Intelligent flexible regulation and control terminal distributed energy management method and system

Abstract

The invention provides an intelligent flexible regulation and control terminal distributed energy management method and system, and relates to the technical field of power systems, and the method comprises the steps: carrying out the prediction based on multi-source data through a prediction model, so as to obtain a future load trend; according to the future load trend, performing cost-benefit analysis to generate a demand response scheduling strategy; and optimizing energy distribution according to the demand response adjustment strategy to generate a dynamic scheduling strategy.

Description

Technical Field

[0001] The present application belongs to the technical field, and specifically relates to a distributed energy management method and system for intelligent flexible control terminals. Background Art

[0002] With the growth of energy demand and increasing demands for environmental protection, smart grids and distributed energy systems (DES) have gradually become the core of modern energy management. However, traditional energy management systems, which rely primarily on centralized control and fixed scheduling strategies, are unable to cope with the increasingly complex fluctuations in electricity demand, the intermittent nature of renewable energy, and changes in user-side responses. Existing load forecasting models are typically based on a single data source and fail to fully utilize multi-source data for comprehensive analysis, resulting in low forecast accuracy. Furthermore, traditional cost-benefit analysis and scheduling strategy generation processes are relatively static and lack flexibility, making them unable to adapt to rapidly changing market conditions and user behavior.

[0003] Furthermore, existing distributed energy resources lack a robust, tightly coupled mechanism for collaboration, making it difficult to achieve a globally optimal configuration. This is especially true in the face of emergencies, where the system's response speed and robustness are limited. These challenges limit the efficiency and sustainability of energy systems, necessitating a comprehensive solution that integrates multi-source data, dynamically adjusts scheduling strategies, and optimizes energy distribution. Summary of the Invention

[0004] The present application provides a method and system for intelligent flexible control terminal distributed energy management to solve the problems of insufficient prediction accuracy, poor scheduling strategy flexibility and imperfect coordination mechanism between distributed energy resources in the existing technology.

[0005] The technical solutions adopted in this application are:

[0006] The present application provides a method for managing distributed energy resources in an intelligent and flexible control terminal, including:

[0007] Based on multi-source data, forecasting models are used to predict future load trends;

[0008] performing a cost-benefit analysis to generate a demand response dispatch strategy based on the future load trend;

[0009] Energy distribution is optimized according to the demand response adjustment strategy to generate a dynamic dispatch strategy.

[0010] According to one embodiment of the present application, the prediction based on multi-source data and the prediction model are used to obtain the future load trend, specifically:

[0011] The multi-source data includes: real-time electricity market prices, historical electricity consumption data, weather forecast information, equipment operating status and user behavior patterns;

[0012] Using edge computing devices to perform preliminary cleaning on the collected multi-source data, remove noise points and outliers, and extract key features of the operating data, including time series features, seasonal patterns, and diurnal cycle changes;

[0013] The short-term load forecasting model is used to perform forecasting to obtain the future load trend.

[0014] According to one embodiment of the present application, the short-term load forecasting model is used to perform forecasting to obtain the future load trend, specifically:

[0015] Training the short-term load forecasting model using the historical electricity consumption data;

[0016] The trained short-term load forecasting model is used for training to obtain the load conditions in the next few hours.

[0017] According to one embodiment of the present application, performing a cost-benefit analysis based on the future load trend to generate a demand response scheduling strategy is specifically as follows:

[0018] Generate an optimal solution using a linear programming or reinforcement learning algorithm to obtain the demand response scheduling strategy;

[0019] Or a reward function is designed to learn the optimal strategy through a trial-and-error process, gradually approaching the global optimal solution to obtain the demand response scheduling strategy.

[0020] According to one embodiment of the present application, the energy distribution is optimized according to the demand response adjustment strategy to generate a dynamic scheduling strategy, specifically:

[0021] Decompose the demand response dispatch strategy into specific tasks;

[0022] According to the characteristics and current status of each subsystem, reasonably allocate the tasks to the corresponding subsystem;

[0023] Through the Lagrange multipliers and penalty parameters in the ADMM algorithm, the solutions of all subsystems are ensured to gradually converge during the iteration process, and ultimately reach the global optimal solution;

[0024] At each time step, the optimization problem is re-solved, and the scheduling strategy is dynamically adjusted based on the future load trend and actual operating status.

[0025] An intelligent flexible control terminal distributed energy management system, comprising:

[0026] The prediction module is used to predict the future load trend based on multi-source data through the prediction model;

[0027] an analysis module, configured to perform a cost-benefit analysis based on the future load trend to generate a demand response scheduling strategy;

[0028] The optimization module is used to optimize energy distribution according to the demand response adjustment strategy to generate a dynamic scheduling strategy.

[0029] According to one embodiment of the present application, the prediction module is specifically:

[0030] The multi-source data includes: real-time electricity market prices, historical electricity consumption data, weather forecast information, equipment operating status and user behavior patterns;

[0031] Using edge computing devices to perform preliminary cleaning on the collected multi-source data, remove noise points and outliers, and extract key features of the operating data, including time series features, seasonal patterns, and diurnal cycle changes;

[0032] The short-term load forecasting model is used to perform forecasting to obtain the future load trend.

[0033] According to one embodiment of the present application, the short-term load forecasting model is used to perform forecasting to obtain the future load trend, specifically:

[0034] Training the short-term load forecasting model using the historical electricity consumption data;

[0035] The trained short-term load forecasting model is used for training to obtain the load conditions in the next few hours.

[0036] According to one embodiment of the present application, the analysis module is specifically:

[0037] Generate an optimal solution using a linear programming or reinforcement learning algorithm to obtain the demand response scheduling strategy;

[0038] Or a reward function is designed to learn the optimal strategy through a trial-and-error process, gradually approaching the global optimal solution to obtain the demand response scheduling strategy.

[0039] According to one embodiment of the present application, the optimization module is specifically:

[0040] Decompose the demand response dispatch strategy into specific tasks;

[0041] According to the characteristics and current status of each subsystem, reasonably allocate the tasks to the corresponding subsystem;

[0042] Through the Lagrange multipliers and penalty parameters in the ADMM algorithm, the solutions of all subsystems are ensured to gradually converge during the iteration process, and ultimately reach the global optimal solution;

[0043] At each time step, the optimization problem is re-solved, and the scheduling strategy is dynamically adjusted based on the future load trend and actual operating status.

[0044] Due to the adoption of the above technical solution, the beneficial effects achieved by this application are as follows:

[0045] By integrating advanced multi-source data forecasting models, dynamic cost-benefit analysis, and efficient distributed coordination mechanisms, this application significantly improves the accuracy of power demand forecasts and the flexibility of dispatch strategies, achieving the globally optimal configuration of distributed energy resources. Compared to existing technologies, this solution not only enhances the system's responsiveness and robustness, ensuring stable operation in the face of market fluctuations and emergencies, but also maximizes the utilization of clean energy, reduces operating costs, and promotes the achievement of sustainable development goals, thereby comprehensively improving the efficiency and reliability of smart grids and distributed energy systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0047] Figure 1 A flow chart of a method for managing distributed energy resources using an intelligent and flexible control terminal is provided in an embodiment of the present application. DETAILED DESCRIPTION

[0048] In order to more clearly illustrate the overall concept of the present application, a detailed description is given below in an illustrative manner in conjunction with the accompanying drawings.

[0049] The following description sets forth many specific details to facilitate a thorough understanding of the present application. However, the present application may also be implemented in other ways than those described herein, and therefore, the scope of protection of the present application is not limited by the specific embodiments disclosed below. It should be noted that the embodiments of the present application and the features of each embodiment may be combined with each other unless there is a conflict.

[0050] In this application, unless otherwise expressly specified and limited, a first feature "above" or "below" a second feature may be that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" 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 application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in an appropriate manner in any one or more embodiments or examples.

[0051] Example 1

[0052] like Figure 1 As shown, a method for managing distributed energy resources with intelligent and flexible control terminals includes:

[0053] Based on multi-source data, prediction models are used to obtain future load trends.

