Electric power and electric quantity balancing method and system

Abstract

The invention discloses an electric power and electric quantity balancing method and system, and relates to the technical field of electric power management. S2, demand prediction; s3, evaluating the power supply capability; s4, carrying out resource optimization configuration; s5, integrating renewable energy sources; s6, demand side management; s7, performing real-time monitoring and adjustment; and S8, performing performance evaluation and feedback, and constructing an electric power and electric quantity balance system based on the method. According to the electric power and electric quantity balancing method and system, the accuracy and efficiency of electric power and electric quantity balancing can be remarkably improved, various electric power demand changes can be effectively handled, and stable operation of a power grid is ensured. Meanwhile, by dynamically adjusting access and demand side management of renewable energy sources, the purposes of maximum utilization of energy sources, energy conservation and emission reduction are achieved. Finally, through regular performance evaluation and feedback, the system performance is continuously optimized, a closed-loop optimization process is formed, and powerful support is provided for sustainable development of a power system.

Description

Technical Field

[0001] The present invention relates to the technical field of power management, and in particular to a power and quantity balancing method and system. Background Art

[0002] In the current power system, power balance is a key link to ensure stable operation of the power grid and efficient power supply. However, existing technologies often face problems such as incomplete data collection and untimely processing, resulting in insufficient demand forecasting accuracy and difficulty in effectively responding to changes in power demand caused by seasonal fluctuations, special events and weather changes. At the same time, the power supply capacity assessment lacks systematicity and fails to fully consider the reliability of power facilities and the capacity limitations of transmission lines, making the power grid operation risky during peak demand periods. In addition, the integration and utilization of renewable energy also face challenges, and there is a lack of dynamic adjustment mechanisms to match the actual needs of the power grid. The demand-side management means are single, making it difficult to effectively regulate user electricity consumption behavior and reduce system load. Finally, the system performance evaluation and feedback mechanism is not sound, and it is impossible to optimize the system in a timely manner based on the evaluation results to form a closed-loop management. Summary of the invention

[0003] In view of the deficiencies of the prior art, the present invention provides a power and quantity balancing method and system to solve the problems raised in the above background technology.

[0004] To achieve the above object, the present invention provides the following technical solution: a power and quantity balancing method, comprising the following steps: S1. Data collection and processing Collect relevant power generation data from each power station. At the same time, collect power consumption data of various users through the automated information system, and perform data quality control to eliminate errors or abnormal data; S2. Demand Forecast Use models to predict future electricity demand based on seasonal changes, special events, and weather conditions to respond to peaks or troughs in demand; S3. Power supply capacity assessment Assess the power supply capacity of existing power facilities, including the maximum power generation and reliability of each power station, and assess the capacity and reliability of transmission lines to ensure stable operation of the transmission system during peak demand periods; S4. Resource optimization configuration Based on the results of demand forecasting and power supply capacity assessment, an optimization algorithm is used to allocate power resources to maximize the economic benefits and energy efficiency of the power grid; S5. Integration of renewable energy Build models to optimize the share of renewable energy in the grid, dynamically adjust the model based on weather forecasts, adjust wind and solar output in real time, and use optimization algorithms to match the actual needs of the grid; S6. Demand-side management Carry out demand-side management to adjust users' electricity consumption behavior through incentives or direct control to reduce the load on the power system; S7, real-time monitoring and adjustment Monitor the operation status of the power grid in real time, quickly detect any abnormal conditions in the grid, and automatically adjust power output or issue an alarm; S8. Performance evaluation and feedback Regularly evaluate the power and electricity balance effect, including analysis of economic benefits, environmental impact and user satisfaction, and provide feedback to system optimization based on the evaluation results.

[0005] To further optimize the technical solution, in step S1, the power generation data includes power generation of wind power, solar power, hydropower and thermal power plants, and various types of users include industrial, commercial and residential users, and the power consumption data is divided into power consumption periods, including peak period, off-peak period and off-peak period; Before data quality control, data standardization and formatting are performed, and all data are stored and analyzed in a unified format and unit; Data quality control includes data verification, outlier detection, and missing value processing. It uses a combination of automated tools and manual review to promptly detect and correct errors and anomalies in the data.

