SCADA system intelligent scheduling and optimal control method oriented to multi-energy integration
By integrating multiple energy sources into a SCADA system, and combining LSTM networks and optimized scheduling algorithms, the problems of flexibility and prediction accuracy in energy scheduling systems are solved, and the efficient and stable operation of energy systems is achieved.
Patent Information
- Application Number
- CN202511473833.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-15
- Publication Date
- 2026-01-09
AI Technical Summary
Existing energy dispatch systems are inadequate in terms of flexibility, optimization capabilities, data fusion and monitoring accuracy, and energy demand forecasting accuracy, making it difficult to adapt to complex and ever-changing energy system environments and rapidly changing energy demands.
By adopting a multi-energy integrated SCADA system, a comprehensive model is established to collect and fuse various energy data in real time. Combined with LSTM network and optimized scheduling algorithm, accurate prediction and dynamic scheduling of energy demand can be achieved.
It has improved the flexibility and optimization capabilities of energy dispatch, enhanced the accuracy of data fusion, improved the accuracy of energy demand forecasting, and ensured the stable operation and efficient utilization of the system.
Smart Images
Figure CN121304384A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of energy monitoring technology, and in particular to a method for intelligent scheduling and optimized control of SCADA systems for multi-energy integration. Background Technology
[0002] In the field of energy management, energy dispatch is crucial for ensuring a stable energy supply and improving energy efficiency. Currently, energy dispatch mainly employs the following methods, each with its own limitations: Firstly, manual dispatching, as a traditional energy management method, relies primarily on operators adjusting the output power of various energy devices based on their experience and historical data to meet energy demands. However, this method is overly dependent on operator experience, highly subjective, and performs poorly in terms of real-time performance, making it difficult to adapt to scenarios with rapidly changing energy demands.
[0003] Secondly, rule-based automated scheduling is a relatively advanced scheduling method. This method automatically adjusts the output power of energy equipment through preset rules and algorithms. However, since the preset rules are fixed, it is not adaptable enough to the complex and ever-changing energy system environment and cannot flexibly respond to various emergencies and dynamically changing energy demands.
[0004] Third, in recent years, with the development of computer technology, some simple optimization scheduling algorithms have been applied to energy management systems. These algorithms are usually based on linear programming or dynamic programming, optimizing energy allocation by constructing mathematical models. However, the assumptions of such algorithms are too idealistic, failing to fully consider the volatility of renewable energy in actual energy systems, resulting in poor performance in practical applications.
[0005] Fourth, some research has begun to explore the introduction of intelligent algorithms into energy scheduling, such as genetic algorithms and particle swarm optimization. These algorithms seek optimal energy allocation schemes by simulating natural evolution or group behavior. However, genetic algorithms suffer from slow convergence speed, while particle swarm optimization is prone to getting trapped in local optima, affecting the accuracy and efficiency of the scheduling results.
[0006] In view of this, the present invention is proposed to solve the above-mentioned technical problems. Summary of the Invention
[0007] The purpose of this invention is to provide an intelligent scheduling and optimization control method for SCADA systems with multi-energy integration, in order to solve the existing technical problems of insufficient energy scheduling flexibility, limited optimization capabilities, low data fusion and monitoring accuracy, and insufficient energy demand forecasting accuracy.
[0008] The purpose of this invention is to provide an intelligent scheduling and optimization control method for SCADA systems with multi-energy integration, comprising the following steps: S1. Multi-energy system modeling: Establish multiple energy models including wind energy model, solar energy model, and traditional energy model, and integrate the wind energy model, solar energy model, and traditional energy model into a multi-energy system model to form a comprehensive model; S2. Model parameter acquisition: By installing sensors on wind power, solar power, and traditional energy equipment, wind speed, light intensity, power generation, and equipment status parameters are collected in real time as input data for the model. Combined with meteorological data and historical operating data, the model parameters are calibrated and optimized. S3. Data fusion and monitoring: Using multi-source data fusion technology, the input data of the model is fused, and the fused data is used as the input data for energy dispatch and optimization control. S4. Demand Forecasting Model Construction: The demand forecasting model construction includes LSTM network structure and energy demand result prediction. An energy demand forecasting model considering weather, load, and time factors is constructed through the LSTM network structure. Time series analysis is used, combined with historical electricity consumption data, to predict the basic trend of future energy demand. At the same time, weather forecast data and user behavior data are introduced, and machine learning algorithms are used to correct and optimize the demand forecasting model. The energy demand forecasting results are updated in real time based on real-time collected weather data and user behavior data. S5. Design an optimized scheduling algorithm. Based on the energy demand forecast results, design an optimized scheduling algorithm, define the optimization direction, and ensure the dynamic balance between energy supply and demand through a dynamic adjustment mechanism.