[0054] Specifically, predicting future load trends using a multi-source data forecasting model involves integrating data from various sources, including real-time electricity market prices, weather forecasts, historical electricity consumption data, equipment operating status, and user behavior patterns. Advanced machine learning algorithms (such as LSTM neural networks) are then used to combine this comprehensive information to accurately predict electricity demand and renewable energy output within the next few hours. This process not only relies on a single data source but also fully utilizes multi-source data for in-depth analysis, thereby improving the accuracy and reliability of the forecast. Specifically, the collected data is first cleaned and standardized to remove outliers and noise points to ensure data quality. Then, advanced forecasting models such as long short-term memory (LSTM) networks are used. These models can capture long-term dependencies in time series and are well-suited for power demand forecasting. In addition, the impact of weather conditions on renewable energy sources (such as photovoltaic and wind power) and the influence of market price fluctuations on scheduling decisions are fully considered in the forecast process, making the forecast results more accurate and accurate. The resulting future load trend forecast provides a solid foundation for subsequent cost-benefit analysis and demand response scheduling strategies, ensuring that the entire energy management system can make optimal decisions based on accurate forecast information.

[0055] For example, multi-source data integration:

[0056] Electricity market prices: Obtain current and historical electricity price information from power exchanges.

[0057] Weather Forecast Information: Obtain weather forecast data such as temperature, humidity, wind speed from meteorological services, especially focusing on their impact on photovoltaic power generation (e.g., solar panel efficiency) and wind power generation.

[0058] Historical Electricity Consumption Data: Obtain hourly electricity consumption records for the past few years from local grid operators, analyze electricity consumption patterns in different seasons and time periods.

[0059] Device Operating Status: Collect real-time operating parameters from various power generation stations, energy storage facilities, and major electricity-consuming devices, such as generator power output, battery charge and discharge levels, etc.

[0060] User Behavior Patterns: Collect user electricity habits through smart home platforms or demand response plans to understand their electricity usage tendencies at different times.

[0061] Data Cleaning and Standardization:

[0062] Pre-process all collected data to remove outliers and noise points, and convert all data to a unified format to ensure the effectiveness of subsequent analysis.

[0063] Prediction Model Construction and Training

[0064] Advanced Prediction Model Selection:

[0065] Use Long Short-Term Memory Network (LSTM), a deep learning algorithm suitable for processing time series data, which can capture long-term dependencies and is very suitable for power demand prediction.

[0066] Use the cleaned multi-source data as input features to build an LSTM model and train it using historical data. Adjust model parameters during training until satisfactory prediction accuracy is achieved.

[0067] Consider External Factors:

[0068] In model design, especially add weather conditions (such as sunlight intensity affecting photovoltaic power generation, wind speed affecting wind power generation) and market price fluctuations (such as higher peak period electricity prices may lead some users to reduce electricity consumption) as additional input features, making the prediction results more close to the actual situation.

[0069] Load Forecasting Execution and Application

[0070] Generate Future Load Trend Forecast:

[0071] Use the trained LSTM model combined with the latest multi-source data to predict the power demand change trend and renewable energy output within the next few hours (e.g., the next 24 hours).

[0072] The prediction results not only include total power demand, but also are subdivided into different types of energy supply (such as traditional energy, photovoltaic, wind power, etc.), so as to more accurately plan the scheduling strategy.

[0073] Provide decision support:

[0074] Based on the prediction results, develop dynamic demand response plans, such as adjusting the time period of non-critical loads, starting backup power, optimizing the charge and discharge mode of energy storage systems, etc., to ensure that user demand is met while minimizing operating costs and maximizing the use of clean energy.

[0075] Real-time monitoring and feedback control:

[0076] The system continuously monitors various indicators to ensure that all equipment operates according to the predetermined strategy and is prepared to respond to any unexpected situations; once deviations are found, corrective measures are taken immediately to maintain optimal operating conditions.

[0077] Further, more types of sensor data can be introduced: for example, temperature, humidity, wind speed, and other environmental parameters, as well as real-time monitoring data of equipment operating status, to further refine the input of the prediction model and improve the accuracy of the prediction.

[0078] Integrate multiple prediction methods: combine the advantages of statistical models, physical models, and machine learning models to develop hybrid prediction models, so as to select the most suitable prediction method in different scenarios and enhance the adaptability and robustness of the system.

[0079] Further, more fine-grained time resolution predictions can be achieved:

[0080] Minute-level or even second-level prediction: for critical loads or important facilities, develop high time resolution prediction models to provide more detailed power demand predictions, support faster response mechanisms and dynamic scheduling strategies.

[0081] Further, user behavior pattern analysis and personalized prediction can be introduced:

[0082] User-side data analysis: through clustering analysis of the electricity consumption behavior of a large number of users, identify different user groups and their typical electricity consumption patterns, and provide personalized power demand predictions for each user.

[0083] Demand response incentive design: design targeted demand response incentives based on user behavior patterns to encourage users to increase electricity consumption during off-peak hours, effectively balancing the load on the power grid.

[0084] Based on the future load trend, conduct a cost-benefit analysis to generate a demand response scheduling strategy.

[0085] Specifically, data input and preprocessing:

[0086] Integrate multi-source data: Based on future load trends previously obtained through advanced forecasting models, combined with multi-source data such as real-time electricity prices, weather forecasts, and equipment status, a comprehensive information foundation is provided for cost-benefit analysis.

[0087] Standardization processing: All collected data are cleaned and standardized to ensure data quality and consistency for use in subsequent economic model construction.

[0088] Economic model construction:

[0089] Objective function definition: Minimizing total operating costs is the objective function, which includes fixed costs (such as equipment investment), variable costs (such as fuel consumption and maintenance costs), and opportunity costs (such as missed power generation revenue). Environmental factors such as carbon emission costs also need to be considered.

[0090] Constraint setting: Consider physical limitations (such as maximum equipment output power), policies and regulations (such as emission standards), user agreements (such as time-of-use electricity price contracts), and other factors as constraints for the optimization problem to ensure the feasibility and legality of the solution.

[0091] Intelligent algorithm selection:

[0092] Linear Programming (LP) / Mixed Integer Linear Programming (MILP): Solve deterministic optimization problems, especially when future demand is known, and find the optimal solution.

[0093] Reinforcement Learning (RL): Designing reward functions to incentivize the system to take actions that are more conducive to cost savings and resource optimization. Learning optimal strategies through trial and error gradually approaches the global optimal solution.

[0094] Genetic Algorithm (GA): simulates the natural selection process, explores different solution spaces through gene recombination and mutation operations, and finds the best configuration that meets the constraints.

[0095] Generate a demand response dispatch strategy:

[0096] Dynamic pricing mechanism: Based on the forecast results, time-of-use electricity prices or real-time electricity price strategies are formulated to encourage users to use electricity during off-peak hours, thereby balancing the grid load.

[0097] Activate backup power sources: If power shortages are predicted and market prices are high, it is recommended to activate backup natural gas generators or other conventional energy facilities.

[0098] Energy storage management: Appropriately increase the discharge rate of energy storage batteries to supplement the power gap, while considering battery life and cost-effectiveness.

[0099] Load adjustment: Provide time-of-use electricity price information to end users through the smart home platform, encouraging them to increase electricity consumption during off-peak hours, thereby balancing the grid load.

[0100] Evaluation and Feedback:

[0101] Performance monitoring: Establish a closed-loop control system to monitor system output in real time and compare it with the expected target, and take corrective measures immediately if deviations are found.

[0102] Rolling Time Optimization (RTO): At each time step (for example, every 5 minutes), the optimization problem is re-solved, the latest prediction results and actual operating status are considered, and the scheduling strategy is dynamically adjusted.

[0103] Adaptive Regulation: When an abnormal situation is detected, the system can adjust its operating mode to restore a stable state. It supports remote diagnostic functions, allowing technicians to check and debug on-site equipment via an Internet connection.

[0104] User-side interaction and incentives:

[0105] Personalized recommendations: Provide personalized energy-saving suggestions and demand response plans based on users' electricity usage habits and historical behavior.

[0106] Incentive mechanism design: Design reasonable economic incentive mechanisms, such as electricity discounts and point rewards, to encourage users to actively participate in demand response activities and achieve a win-win situation.