[0006] To further optimize the technical solution, in step S2, in the power demand forecasting, time series analysis and multivariate regression analysis are combined to consider various factors affecting power demand, and the basic demand forecasting model is as follows:

[0007] in, ( ) is at time ( )’s forecast electricity demand; ( ) is a constant term, representing the basic electricity demand; ( ) represents the combination of seasonal and holiday factors, represented by dummy variables; ( ) is a numerical indicator of weather conditions, including temperature and humidity; ( ) indicators of economic activity, such as the industrial production index; ( ) is the error term, which represents the variation in the data that cannot be explained in the model; When performing model calculations, various holiday factors are preset, and seasonal factors need to be calculated and adjusted. The seasonal factor adjustment algorithm is as follows:

[0008] in,( ) is the time ( ), where ( ) represents the total time unit in a year; ( )and( ) is a parameter to adjust for seasonal fluctuations.

[0009] To further optimize the technical solution, in step S3, a power station capacity reliability model and a transmission line stability model are constructed to evaluate the maximum power generation and reliability of the power station, and the capacity and reliability of the transmission line, respectively; The power station capacity reliability model is as follows:

[0010] in, ( ) is the effective power supply capacity of the power station; ( ) is the rated power supply capacity of the power station; ( ) is the equipment aging or failure rate, calculated based on historical maintenance records and equipment failure data; ( ) is the seasonal factor, which takes into account the impact of different seasons on power supply capacity; The transmission line stability model is as follows:

[0011] in, ( ) is the stability score of the transmission line; ( ) is the maximum design load capacity of the line; ( ) is an environmental factor, adjusted according to weather conditions and geographical location; ( ) is the resilience index of the line, calculated based on the line’s maintenance record and historical performance.

[0012] Further optimizing the technical solution, in step S4, constructing a power resource optimization configuration model not only considers the operation cost and environmental emissions of the power station, but also includes its contribution to the stability of the power grid. At the same time, the constraints of the model also include the total power demand satisfaction constraint and the power station output capacity limitation; The power resource optimization configuration model is as follows:

[0013] in, ( ) is the first ( ) unit electricity cost of each power station; ( ) is the first ( ) unit power environmental impact coefficient of each power station; ( ) is the first ( ) reliability indicators of each power station; ( ) is the first ( ) power station’s electricity output; ( ) are weight factors of cost, environment and reliability, reflecting the different emphases of scheduling strategies; Among the constraints, the total power demand satisfies the following constraints:

[0014] in,( ) is the time ( )’s total electricity demand; The power station output capacity limitations are as follows:

[0015] in,( )and( ) are power stations ( )'s minimum and maximum output capabilities.

[0016] Further optimizing the technical solution, in step S5, the dynamic adjustment model based on weather forecast is used to predict wind speed and solar radiation intensity through meteorological technology, and the dynamic adjustment model includes a wind power output prediction model and a solar power output prediction model; The wind power output prediction model is as follows:

[0017] in, ( ) is at time ( ) of wind power output; ( ) is the air density, which depends on local climatic conditions; ( ) is the swept area of ​​the wind rotor; ( ) is the predicted wind speed; ( ) is the power coefficient, which depends on the blade pitch angle ( ) and tip speed ratio ( ); The solar output prediction model is as follows:

[0018] in, ( ) is at time ( )’s solar power output; ( ) is the area of ​​the photovoltaic panel; ( ) is the predicted solar radiation intensity; ( ) is the conversion efficiency of the photovoltaic panel.

[0019] To further optimize the technical solution, in step S5, the output of renewable energy is optimized to match the demand of the power grid based on a linear programming algorithm, and the objective function of the linear programming algorithm is as follows:

[0020] in,( ) is the cost of purchasing additional electricity from the traditional grid, ( ) is the cost of regulating renewable energy output; The constraints are as follows:

[0021] in,( ) is the time( ) power demand, ( ) is the amount of electricity purchased from the traditional grid; Use the model's predicted weather data to adjust ( )and( ) to minimize the total cost ( ).

[0022] To further optimize the technical solution, in step S6, the demand side management has a built-in power regulation model, which optimizes the load distribution of the power system by adjusting the power consumption behavior. The model integrates the peak and valley electricity price policy to encourage users to change their electricity consumption habits, thereby achieving smoothing of power demand; The power regulation model constructs a power consumption behavior response function, which describes the user's sensitivity to changes in electricity prices and the adjustment of electricity consumption. The power consumption behavior response function is as follows:

[0023] in, ( ) is the time without the influence of peak-valley electricity price policy ( )’s basic electricity consumption; ( ) is the price elasticity coefficient, which reflects the impact of price changes on electricity consumption; ( ) is the time ( ) in the daytime ( ) The rate of change of electricity prices (peak or off-peak); ( ) is the adjusted day segment ( ) at time ( )’s electricity consumption; Electricity price change rate definition:

[0024] in, ( ) is the day segment ( )’s actual electricity price; ( ) is the benchmark electricity price, i.e. the off-peak electricity price.

