison difference records;

[0073] The main purpose of aligning historical and real-time power data records is to accurately match historical power usage data with real-time grid load data. First, the time tags of all data are verified and corrected to ensure that the timestamp of each record is accurate. In this process, for each record in the historical data, check whether its timestamp corresponds to the timestamp of the real-time load data. If the time is inconsistent, adjust or delete the mismatched records. Then, directly compare the peak and valley values ​​of historical power usage with the real-time grid load at the same time point, calculate the difference between each day's power usage and real-time load, so as to quantify the daily load fluctuations, and then output the daily comparison difference records.

[0074] Based on the daily comparison difference record, the formula is used.

[0075] ;

[0076] Calculate the sum of squares of the normalized differences for each day , generate power load comparison analysis results, where, Indicates The difference between the peak and valley values ​​of electricity usage during the day, Indicates The real-time load value of the day, Represents the total number of days;

[0077] Collect electricity data for 3 days, the first day 50MW, real-time load 500MW, the next day 30MW, real-time load The third day's 20MW, real-time load It is 550MW.

[0078] Substitute into the formula and calculate:

[0079]

[0080]

[0081] The results show that the normalized square difference between the peak and valley values ​​of electricity usage and the real-time load during the entire period (within 3 days) is 0.015766, which reflects the volatility between electricity usage and load (small values ​​indicate small volatility), and can be used to quantitatively evaluate the effect of load management between different days.

[0082] See also Figure 3 , the steps to adjust the output ratio of renewable energy and traditional energy are:

[0083] Based on the power load comparison results, trend analysis and pattern recognition are carried out to determine the key factors of power supply and demand changes and generate a comprehensive evaluation record of the supply and demand relationship;

[0084] When performing the power load comparison analysis, time series analysis and pattern recognition techniques were used to deeply analyze the power load comparison data. Through time series analysis (including methods such as autoregressive model (AR), moving average model (MA) and autoregressive integrated moving average model (ARIMA)), it is possible to effectively identify the cyclical fluctuations, seasonal changes and long-term trends of power consumption, and to predict future power demand and supply patterns and evaluate historical volatility. Pattern recognition techniques (including cluster analysis and principal component analysis (PCA)) are used to discover abnormal patterns and mutation points in the data and to more accurately divide different power supply and demand states. The analysis results are integrated and compiled into a comprehensive evaluation report, which details the dynamic relationship between power demand and supply, providing a decision-making basis for energy strategy adjustments.

[0085] Based on the comprehensive evaluation records of supply and demand, the formula is adopted.

[0086] ;

[0087] Calculate the output ratio of renewable energy to traditional energy , output energy output ratio scheme, where, Representative The value of renewable energy supply per day, Represents the total energy demand value for the same day, is the difference between demand and supply on that day, Represents the total number of days;

[0088] set up Day (one month), unit, unit, Units, plug in the formula and calculate:

[0089]

[0090]

[0091] The results show that, on average, renewable energy should account for 4.44 units of total energy output, which can help adjust energy output strategies to better meet market demand.

[0092] Apply the energy output ratio scheme, adjust the power generation plan and load response strategy, ensure the dynamic balance between supply and load, and form a record of the adjusted energy output ratio;

[0093] The implementation of the energy output ratio plan requires the collaboration of multiple departments, including the energy management department, technical support team and operation department. First, the technical team needs to update the parameter settings in the energy management system and adjust the power generation plan according to the new energy output ratio, which involves adjusting the operation mode and output level of the power plant. At the same time, the operation department needs to adjust the load response strategy of the power grid according to the new output ratio to ensure that renewable energy can be used first during high-demand periods and reduce dependence on traditional energy. In this process, it is also necessary to closely monitor market reactions and environmental impacts to ensure the continuity and security of energy supply. Finally, all adjustment measures and implementation results will be recorded in detail as an important basis for evaluating the effectiveness of energy strategies, and also provide a reference for possible future strategy adjustments.

[0094] See also Figure 4 , the steps to obtain the optimal configuration plan of power resources are:

[0095] Based on the adjusted energy output ratio record, collect the required energy usage data and charging and discharging cycles according to the new ratio, and re-formulate and output the preliminary charging and discharging plan;

[0096] Based on the adjusted energy output ratio record, the load data of the power grid is systematically collected by connecting to real-time data monitoring, including but not limited to peak power demand, valley consumption and total energy consumption. The collected data is subject to detailed quality review, including screening of data anomalies and necessary data standardization, and then through regression analysis and variance analysis, the main factors affecting power supply and demand are identified, so as to formulate preliminary charging and discharging plans to adapt to the changing market demand and resource supply.