[0107] Long-term planning and strategic decision support:

[0108] Seasonal and annual load forecasting: Develop forecasting tools suitable for long-term planning to provide a scientific basis for investment, construction, and renovation of energy infrastructure.

[0109] Policy simulation and impact assessment: Simulate changes in electricity demand under different policy scenarios, evaluate the impact of new policies on the energy market, and provide reference for governments and businesses to formulate relevant policies.

[0110] For example, consider a smart campus with diverse energy resources, including photovoltaic systems, wind turbines, natural gas power plants, battery storage, and a range of smart loads. The campus's energy management system needs to ensure a stable power supply while minimizing costs and maximizing the use of renewable energy. One morning, the system predicts peak electricity demand that afternoon, when photovoltaic and wind power output is expected to be insufficient.

[0111] Detailed process

[0112] Data collection and processing

[0113] Real-time data collection: Obtain real-time data from multiple channels, including current electricity prices, weather forecasts, equipment status, etc., and perform pre-processing.

[0114] Current electricity price: The real-time market electricity price is displayed during high-price periods.

[0115] Weather forecast: Light intensity is expected to decrease in the afternoon, with lower wind speeds, leading to reduced photovoltaic and wind power production.

[0116] Historical data: Electricity demand and renewable energy output under similar weather conditions in the past.

[0117] Equipment status: The current operating status of all power generation equipment and energy storage systems.

[0118] Load forecasting and cost-benefit analysis

[0119] Short-term load forecasting model: Using an LSTM neural network combined with the above data, we predict that electricity demand will increase significantly in the next few hours, especially between 3 pm and 6 pm.

[0120] Cost-Benefit Analysis:

[0121] Definition of objective function: Set the minimization of total operating cost as the objective function, taking into account fixed costs (such as equipment investment), variable costs (such as fuel consumption, maintenance costs) and opportunity costs (such as missed power generation benefits).

[0122] Constraint setting: Consider factors such as physical limitations (such as maximum output power of equipment), policies and regulations (such as emission standards), and user agreements (such as time-of-use electricity price contracts).

[0123] Demand response dispatch strategy generation

[0124] Dynamic pricing: Based on the forecast results, time-of-use electricity prices or real-time electricity price strategies are formulated to encourage users to use electricity during off-peak hours.

[0125] 1pm to 3pm: Offer preferential electricity prices to encourage users to start large electrical appliances or factory production lines early.

[0126] After 6 p.m.: Preferential electricity prices are offered again to attract users to postpone the use of certain non-critical loads.

[0127] Activate backup power: If power shortages are predicted and market prices are high, it is recommended to activate backup natural gas generator sets.

[0128] 3pm to 6pm: Natural gas generators are activated to ensure adequate power supply while avoiding high costs associated with peak electricity prices.

[0129] Energy storage management:

[0130] 9 a.m. to 1 p.m.: When photovoltaic power generation is large, charging of energy storage batteries is given priority.

[0131] 3pm to 6pm: Appropriately increase the discharge rate of energy storage batteries to supplement the power gap, while considering battery life and cost-effectiveness.

[0132] User-side demand response:

[0133] Smart home platform: Provide time-of-use electricity price information to end users through the smart home platform, encouraging them to increase electricity consumption during off-peak hours, thereby balancing the grid load.

[0134] Industrial users: For industrial enterprises that have signed demand response agreements, notices will be issued before peak hours, requiring them to adjust production plans and reduce electricity demand during peak hours.

[0135] Dynamic adjustment and optimization

[0136] Rolling Time Optimization (RTO): At each time step (for example, every 5 minutes), the optimization problem is re-solved, the latest prediction results and actual operating status are considered, and the scheduling strategy is dynamically adjusted.

[0137] Adaptive adjustment mechanism: When an abnormal situation is detected, the system can adjust its operating mode to restore a stable state. It supports remote diagnostic functions, allowing technicians to check and debug on-site equipment through an Internet connection.

[0138] Through the above-mentioned specific measures, the park can effectively alleviate the contradiction between electricity supply and demand during peak periods, reduce operating costs, and maximize the use of clean energy. Specifically:

[0139] Cost savings: By rationally arranging power generation and energy storage, the need to purchase high-priced electricity during peak hours is reduced, lowering overall operating costs.

[0140] Optimal resource allocation: Fully utilizing renewable energy sources such as photovoltaic and wind power, reducing dependence on traditional fossil fuels and improving energy efficiency.

[0141] User interaction: Time-of-use electricity prices encourage users to adjust their electricity usage habits, promote active user participation, and enhance the flexibility and robustness of the system.

[0142] Furthermore, a dynamic cost-benefit model can be introduced:

[0143] Real-time update of cost parameters: Based on the latest market prices, policy and regulatory changes, equipment maintenance status and other information, the various parameters in the cost-effectiveness model are updated in real time to ensure that the model always reflects the most accurate cost structure.

[0144] Introduce opportunity cost analysis: In addition to considering direct operating costs (such as fuel consumption and maintenance costs), opportunity costs such as missed power generation revenue should also be evaluated to comprehensively measure the impact of different scheduling decisions on overall economic benefits.

[0145] Furthermore, we can also introduce enhanced user participation and interactivity:

[0146] Personalized demand response plan: Based on the electricity consumption behavior patterns of different users, formulate personalized time-of-use electricity prices or real-time electricity price strategies to encourage users to increase electricity consumption during non-peak hours, thereby balancing the grid load.

[0147] Incentive mechanism design: Design diverse demand response incentives, such as electricity discounts and point rewards, to increase user participation, and continuously optimize the effectiveness of these incentives through data analysis.

[0148] Furthermore, refined constraint settings can be introduced:

[0149] Detailed physical limitation modeling: Accurately simulate the physical characteristics of each device, such as maximum output power and minimum startup time, to ensure that the scheduling strategy meets actual operating conditions and avoid equipment damage due to overload operation.

[0150] Environmental impact assessment: Incorporate factors such as carbon emissions and pollutant emissions into constraints, promote the priority scheduling of green energy, and promote the realization of sustainable development goals.

[0151] Furthermore, intelligent algorithm optimization and innovation can be introduced:

[0152] Application of reinforcement learning (RL): Using reinforcement learning algorithms to design reward functions, we incentivize the system to take actions that are more conducive to cost savings and resource optimization. Through a continuous trial-and-error process, we gradually approach the global optimal solution.

[0153] Genetic Algorithm (GA) Exploration: Through gene recombination and mutation operations, it explores different solution spaces to find the optimal configuration that meets all constraints, especially for the complex and changing power market environment.

[0154] Furthermore, rolling horizon optimization (RTO) and feedback control can be introduced:

[0155] Combining short-term and long-term: At each time step (for example, every 5 minutes), the optimization problem is re-solved, and the short-term prediction results and long-term planning goals are combined to dynamically adjust the scheduling strategy to ensure that the system can respond to changes in the short term and maintain stable operation in the long term.

[0156] Closed-loop control system: Establish a closed-loop control system to monitor system output in real-time and compare it with expected targets. Take corrective measures immediately if deviations are found to ensure effective implementation of scheduling strategies.

[0157] Further, multi-objective optimization can also be introduced:

[0158] Integrated evaluation system construction: In addition to minimizing total operating costs, other important indicators such as reliability, environmental performance, and service quality should also be considered. A multi-objective optimization model should be constructed to achieve the best trade-off between multiple objectives.

[0159] Dynamic weight adjustment: According to changes in different time periods and market demand, dynamically adjust the weights of each target to ensure that the scheduling strategy can meet both economic benefit requirements and social and environmental benefits.

[0160] Further, scenario simulation and risk assessment can also be introduced:

[0161] Optimization analysis under multiple scenarios: Simulate changes in power demand under different market conditions, weather conditions, and unexpected events to evaluate the effectiveness of various scheduling strategies and provide references for decision-making.

[0162] Risk management and emergency plan: Develop emergency plans in advance to ensure rapid response and recovery of system normal operation in emergency situations.

[0163] Further, data-driven continuous improvement can also be introduced:

[0164] Historical data review and learning: Regularly review historical data, analyze the actual effectiveness of scheduling strategies, summarize lessons learned, and continuously optimize cost-effectiveness models and scheduling algorithms.