[0025] Further optimizing the technical solution, in step S8, an economic benefit evaluation model and an environmental impact evaluation model are constructed, wherein the economic benefit evaluation model considers costs, benefits and energy cost savings, and the environmental impact evaluation model performs environmental evaluation based on carbon emissions; The economic benefit evaluation model is as follows:

[0026] in, ( ) is the economic efficiency of the power system; ( ) is the total revenue, assumed to include electricity sales and energy-saving subsidies; ( ) is a fixed cost, including equipment depreciation and basic maintenance costs; ( ) are variable costs, including fuel costs and electricity purchase costs; ( ) is the maintenance cost, including additional maintenance and fault repair; The environmental impact assessment model is as follows:

[0027] in, ( ) is the total environmental impact, expressed in CO2 equivalent; ( ) is the The amount of electricity generated by the energy source; ( ) is the CO2 emission factor per unit of electricity generated.

[0028] An electric power balance system is constructed based on the above-mentioned electric power balance method, including a data collection and processing module, a demand forecasting module, a power supply capacity evaluation module, a resource optimization configuration module, a renewable energy integration module, a demand side management module, a real-time monitoring and adjustment module, and a performance evaluation and feedback module; The data collection and processing module aggregates and cleans power data from power stations and users. The demand forecasting module predicts future power demand based on data and external factors. The power supply capacity assessment module assesses the power supply potential and reliability of existing power facilities. The resource optimization allocation module uses optimization algorithms to reasonably allocate power resources based on predicted demand and power supply capacity. The renewable energy integration module dynamically adjusts the access of wind power and solar energy to maximize utilization and match grid demand. The demand side management module adjusts user electricity consumption through incentives or control measures to reduce system load. The real-time monitoring and adjustment module continuously monitors the grid status, responds quickly to abnormalities and automatically adjusts or alarms. Finally, the performance evaluation and feedback module regularly evaluates system performance and feeds back the results to each module to continuously optimize system performance, forming a closed-loop optimization process.

[0029] Compared with the prior art, the present invention provides a method and system for balancing electric power and electricity, which has the following beneficial effects: This power and electricity balance method and system achieves comprehensive coverage from data source to system optimization by integrating data collection and processing, demand forecasting, power supply capacity assessment, resource optimization allocation, renewable energy integration, demand-side management, real-time monitoring and adjustment, and performance evaluation and feedback; this method and system can significantly improve the accuracy and efficiency of power and electricity balance, effectively respond to various changes in power demand, and ensure stable operation of the power grid. At the same time, by dynamically adjusting the access of renewable energy and demand-side management, the maximum utilization of energy and the goal of energy conservation and emission reduction are achieved. Finally, through regular performance evaluation and feedback, the system performance is continuously optimized, forming a closed-loop optimization process, which provides strong support for the sustainable development of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 A schematic diagram of a flow chart of a power and quantity balancing method proposed by the present invention; Figure 2 This is a structural schematic diagram of an electric power and quantity balancing system proposed by the present invention. DETAILED DESCRIPTION

[0031] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] Embodiment 1: See also Figure 1 , a power and quantity balancing method, comprising the following steps: S1. Data collection and processing Collect relevant power generation data from each power station. At the same time, collect power consumption data of various users through the automated information system, and perform data quality control to eliminate erroneous or abnormal data.

[0033] In this embodiment, the power generation data includes the power generation of wind power, solar power, hydropower and thermal power plants, and the various users include industrial, commercial and residential users. The power consumption data is divided into power consumption periods, including peak period, off-peak period and off-peak period. Before data quality control, data standardization and formatting are performed, and all data are stored and analyzed in a unified format and unit; Data quality control includes data verification, outlier detection, and missing value processing. It uses a combination of automated tools and manual review to promptly detect and correct errors and anomalies in the data.

[0034] S2. Demand Forecast Based on seasonal changes, special events (such as holidays), and weather conditions, models are used to predict future electricity demand to cope with peaks or troughs in demand.

[0035] In this embodiment, in the power demand forecasting, time series analysis and multivariate regression analysis are combined to consider various factors affecting power demand. The basic demand forecasting model is as follows:

[0036] in, ( ) is at time ( )’s forecast electricity demand; ( ) is a constant term, representing the basic electricity demand; ( ) represents the combination of seasonal and holiday factors, represented by dummy variables; ( ) is a numerical indicator of weather conditions, including temperature, humidity, etc.; ( ) indicators of economic activity, such as the industrial production index; ( ) is the error term, which represents the variation in the data that cannot be explained in the model; When performing model calculations, various holiday factors are preset, and seasonal factors need to be calculated and adjusted. The seasonal factor adjustment algorithm is as follows:

[0037] in,( ) is the time ( ), where ( ) represents the total time unit in a year; ( )and( ) is a parameter to adjust for seasonal fluctuations.