[0097] Based on the preliminary charge and discharge plan, an environmental simulation is performed to simulate the performance under different power load conditions, using the formula,

[0098] ;

[0099] Calculate the overall efficiency index of the charging and discharging plan , and get the simulation test results, where Indicates Daily charging capacity, is the amount of electricity consumed that day, and Represent the predicted and actual loads, respectively. Indicates the total number of days;

[0100] collect (One month) data, kWh, kWh, kWh, kWh.

[0101] Substitute into the formula and calculate:

[0102]

[0103]

[0104] The results show that within a certain period of time (within one month), the total efficiency index of the charging and discharging plan is 2.5, which can measure the matching degree and efficiency between the charging strategy and the actual power demand (ie, load). The result of 2.5 is high, indicating that the adjusted charging and discharging plan can effectively improve energy utilization and adapt to different load conditions.

[0105] According to the simulation test results, the charging and discharging parameters, including charging time and discharging rate, are adjusted in real time to determine the final charging and discharging plan, maximize the use of renewable energy, meet the stable operation requirements of the power grid at the same time, and obtain the optimal configuration plan for power resources;

[0106] Based on the simulation test results, it is particularly important to refine the charging and discharging plan. This includes adjusting the charging time, discharge rate, and possible energy storage strategies to ensure the efficient operation of the power system. In actual operation, the adjustment strategy needs to consider not only the stability of energy supply, but also optimize the grid's ability to respond to peak and valley loads. By monitoring the grid load and energy output in real time, the charging and discharging plans can be adjusted dynamically. For example, the future power demand can be predicted based on weather conditions and user behavior, and the charging and discharging plans can be adjusted in a timely manner. The adjusted plan will be documented through a comprehensive power resource optimization configuration plan and provided to the operation team for execution to ensure the optimal use of energy and the long-term stability of the power grid.

[0107] See also Figure 5 , the steps to identify configuration deviation are:

[0108] Establish a communication connection with the power grid data center through a secure network protocol, receive the load and energy distribution data of the power grid in real time, and generate a real-time power grid data stream;

[0109] A communication connection is established with the power grid data center through a secure network protocol. Through this connection, load and energy distribution data from the power grid center is received in real time. The data includes core power grid data such as power consumption in major cities and output power of major power stations. It ensures that the received data has the latest timestamp and the data interval remains within the preset real-time range. Through data encryption and security verification processes, the security and integrity of the data during data transmission are guaranteed. During this process, the received data is preliminarily formatted, such as data type conversion, time series synchronization, etc., to facilitate the next step of data processing and analysis, and the generation of real-time power grid data streams.

[0110] Using real-time grid data streams, the current values ​​of grid load and energy distribution are compared with the preset values ​​of the power resource optimization configuration plan, and the formula is used.

[0111] ;

[0112] Calculate the average of the weighted absolute deviations of the parameters , generate configuration deviation analysis results; where, Indicates the real-time parameter value, Indicates the parameter value of the configuration scheme. Represents the parameter weight, which adjusts the influence of each parameter deviation. Indicates the total number of parameters;

[0113] There are three different power parameters in the system (generation, peak demand, and spare capacity). The specific real-time data and configuration plan data collected are as follows (all standard unit values): , , .

[0114] Substituting into the formula, we get:

[0115]

[0116]

[0117]

[0118]

[0119] The results show that there is a certain deviation between the current real-time power consumption and the preset configuration scheme ( ), indicating that the actual state of power distribution is relatively close to the preset configuration, but it is still necessary to pay attention to monitoring parameters with large deviations to further optimize the stability and efficiency of power grid operation.

[0120] According to the configuration deviation analysis results, set thresholds and identify deviation items that exceed the thresholds, and classify them as critical configuration deviations to generate critical deviation identification results;

[0121] According to the results of configuration deviation analysis, deviation items exceeding the threshold are identified, including evaluating the deviation of each power parameter, comparing the evaluation results with the set deviation threshold, and identifying parameters exceeding the threshold. The parameters involve the operation of key equipment or power supply problems in a certain area. Through real-time monitoring data, key deviation data are marked and classified to provide decision support for power grid operation and management. Through further analysis, they are classified as key configuration deviations and key deviation identification results are generated, which will be used for daily monitoring and emergency response adjustments of the power dispatching center to ensure safe and stable operation of the power grid.