[0165] User feedback mechanism: Establish a user feedback channel to collect users' opinions and suggestions on demand response plans, and timely adjust and improve relevant strategies to improve user satisfaction and participation.

[0166] According to the demand response adjustment strategy, optimize energy distribution to generate a dynamic scheduling strategy.

[0167] Specifically, the application of distributed coordination mechanism:

[0168] ADMM (Alternating Direction Method of Multipliers) algorithm: Use the ADMM algorithm to solve the optimal allocation problem of distributed energy resources to ensure that the collaboration between subsystems maximizes overall benefits. Each subsystem independently solves its local optimal solution according to the received demand response instructions and maintains consistency through shared auxiliary variables (such as Lagrange multipliers).

[0169] P2P communication protocol: This allows for direct information exchange between SFCTs, reducing the burden on the central control unit while improving system robustness and flexibility. Communication between all devices follows a unified message format, ensuring efficient information transfer and processing.

[0170] Green energy priority dispatch:

[0171] Clean energy prioritization: Develop rules that prioritize the use of zero-carbon renewable energy (such as photovoltaic and wind power) and, when necessary, activate backup conventional energy facilities. This will not only help reduce carbon emissions but also fully utilize natural resources and reduce dependence on fossil fuels.

[0172] User-side interaction: Smart home platforms provide end users with time-of-use electricity price information, encouraging them to increase electricity consumption during off-peak hours, thereby balancing the grid load. This interactive mechanism not only increases user participation but also effectively alleviates power pressure during peak hours.

[0173] Real-time monitoring and feedback control:

[0174] SCADA system integration: Connect all devices to the SCADA system for remote monitoring and automated control, ensuring the system operates according to pre-defined policies. The SCADA system can collect and analyze the operating status of each subsystem in real time, providing data support for subsequent decision-making.

[0175] Adaptive adjustment mechanism: When an abnormality is detected, the system can automatically adjust its operating mode to restore stability. It also supports remote diagnostics, allowing technicians to inspect and debug on-site equipment via an internet connection. This mechanism ensures the system's self-healing capabilities and ability to respond to emergencies.

[0176] Rolling Time Optimization (RTO):

[0177] Dynamically adjust the scheduling strategy: At each time step (e.g., every 5 minutes), the optimization problem is re-solved, taking into account the latest forecast results and actual operating status, and the scheduling strategy is dynamically adjusted. This rolling optimization method can quickly respond to changes and ensure that the system is always in optimal operating condition.

[0178] Feedback loops: Establish a closed-loop control system to monitor system output in real time and compare it with the expected target, taking corrective measures immediately if deviations are detected. This helps maintain system stability and efficiency and ensures the effective execution of scheduling strategies.

[0179] Performance evaluation and improvement:

[0180] Periodic Performance Evaluation: Regularly assess the system's performance in terms of scheduling effectiveness, cost savings, environmental impact, and other aspects. Adjust the scheduling algorithms and optimization model parameters as needed based on the evaluation results. Through continuous improvement, continuously improve the system's operational efficiency and economic benefits.

[0181] User Feedback Mechanism: Collect users' opinions and suggestions on demand response plans, and timely adjust and improve relevant strategies to improve users' satisfaction and participation.

[0182] Multi-objective Optimization:

[0183] Comprehensive Evaluation System Construction: In addition to minimizing total operating costs, other important indicators such as reliability, environmental performance, and service quality should also be considered. A multi-objective optimization model is constructed to achieve the best trade-off between multiple objectives.

[0184] Dynamic Weight Adjustment: According to the changes of different time periods and market demand, dynamically adjust the weights of each target to ensure that the scheduling strategy can meet the requirements of economic benefits, and also consider social and environmental benefits.

[0185] For example, small urban area smart grid

[0186] Input of demand response adjustment strategy

[0187] Future Load Trend Forecast: Based on multi-source data (such as weather forecast, historical electricity consumption data, market electricity price, etc.) and advanced machine learning algorithms (such as LSTM neural network), it is predicted that the electricity demand will increase significantly within the next 24 hours, especially between 5 pm and 9 pm.

[0188] Cost-benefit analysis results: Through economic models and intelligent algorithms, the optimal cost-saving scheme is determined, including starting standby natural gas generators, appropriately increasing the discharge rate of energy storage batteries, and encouraging users to increase electricity consumption during off-peak hours.

[0189] Application of distributed coordination mechanism

[0190] Selection of distributed optimization algorithm

[0191] ADMM (Alternating Direction Method of Multipliers): Use ADMM algorithm to solve the optimization configuration problem of distributed energy resources, ensure the collaboration between each subsystem to maximize the overall benefit. Each subsystem independently solves its local optimal solution according to the received demand response instructions, and maintains consistency through shared auxiliary variables.

[0192] P2P communication protocol

[0193] Direct information exchange: Allows each SFCT to exchange information directly with each other, reducing the burden on the central control unit while improving the robustness and flexibility of the system. Communication between all devices follows a unified message format for efficient information transfer and processing.

[0194] Generation of dynamic scheduling strategies

[0195] Green energy priority dispatch

[0196] Clean energy prioritization: Establish rules to prioritize the use of zero-carbon renewable energy sources (such as photovoltaic and wind power). Since the weather forecast indicates abundant daylight and high photovoltaic power generation, this energy source will be prioritized to meet part of the daytime demand.

[0197] User-side interaction

[0198] Time-of-use electricity price information release: Smart home platforms provide end users with time-of-use electricity price information, encouraging them to increase electricity consumption during off-peak hours (e.g., 1:00 AM to 7:00 AM), thereby balancing the grid load. For example, electricity discounts or points rewards can be offered to encourage users to use appliances such as washing machines and dishwashers during off-peak hours.

[0199] Start backup power supply and energy storage management

[0200] Start the backup natural gas generator set: Based on the predicted evening peak load, it is recommended to start the backup natural gas generator set around 4 pm and gradually increase its output power to ensure sufficient power supply during the evening peak period.

[0201] Energy storage battery charge and discharge management: Appropriately increase the discharge rate of energy storage batteries to supplement power shortages, especially during peak hours in the evening. At the same time, when there is excess photovoltaic production capacity during the day, excess electricity is stored to prepare for high demand in the evening.

[0202] Real-time monitoring and feedback control

[0203] SCADA system integration: Connect all devices to the SCADA system for remote monitoring and automated control, ensuring the system operates according to pre-defined policies. The system continuously monitors various indicators and takes immediate corrective action if any deviation is detected.

[0204] Rolling Time Optimization (RTO): At each time step (e.g., every 5 minutes), the optimization problem is re-solved, taking into account the latest forecast results and actual operating status, and dynamically adjusting the scheduling strategy. For example, if real-time monitoring indicates a device failure, the system immediately adjusts the operating parameters of other devices to compensate for the loss.

[0205] Performance evaluation and improvement

[0206] Regular evaluation: Regularly evaluate the performance of the entire system, analyzing scheduling effectiveness, cost savings, environmental impact, and other aspects. Based on the evaluation results, the scheduling algorithm and optimization model parameters should be adjusted in a timely manner. For example, analyze whether the charging and discharging frequency of the energy storage battery is reasonable and whether there is room for optimization.

[0207] Adaptive adjustment mechanism: When an abnormal situation is detected, the system can adjust its operating mode to restore a stable state. It supports remote diagnostic functions, allowing technicians to check and debug on-site equipment through an Internet connection.

[0208] Specific case display

[0209] Imagine one morning when the peak electricity demand period in the park is approaching, and the photovoltaic power generation is insufficient, and the wind power generation is also affected by the weather and cannot operate at full power. After the system learns this through real-time data collection:

[0210] Data Collection and Processing:

[0211] Acquire real-time data from multiple channels, including current electricity prices, weather forecasts, equipment status, etc., and perform preprocessing.

[0212] Prediction model calculation:

[0213] Based on historical data and real-time information, a machine learning algorithm was used to predict electricity demand in the coming hours. The short-term load forecasting model (LSTM neural network) predicted that electricity demand would increase significantly in the coming hours, especially between 5 pm and 9 pm.