[0038] In this embodiment, the prediction model is integrated into the decision support system of the power grid to help operators respond to demand changes in real time, optimize power resource allocation, reduce waste, and ensure the stable operation of the power grid.

[0039] The parameters of the above models are fitted and optimized through machine learning algorithms (such as gradient boosting trees or neural networks). These algorithms can automatically adjust parameters based on historical data to adapt to the changing power demand patterns. The models are trained using historical power data and relevant influencing factor data, and the generalization ability of the models is ensured through cross-validation.

[0040] When applied, the forecast model is updated daily, using the latest weather forecasts and economic indicator data to predict power demand in the next few days. These forecast results will directly affect the operational decisions of the power grid, such as the allocation of power resources and the preparation of emergency measures.

[0041] S3. Power supply capacity assessment Assess the power supply capacity of existing power facilities, including the maximum power generation and reliability of each power station, and assess the capacity and reliability of transmission lines so that the transmission system can operate stably during peak demand periods.

[0042] In this embodiment, a power station capacity reliability model and a transmission line stability model are constructed to evaluate the maximum power generation and reliability of the power station, and the capacity and reliability of the transmission line, respectively; The power station capacity reliability model is as follows:

[0043] in, ( ) is the effective power supply capacity of the power station; ( ) is the rated power supply capacity of the power station; ( ) is the equipment aging or failure rate, calculated based on historical maintenance records and equipment failure data; ( ) is the seasonal factor, which takes into account the impact of different seasons on power supply capacity; The transmission line stability model is as follows:

[0044] in, ( ) is the stability score of the transmission line; ( ) is the maximum design load capacity of the line; ( ) is an environmental factor, adjusted according to weather conditions and geographical location; ( ) is the resilience index of the line, calculated based on the line’s maintenance record and historical performance.

[0045] First, the power station capacity reliability model is used to calculate the effective power supply capacity of each power station, and then the transmission line stability model is used to evaluate the stability of the main transmission lines. Through these calculations, operators can obtain the expected performance of each power station and transmission line during high demand periods, so as to formulate corresponding dispatch strategies and emergency plans.

[0046] S4. Resource optimization configuration Based on the results of demand forecasting and power supply capacity assessment, an optimization algorithm is used to allocate power resources to maximize the economic benefits and energy efficiency of the power grid. For example, power stations with low costs and low pollution can be prioritized to improve the environmental friendliness of the overall power grid.

[0047] In this embodiment, the power resource optimization configuration model is constructed, which not only considers the operation cost and environmental emissions of the power station, but also includes its contribution to the stability of the power grid. At the same time, the constraints of the model also include the total power demand satisfaction constraint and the power station output capacity limitation; The power resource optimization configuration model is as follows:

[0048] in, ( ) is the first ( ) unit electricity cost of each power station; ( ) is the first ( ) unit power environmental impact coefficient of each power station; ( ) is the first ( ) reliability indicators of each power station; ( ) is the first ( ) power station’s electricity output; ( ) are weight factors of cost, environment and reliability, reflecting the different emphases of scheduling strategies; Among the constraints, the total power demand satisfies the following constraints:

[0049] in,( ) is the time ( )’s total electricity demand; The power station output capacity limitations are as follows:

[0050] in,( )and( ) are power stations ( )'s minimum and maximum output capabilities.

[0051] In this embodiment, first determine The values ​​of can be adjusted according to market demand and environmental protection requirements. Next, the above optimization problem is solved using algorithms and other methods to determine the optimal power output of each power station at different time points.

[0052] S5. Integration of renewable energy Build models to optimize the share of renewable energy in the power grid, dynamically adjust the model based on weather forecasts, adjust the output of wind and solar power in real time, and use optimization algorithms to match the actual needs of the power grid.