[0009] Furthermore, before step S3, the input data of the collected model is cleaned to remove noise and outliers.
[0010] Furthermore, the LSTM network architecture includes: Input layer: Historical load data over 24 time steps; Hidden layers: 2 LSTM layers, 64 neurons per layer, activation function is tanh; Output layer: Fully connected layer, outputs the load forecast for the next 24 hours; Attention mechanism formula: = , =MLP( , ); in, Let be the hidden state of the LSTM at time step i. External features For feature weights, MLP is the input weights. and Neural networks for nonlinear feature fusion Hidden state for LSTM External features The correlation score.
[0011] Furthermore, model training and validation: The demand forecasting model is trained using historical data, and the prediction accuracy and generalization ability of the model are evaluated through cross-validation. The model parameters are continuously adjusted and the model structure is optimized.
[0012] The optimized scheduling algorithm uses a fitness function F; F= · + · ; in, =0.6, =0.4, which is the weighting coefficient; Total energy cost; The maximum allowable cost; For renewable energy utilization rate; Total energy utilization rate (renewable energy + traditional energy).
[0013] Furthermore, the dynamic adjustment mechanism includes: Renewable energy fluctuations: Output changes exceeding ±20% within 15 minutes; Load mutation: Demand changes exceeding ±15% within 5 minutes; Equipment failure: Any generator set is in an abnormal state.
[0014] Furthermore, the wind energy model in the multi-energy model is based on the wind speed-power curve and wind turbine parameters to establish a wind power generation model, in which the output power of the wind turbine is... Based on the wind speed-power curve: = ; in, For real-time wind speed, For fan efficiency, , , These are the cut-in wind speed, rated wind speed, and cut-out wind speed, respectively; calibration mechanism: dynamically corrected quarterly by comparing historical wind speed data with measured power. .
[0015] Furthermore, the photovoltaic power generation of the solar energy model in the multi-energy model. calculate: =η·S·G·(1- ; Where η is the photoelectric conversion efficiency, S is the effective area of the photovoltaic panel, and G is the light intensity. For temperature coefficient, This refers to the real-time surface temperature of the photovoltaic panel. This is a reference temperature.
[0016] Furthermore, the combustion cost function C(P) of the traditional energy model in various energy models is: C(P) = a +bP+c; P represents the generator set output power, and a, b, and c represent combustion cost coefficients.
[0017] By adopting the above technical solution, the present invention has the following beneficial effects: 1. Greater scheduling flexibility: Intelligent scheduling based on SCADA system can respond to changes in energy supply and demand in real time and adapt to the volatility of renewable energy, which is superior to manual and rule-based scheduling methods.
[0018] 2. Enhanced optimization capabilities: By integrating genetic algorithms and particle swarm optimization algorithms, it can quickly find the global optimal solution, improve energy utilization efficiency, reduce costs, and outperform simple optimization scheduling algorithms.
[0019] 3. Higher data fusion accuracy: By adopting multi-source data fusion technology and combining real-time sensor and meteorological data, more accurate real-time information is provided, providing reliable support for intelligent scheduling.
[0020] 4. More accurate demand forecasting: By comprehensively considering multiple factors and using machine learning to optimize the forecasting model, the accuracy of energy demand forecasting is improved, which is superior to traditional statistical models.