[0214] Demand response scheduling strategy generation:

[0215] Economic Model Solution: Apply linear programming or reinforcement learning algorithms, combining forecast results with existing resource availability, to generate an optimal scheduling plan. Recommendations include activating backup natural gas generators, appropriately increasing the discharge rate of energy storage batteries, and distributing time-of-use electricity pricing information through smart home platforms to encourage users to increase electricity consumption during off-peak hours.

[0216] Application of distributed coordination mechanism:

[0217] Task allocation: Based on the demand response scheduling strategy, tasks are reasonably allocated to each subsystem, such as starting the backup natural gas generator set and adjusting the charge and discharge mode of the energy storage battery.

[0218] Consistency check: Ensure that the solutions of all subsystems are consistent. If they are inconsistent, adjust the Lagrange multiplier and penalty parameters to encourage the subsystem to converge to the global optimal solution as quickly as possible.

[0219] Feedback mechanism: Establish a closed-loop control system to monitor system output in real time and compare it with the expected target, and take corrective measures immediately if deviations are found.

[0220] Performance Monitoring and Feedback Loops:

[0221] The system continuously monitors various indicators to ensure that all devices operate according to predetermined strategies and are prepared to respond to any emergencies.

[0222] Rolling Time Optimization (RTO): At each time step (for example, every 5 minutes), the optimization problem is re-solved, the latest prediction results and actual operating status are considered, and the scheduling strategy is dynamically adjusted.

[0223] System evaluation and improvement:

[0224] Regularly evaluate the performance of the entire system, analyze the scheduling effect, cost savings, environmental impact and other aspects, and adjust the scheduling algorithm and optimize the model parameters in a timely manner according to the evaluation results.

[0225] Furthermore, enhancements to the distributed coordination mechanism can be introduced:

[0226] More efficient communication protocol: Optimize P2P communication protocol to ensure the speed and reliability of information transmission, reduce latency and improve the real-time responsiveness of the system.

[0227] Multi-layer coordination architecture: Build a multi-level coordination mechanism to allow subsystems at different levels to make different degrees of autonomous decisions based on their characteristics and task importance, thereby achieving more flexible and efficient resource allocation.

[0228] Furthermore, adaptive regulation and feedback control can be introduced:

[0229] Intelligent feedback mechanism: Introduce an intelligent feedback control system to monitor the operating status of each subsystem in real time and compare it with the expected target. Once a deviation is found, corrective measures will be taken immediately to ensure that the system is always in the best operating state.

[0230] Rolling Time Optimization (RTO): At each time step (for example, every 5 minutes), the optimization problem is re-solved, and the scheduling strategy is dynamically adjusted based on the latest prediction results and actual operating status to ensure continuous optimization of the system.

[0231] Furthermore, green energy priority scheduling can be introduced:

[0232] Clean energy prioritization: Establish rules to give priority to the use of zero-carbon emission renewable energy (such as photovoltaic power generation and wind power generation), and activate backup traditional energy facilities when necessary to maximize the use of clean energy and reduce carbon footprint.

[0233] Energy storage system optimization management: Through precise charging and discharging strategies, the efficiency of the energy storage system is maximized, especially in situations where the power supply is unstable or the market price fluctuates greatly, ensuring that the energy storage system can provide support when it is most needed.

[0234] Furthermore, user-side interaction and incentive mechanisms can be introduced:

[0235] Personalized demand response plan: Based on the electricity consumption behavior patterns of different users, formulate personalized time-of-use electricity prices or real-time electricity price strategies to encourage users to increase electricity consumption during non-peak hours, thereby balancing the grid load.

[0236] User incentive design: Design diverse demand response incentives, such as electricity discounts and point rewards, to increase user participation, and continuously optimize the effectiveness of these incentives through data analysis.

[0237] Furthermore, multi-objective optimization and comprehensive evaluation can be introduced:

[0238] Construction of a comprehensive evaluation system: In addition to minimizing the total operating cost, other important indicators should also be considered, such as reliability, environmental performance, and service quality, to build a multi-objective optimization model to achieve the best trade-off between multiple objectives.

[0239] Dynamic weight adjustment: Dynamically adjust the weight of each target according to changes in different time periods and market demand to ensure that the scheduling strategy can meet the requirements of economic benefits while taking into account social and environmental benefits.

[0240] Furthermore, scenario simulation and risk assessment can be introduced:

[0241] Optimization analysis under various scenarios: Simulate changes in power demand under different market conditions, weather conditions, and emergencies, evaluate the effectiveness of various scheduling strategies, and provide reference for decision-making.

[0242] Risk management and emergency plans: Develop emergency plans in advance for possible risk events to ensure rapid response and restoration of normal system operations in emergency situations.

[0243] Furthermore, data-driven continuous improvement can be introduced:

[0244] Historical data review and learning: Regularly review historical data, analyze the actual effects of scheduling strategies, summarize lessons learned, and continuously optimize cost-benefit models and scheduling algorithms.

[0245] User feedback mechanism: Establish user feedback channels to collect users’ opinions and suggestions on demand response plans, adjust and improve relevant strategies in a timely manner, and improve user satisfaction and participation.

[0246] Furthermore, the application of advanced optimization algorithms can also be introduced:

[0247] Application of reinforcement learning (RL): Using reinforcement learning algorithms to design reward functions, we incentivize the system to take actions that are more conducive to cost savings and resource optimization. Through a continuous trial-and-error process, we gradually approach the global optimal solution.

[0248] Genetic Algorithm (GA) Exploration: Through gene recombination and mutation operations, it explores different solution spaces to find the optimal configuration that meets all constraints, especially for the complex and changing power market environment.

[0249] Furthermore, it is possible to introduce and integrate emerging technologies and innovative applications:

[0250] Internet of Things (IoT) and edge computing: Leveraging IoT technology and edge computing capabilities, real-time data collection and local pre-processing can be achieved, reducing the burden on central servers and improving system response speed.

[0251] Blockchain technology: Explore the application of blockchain technology in energy trading, data verification and transparency improvement to enhance the credibility and security of the system.

[0252] Refined task allocation: Based on the characteristics and current status of each subsystem, tasks are reasonably allocated to ensure that each subsystem receives clear operating instructions, including when to execute, what to execute, and the expected results.

[0253] Consistency maintenance: Through the Lagrange multipliers and penalty parameters in the ADMM algorithm, the solutions of all subsystems are ensured to gradually converge during the iteration process, ultimately reaching the global optimal solution.

[0254] Dynamic Adjustment: When an abnormal situation is detected, the system is able to adjust its operating mode to restore a stable state. It supports remote diagnostic functions, allowing technicians to check and debug on-site equipment through an Internet connection.

[0255] In some embodiments provided herein, the prediction model is used to predict future load trends based on multi-source data, specifically:

[0256] The multi-source data includes: real-time electricity market prices, historical electricity consumption data, weather forecast information, equipment operating status and user behavior patterns;

[0257] Using edge computing devices to perform preliminary cleaning on the collected multi-source data, remove noise points and outliers, and extract key features of the operating data, including time series features, seasonal patterns, and diurnal cycle changes;

[0258] The short-term load forecasting model is used to perform forecasting to obtain the future load trend.

[0259] Specifically, the integration and preprocessing of multi-source data

[0260] Technical feature description:

[0261] The multi-source data includes: real-time electricity market prices, historical electricity consumption data, weather forecast information, equipment operating status and user behavior patterns.

[0262] Detailed technical means:

[0263] Data collection channels:

[0264] Real-time electricity market prices: Obtain the latest electricity price information from the electricity trading market through API interfaces or dedicated communication protocols.

[0265] Historical electricity usage data: Extract electricity usage records from the past few years from the energy management system (EMS) or SCADA system to ensure that the data covers a sufficient time span.

[0266] Weather forecast information: Use the weather service API to obtain environmental parameters such as temperature, humidity, wind speed, as well as short-term and long-term weather forecasts.

[0267] Equipment operating status: Through Internet of Things (IoT) sensors and monitoring systems, the operating status of key equipment such as generator sets, energy storage systems, and transformers can be monitored in real time.