[0053] In this embodiment, the dynamic adjustment model based on weather forecast predicts wind speed and solar radiation intensity through meteorological technology, and the dynamic adjustment model includes a wind power output prediction model and a solar power output prediction model; The wind power output prediction model is as follows:

[0054] in, ( ) is at time ( ) of wind power output; ( ) is the air density, which depends on local climatic conditions; ( ) is the swept area of ​​the wind rotor; ( ) is the predicted wind speed; ( ) is the power coefficient, which depends on the blade pitch angle ( ) and tip speed ratio ( ); The solar output prediction model is as follows:

[0055] in, ( ) is at time ( )’s solar power output; ( ) is the area of ​​the photovoltaic panel; ( ) is the predicted solar radiation intensity; ( ) is the conversion efficiency of the photovoltaic panel.

[0056] Furthermore, the output of renewable energy is optimized to match the demand of the power grid based on a linear programming algorithm. The objective function of the linear programming algorithm is as follows:

[0057] in,( ) is the cost of purchasing additional electricity from the traditional grid, ( ) is the cost of regulating renewable energy output; The constraints are as follows:

[0058] in,( ) is the time( ) power demand, ( ) is the amount of electricity purchased from the traditional grid; Use the model's predicted weather data to adjust ( )and( ) to minimize the total cost ( ).

[0059] In actual operation, the model updates the power output forecast every day based on the latest weather forecast data. Grid operators use these forecast results to formulate power dispatch plans for the next day to ensure the balance of power supply and demand while optimizing the utilization rate and cost-effectiveness of renewable energy. High-frequency updated weather forecasts and real-time grid monitoring data are important for the accuracy and feasibility of the model.

[0060] S6. Demand-side management Carry out demand-side management to adjust users' electricity consumption behavior through incentives or direct control to reduce the load on the power system.

[0061] In this embodiment, the demand-side management has a built-in power regulation model, which optimizes the load distribution of the power system by adjusting the power consumption behavior. The model integrates the peak and valley electricity price policy to encourage users to change their electricity consumption habits, thereby achieving smoothing of power demand; The power regulation model constructs a power consumption behavior response function, which describes the user's sensitivity to changes in electricity prices and the adjustment of electricity consumption. The power consumption behavior response function is as follows:

[0062] in, ( ) is the time without the influence of peak-valley electricity price policy ( )’s basic electricity consumption; ( ) is the price elasticity coefficient, which reflects the impact of price changes on electricity consumption; ( ) is the time ( ) in the daytime ( ) The rate of change of electricity prices (peak or off-peak); ( ) is the adjusted day segment ( ) at time ( )’s electricity consumption; Electricity price change rate definition:

[0063] in, ( ) is the day segment ( )’s actual electricity price; ( ) is the benchmark electricity price, i.e. the off-peak electricity price.

[0064] The grid operator first sets the base price and the difference between peak and valley prices, as well as the estimated price elasticity coefficient. The operator can determine the price elasticity coefficients for different user groups (such as industry, commerce, and residents) through historical data analysis. By adjusting (Peak and off-peak electricity prices) can incentivize users to use more electricity when electricity demand is low, and vice versa.

[0065] In addition, the model can be extended to data-dependent feedback systems, allowing grid operators to dynamically adjust electricity prices based on real-time market reactions and more precisely control electricity demand, thereby optimizing the load balance and overall energy efficiency of the grid.

[0066] By combining incentives and direct control, not only is effective load management achieved, but the economy and reliability of the power system are also improved.

[0067] S7, real-time monitoring and adjustment Monitor the operating status of the power grid in real time, quickly detect any abnormal conditions in the grid, and automatically adjust power output or issue an alarm.

[0068] The real-time status of the power grid and various important indicators are displayed to the operator through a graphical user interface (GUI). Information is updated in real time and displayed in easy-to-understand formats such as charts, dashboards, and maps, allowing operators to quickly identify any anomalies or deviations in the power grid. It also allows operators to manually input control instructions or adjust automatic control parameters.

[0069] Alert operators before situations develop to a critical stage. Use preset rules and algorithms to analyze real-time data and activate the alarm system immediately if parameters are detected to be outside the normal range. Alerts can be in various forms, including sound, light signals, and automatic text messages or emails.

[0070] S8. Performance evaluation and feedback Regularly evaluate the power and electricity balance effect, including analysis of economic benefits, environmental impact and user satisfaction, and provide feedback to system optimization based on the evaluation results.

[0071] In this embodiment, an economic benefit evaluation model and an environmental impact evaluation model are constructed, wherein the economic benefit evaluation model considers costs, benefits and energy cost savings, and the environmental impact evaluation model performs environmental evaluation based on carbon emissions; The economic benefit evaluation model is as follows:

[0072] in, ( ) is the economic efficiency of the power system; ( ) is the total revenue, assumed to include electricity sales and energy-saving subsidies; ( ) is a fixed cost, including equipment depreciation and basic maintenance costs; ( ) are variable costs, including fuel costs and electricity purchase costs; ( ) is the maintenance cost, including additional maintenance and fault repair; The environmental impact assessment model is as follows:

[0073] in, ( ) is the total environmental impact, expressed in CO2 equivalent; ( ) is the The amount of electricity generated by the energy source; ( ) is the CO2 emission factor per unit of electricity generated.