[0122] See also Figure 6 ,The steps for obtaining real-time power adjustment feedback data are:

[0123] Analyze the key deviation identification results, check the areas that exceed the threshold in the power configuration, determine the power configuration items that need to be adjusted and prioritize them, and generate a power adjustment priority list;

[0124] Based on the data from real-time monitoring of the power system, the areas where deviations exist in the power configuration are analyzed and identified, and the areas that exceed the preset thresholds are sorted to determine which areas' power configurations need urgent adjustment. The priority of the adjustment is determined according to the size of the deviation and the scope of impact. The data is processed systematically to generate a detailed power adjustment priority list to guide the implementation of the next power adjustment strategy, ensuring the accuracy and timeliness of power adjustment, and effectively responding to actual demand changes in the power grid. The generated power adjustment priority list directly affects the next adjustment decision.

[0125] According to the power adjustment priority list, make real-time adjustments to power configuration, increase power supply in key areas and reduce power supply in non-key areas, optimize power resource allocation, and generate power adjustment execution records;

[0126] According to the power adjustment priority list, real-time adjustments to power configuration are performed. Adjustment measures include but are not limited to increasing power supply in key areas, reducing power supply in non-key areas, and adjusting the output power of power stations, ensuring that each adjustment is accurate to the specific power supply line and time point. The adjustment execution record is automatically generated by the system and saved in the database for subsequent review and analysis. The generated adjustment execution record provides transparency and traceability of operations for the power system.

[0127] Based on the power adjustment execution records, collect the usage data after the power adjustment, evaluate the effectiveness of the adjustment measures by comparing the power usage and user feedback data before and after the adjustment, and generate real-time power adjustment feedback data;

[0128] Collect and analyze adjusted electricity usage data, including comparison of load data before and after adjustment, user satisfaction survey results, and system stability indicators. Through data analysis, evaluate the effectiveness of adjustment measures, help power companies understand the impact of power adjustments, and provide data support for improving power resource allocation. The generated real-time power adjustment feedback data provides real-time feedback for grid operations, ensuring the best match between power supply and consumer demand, and further optimizing the operating efficiency and reliability of the grid.

[0129] See also Figure 7 , the steps for dynamic adjustment of inter-regional power flow are:

[0130] Based on the real-time power adjustment feedback data, record the power usage of each area after adjustment, including power consumption and power supply, and generate regional power usage records;

[0131] By accessing the power grid management system, we collect real-time feedback data from each region after power adjustment. The data records in detail the power consumption and supply of each region. Using this data, we conduct a comprehensive analysis of the power usage of each region and generate a power usage report for each region. These reports show the power supply and demand situation of each region in detail, providing accurate basic data for the next step of supply and demand evaluation.

[0132] Using regional electricity usage records, the formula

[0133] ;

[0134] Calculate regional power supply and demand ratio , analyze and output the power balance state identification results, where, and Respectively represent The power supply and demand in the region, is the total number of regions, It is an adjustment factor set according to regional priorities, adjusted according to the intensity of regional economic activity or population density;

[0135] The power supply collected in the three areas is , and MW, the demand is , and Megawatts, with the adjustment factor set to , and . Substitute into the formula to calculate:

[0136]

[0137]

[0138] The results show that the overall supply and demand relationship is relatively balanced ( ), but individual regions may need further electricity adjustments to achieve a better balance between supply and demand.

[0139] According to the results of power balance status identification, the inter-regional power is dynamically adjusted to achieve the optimal allocation of inter-regional power supply and demand, and the results of dynamic adjustment of inter-regional power flow are output;

[0140] Based on the supply-demand ratio calculated above, we can analyze which areas have power shortage or power surplus problems, and make appropriate adjustments to the power in the relevant areas, such as increasing the power supply to the areas with power shortage or reducing the power supply to the areas with power surplus. Through this dynamic adjustment, we can ultimately achieve the optimal balance between power supply and demand among regions. This adjustment process ensures the rational allocation of power resources and the efficient and stable operation of the power grid. The output results of the dynamic adjustment of power flows among regions clearly point out the future adjustment direction and strategy of each region.

[0141] See also Figure 8 , the steps to obtain the results of regional power generation coordinated dispatch are:

[0142] According to the results of the dynamic adjustment of inter-regional power flow, the current power supply and demand status of the difference regions and the adjusted power flow information are extracted to generate preliminary regional power load balance records;

[0143] The power flow data obtained from the real-time monitoring system is analyzed in detail, the differences in power supply and demand between different regions are analyzed, and the power load conditions in each region are accurately assessed. The data collected from hundreds of sensors are synchronously processed and outliers are filtered to ensure the accuracy and real-time nature of the data obtained. According to the analysis results, the power flow in the power grid is dynamically adjusted to optimize the energy efficiency and response speed of the entire power grid, ensuring the stability and safety of power grid operations. The preliminary power balance report records the output results of this series of operations, providing data support for the next step of decision-making and optimization.