[0021] 5. More efficient dynamic adjustment: It has the ability to adjust the scheduling strategy in real time, quickly respond to changes in supply and demand, and ensure stable system operation, which is superior to the adjustment mechanism of existing technologies. Attached Figure Description
[0022] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments and descriptions of the invention are used to explain the invention, but do not constitute an undue limitation of the invention. Obviously, the drawings described below are merely some embodiments, and those skilled in the art can obtain other drawings based on these drawings without creative effort. In the drawings: Figure 1 This application provides a flowchart of the data fusion and monitoring process for the intelligent scheduling and optimization control method of SCADA system for multi-energy integration in this embodiment. Figure 2 A flowchart of the energy demand forecasting method for intelligent scheduling and optimization control of SCADA system for multi-energy integration provided in this embodiment of the application; Figure 3 The flowchart of the optimization scheduling algorithm for the intelligent scheduling and optimization control method of SCADA system for multi-energy integration provided in this embodiment of the application is shown.
[0023] It should be noted that these accompanying drawings and textual descriptions are not intended to limit the scope of the invention in any way, but rather to illustrate the concept of the invention to those skilled in the art by referring to specific embodiments. Detailed Implementation
[0024] This application provides an intelligent scheduling and optimization control method for SCADA systems with multi-energy integration, including the following steps: S1. Multi-energy system modeling: Establish multiple energy models including wind energy model, solar energy model, and traditional energy model. For the solar energy model, establish a photovoltaic power generation model based on light intensity, temperature, and photovoltaic panel parameters. For the traditional energy model, establish a traditional energy power generation model considering the power output range and cost function of the generator set. Integrate the wind energy model, solar energy model, and traditional energy model into a multi-energy system model to form a comprehensive model, considering the coupling relationship and complementary characteristics between the various energy models. S2. Model parameter acquisition: By installing sensors on wind power, solar power, and traditional energy equipment, wind speed, light intensity, power generation, and equipment status parameters are collected in real time as input data for the model. Combined with meteorological data and historical operating data, the model parameters are calibrated and optimized to ensure the accuracy and reliability of the model. S3. Data Fusion and Monitoring: Utilizing multi-source data fusion technology, the input data of the model is fused, and the fused data is used as input data for energy dispatch and optimization control. This provides accurate real-time information for intelligent dispatch algorithms, supporting fast and accurate decision-making. For wind and solar energy models, meteorological data and real-time sensor data are combined, and the power generation is estimated and predicted using the Kalman filter algorithm, improving the accuracy and reliability of the data. For traditional energy models, real-time operating data and historical data of generator units are combined, and the power generation cost model is optimized through data mining and machine learning algorithms. See Figure 1 As shown, the data fusion and monitoring process is as follows: Data acquisition: Sensors in each energy device collect real-time data, including power generation, equipment status, environmental parameters, etc., and send it to the SCADA system.
[0025] Data preprocessing: The SCADA system cleans the collected data, removing noise and outliers to ensure the accuracy and integrity of the data.
[0026] Data fusion: Multi-source data fusion technology is employed to combine data from different energy devices and sensors. For example, for wind and solar power, meteorological data and real-time sensor data are combined, and algorithms such as Kalman filtering are used to estimate and predict power generation, improving the accuracy and reliability of the data.
[0027] Data storage: The merged data is stored in the data storage and management module to provide real-time data support for energy dispatch and optimization control.
[0028] Real-time monitoring and feedback: The SCADA system monitors the operating status of the energy system in real time, and controls and adjusts each energy device based on real-time data and scheduling strategies to ensure the stable operation of the system.
[0029] S4. Demand Forecasting Model Construction: This includes the construction of an LSTM network structure and energy demand forecasting. An energy demand forecasting model considering weather, load, and time factors is constructed using an LSTM network structure. Time series analysis is employed, combined with historical electricity consumption data, to predict the basic trend of future energy demand. Simultaneously, weather forecast data and user behavior data are introduced, and machine learning algorithms are used to correct and optimize the demand forecasting model. Based on real-time collected weather data and user behavior data, the energy demand forecasting results are updated in real time. The energy demand forecasting results serve as an important basis for energy dispatching, supporting intelligent dispatching algorithms to formulate optimal energy allocation and operation strategies, ensuring a dynamic balance between energy supply and demand. See Figure 2 As shown, the process for building a demand forecasting model is as follows: Data collection: Collect historical electricity consumption data, meteorological data, and user behavior data as input data for demand forecasting.