[0074] The economic benefit assessment model and the environmental impact assessment model make it easier for operators to monitor and report their impact on the environment, while supporting the formulation of environmentally friendly power generation strategies.

[0075] These indicators are calculated regularly using actual data, and operations and policies are adjusted based on the results. For example, if the economic benefit assessment model shows that a certain strategy leads to increased costs, the strategy may need to be optimized or replaced. Similarly, if the environmental impact assessment model shows an increase in environmental impact, the operator may increase the proportion of renewable energy or invest in more efficient technologies.

[0076] Embodiment 2: See also Figure 2 A power and electricity balancing system is constructed based on a power and electricity balancing method described in Example 1, and includes a data collection and processing module, a demand forecasting module, a power supply capacity assessment module, a resource optimization configuration module, a renewable energy integration module, a demand side management module, a real-time monitoring and adjustment module, and a performance evaluation and feedback module.

[0077] The data collection and processing module aggregates and cleans the power data from power stations and users.

[0078] The demand forecasting module predicts future electricity demand based on data and external factors.

[0079] In this embodiment, based on the four seasons and special event days (holidays), the data obtained by the demand forecasting module is shown in Table 1. The seasonal index, special event impact coefficient, and weather condition impact coefficient are all within the preset threshold range and are used to adjust the basic power demand forecast.

[0080] Time period Spring Daytime Peak Summer Night Lows Average daily autumn Winter daytime peak Special Event Days Seasonal Index 1.2 0.8 1.0 1.3 1.1 Special event impact coefficient 0.95 1.05 0.98 0.85 1.5 Weather Conditions Impact Factor 0.98 0.75 0.9 1.1 0.95 Forecasted power demand (MW) 5000 3500 4200 6000 7500 Table 1 The power supply capacity assessment module evaluates the power supply potential and reliability of existing power facilities.

[0081] The resource optimization configuration module uses optimization algorithms to rationally configure power resources based on predicted demand and power supply capacity.

[0082] In this embodiment, during the resource optimization configuration process, the power generation of different power stations is adjusted according to demand forecasts and power supply capacity assessments, while considering the improvement of economic benefits and energy efficiency. The power generation of wind power stations and solar power stations is dynamically adjusted according to weather forecasts, and the energy storage system participates in the optimization as a balancing means. The improvement of economic benefits and energy efficiency is a relative value calculated based on a preset threshold range. The configuration parameters are shown in Table 2.

[0083] Power station number Power generation before optimization (MW) Optimized power generation (MW) Improvement in economic benefits (%) Energy efficiency improvement (%) A01 800 900 5 3 A02 650 600 (transferred to energy storage) 2 1 B01 1200 1150 4 2 C01 (Wind power station) 400 500 (Weather forecast is good) 8 5 D01 (Solar) 300 250 (Period adjustment) 6 4 Energy Storage System - 200 (transferred from A02) - - (as a means of regulation) Table 2 The renewable energy integration module dynamically adjusts the access of clean energy such as wind power and solar power to maximize utilization and match grid demand.

[0084] The demand side management module regulates user electricity consumption through incentives or control measures to reduce system load.

[0085] The real-time monitoring and adjustment module continuously monitors the power grid status, quickly responds to abnormalities and automatically adjusts or alarms.

[0086] Finally, the performance evaluation and feedback module regularly evaluates system performance and feeds the results back to each module to continuously optimize system performance, forming a closed-loop optimization process.

[0087] The beneficial effects of the present invention are: This power and electricity balance method and system achieves comprehensive coverage from data source to system optimization by integrating data collection and processing, demand forecasting, power supply capacity assessment, resource optimization allocation, renewable energy integration, demand-side management, real-time monitoring and adjustment, and performance evaluation and feedback; this method and system can significantly improve the accuracy and efficiency of power and electricity balance, effectively respond to various changes in power demand, and ensure stable operation of the power grid. At the same time, by dynamically adjusting the access of renewable energy and demand-side management, the maximum utilization of energy and the goal of energy conservation and emission reduction are achieved. Finally, through regular performance evaluation and feedback, the system performance is continuously optimized, forming a closed-loop optimization process, which provides strong support for the sustainable development of the power system.