[0268] User behavior patterns: Collect information such as users’ electricity usage habits and device usage through smart home platforms or user-side terminal devices.

[0269] Data integration platform:

[0270] Use big data platforms (such as Hadoop and Spark) to centrally manage and store data from different channels to ensure data consistency and integrity.

[0271] The data format is standardized, using a unified data structure and encoding method to facilitate subsequent processing and analysis.

[0272] Preliminary cleaning and key feature extraction

[0273] Technical feature description:

[0274] Use edge computing devices to perform preliminary cleaning on the collected multi-source data, remove noise points and outliers, and extract key features of the operating data, which include time series features, seasonal patterns, and diurnal cycle changes.

[0275] Detailed technical means:

[0276] Edge computing devices:

[0277] Data processing nodes deployed locally can process large amounts of data in real time, reducing the burden on central servers and improving response speed.

[0278] Edge computing devices are equipped with high-performance processors and sufficient memory resources to support complex algorithm operations.

[0279] Data cleaning:

[0280] Remove noise points: Apply statistical methods (such as Z-score, IQR) to identify and eliminate data points that are outside the normal range.

[0281] Handling missing values: Use interpolation methods (such as linear interpolation and spline interpolation) to fill missing data and ensure the continuity of the time series.

[0282] Outlier detection: Combine machine learning algorithms (such as Isolation Forest and LOF) to identify and process abnormal data to prevent it from affecting subsequent analysis results.

[0283] Feature extraction:

[0284] Time series features: Extract time-related features such as timestamps and time intervals to provide a basis for subsequent predictions.

[0285] Seasonal patterns: Analyze the periodic components in the data through Fourier transform or wavelet transform to identify seasonal patterns on different time scales such as years, months, and weeks.

[0286] Daily Cycle Variation: Analyze the daily variation of electricity consumption and capture the differences between weekdays and weekends, and daytime and nighttime.

[0287] Application of short-term load forecasting models

[0288] Technical feature description:

[0289] The short-term load forecasting model is used to perform forecasting to obtain the future load trend.

[0290] Detailed technical means:

[0291] Choose an appropriate forecasting model:

[0292] Long Short-Term Memory (LSTM): Suitable for capturing long-term dependencies in time series, it is particularly well-suited for power demand forecasting. LSTM can automatically learn complex patterns in data without the need for manual feature design.

[0293] Prophet: A time series forecasting tool developed by Facebook that excels at processing data with significant seasonality and holiday effects.

[0294] ARIMA / Seasonal ARIMA: A classic statistical model suitable for time series data that is stationary or tends to be stationary after difference processing.

[0295] Model training and validation:

[0296] Data splitting: Divide the data into training, validation, and test sets to ensure that the model can perform well on unknown data.

[0297] Hyperparameter tuning: Use methods such as grid search, random search, or Bayesian optimization to find the best combination of model parameters.

[0298] Cross-validation: K-fold cross-validation is used to evaluate model performance and avoid overfitting.

[0299] Prediction result output:

[0300] Short-term forecast: Generate power demand forecasts for the next few hours to days to provide a basis for scheduling decisions.

[0301] Visual display: The forecast results are presented intuitively through charts and dashboards to help operators understand and apply them.

[0302] In some embodiments provided in this application, the short-term load forecasting model is used to forecast and obtain the future load trend, specifically:

[0303] Training the short-term load forecasting model using the historical electricity consumption data;

[0304] The trained short-term load forecasting model is used for training to obtain the load conditions in the next few hours.

[0305] Specifically, historical electricity consumption data is used to train the short-term load forecasting model

[0306] Technical feature description:

[0307] The short-term load forecasting model is trained using the historical electricity consumption data.

[0308] Detailed technical means:

[0309] Data preparation and preprocessing:

[0310] Data cleaning: Use statistical methods (such as Z-score, IQR) to remove noise points and outliers to ensure the accuracy and consistency of the data.

[0311] Missing value processing: Use interpolation methods (such as linear interpolation and spline interpolation) to fill in missing data to ensure the continuity of the time series.

[0312] Feature Engineering: Extract time-related features such as timestamps and time intervals, identify and add seasonal patterns (year, month, week) and diurnal variations (weekdays vs. weekends, daytime vs. nighttime) to enhance the model’s predictive power.

[0313] Choose an appropriate forecasting model:

[0314] Long Short-Term Memory (LSTM): Suitable for capturing long-term dependencies in time series, it is particularly well-suited for power demand forecasting. LSTM can automatically learn complex patterns in data without the need for manual feature design.

[0315] Prophet: A time series forecasting tool developed by Facebook that excels at processing data with significant seasonality and holiday effects.

[0316] ARIMA / Seasonal ARIMA: A classic statistical model suitable for time series data that is stationary or tends to be stationary after difference processing.

[0317] Model training:

[0318] Data Split: Split historical electricity consumption data into training, validation, and test sets. Typically, the ratio is 70% training, 15% validation, and 15% test.

[0319] Hyperparameter tuning: Use methods such as grid search, random search, or Bayesian optimization to find the optimal combination of model parameters. For example, for an LSTM model, you can adjust hyperparameters such as the number of hidden layer nodes, learning rate, and batch size.

[0320] Cross-validation: K-fold cross-validation is used to evaluate model performance and avoid overfitting. A common k-value of 5 or 10 is used to ensure that the model performs consistently across different subsets.

[0321] Model Evaluation:

[0322] Error metrics: Use metrics such as mean squared error (MSE), mean absolute error (MAE), and root mean square error (RMSE) to evaluate the prediction accuracy of the model.

[0323] Visual analysis: By plotting a comparison chart of actual values ​​and predicted values, the model's prediction effect is intuitively displayed to help identify potential problems.

[0324] Use the trained short-term load forecasting model to make forecasts

[0325] Technical feature description:

[0326] The trained short-term load forecasting model is used to perform forecasting to obtain the load conditions in the next few hours.

[0327] Detailed technical means:

[0328] Real-time data input:

[0329] Data collection: Acquire the latest data from multiple sources such as real-time power market prices, weather forecast information, equipment operating status, and user behavior patterns, and perform preliminary cleaning and preprocessing.

[0330] Feature extraction: Extract key features of real-time data, including time series characteristics, seasonal patterns, and diurnal variations, ensuring that the input data is consistent with the training data format.

[0331] Model predictions:

[0332] Short-term forecasting: Use the trained short-term load forecasting model to predict the power demand in the next few hours. The specific steps are as follows:

[0333] Input real-time data: Use the latest collected real-time data as model input to ensure that forecasts are based on the latest market conditions and environmental factors.

[0334] Generate forecast results: The model outputs load forecast values ​​for the next few hours, providing detailed time series forecasts.

[0335] Rolling Forecast: Using the Rolling Time Optimization (RTO) method, the optimization problem is re-solved at each time step (for example, every 5 minutes), and the scheduling strategy is dynamically adjusted based on the latest forecast results and actual operating status.

[0336] Prediction result processing:

[0337] Error correction: Correct the prediction results based on actual operational data to improve prediction accuracy. For example, this can be done by using a Kalman filter or other adaptive algorithms to continuously update the prediction model.

[0338] Visualization: Charts and dashboards provide intuitive visualization of forecast results, helping operators understand and apply them. Multiple visualization methods, such as line charts and bar charts, are supported, facilitating quick interpretation of data.

[0339] Feedback mechanism:

[0340] Performance monitoring: Establish a closed-loop control system to monitor system output in real time and compare it with the expected target, and take corrective measures immediately if deviations are found.

[0341] Continuous improvement: Regularly review historical data, analyze the actual effects of scheduling strategies, summarize lessons learned, and continuously optimize cost-benefit models and scheduling algorithms.

[0342] In some embodiments provided herein, the cost-benefit analysis is performed based on the future load trend to generate a demand response scheduling strategy, specifically:

[0343] Generate an optimal solution using a linear programming or reinforcement learning algorithm to obtain the demand response scheduling strategy;

[0344] Or a reward function is designed to learn the optimal strategy through a trial-and-error process, gradually approaching the global optimal solution to obtain the demand response scheduling strategy.