[0030] Data preprocessing: The collected data is cleaned and normalized to remove noise and outliers, ensuring data quality and consistency.
[0031] Model training: The demand forecasting model is trained using historical data, and its prediction accuracy and generalization ability are evaluated through methods such as cross-validation. Model parameters are continuously adjusted and the model structure optimized to improve the accuracy and reliability of predictions.
[0032] Real-time forecasting: Based on real-time collected weather and user load data, energy demand forecasts are updated in real time. These forecasts serve as a crucial basis for energy dispatching, supporting intelligent dispatching algorithms in formulating optimal energy allocation and operational strategies to ensure a dynamic balance between energy supply and demand.
[0033] Prediction result feedback: The prediction results are fed back to the SCADA system to optimize the generation and adjustment of scheduling strategies.
[0034] S5. Design an optimized scheduling algorithm. Based on the energy demand forecast results, design an optimized scheduling algorithm, define the optimization direction, and ensure the dynamic balance between energy supply and demand through a dynamic adjustment mechanism.
[0035] See Figure 3 As shown, the optimized scheduling algorithm flow is as follows: Initialization: Based on the initial state of the multi-energy system, initialize the parameters of the genetic algorithm and particle swarm optimization algorithm, including population size, crossover rate, mutation rate, particle inertia weight, etc.
[0036] Encoding and initializing the population: Encode the output power and operating status of each energy device into the position of chromosomes or particles, and initialize the population or particle swarm.
[0037] Fitness function calculation: The fitness value of a chromosome or particle is calculated based on factors such as the matching degree between energy supply and demand, energy utilization efficiency, and cost.
[0038] Genetic algorithm operations: For genetic algorithms, crossover, mutation, and selection operations are performed to generate a new population. Through the crossover operation, some genes of two chromosomes are exchanged to produce new chromosomes; through the mutation operation, some genes of chromosomes are randomly modified to increase the diversity of the population; through the selection operation, chromosomes with better fitness values are selected to enter the next generation of the population.
[0039] Particle Swarm Optimization (PSO) Operation: For the PSO algorithm, the velocity and position of particles are updated based on individual and swarm experience. By calculating the optimal individual and swarm positions, the particle's direction and velocity are adjusted to guide the particles toward the optimal solution.
[0040] Algorithm fusion and collaborative optimization: By fusing genetic algorithms and particle swarm optimization algorithms, and through information sharing and collaborative evolution, the global search capability and local search capability of the algorithms are improved, avoiding getting trapped in local optima and quickly finding the globally optimal energy allocation scheme.
[0041] Scheduling strategy generation: Based on the output of the optimized scheduling algorithm, a specific energy scheduling strategy is generated. The scheduling strategy includes information such as the output power, operating status, and start / stop times of each energy device.
[0042] Dynamic adjustment mechanism: A dynamic adjustment mechanism is designed to adjust the dispatch strategy in real time based on real-time monitoring data and energy demand forecasts. When energy supply and demand change, the dispatch strategy is updated promptly to ensure stable system operation and efficient energy utilization.
[0043] Execution and Feedback: The scheduling strategy is sent to the SCADA system, which then controls the operation of each energy device to achieve intelligent scheduling and optimized control of energy. Based on the actual operation of the system, the scheduling strategy is evaluated and feedback is provided to further optimize the scheduling algorithm and strategy.
[0044] It should be noted that the multi-energy system structure mainly consists of the following: Wind power generation module: Includes wind turbine, anemometer and other equipment, used to collect wind speed data and power generation data and send them to the SCADA system.
[0045] Solar power generation module: Includes photovoltaic panels, light sensors and other equipment, used to collect light intensity data and power generation data, and send them to the SCADA system.
[0046] Traditional energy generation modules include generator sets, power sensors, and other equipment, which are used to collect power generation and equipment status data and send them to the SCADA system.
[0047] SCADA system: As the core of energy dispatching and optimization control, it receives data from various energy modules, performs data fusion, demand forecasting and optimization scheduling, generates scheduling strategies and controls the operation of various energy devices.