[0088] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0089] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A power and quantity balancing method, characterized in that: The following steps are involved: S1. Data collection and processing Collect relevant power generation data from each power station. At the same time, collect power consumption data of various users through the automated information system, and perform data quality control to eliminate errors or abnormal data; S2. Demand Forecast Use models to predict future electricity demand based on seasonal changes, special events, and weather conditions to respond to peaks or troughs in demand; S3. Power supply capacity assessment Assess the power supply capacity of existing power facilities, including the maximum power generation and reliability of each power station, and assess the capacity and reliability of transmission lines to ensure stable operation of the transmission system during peak demand periods; S4. Resource optimization configuration Based on the results of demand forecasting and power supply capacity assessment, an optimization algorithm is used to allocate power resources to maximize the economic benefits and energy efficiency of the power grid; S5. Integration of renewable energy Build models to optimize the share of renewable energy in the grid, dynamically adjust the model based on weather forecasts, adjust wind and solar output in real time, and use optimization algorithms to match the actual needs of the grid; S6. Demand-side management Carry out demand-side management to adjust users' electricity consumption behavior through incentives or direct control to reduce the load on the power system; S7, real-time monitoring and adjustment Monitor the operation status of the power grid in real time, quickly detect any abnormal conditions in the grid, and automatically adjust power output or issue an alarm; S8. Performance evaluation and feedback Regularly evaluate the power and electricity balance effect, including analysis of economic benefits, environmental impact and user satisfaction, and provide feedback to system optimization based on the evaluation results.

2. A power and quantity balancing method according to claim 1, characterized in that: In step S1, the power generation data includes power generation of wind power, solar power, hydropower and thermal power plants, and the various users include industrial, commercial and residential users. The power consumption data is divided into power consumption periods, including peak period, off-peak period and off-peak period. Before data quality control, data standardization and formatting are performed, and all data are stored and analyzed in a unified format and unit; Data quality control includes data verification, outlier detection, and missing value processing. It uses a combination of automated tools and manual review to promptly detect and correct errors and anomalies in the data.

3. The power and quantity balancing method according to claim 1, characterized in that: In step S2, in the power demand forecast, time series analysis and multivariate regression analysis are combined to consider various factors affecting power demand. The basic demand forecast model is as follows: in, ( ) is at time ( )’s forecast electricity demand; ( ) is a constant term, representing the basic electricity demand; ( ) represents the combination of seasonal and holiday factors, represented by dummy variables; ( ) is a numerical indicator of weather conditions, including temperature and humidity; ( ) indicators of economic activity, such as the industrial production index; ( ) is the error term, which represents the variation in the data that cannot be explained in the model; When performing model calculations, various holiday factors are preset, and seasonal factors need to be calculated and adjusted. The seasonal factor adjustment algorithm is as follows: in,( ) is the time ( ), where ( ) represents the total time unit in a year; ( )and( ) is a parameter to adjust for seasonal fluctuations.

4. The power and quantity balancing method according to claim 1, characterized in that: In the step S3, a power station capacity reliability model and a transmission line stability model are constructed to evaluate the maximum power generation and reliability of the power station, and the capacity and reliability of the transmission line, respectively; The power station capacity reliability model is as follows: in, ( ) is the effective power supply capacity of the power station; ( ) is the rated power supply capacity of the power station; ( ) is the equipment aging or failure rate, calculated based on historical maintenance records and equipment failure data; ( ) is the seasonal factor, which takes into account the impact of different seasons on power supply capacity; The transmission line stability model is as follows: in, ( ) is the stability score of the transmission line; ( ) is the maximum design load capacity of the line; ( ) is an environmental factor, adjusted according to weather conditions and geographical location; ( ) is the resilience index of the line, calculated based on the line’s maintenance record and historical performance.

5. The power and quantity balancing method according to claim 1, characterized in that: In step S4, a power resource optimization configuration model is constructed, which not only considers the operation cost and environmental emissions of the power station, but also includes its contribution to the stability of the power grid. At the same time, the constraints of the model also include the total power demand satisfaction constraint and the power station output capacity limitation; The power resource optimization configuration model is as follows: in, ( ) is the first ( ) unit electricity cost of each power station; ( ) is the first ( ) unit power environmental impact coefficient of each power station; ( ) is the first ( ) reliability indicators of each power station; ( ) is the first ( ) power station’s electricity output; ( ) are weight factors of cost, environment and reliability, reflecting the different emphases of scheduling strategies; Among the constraints, the total power demand satisfies the following constraints: in,( ) is the time ( )’s total electricity demand; The power station output capacity limitations are as follows: in,( )and( ) are power stations ( )'s minimum and maximum output capabilities.