[0345] Specifically, linear programming or reinforcement learning algorithms are used to generate optimal solutions

[0346] Technical feature description:

[0347] A linear programming (LP) or reinforcement learning (RL) algorithm is used to generate an optimal solution to obtain the demand response scheduling strategy.

[0348] Detailed technical means:

[0349] Linear Programming (LP)

[0350] Problem Modeling:

[0351] Objective function: Set the minimization of total operating costs as the objective function, including fixed costs (such as equipment investment), variable costs (such as fuel consumption, maintenance costs) and opportunity costs (such as missed power generation benefits). For example, the formula is:

[0352]

[0353] Among them C i 、V j and O k represent fixed cost, variable cost and opportunity cost respectively, and x i 、y j and z k is the corresponding decision variable.

[0354] Constraints: Consider factors such as physical limitations (such as the maximum output power of the equipment), policies and regulations (such as emission standards), and user agreements (such as time-of-use electricity price contracts). For example, physical limitations can be expressed as:

[0355]

[0356] Among them, P max is the maximum output power of the device, P t is the actual output power at time t.

[0357] Solution:

[0358] Optimization solver: Use an efficient linear programming solver (such as CPLEX or Gurobi) to solve the above model and find the optimal solution that satisfies all constraints.

[0359] Dynamic adjustment: Combined with rolling horizon optimization (RTO), the optimization problem is re-solved at each time step, taking into account the latest prediction results and actual operating status, and dynamically adjusting the scheduling strategy.

[0360] Reinforcement Learning (RL)

[0361] Environmental Modeling:

[0362] State space: defines the state of the system, including current power demand, renewable energy output, energy storage system status, etc. For example, state S t It can be expressed as:

[0363] s t =(D t , R t , S t )

[0364] Among them D t is the power demand, R t is the renewable energy output, S t is the state of the energy storage system.

[0365] Action space: defines available actions, such as starting backup power, adjusting energy storage charge and discharge rates, etc. For example, action a t It can be expressed as:

[0366] a t =(start_backup,adjust_storage)

[0367] Reward function design:

[0368] Immediate reward: Define the immediate reward r t , encouraging the system to take actions that are conducive to cost savings and resource optimization. For example, immediate rewards can be calculated based on indicators such as cost savings and reduced carbon emissions.

[0369] Long-term rewards: A discount factor γ is introduced to balance short-term and long-term rewards, ensuring that the system not only focuses on immediate economic benefits but also takes long-term development into consideration.

[0370] Trial and error learning:

[0371] Q-learning or DQN: Using algorithms such as Q-learning or Deep Q Network (DQN), the algorithm gradually approaches the global optimal solution by continuously trying different combinations of actions. After each action is taken, the Q value or network parameters are updated based on the actual feedback.

[0372] Exploration and Exploitation: Adopting an ∈-greedy strategy, it explores more possibilities of different actions in the early stages and gradually increases the frequency of choosing the best known action as the learning process progresses.

[0373] Strategy improvements:

[0374] Strategy evaluation: Regularly evaluate the effectiveness of the current strategy, analyze its performance in different scenarios, and adjust the reward function and learning parameters based on the evaluation results.

[0375] Policy iteration: The strategy is continuously improved through multiple iterations, eventually converging to a stable and efficient scheduling solution.

[0376] Design a reward function and learn the optimal strategy through trial and error

[0377] Technical feature description:

[0378] A reward function is designed, and the optimal strategy is learned through a trial-and-error process, gradually approaching the global optimal solution to obtain the demand response scheduling strategy.

[0379] Detailed technical means:

[0380] Reward function design:

[0381] Multi-objective comprehensive evaluation: In addition to direct economic costs, it also includes reliability, environmental performance, service quality, and other aspects. For example, the reward function can be expressed as:

[0382] r t =w1·cost_saving t +w2·reliability t +w3·environmental_impact t +w4·service_quality t

[0383] Among them, w1, w2, w3, and w4 are the weights of each target, which can be dynamically adjusted according to specific needs.

[0384] Trial and error learning process:

[0385] Initialization strategy: Initialize with a random or simple heuristic strategy, gradually accumulate experience and optimize.

[0386] Action selection: At each time step t, according to the current state s t And the learned strategy π(s), choose an action a t .

[0387] Environmental feedback: After executing the selected action, observe the feedback from the environment (i.e. the new state st+1 and instant rewards t , and record it.

[0388] Strategy update: Based on the collected data, update the strategy π(s) so that it can make better choices when encountering similar states in the future.

[0389] Continuous improvement and verification:

[0390] Simulation testing: Conduct a large number of simulation tests in a virtual environment to verify the effectiveness and stability of the learned strategies.

[0391] Real-world deployment: Deploy fully tested strategies into real-world environments, monitor their performance in real time, and further adjust and optimize based on actual conditions.

[0392] In some embodiments provided herein, the energy distribution is optimized according to the demand response adjustment strategy to generate a dynamic scheduling strategy, specifically:

[0393] Decompose the demand response dispatch strategy into specific tasks;

[0394] According to the characteristics and current status of each subsystem, reasonably allocate the tasks to the corresponding subsystem;

[0395] Through the Lagrange multipliers and penalty parameters in the ADMM algorithm, the solutions of all subsystems are ensured to gradually converge during the iteration process, and ultimately reach the global optimal solution;

[0396] At each time step, the optimization problem is re-solved, and the scheduling strategy is dynamically adjusted based on the future load trend and actual operating status.

[0397] Specifically, the demand response scheduling strategy is decomposed into specific tasks

[0398] Technical feature description:

[0399] Decompose the demand response dispatch strategy into specific tasks.

[0400] Detailed technical means:

[0401] Task definition: Identify and define independently executable tasks, such as starting or stopping certain equipment, adjusting power generation, and regulating the charging and discharging of energy storage systems. Each task should have a clear goal (e.g., reducing power consumption during peak hours), constraints (e.g., not exceeding the maximum power limit of the equipment), and expected results (e.g., cost savings, efficiency improvements).

[0402] Prioritize tasks: Sort tasks by importance and urgency to ensure critical tasks are addressed first. This can be achieved by assigning weights or scoring mechanisms, such as impact on overall objectives (e.g., cost savings, environmental benefits) or time sensitivity (e.g., tasks that must be completed within a specific timeframe).

[0403] According to the characteristics and current status of each subsystem, the tasks are reasonably assigned to the corresponding subsystems.

[0404] Technical feature description:

[0405] According to the characteristics and current status of each subsystem, the tasks are reasonably allocated to the corresponding subsystems.

[0406] Detailed technical means:

[0407] Subsystem assessment: Conduct a comprehensive assessment of each subsystem (such as power stations, energy storage devices, user-end equipment, etc.) to understand its capabilities (such as maximum output power and response speed), operating status (such as whether it is in the maintenance period), and its interaction with other systems.

[0408] Task Matching Algorithms: Develop intelligent algorithms to match specific tasks with the most appropriate subsystems. These algorithms should consider multiple factors, including but not limited to task requirements, subsystem availability and performance metrics, and potential synergies. For example, rule-based approaches or machine learning models can be used for prediction and decision-making.

[0409] Resource Coordination Platform: Build a centralized resource coordination platform that monitors the status of all subsystems in real time and dynamically adjusts task allocation accordingly. It should also be flexible enough to allow for manual intervention or custom settings to accommodate special scheduling needs.

[0410] Through the Lagrange multiplier and penalty parameter in the ADMM algorithm, the solutions of all subsystems are ensured to gradually converge during the iteration process, and finally the global optimal solution is reached.

[0411] Technical feature description:

[0412] Through the Lagrange multipliers and penalty parameters in the ADMM algorithm, the solutions of all subsystems are ensured to gradually converge during the iteration process and eventually reach the global optimal solution.

[0413] Detailed technical means:

[0414] Alternating Direction Method of Multipliers (ADMM): This is an efficient distributed optimization algorithm suitable for large-scale problems, especially when the problem can be naturally divided into several smaller subproblems. The core idea of ​​ADMM is to transform the original problem into a series of more solvable subproblems. By introducing auxiliary variables and Lagrange multipliers, the solution of these subproblems is guaranteed to converge to a uniform solution.