[0048] Data storage and management module: Used to store historical and real-time data, providing data support for data fusion, demand forecasting and optimized scheduling.
[0049] Before step S3, data acquisition and preprocessing are performed. Using the SCADA system, real-time data is collected from the sensors of various energy devices, including power generation, equipment status, environmental parameters, etc. The input data of the collected model is cleaned to remove noise and outliers to ensure the accuracy and integrity of the data.
[0050] The LSTM network architecture includes: Input layer: Historical load data over 24 time steps; Hidden layers: 2 LSTM layers, 64 neurons per layer, activation function is tanh; Output layer: Fully connected layer, outputs the load forecast for the next 24 hours; Attention mechanism formula: , ; in, Let be the hidden state of the LSTM at time step i. External features For feature weights, MLP is the input weights. and Neural networks for nonlinear feature fusion Hidden state for LSTM External features The correlation score.
[0051] Model training and validation: The demand forecasting model is trained using historical data, and the prediction accuracy and generalization ability of the model are evaluated through cross-validation. The model parameters are continuously adjusted and the model structure is optimized.
[0052] The optimized scheduling algorithm uses a fitness function F; ; in, =0.6, =0.4, which is the weighting coefficient; Total energy cost; Maximum allowable cost (set according to budget); The utilization rate of renewable energy (the proportion of wind and solar energy in total consumption); Total energy utilization rate (renewable energy + traditional energy).
[0053] The dynamic adjustment mechanism includes: Renewable energy fluctuations: Output changes exceeding ±20% within 15 minutes; Load mutation: Demand changes exceeding ±15% within 5 minutes; Equipment failure: Any generator set is in an abnormal state (such as excessive temperature or excessive vibration).
[0054] The wind energy model in various energy models is based on the wind speed-power curve and wind turbine parameters to establish a wind power generation model, in which the output power of the wind turbine is... Based on the wind speed-power curve: = ; in, For real-time wind speed, For fan efficiency, , , These are the cut-in wind speed, rated wind speed, and cut-out wind speed, respectively; calibration mechanism: dynamically corrected quarterly by comparing historical wind speed data with measured power. .
[0055] Photovoltaic power generation in the solar energy model of multiple energy models calculate: =η·S·G·(1- ; Where η is the photoelectric conversion efficiency, S is the effective area of the photovoltaic panel, and G is the light intensity. For temperature coefficient, This refers to the real-time surface temperature of the photovoltaic panel. Reference temperature (standard test condition temperature, usually 25℃).
[0056] The combustion cost function C(P) of the traditional energy model in the multi-energy model is: C(P) = a +bP+c; P is the output power of the generator set, and a, b, and c are the combustion cost coefficients (the combustion cost coefficients are determined according to the type of generator set, such as a=0.02, b=30, and c=1000 for coal-fired generator sets).
[0057] The implementation steps of the technical solution in this application are as follows: (1) Hardware equipment installation Sensors are installed on various energy equipment, including anemometers for wind turbines, light sensors for photovoltaic panels, and power sensors for generator sets, to collect real-time data.
[0058] Deploy a SCADA system, including servers, data acquisition terminals, communication networks, and other equipment, for data acquisition, fusion, prediction, and scheduling control.
[0059] Configure the data storage and management module to store historical and real-time data, and support data query and analysis.
[0060] (2) Software system deployment Deploy a data fusion module, an energy demand forecasting module, and an optimized scheduling module in the SCADA system to realize data processing, forecasting, and scheduling control functions.
[0061] Configure communication protocols and interfaces to ensure smooth data communication between various hardware devices and the SCADA system.
[0062] Perform system debugging and testing to verify the system's functions and performance, and ensure the system operates normally.
[0063] (3) Data collection The sensors of each energy device are activated to collect data such as power generation, equipment status, and environmental parameters in real time, and then send them to the SCADA system.
[0064] Collect meteorological data and user behavior data as input data for energy demand forecasting.
[0065] (4) Data preprocessing The SCADA system cleans the received data, removing noise and outliers to ensure data accuracy and integrity.