6. The power and quantity balancing method according to claim 1, characterized in that: In step S5, a dynamic adjustment model based on weather forecast is used to predict wind speed and solar radiation intensity by meteorological technology, and the dynamic adjustment model includes a wind power output prediction model and a solar power output prediction model; The wind power output prediction model is as follows: in, ( ) is at time ( ) of wind power output; ( ) is the air density, which depends on local climatic conditions; ( ) is the swept area of ​​the wind rotor; ( ) is the predicted wind speed; ( ) is the power coefficient, which depends on the blade pitch angle ( ) and tip speed ratio ( ); The solar output prediction model is as follows: in, ( ) is at time ( )’s solar power output; ( ) is the area of ​​the photovoltaic panel; ( ) is the predicted solar radiation intensity; ( ) is the conversion efficiency of the photovoltaic panel.

7. The power and quantity balancing method according to claim 1, characterized in that: In step S5, the matching between the output of renewable energy and the demand of the power grid is optimized based on a linear programming algorithm, and the objective function of the linear programming algorithm is as follows: in,( ) is the cost of purchasing additional electricity from the traditional grid, ( ) is the cost of regulating renewable energy output; The constraints are as follows: in,( ) is the time( ) power demand, ( ) is the amount of electricity purchased from the traditional grid; Use the model's predicted weather data to adjust ( )and( ) to minimize the total cost ( ).

8. The power and quantity balancing method according to claim 1, characterized in that: In step S6, the demand side management has a built-in power regulation model, which optimizes the load distribution of the power system by adjusting the power consumption behavior. The model integrates the peak and valley electricity price policy to encourage users to change their electricity consumption habits, thereby achieving smoothing of power demand; The power regulation model constructs a power consumption behavior response function, which describes the user's sensitivity to changes in electricity prices and the adjustment of electricity consumption. The power consumption behavior response function is as follows: in, ( ) is the time without the influence of peak-valley electricity price policy ( )’s basic electricity consumption; ( ) is the price elasticity coefficient, which reflects the impact of price changes on electricity consumption; ( ) is the time ( ) in the daytime ( ) The rate of change of electricity prices (peak or off-peak); ( ) is the adjusted day segment ( ) at time ( )’s electricity consumption; Electricity price change rate definition: in, ( ) is the day segment ( )’s actual electricity price; ( ) is the benchmark electricity price, i.e. the off-peak electricity price.

9. The power and quantity balancing method according to claim 1, characterized in that: In the step S8, an economic benefit evaluation model and an environmental impact evaluation model are constructed, wherein the economic benefit evaluation model takes into account costs, benefits and energy cost savings, and the environmental impact evaluation model performs an environmental evaluation based on carbon emissions; The economic benefit evaluation model is as follows: in, ( ) is the economic efficiency of the power system; ( ) is the total revenue, assumed to include electricity sales and energy-saving subsidies; ( ) is a fixed cost, including equipment depreciation and basic maintenance costs; ( ) are variable costs, including fuel costs and electricity purchase costs; ( ) is the maintenance cost, including additional maintenance and fault repair; The environmental impact assessment model is as follows: in, ( ) is the total environmental impact, expressed in CO2 equivalent; ( ) is the The amount of electricity generated by the energy source; ( ) is the CO2 emission factor per unit of electricity generated.

10. A power and quantity balancing system, constructed based on a power and quantity balancing method according to any one of claims 1 to 9, characterized in that: It includes data collection and processing module, demand forecasting module, power supply capacity assessment module, resource optimization configuration module, renewable energy integration module, demand side management module, real-time monitoring and adjustment module, and performance evaluation and feedback module; The data collection and processing module aggregates and cleans power data from power stations and users. The demand forecasting module predicts future power demand based on data and external factors. The power supply capacity assessment module assesses the power supply potential and reliability of existing power facilities. The resource optimization allocation module uses optimization algorithms to reasonably allocate power resources based on predicted demand and power supply capacity. The renewable energy integration module dynamically adjusts the access of wind power and solar energy to maximize utilization and match grid demand. The demand side management module adjusts user electricity consumption through incentives or control measures to reduce system load. The real-time monitoring and adjustment module continuously monitors the grid status, responds quickly to abnormalities and automatically adjusts or alarms. Finally, the performance evaluation and feedback module regularly evaluates system performance and feeds back the results to each module to continuously optimize system performance, forming a closed-loop optimization process.

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