[0144] Based on preliminary regional electricity load balance records, combined with regional renewable energy generation data and electricity consumption patterns, the formula

[0145] ;

[0146] Calculate the efficiency improvement ratio of power flow , and the power flow adjustment result is obtained, where Indicates Renewable energy production in the region, Indicates The regional electricity demand, is the weight coefficient adjusted according to the regional priority, Indicates the total number of regions;

[0147] The production capacities of the three regions are 100, 150, and 120 units, respectively, and the demands are 80, 160, and 100 units, respectively. The weight coefficients are 1, 1.5, and 1.2, respectively. Substituting them into the formula, we get:

[0148]

[0149]

[0150] result Represents the percentage of efficiency improvement of power flow after dynamic adjustment of inter-regional power flow, reflecting the degree of efficiency improvement from the baseline power flow state to the adjusted state. % indicates that the power flow adjustment measures can improve the overall efficiency of the power grid %, which means that the efficiency of power transmission and distribution has been significantly improved, reducing energy losses and optimizing the utilization of power resources.

[0151] According to the power flow adjustment results, perform inter-regional coordinated power generation dispatching to ensure that the region receives or supplies the corresponding power as planned, monitor the power flow and status in real time, dynamically match sudden changes, and output regional power generation coordinated dispatching results;

[0152] After completing the dynamic adjustment of inter-regional power flows, a detailed power exchange plan was formulated by comprehensively considering the renewable energy power generation and actual consumption patterns of each region. By simulating various supply and demand scenarios, the maximum operating efficiency and stability of the power grid under different conditions were ensured. In this process, factors such as seasonal changes, weather conditions and forecast errors were considered to dynamically adjust power distribution, reduce energy waste, and increase system reliability. The final regional power generation coordinated dispatching results not only improved the economic operation efficiency of the power grid, but also enhanced the utilization rate of renewable energy. In addition, through the real-time data feedback mechanism, the dispatching system can continuously monitor the implementation effect and adjust strategies in time to cope with changes in power grid load, ensuring the continuous balance and efficient operation of the power system.

[0153] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. The power generation dispatching system based on cloud computing is characterized by: The system comprises: The power demand forecasting module collects historical power usage data, calculates the daily peak and valley values ​​of power usage, compares and analyzes them with real-time grid load data, and generates power load comparison results; The resource allocation optimization module analyzes and evaluates the power supply and demand relationship based on the power load comparison result, adjusts the output ratio of renewable energy and traditional energy, synchronously adjusts the charging and discharging plan according to the output ratio adjustment result, matches the power load change, and generates a power resource optimization configuration plan; The data monitoring and feedback module connects to the real-time data stream of the power grid, monitors the real-time status of the current power grid load and energy distribution, compares the real-time data with the power resource optimization configuration plan, identifies the configuration deviation, and adjusts the power configuration in real time according to the configuration deviation identification result to match the actual power usage and obtain real-time power adjustment feedback data; The regional power dispatch coordination module uses the real-time power adjustment feedback data to evaluate the power supply and demand situation between regions, dynamically adjust the power flow between regions, optimize the load balance between regions based on the dynamic adjustment results of the power flow between regions, and formulate a power exchange plan based on the renewable energy power generation and consumption patterns between regions to maximize the power grid operation efficiency and stability and generate regional power generation coordinated dispatch results.

2. The cloud computing-based power generation dispatching system according to claim 1, characterized in that: The steps for obtaining the power load comparison result are: Collect historical power data and real-time grid load data, extract the maximum and minimum values ​​of daily power usage from the historical data, and record the real-time load on the corresponding date to generate historical and real-time power data records; Performing time series alignment on the historical and real-time power data records, confirming the consistency of the time label of each data point, comparing the difference between the historical power peak and valley values ​​and the real-time load on the same day, and obtaining a daily comparison difference record; Based on the daily comparison difference record, the formula is used: ； Calculate the sum of squares of the normalized differences for each day , generate power load comparison analysis results, where, Indicates The difference between the peak and valley values ​​of electricity usage during the day, Indicates The real-time load value of the day, Represents the total number of days.