[0066] Normalize the data to unify the data format and units, which will facilitate subsequent data fusion and processing.
[0067] (5) Data fusion Multi-source data fusion technology is employed to combine data from different energy devices and sensors. For example, for wind and solar power, meteorological data and real-time sensor data are combined, and algorithms such as Kalman filtering are used to estimate and predict power generation, thereby improving the accuracy and reliability of the data.
[0068] The merged data is stored in the data storage and management module to provide real-time data support for energy dispatch and optimization control.
[0069] (6) Model training Historical electricity consumption data, meteorological data, and user behavior data are used to train an energy demand forecasting model. The model's prediction accuracy and generalization ability are evaluated through methods such as cross-validation. Model parameters are continuously adjusted and the model structure optimized to improve the accuracy and reliability of predictions.
[0070] The trained model is stored in the SCADA system for real-time demand forecasting.
[0071] (7) Real-time prediction Based on real-time collected weather and user load data, energy demand forecasts are updated in real time. These forecasts serve as a crucial basis for energy dispatching, supporting intelligent dispatching algorithms in formulating optimal energy allocation and operational strategies to ensure a dynamic balance between energy supply and demand.
[0072] The prediction results are fed back to the SCADA system to optimize the generation and adjustment of scheduling strategies.
[0073] (8) Initialization of the optimized scheduling algorithm Based on the initial state of the multi-energy system, initialize the parameters of the genetic algorithm and particle swarm optimization algorithm, including population size, crossover rate, mutation rate, particle inertia weight, etc.
[0074] The output power and operating status of each energy device are encoded as the position of chromosomes or particles, and the population or particle swarm is initialized.
[0075] (9) Optimize the operation of the scheduling algorithm The fitness value of chromosomes or particles is calculated based on factors such as the matching degree between energy supply and demand, energy utilization efficiency, and cost.
[0076] For genetic algorithms, crossover, mutation, and selection operations are performed to generate a new population. Crossover involves exchanging some genes between two chromosomes to create a new chromosome; mutation randomly modifies certain genes on chromosomes to increase population diversity; and selection selects superior chromosomes based on their fitness values to enter the next generation of the population.
[0077] For particle swarm optimization (PSO), the velocity and position of particles are updated based on individual and swarm experience. By calculating the optimal individual and swarm positions, the particle's direction and velocity are adjusted to guide it toward the optimal solution.
[0078] By integrating genetic algorithms and particle swarm optimization algorithms, and through information sharing and co-evolution, the global and local search capabilities of the algorithms are improved, avoiding getting trapped in local optima and quickly finding the globally optimal energy allocation scheme.
[0079] (10) Scheduling strategy generation and execution Based on the output of the optimized scheduling algorithm, a specific energy scheduling strategy is generated. The scheduling strategy includes information such as the output power, operating status, and start / stop times of each energy device.
[0080] The scheduling strategy is sent to the SCADA system, which then controls the operation of each energy device to achieve intelligent scheduling and optimized control of energy.
[0081] (11) Dynamic adjustment mechanism The system is designed with a dynamic adjustment mechanism to adjust the dispatch strategy in real time based on real-time monitoring data and energy demand forecasts. When energy supply and demand change, the dispatch strategy is updated promptly to ensure stable system operation and efficient energy utilization.
[0082] Based on the actual operation of the system, the scheduling strategy is evaluated and feedback is provided to further optimize the scheduling algorithm and strategy.
[0083] This specific embodiment is merely an explanation of the invention and is not intended to limit the invention. After reading this specification, those skilled in the art can make modifications to this embodiment without contributing any inventive step, but as long as they are within the scope of protection of this invention, they are protected by patent law.