[0415] Lagrange multiplier update: In each iteration, the Lagrange multiplier is updated based on the difference between the current solutions. This process helps correct inconsistencies between subsystems and push them towards a common direction. The formula is usually expressed as:

[0416] λ k+1 =λ k +ρ(Ax-b)

[0417] Where A and b represent the coefficient matrix and constant term respectively, x is the decision variable, λ is the Lagrange multiplier, and ρ is the penalty parameter.

[0418] Penalty Parameter Adjustment: Appropriate selection and timely adjustment of the penalty parameter ρ is crucial for ensuring convergence speed and stability. A larger ρ can accelerate convergence but may lead to numerical instability; a smaller ρ can have the opposite effect. In practice, experimental adjustments can be made based on the specific application, or adaptive methods can be used to automatically adjust the value of ρ.

[0419] At each time step, the optimization problem is re-solved, and the scheduling strategy is dynamically adjusted based on the future load trend and actual operating status.

[0420] Technical feature description:

[0421] At each time step, the optimization problem is re-solved, and the scheduling strategy is dynamically adjusted based on the future load trend and actual operating status.

[0422] Detailed technical means:

[0423] Rolling Horizon Optimization (RTO): This method re-solves the optimization problem at each point in time based on the latest forecast information (such as weather forecasts, market electricity price changes, etc.) and current actual operating data. The goal is to ensure that the scheduling strategy always keeps up with the latest developments and better cope with uncertainty.

[0424] Short-term forecasting models: Build highly accurate short-term load forecasting models to predict power demand over the next few hours. These models can be trained based on historical data or continuously updated with real-time monitoring data to improve forecast accuracy.

[0425] Real-time feedback mechanism: Create a fast-response feedback loop that enables the system to react quickly when abnormal conditions (such as sudden load surges or equipment failures) are detected. This includes not only automatically triggering emergency measures but also timely adjusting optimization objectives and constraints to ensure the effectiveness of the overall scheduling strategy.

[0426] Example 2

[0427] An intelligent flexible control terminal distributed energy management system, comprising:

[0428] The prediction module is used to predict the future load trend based on multi-source data through the prediction model;

[0429] an analysis module, configured to perform a cost-benefit analysis based on the future load trend to generate a demand response scheduling strategy;

[0430] The optimization module is used to optimize energy distribution according to the demand response adjustment strategy to generate a dynamic scheduling strategy.

[0431] In some embodiments provided in this application, the prediction module is specifically:

[0432] The multi-source data includes: real-time electricity market prices, historical electricity consumption data, weather forecast information, equipment operating status and user behavior patterns;

[0433] Using edge computing devices to perform preliminary cleaning on the collected multi-source data, remove noise points and outliers, and extract key features of the operating data, including time series features, seasonal patterns, and diurnal cycle changes;

[0434] The short-term load forecasting model is used to perform forecasting to obtain the future load trend.

[0435] In some embodiments provided in this application, the short-term load forecasting model is used to forecast and obtain the future load trend, specifically:

[0436] Training the short-term load forecasting model using the historical electricity consumption data;

[0437] The trained short-term load forecasting model is used for training to obtain the load conditions in the next few hours.

[0438] In some embodiments provided in this application, the analysis module is specifically:

[0439] Generate an optimal solution using a linear programming or reinforcement learning algorithm to obtain the demand response scheduling strategy;

[0440] Or a reward function is designed to learn the optimal strategy through a trial-and-error process, gradually approaching the global optimal solution to obtain the demand response scheduling strategy.

[0441] In some embodiments provided in this application, the optimization module is specifically:

[0442] Decompose the demand response dispatch strategy into specific tasks;

[0443] According to the characteristics and current status of each subsystem, reasonably allocate the tasks to the corresponding subsystem;

[0444] Through the Lagrange multipliers and penalty parameters in the ADMM algorithm, the solutions of all subsystems are ensured to gradually converge during the iteration process, and ultimately reach the global optimal solution;

[0445] At each time step, the optimization problem is re-solved, and the scheduling strategy is dynamically adjusted based on the future load trend and actual operating status.

[0446] Anything not described in this application can be achieved by adopting or drawing on existing technologies.

[0447] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0448] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A method for managing distributed energy resources with intelligent and flexible control terminals, characterized in that: include: Based on multi-source data, forecasting models are used to predict future load trends; performing a cost-benefit analysis to generate a demand response dispatch strategy based on the future load trend; Energy distribution is optimized according to the demand response adjustment strategy to generate a dynamic dispatch strategy.

2. The method according to claim 1, characterized in that The prediction model is used to predict the future load trend based on multi-source data, specifically: The multi-source data includes: real-time electricity market prices, historical electricity consumption data, weather forecast information, equipment operating status and user behavior patterns; Using edge computing devices to perform preliminary cleaning on the collected multi-source data, remove noise points and outliers, and extract key features of the operating data, including time series features, seasonal patterns, and diurnal cycle changes; The short-term load forecasting model is used to perform forecasting to obtain the future load trend.

3. The method according to claim 2, characterized in that The short-term load forecasting model is used to predict and obtain the future load trend, specifically: Training the short-term load forecasting model using the historical electricity consumption data; The trained short-term load forecasting model is used for training to obtain the load conditions in the next few hours.

4. The method according to claim 1, wherein The cost-benefit analysis is performed based on the future load trend to generate a demand response scheduling strategy, specifically: Generate an optimal solution using a linear programming or reinforcement learning algorithm to obtain the demand response scheduling strategy; Or a reward function is designed to learn the optimal strategy through a trial-and-error process, gradually approaching the global optimal solution to obtain the demand response scheduling strategy.

5. The method according to claim 1, wherein The energy allocation is optimized according to the demand response adjustment strategy to generate a dynamic scheduling strategy, specifically: Decompose the demand response dispatch strategy into specific tasks; According to the characteristics and current status of each subsystem, reasonably allocate the tasks to the corresponding subsystem; Through the Lagrange multipliers and penalty parameters in the ADMM algorithm, the solutions of all subsystems are ensured to gradually converge during the iteration process, and ultimately reach the global optimal solution; At each time step, the optimization problem is re-solved, and the scheduling strategy is dynamically adjusted based on the future load trend and actual operating status.

6. An intelligent flexible control terminal distributed energy management system, characterized in that: include: The prediction module is used to predict the future load trend based on multi-source data through the prediction model; an analysis module, configured to perform a cost-benefit analysis based on the future load trend to generate a demand response scheduling strategy; The optimization module is used to optimize energy distribution according to the demand response adjustment strategy to generate a dynamic scheduling strategy.

7. The system according to claim 6, characterized in that The prediction module is specifically: The multi-source data includes: real-time electricity market prices, historical electricity consumption data, weather forecast information, equipment operating status and user behavior patterns; Using edge computing devices to perform preliminary cleaning on the collected multi-source data, remove noise points and outliers, and extract key features of the operating data, including time series features, seasonal patterns, and diurnal cycle changes; The short-term load forecasting model is used to perform forecasting to obtain the future load trend.

8. The system according to claim 7, characterized in that The short-term load forecasting model is used to predict and obtain the future load trend, specifically: Training the short-term load forecasting model using the historical electricity consumption data; The trained short-term load forecasting model is used for training to obtain the load conditions in the next few hours.

9. The system according to claim 6, wherein: The analysis module is specifically: Generate an optimal solution using a linear programming or reinforcement learning algorithm to obtain the demand response scheduling strategy; Or a reward function is designed to learn the optimal strategy through a trial-and-error process, gradually approaching the global optimal solution to obtain the demand response scheduling strategy.

10. The system according to claim 6, wherein: The optimization module is specifically: Decompose the demand response dispatch strategy into specific tasks; According to the characteristics and current status of each subsystem, reasonably allocate the tasks to the corresponding subsystem; Through the Lagrange multipliers and penalty parameters in the ADMM algorithm, the solutions of all subsystems are ensured to gradually converge during the iteration process, and ultimately reach the global optimal solution; At each time step, the optimization problem is re-solved, and the scheduling strategy is dynamically adjusted based on the future load trend and actual operating status.

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