3. The cloud computing-based power generation dispatching system according to claim 2, characterized in that: The steps for adjusting the output ratio of renewable energy to traditional energy are as follows: Based on the power load comparison results, trend analysis and pattern recognition are performed to determine the key factors of power supply and demand changes and generate a comprehensive supply and demand relationship evaluation record; Based on the comprehensive evaluation records of the supply and demand relationship, the formula is used: ； Calculate the output ratio of renewable energy to traditional energy , output energy output ratio scheme, where, Representative The value of renewable energy supply per day, Represents the total energy demand value for the same day, is the difference between demand and supply on that day, Represents the total number of days; The energy output ratio scheme is applied to adjust the power generation plan and load response strategy to ensure a dynamic balance between supply and load, and form a record of the adjusted energy output ratio.

4. The cloud computing-based power generation dispatching system according to claim 3, characterized in that: The steps for obtaining the power resource optimization configuration plan are: Based on the adjusted energy output ratio record, according to the new ratio, the required energy usage data and charging and discharging cycle are collected, and a preliminary charging and discharging plan is re-formulated and output; Based on the preliminary charge and discharge plan, an environmental simulation is performed to simulate the performance under different power load conditions, using the formula, ； Calculate the overall efficiency index of the charging and discharging plan , and get the simulation test results, where Indicates Daily charging capacity, is the amount of electricity consumed that day, and Represent the predicted and actual loads, respectively. Indicates the total number of days; According to the simulation test results, the charging and discharging parameters, including charging time and discharging rate, are adjusted in real time to determine the final charging and discharging plan, maximize the use of renewable energy, simultaneously meet the stable operation requirements of the power grid, and obtain an optimal configuration plan for power resources.

5. The cloud computing-based power generation dispatching system according to claim 4, characterized in that: The steps of identifying the configuration deviation are: Establish a communication connection with the power grid data center, receive the load and energy distribution data of the power grid in real time, and generate real-time power grid data stream; By using the real-time grid data stream, the current values ​​of grid load and energy distribution are compared with the preset values ​​of the power resource optimization configuration scheme, and the formula is used: ； Calculate the average of the weighted absolute deviations of the parameters , generate configuration deviation analysis results; where, Indicates the real-time parameter value. Indicates the parameter value of the configuration scheme. Represents the parameter weight, which adjusts the influence of each parameter deviation. Indicates the total number of parameters; According to the configuration deviation analysis result, a threshold is set and deviation items exceeding the threshold are identified and classified as key configuration deviations, and a key deviation identification result is generated.

6. The cloud computing-based power generation dispatching system according to claim 5, characterized in that: The steps for acquiring the real-time power adjustment feedback data are as follows: Analyze the key deviation identification results, check the areas exceeding the threshold in the power configuration, determine the power configuration items that need to be adjusted and prioritize them, and generate a power adjustment priority list; According to the power adjustment priority list, real-time adjustment of power configuration is performed to increase power supply in key areas and reduce power supply in non-key areas, optimize power resource allocation, and generate power adjustment execution records; Based on the power adjustment execution record, the usage data after the power adjustment is collected, and by comparing the power usage and user feedback data before and after the adjustment, the effectiveness of the adjustment measures is evaluated to generate real-time power adjustment feedback data.

7. The cloud computing-based power generation dispatching system according to claim 6, characterized in that: The steps of dynamically adjusting the inter-regional power flow are as follows: Based on the real-time power adjustment feedback data, record the power usage of each area after adjustment, including power consumption and power supply, and generate a regional power usage record; Using the regional electricity usage records, the formula is adopted, ； Calculate regional power supply and demand ratio , analyze and output the power balance state identification results, where, and Respectively represent The power supply and demand in the region, is the total number of regions, It is an adjustment factor set according to regional priorities, adjusted according to the intensity of regional economic activity or population density; According to the power balance state identification result, the inter-regional power is dynamically adjusted to achieve the optimal allocation of inter-regional power supply and demand, and the inter-regional power flow dynamic adjustment result is output.

8. The cloud computing-based power generation dispatching system according to claim 7, characterized in that: The steps for obtaining the regional power generation coordinated dispatch result are: According to the inter-regional power flow dynamic adjustment result, extract the current power supply and demand status of the difference region and the adjusted power flow information to generate a preliminary regional power load balance record; Based on the preliminary regional power load balance records, combined with regional renewable energy generation data and power consumption patterns, the formula is used: ； Calculate the efficiency improvement ratio of power flow , and the power flow adjustment result is obtained, where Indicates Renewable energy production in the region, Indicates The regional electricity demand, is the weight coefficient adjusted according to the regional priority, Indicates the total number of regions; According to the power flow adjustment results, inter-regional power generation coordinated scheduling is performed to ensure that the region receives or supplies the corresponding power as planned, monitor the power flow and status in real time, dynamically match sudden changes, and output regional power generation coordinated scheduling results.

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