Claims
1. A method for intelligent scheduling and optimized control of SCADA systems with multi-energy integration, characterized in that, Includes the following steps: S1. Multi-energy system modeling: Establish multiple energy models including wind energy model, solar energy model, and traditional energy model, and integrate the wind energy model, solar energy model, and traditional energy model into a multi-energy system model to form a comprehensive model; S2. Model parameter acquisition: By installing sensors on wind power, solar power, and traditional energy equipment, wind speed, light intensity, power generation, and equipment status parameters are collected in real time as input data for the model. Combined with meteorological data and historical operating data, the model parameters are calibrated and optimized. S3. Data fusion and monitoring: Using multi-source data fusion technology, the input data of the model is fused, and the fused data is used as the input data for energy dispatch and optimization control. S4. Demand forecasting model construction: The demand forecasting model construction includes LSTM network structure and energy demand result prediction. An energy demand forecasting model considering weather, load, and time factors is constructed through the LSTM network structure. Time series analysis is used, combined with historical electricity consumption data, to predict the basic trend of future energy demand. At the same time, weather forecast data and user behavior data are introduced, and the demand forecasting model is corrected and optimized through machine learning algorithms. The energy demand forecasting results are updated in real time based on real-time collected weather data and user behavior data. S5. Design an optimized scheduling algorithm. Based on the energy demand forecast results, design an optimized scheduling algorithm, define the optimization direction, and ensure the dynamic balance between energy supply and demand through a dynamic adjustment mechanism.
2. The intelligent scheduling and optimization control method for SCADA systems oriented towards multi-energy integration as described in claim 1, characterized in that, Before step S3, the collected input data of the model is cleaned to remove noise and outliers.
3. The intelligent scheduling and optimization control method for SCADA systems oriented towards multi-energy integration as described in claim 2, characterized in that, The LSTM network structure includes: Input layer: Historical load data over 24 time steps; Hidden layers: 2 LSTM layers, 64 neurons per layer, activation function is tanh; Output layer: Fully connected layer, outputs the load forecast for the next 24 hours; Attention mechanism formula: ， ； in, Let be the hidden state of the LSTM at time step i. External features For feature weights, MLP is the input weights. and Neural networks for nonlinear feature fusion Hidden state for LSTM External features The correlation score.
4. The intelligent scheduling and optimization control method for SCADA systems oriented towards multi-energy integration as described in claim 3, characterized in that, Model training and validation: The demand forecasting model is trained using historical data, and the prediction accuracy and generalization ability of the model are evaluated through cross-validation. The model parameters are continuously adjusted and the model structure is optimized.
5. The intelligent scheduling and optimization control method for SCADA systems oriented towards multi-energy integration as described in claim 4, characterized in that, The optimized scheduling algorithm employs a fitness function F; ； in, =0.6, =0.4, which is the weighting coefficient; Total energy cost; The maximum allowable cost; For renewable energy utilization rate; Total energy utilization rate (renewable energy + traditional energy).
6. The intelligent scheduling and optimization control method for SCADA systems oriented towards multi-energy integration as described in claim 5, characterized in that, The dynamic adjustment mechanism includes: Renewable energy fluctuations: Output changes exceeding ±20% within 15 minutes; Load mutation: Demand changes exceeding ±15% within 5 minutes; Equipment failure: Any generator set is in an abnormal state.
7. The intelligent scheduling and optimization control method for SCADA systems oriented towards multi-energy integration as described in claim 1, characterized in that, The wind energy model among the various energy models is based on the wind speed-power curve and wind turbine parameters to establish a wind power generation model, wherein the output power of the wind turbine is... Based on the wind speed-power curve: = ； in, For real-time wind speed, For fan efficiency, , , These are the cut-in wind speed, rated wind speed, and cut-out wind speed, respectively; calibration mechanism: dynamically corrected quarterly by comparing historical wind speed data with measured power. .
8. The intelligent scheduling and optimization control method for SCADA systems oriented towards multi-energy integration according to claim 1, characterized in that, Photovoltaic power generation of the solar energy model among the various energy models calculate: =η·S·G·(1- ; Where η is the photoelectric conversion efficiency, S is the effective area of the photovoltaic panel, and G is the light intensity. For temperature coefficient, This refers to the real-time surface temperature of the photovoltaic panel. This is a reference temperature.
9. The intelligent scheduling and optimization control method for SCADA systems oriented towards multi-energy integration according to claim 1, characterized in that, The combustion cost function C(P) of the traditional energy model among the various energy models is: C(P)=a +bP+c; P represents the generator set output power, and a, b, and c represent combustion cost coefficients.